Calibration of the Mars Science Laboratory Alpha Particle X-Ray Spectrometer for Analysis of Visible Elements and Light Invisible Components

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Calibration of the Mars Science Laboratory Alpha Particle X-ray Spectrometer for Analysis of Visible Elements and Light Invisible Components

Glynis Mary Perrett

A Thesis Presented to The University of Guelph

In partial fulfilment of requirements for the degree of Doctor of Philosophy in Land Resource Science

Guelph, Ontario, Canada

CALIBRATION OF THE MARS SCIENCE LABORATORY ALPHA

PARTICLE X-RAY SPECTROMETER FOR ANALYSIS OF VISIBLE

ELEMENTS AND LIGHT INVISIBLE COMPONENTS

Glynis Mary Perrett Advisors: University of Guelph, 2015 Dr. S. Glasauer Dr. J. L. (Iain) Campbell

The Alpha Particle X-ray Spectrometer (APXS) is a small, lightweight instrument capable of detecting geologically significant elements (Na-Y) in a sample through particle induced

X-ray emission and X-ray fluorescence. This makes it an ideal instrument for planetary exploration and it has been on every successful NASA rover mission. This thesis covers the fundamental parameters elemental calibration for the newest APXS instrument onboard the

Mars Science Laboratory (MSL) rover, Curiosity. This calibration approach assumes all samples are homogeneous at the sub-micron scale, which is incorrect for geologic materials, the primary target materials of the APXS. Specific elements in geochemical reference materials were discovered to systematically deviate from certificate values; these “mineral phase effects (MPEs)”, have been quantitatively described for the three lightest detectable elements (Na, Mg, Al), which display the largest deviations. Examination of mineral theoretical X-ray yields and bulk elemental theoretical X-ray yields of a geochemical reference material (GRM) showed differences that agree in magnitude with the differences observed in the calibration. This verified that the root cause of the MPEs is the necessary homogeneity assumption. To complete the theoretical yield calculations, accurate mineral abundances and elemental composition must be known. Mineral abundances were determined by X-ray diffraction (XRD) and Rietveld analysis. Mineral elemental composition calculations relyon the bulk chemistry and mineral abundances. These calculations are complex, so a program was developed (APXRD) to compute mineral elemental compositions. APXRD was tested against by-hand calculations and the results agree. APXRD may be used in the future to simplify the study of MPEs in more complex GRMs and Martian APXS targets. MPEs have also been studied using the Guelph proton microprobe, which has replicated MPEs observed in select APXS spectra. These analyses show the value of further MPEs studies with the proton microprobe. Calibration of the L훼 scatter peaks method for determining additional light invisible components (ALICs) of MSL APXS targets has been completed.

This calibration was tested on GRMs with known ALIC content and it was able to reproduce the known GRM ALIC content where ALICs are greater than 5 oxide wt%. Preliminary analyses of MPE corrections and ALIC content of MSL APXS spectra are presented. Acknowledgements

The six years I have dedicated to the completion of my thesis have been both challenging and rewarding. The additional complications of being an interdisciplinary student straddled between two departments were met with amazing support from many individuals in various capacities. For this I am so very grateful. I would like to thank those in both Physics (Reggi Vallille, Steve Kempf, Janice Hall) and SES (Marie Vickary, Linda Bissell) who helped facilitate my unique situation in an administrative context. I am grateful for the excellent work by Steve Wilson in the Physics machine shop. His insightful input for experimental design, and speedy, high-quality work helped smooth experimental procedures. David Atkinson in the Physics chemical laboratory was a great help in the collection of chemical standards, development of laboratory procedures, and general discussions. Steve Sadura in SES provided invaluable geochemical and mineralogical information on numerous occasions. The programming support provided by John Maxwell was crucial to my thesis and I am so thankful for his great work and the time he saved me. Several aspects of my thesis have also benefited from various discussions we have had over the years. Thank you to the APXS crew past, present,at Guelph, and at other institutions. You have been an excellent team to work with and I appreciate all of the insightful conversations I have had with each of you. I particularly appreciate the additional support and guidance provided by Irina Pradler, Stefan Andrushenko, and Chris Heirwegh. I am indebted to my advisory committee for all of the time each member has spent advising me throughout my degree. Ralf Gellert, Penny King, and Mariek Schmidt have provided excellent guidance not only regarding my PhD research, but they have also provided guidance towards improving my skills as a scientist and researcher. My advisors, Susan Glasauer and Iain Campbell, have gone above and beyond to support me throughout my degree. Thank you for taking me on in such a unique, interdepartmental, arrangement, and for going out of your way to make it work. I have learned so much from both Susan and Iain, from physics and geochemistry, to laboratory techniques, diplomacy, and how to write scientifically. I cannot express my gratitude enough. Last but not least, I would like to thank my friends and family. To my friends, thank you for always being there to listen when times were tough and being there to celebrate the big moments. Your constant encouragement has been a source of strength these past six years. Thank you, Dustin, for your excellent cooking, editorial skills, insightful research- based conversations, and never-ending patience. Without your daily support these last few months I can guarantee this process would have been much more challenging. To Mom, Dad, and Braden, thank you for your ceaseless love and support, particularly when I have encountered difficult times. Thank you for pushing me to be the best I can be,especially when I was younger, no matter the circumstances. Without your belief in my ability, I most certainly would not have had the courage or capacity to complete this PhD.

iv Contents

List of Acronyms xiv

1 Introduction 1 1.1 The Alpha Particle X-ray Spectrometer in Detail ...... 3 1.2 APXS Spectrum Treatment Methods ...... 5 1.2.1 Semi-Empirical Method Used on the Mars Exploration Rovers . . . . .6 1.2.2 GUAPX: A Fundamental Parameters Approach to Interpretation of APXS Spectra ...... 8

2 Elemental Calibration of the Mars Science Laboratory Alpha Particle X- ray Spectrometer by a Fundamental Parameters Approach 12 2.1 Introduction and Overview ...... 12 2.2 Geochemical Reference Materials ...... 13 2.3 Sample Preparation ...... 17 2.4 Instrument Parameters Required for the GUAPX Fitting Program ...... 21

2.4.1 Defining the Transmissivity (푡푍 ) of the APXS Window ...... 21 2.4.2 Effective Angles for the MSL APXS ...... 22

2.4.3 훼/퐿푥 and H-value Determination ...... 23 2.4.4 FEU Low Energy Cut Off Correction ...... 25 2.4.5 Spectrum Calibration in GUAPX ...... 25 2.5 GUAPX Calibration Procedure ...... 28 2.6 Element Concentration Offsets in the FEU ...... 29 2.6.1 Aluminum Offset ...... 30 2.6.2 Calcium Offset ...... 31 2.6.3 Titanium Offset ...... 31 2.6.4 Chromium, Nickel, and Copper Offsets ...... 31 2.6.5 Yttrium Offset ...... 31 2.6.6 Phosphorous Offset ...... 32 2.6.7 Final Remarks ...... 32 2.7 FEU Calibration Results and Discussion ...... 33 2.7.1 Major Elements ...... 34 2.7.2 Minor and Trace Elements ...... 37 2.7.2.1 Elements Significantly Excited by PIXE: P, S, Cl . . . . 38 2.7.2.2 Ti: Approximately Equal Excitation by PIXE and XRF . . . 45 2.7.2.3 Elements Excited Predominantly by XRF ...... 46 2.8 Limits of Detection and Limits of Quantitation ...... 52 2.9 Errors ...... 54

v 2.10 Cross Calibration with the PFM ...... 56 2.11 Summary ...... 57

3 Mineral Phase Effects 60 3.1 Introduction ...... 60 3.2 Empirical Correction Factors ...... 63 3.3 Mineral Phase Effects: Qualitative Description ...... 64 3.3.1 Aluminum ...... 65 3.3.2 Sodium ...... 66 3.3.3 Magnesium ...... 66 3.4 Mineralogy of GRMs ...... 68 3.4.1 Detailed Mineralogy of Five Selected GRMs ...... 70 3.4.1.1 Mineralogy of GA and GH ...... 73 3.4.1.2 Mineralogy of ISH-G and MDO-G ...... 73 3.4.1.3 Mineralogy of BCR-2 ...... 74 3.5 Mineral Phase Effects: Quantitative Determination ...... 75 3.5.1 APX-Yield ...... 75 3.5.2 Determining Elemental Compositions of Minerals ...... 75 3.5.3 Calculating Weighted Mineral Y1 Values ...... 77 3.5.4 Calculating R(Y1) ...... 78 3.6 Discussion of Results ...... 80 3.6.1 Silicon ...... 80 3.6.2 Aluminum ...... 80 3.6.3 Sodium ...... 80 3.6.4 Magnesium ...... 81 3.6.5 Potassium, Calcium, and Iron in ISH-G and MDO-G ...... 82 3.6.6 Summary ...... 83 3.7 PIXE Emulation of the APXS to Test for Mineral Phase Effects ...... 83 3.7.1 Results for the BT-2 Pressed Pellet ...... 85 3.7.2 Results for the BT-2 Solid Rock Slab and Thin Section ...... 86 3.7.3 Summary ...... 87 3.8 Conclusion ...... 87

4 APXRD: A Computer Program to Determine Mineral Composition 89 4.1 Introduction ...... 89 4.2 Details of the APXRD Program ...... 90 4.3 Comparison of APXRD to By-Hand Results ...... 92 4.3.1 Mineral Elemental Compositions ...... 92 4.3.1.1 BCR-2 ...... 92 4.3.1.2 ISH-G and MDO-G ...... 94 4.3.1.3 GA and GH ...... 95 4.3.2 APXRD Derived R(Y1) Values for the Five Test GRMs ...... 96 4.3.2.1 BCR-2 ...... 97 4.3.2.2 ISH-G and MDO-G ...... 99 4.3.2.3 GA and GH ...... 100 4.3.3 Summary ...... 101 4.4 New GRM R(Y1) Results and Discussion ...... 101 4.4.1 Basalt GRMs ...... 102

vi 4.4.2 BT-2 ...... 105 4.4.3 Summary ...... 106 4.5 Conclusion ...... 106

5 Calibration of the MSL APXS Scatter Peaks for Determining Light Ele- ment Abundance 108 5.1 Introduction ...... 108

5.2 L훼 K-value Method Calibration ...... 110 5.2.1 Selection of GRMs and Data ...... 111 5.2.2 The Marsgeom Simulation Program ...... 112

5.2.3 L훼 K-value Calibration for the MSL FEU ...... 114 5.3 Effects on C/R ...... 115 5.3.1 Effect of Energy Region on Experimental C/R ...... 116 5.3.2 Effect of Spectrum Duration on Experimental C/R ...... 117 5.3.3 Effect of Marsgeom ALIC Type on Simulated C/R ...... 118 5.4 Accuracy of the K-value Method and Calibration ...... 119 5.4.1 Method for Calculating ALICs ...... 119

5.4.2 H2O Simulations ...... 124 5.4.3 CO2 Simulations ...... 127 5.5 Detection Limit and Error Estimate ...... 130 5.6 Conclusion ...... 133

6 Application of the Elemental and K-value Method Calibrations to MSL APXS Targets and First Alterations of Martian APXS Results for Mineral Phase Effects 134 6.1 Introduction ...... 134 6.2 Extension of the Elemental Calibration to Mars ...... 135 6.2.1 FEU and PFM Cross Calibration ...... 135 6.2.2 Martian ECF Files: Adjustment of Phosphorous and Chlorine . . . . . 137 6.2.3 Effect of Sample-to-Instrument Standoff on Concentrations . 138 6.2.4 Geometric Normalization and Standoff ...... 141 6.2.5 Effect of Pressure Variations on Element Concentrations . . . 144 6.2.6 Optimal Manganese Full Width Half Maximum for Concentration De- termination ...... 146 6.2.7 Consideration of Mineral Phase Effects: Collaboration of APXS Bulk Chemistry and CheMin Mineralogy ...... 150 6.3 Application of the K-value Method to MSL Spectra ...... 154 6.3.1 Preliminary ALIC Results ...... 155 6.3.2 Dust Effects ...... 158 6.4 Conclusion ...... 160

7 Concluding Remarks 162

vii List of Tables

2.1 GRMs used in the calibration of the MSL APXS ...... 17 2.2 Comparison of certificate concentration means to concentration means produced by Activation Laboratories Inc. Errors are one standard deviation. . . . 18 2.3 Instrument offsets ...... 30 2.4 Mean and best R-values ...... 34 2.5 R-values by rock type for Si, K, Ca, and Fe ...... 35 2.6 R-values by rock type for Na, Mg, and Al ...... 35 2.7 R-values by rock type for P, S, and Cl ...... 41 2.8 R-values by rock type for Ti ...... 45 2.9 Minor and trace element R-value (24≤Z≤31) means by rock type ...... 46 2.10 Minor and trace element R-value (Z>31) means by rock type ...... 50 2.11 Limits of Quantitation ...... 55 2.12 Accuracies based on rock type ...... 59

3.1 ECFs by rock type (homogeneous, high alkali rock, granite, trachyte, andesite, basalt, ultra mafic, and sediment). These values have been updated fromthe Campbell et al. MSL APXS calibration paper [12]...... 63 3.2 Mineral abundance (wt%) for mineral and HAR GRMs...... 69 3.3 Mineral abundance (wt%) for granite GRMs...... 69 3.4 Mineral abundance (wt%) for andesite GRMs. * 1. BCR-2 Rietveld analysis was provided by Dr. S. Wilson of the USGS [76]...... 70 3.5 Mineral abundance (wt%) for andesite and basalt GRMs...... 70 3.6 Mineral abundance (wt%) for basalt GRMs...... 71 3.7 Mineral abundance (wt%) for UM, trachyte, and anorthosite GRMs...... 71 3.8 Mineral abundance (wt%) for sediment and special GRMs...... 71 3.9 Interrogation depth for the major minerals of BCR-2 ...... 72 3.10 Areal abundance of the 5 study GRMs ...... 72 3.11 Distribution of elements into BCR-2 minerals ...... 77 3.12 Error weighted mean R-values for aluminum by rock type...... 79 3.13 Renormalized R-values and calculated R(Y1) values for Na, Mg, Al, and Si [61]. 79 3.14 Theoretical yield comparison to calibration R-values for K, Ca and Fe in trachyte GRMs...... 82 3.15 90% escape depth for characteristic X-rays of sodium to silicon for 5 MeV alpha particle and 3 MeV proton emitters...... 84 3.16 Penetration depth (range) of 5 MeV alpha particles and 3 MeV protons in an iron rich mineral (augite) and an iron free mineral (labradorite)...... 84 3.17 BT-2 element R-values from APXS and proton beam analyses (2휎 error.) . . 86 3.18 Modal mineralogy of the BT-2 calibration target...... 87

viii 4.1 Three test results comparing by-hand and APXRD mineral elemental concentrations using BCR-2. Simplex 4 results have been used in all cases. . . . . 94 4.2 Comparison of by-hand and APXRD mineral elemental concentrations for ISH-G and MDO-G...... 95 4.3 Comparison of by-hand and APXRD mineral elemental concentrations for GA and GH...... 96 4.4 Comparison of APXRD R(Y1) values to the by-hand R(Y1) and calibration R values...... 97 4.5 Effect on R(Y1) values for BCR-2 when pyroxene is run through APXRD as augite (test 1) and hypersthene (test 2)...... 98 4.6 Comparison of the “test 2” simplex 4 results to the “no elemental iron” simplex 4 results for BCR-2...... 99 4.7 R(Y1) values for major elements and calibration R-values for BHVO-2. . . . . 103 4.8 Final APXRD-derived mineral compositions for BIR-1a...... 103 4.9 Simplex 4 R(Y1) values for three rounds of mineralogy adjustments and calibration R-values for BIR-1a...... 104 4.10 R(Y1) values from simplex 4 of APXRD and R-values for the calibration target, BT-2...... 105

5.1 GRMs and minerals selected for the L훼 K-calibration line. Their Rb and Sr concentrations are below 500 ppm weight and their ALICs are all below 2 wt%.113 5.2 Ratio of the Compton and Rayleigh differential scattering cross sections휎 (d /dΩ) for select light elements at 14.26 keV...... 119

5.3 Calculated ALICs using H2O based simulations...... 125 5.4 Calculated ALICs from CO2 based simulations...... 128 6.1 BT-2 calibration target concentrations on Earth, sol 34, and sol 179. Elevated Mg, S, Cl, and Fe indicate a layer of dusty material was deposited during landing...... 136 6.2 Oxide ranges for the GRM calibration suite and MSL APXS targets up to sol 790. Only elements with ECF corrections are considered...... 137 6.3 Ratios of the average elemental concentrations for middle or high Mn FWHM over the average low FWHM. Major and select minor elements are shown,

along with the L훼 C/R values...... 148 6.4 Predicted mineral phase effects in Rocknest, John Klein, and Cumberland targets. R(Y1) for these three targets have been renormalized to the “best” R-value found from the calibration. These “best” values are inherent to the ECF values...... 152 6.5 General concentrations, basalt ECF corrected concentrations, and target specific mineral phase effect corrected concentrations for Martian targets Rock- nest, John Klein, and Cumberland...... 153 6.6 Calculated ALICs for select MSL APXS targets. Adjusted ALICs have had a correction factor of 1.13 applied. Several target types are included, such as rocks (R), brushed rocks (RB), soils (S), and drill fines (F). The standoff values are taken from Professor R. Gellert’s calculations. Targets labelled with a ’D’ indicate those with dusty surfaces...... 156 6.7 Average ALICs for targets at Yellowknife Bay, Darwin, and The Kimberley waypoints. Uncertainties are 2휎 standard deviations...... 158

ix 6.8 Concentrations for select elements and L훼 C/R values in targets that have been measured with unbrushed and brushed surfaces...... 159

x List of Figures

1.1 Image of the APXS on the turret of the MSL rover. The six APXS sources surrounding the central detector can be clearly seen...... 4 1.2 Cross section of the MSL APXS showing source and detector configuration. .5 1.3 Example of an MSL flight equivalent unit (FEU) APXS spectrum. . .6

2.1 TAS diagram with extrusive igneous classification ...... 14 2.2 TAS diagram with intrusive igneous classification ...... 15 2.3 Petri dish cross section ...... 19 2.4 Sample procedure reproducibility test ...... 20 2.5 Low energy cut-off in FEU APXS spectra ...... 26 2.6 Aluminum Offset ...... 30 2.7 Si and Fe R-values...... 36 2.8 K and Ca R-values...... 37 2.9 Na R-values ...... 38 2.10 Mg R-values ...... 39 2.11 Al R-values ...... 40 2.12 P R-values ...... 42 2.13 S R-values ...... 43 2.14 Cl R-values ...... 44 2.15 Ti R-values ...... 46 2.16 Cr and Mn R-values ...... 47 2.17 Ni and Cu R-values ...... 48 2.18 Zn and Ga R-values ...... 49 2.19 Br and Rb R-values ...... 51 2.20 Sr and Y R-values ...... 52 2.21 W and Pb R-values ...... 53

3.1 The escape depth of higher Z elements detected by the APXS is larger than the particle size. These elements are less susceptible to mineral phase effects. 61 3.2 Elements predominantly excited by PIXE have interrogation depths likely much smaller than the typical grain size. These elements are therefore much more susceptible to mineral phase effects...... 62 3.3 Correlation between Na and Al “best” R-values [61] ...... 67 3.4 R-values as area coverage increases through spectrum summation...... 85

5.1 High energy cut-off in pre-2012 MSL FEU spectra ...... 110

5.2 K-method calibration for the full energy MSL L훼 scatter peaks...... 115 5.3 MER (a) and cut-off MSL (b) K-value calibrations forL훼 scatter peaks . . . . 116

xi 5.4 Effect of spectrum duration on experimental C/R for BT-2 (sol 179)and Wernecke (sol 173)...... 118 5.5 Point of intersection and associated errors for the K-calibration line and the GRM (UB-N) K-line with 0, 5, 10, and 15 wt% simulated ALICs...... 121 5.6 Graph to convert F(I) to ALIC content for UB-N. F(O) values are also represented to demonstrate the discrepancy between these values...... 122 5.7 Calculated ALICs of high rubidium and strontium GRMs, compared to their expected ALIC content...... 123

5.8 Experimentally determined ALICs (H2O simulations) compared to Activation Laboratories ALICs...... 126

5.9 Experimentally determined ALICs (H2O simulations) compared to Activation Laboratories ALICs for GRMs with known ALIC content greater than 5.0 wt%.127

5.10 Experimentally determined ALICs (CO2 simulations) compared to Activation Laboratories ALICs for GRMs with known ALIC content greater than 5.0 wt%.128

5.11 Difference between calculated ALIC content with2 CO and H2O Marsgeom simulations...... 129 5.12 Demonstration of the inverse square relation of C/R on mean Z...... 130 5.13 Ratio of calculated to Activation Laboratories ALICs compared to known ALIC values. Around 5 wt% the calculated to Activation Laboratories ratio becomes consistent...... 131 5.14 Ratio of calculated to Activation Laboratories ALICs for GRMs with known ALIC values less than 5.0 wt%...... 131 5.15 Ratio of calculated to Activation Laboratories ALICs for GRMs with known ALIC values greater than 5.0 wt%...... 132

6.1 Effect of instrument standoff on sodium, silicon, and iron peak areas. . 139 6.2 Effect of standoff in IM mode GUAPX analyses of Martian spectra uptosol 400 for Na to Si...... 140 6.3 Effect of standoff in IM mode GUAPX analyses of Martian spectra uptosol 400 for select elements from P to Fe...... 140 6.4 GUAPX geometric normalization factor versus standoff using GRM FEU data.142 6.5 GUAPX geometric normalization factor versus standoff using APXS data collected at Gale Crater, Mars...... 143 6.6 Calculated standoffs using the GUAPX geometric normalization factor versus standoffs calculated by Professor R. Gellert...... 144 6.7 Effect of pressure on Martian APXS concentrations for sodium up to silicon. MSL APXS spectra up to sol 400 were used and were fit with IM mode GUAPX.145 6.8 Effect of varying pressure on Martian APXS concentrations for high Zele- ments beyond silicon. MSL APXS spectra up to sol 400 were used and were fit with IM mode GUAPX...... 146 6.9 Na concentration comparison among six MSL targets with low, middle, and high Mn FWHM values...... 147 6.10 Si concentration comparison among six MSL targets with low, middle, and high Mn FWHM values...... 149 6.11 Fe concentration comparison among six MSL targets with low, middle, and high Mn FWHM values...... 149 6.12 Demonstration of mineral phase effect corrections on select MSL targets . 152 6.13 Effect of standoff on the C/R value in Martian spectra...... 155

xii 6.14 L훼 C/R values for select MSL targets organized by sol...... 157 6.15 Effect of surface dust on calculated ALIC content in MSL APXS targets. The data points represented by the circles are unbrushed targets and the square data points are brushed targets...... 160

xiii List of Acronyms

The following is a list of common acronyms that are used throughout this thesis:

ALICs: Additional Light Invisible Components

APXS: Alpha Particle X-ray Spectrometer

DAN: Dynamic Albedo of Neutrons

ECF: Empirical Correction Factor

FEU: Flight Equivalent Unit

FM: Fixed Matrix

FWHM: Full Width Half Maximum

GRM: Geochemical Reference Material

HAR: High Alkali Rock

IB: Icelandic Basalt

ICC: Incomplete Charge Collection

IM: Iterative Matrix

LOD: Limit of Detection

LOQ: Limit of Quantitation

MER: Mars Exploration Rover

MPE: Mineral Phase Effect

xiv MSL: Mars Science Laboratory

PFM: Pre Flight Model

PIXE: Particle Induced X-ray Emission

SAM: Sample Analysis at Mars

TAS: Total Alkali and Silica

UM: Ultramafic

WEH: Water Equivalent Hydrogen

XRD: X-Ray Diffraction

XRF: X-Ray fluorescence

xv Chapter 1

Introduction

The planet Mars has been a target of scientific interest to humans for hundreds of years.

The observations of channels by Giovanni V. Schiaparelli in the late 1800s, which resembled linear, man-made canals on Earth, stimulated imaginations and many began to imagine that perhaps Earth was not the only inhabited planet in our solar system [45]. Much discussion was made over Schiaparelli’s canali, incorrectly translated to canals, and was fuelled by writings by Percival L. Lowell around the turn of the 20th century that stated that these canals were made by intelligent life [45]. It was not until 1965 that Mars was successfully visited by a human-made spacecraft; Mariner 4 (NASA) flew by Mars and took the first up close images of another planet [54]. The first successful mission tolandon the surface of Mars was the Viking mission (NASA), landing in 1976 [18]. Mars was not revisited for another two decades, but since the successful landing and operation of the 1997

Pathfinder mission (NASA), composed of a lander and rover, Mars has been continuously monitored. There are currently five orbiters (Odyssey (NASA, 2001), Mars Express (ESA,

2003), Mars Reconnaissance Orbiter (NASA, 2006), Maven(NASA, 2014), and MOM (India,

2014)) and two rovers (MER-Opportunity (NASA, 2004) and MSL-Curiosity (NASA, 2012))

[18] operating on Mars, with missions planned for 2016, 2018, and 2020 [64].

Those first images taken by Mariner 4 of the Martian surface showed that therewas likely no intelligent life constructing canals on the surface of Mars. Images taken since from orbiters, landers, and rovers, confirm that those so-called canals were naturally formed geologic features. What these missions have not been able to exclude is the presence of past life on Mars. There is indisputable evidence that water once flowed on the surface of

1 Mars, which is a key component for life on Earth. Geomorphic features, such as outflow channels, distributary fans, and river-like channels ending in potential ancient lake basins, cover the majority of the Martian surface (e.g. [18, 19]). Orbiter and surface-based mission data indicate the presence of sub-surface water ice (e.g. [18, 34, 53]). Rover-based in situ geochemistry and mineralogy indicate aqueous alteration and the presence of minerals that require water to form. They have also detected mineralogically bound water (e.g. [9, 27,

37, 42, 50, 75]). All of this evidence is suggestive that the Martian surface was once very different from what it is today and could have been a habitable environment for lifeatone time [30].

The alpha particle X-ray spectrometer (APXS) has been a crucial instrument in the study of in situ Martian geochemistry. It was first sent to Mars on-board the Mars Pathfinder

Mission microrover, Sojourner [67]. The APXS for that mission was termed the alpha proton

X-ray spectrometer. It used nine 244Cm sources with a combined activity of 50 mCi, and

had both an X-ray detector and a dual particle detector (able to detect both alpha particles

and protons)[67]. Before Pathfinder, all that was known of Martian geochemistry wasthe

elementally constrained soil composition measured by the Viking X-ray fluorescence (XRF)

instruments, and the composition of meteorites that were Martian in origin [67, 74]. The

Pathfinder APXS instrument was capable of detecting all elements of geologic importance,

starting at sodium, via particle induced X-ray emission (PIXE) and XRF. Lighter elements

from carbon to fluorine could be detected by the alpha and proton modes, providing amuch

larger element range than what was obtained by the Viking XRF instruments. Five rocks

and six soils were analyzed by the Pathfinder APXS, providing the first in situ chemical analysis of Martian rocks [66].

Two updated APXS instruments, renamed the alpha particle X-ray spectrometer (containing a silicon drift detector for X-rays and a particle detector for Rutherford backscat- tering), were sent to Mars in 2003 on-board the Mars Exploration Rovers, named Spirit and Opportunity [25, 66]. The APXS was the only instrument on these rovers capable of analyzing the bulk chemistry of the Martian rocks and soils, and our knowledge of Martian geochemistry today is largely based upon the overwhelming success of this mission and the wealth of data collected. In 2012, the Mars Science Laboratory (MSL) rover was sent to

2 Mars with the most ambitious payload to date. It includes ten scientific instruments. Of note are the X-ray diffraction instrument, CheMin; the wet chemistry lab, SAM (Sample

Analysis at Mars); and yet another APXS instrument. This APXS instrument has been updated and improved from the MER mission (e.g. four times higher counting rate), but the principle remains the same. The APXS instruments on the MER rover, Opportunity, and on

MSL’s Curiosity, are both currently operational, “unearthing” new and exciting geochemical trends on a regular basis.

Operating an instrument on another planet is an entirely unique experience compared to operating such an instrument in a terrestrial laboratory. On Earth, if an instrument malfunctions or gets damaged, it may be easily examined and fixed or replaced. On Mars there is no such luxury. Instrument safety is of ultimate concern and if any damage does incur and remote troubleshooting does not yield any improvements or work-arounds, that is the end of the research.

On Earth, instruments should typically undergo initial calibration and testing. If, however, new approaches or updates to the instrument are required, the instrument may undergo recalibration and adjustment. This is not possible when the instrument is on Mars. The initial calibration of the instrument must be performed thoroughly and with as much foresight as possible. This is typically performed under tight deadlines since many space missions have short launch windows during select years, as is the case for missions to Mars. If an instrument destined for planetary or space exploration is properly calibrated beforehand and is operated safely through out its mission, it will likely yield ground breaking results.

1.1 The Alpha Particle X-ray Spectrometer in Detail

All Martian APXS instruments have used radioactive 244Cm sources to excite targeted material. 244Cm decays according to equation 1.1 to produce both alpha particles at 5.806

MeV, and plutonium L X-rays [10, 57]. The alpha particle energy for the MSL APXS has been decreased to an average value of approximately 5 MeV due to the thin titanium cover foils used to prevent self sputtering of the open curium sources.

244Cm 240Pu 4 He (1.1) 96 = 94 +2

3 There are six sources on the MSL APXS, organized in a concentric circle around the

X-ray detector. Figure 1.1 shows a face-on view of the source and detector configuration in the MSL APXS instrument on Mars, taken by Curiosity’s Mast Camera on sol 32 [63].

Figure 1.2 gives the cross sectional view of the MSL APXS sources, collimator, detector, and general electronics configuration [13].

Figure 1.1: Image of the APXS on the turret of the MSL rover. The six APXS sources surrounding the central detector can be clearly seen.

The combined activity of the sources on MSL has been increased to approximately 60 mCi over the combined 30 mCi used for the MER APXS instruments. This increase in source activity, along with a shorter sample-to-detector distance, has increased the count rate by approximately four times over that of MER. MSL also differs greatly from MER in that half of its sources are closed, whereas all sources were left uncovered on MER. The three closed sources of MSL are only able to contribute 240Pu X-rays. This suppression of alpha particles was done to lessen the relative effect of the strong visible light element peaks, which are excited predominately by PIXE, in relation to the heavier element peaks, which are predominantly excited by XRF. This increased heavy element relative sensitivity improves trace element detection and increases the count rate of the plutonium L X-ray

4 scatter peaks.

Figure 1.2: Cross section of the MSL APXS showing source and detector configuration.

The MSL APXS is located on the turret at the end of Curiosity’s arm (Figure 1.1). The arm is able to place the APXS contact sensor on or near Martian rocks and soils that are to be analyzed. The alpha particles and X-rays from the 244Cm sources excite the Martian targets via PIXE and XRF processes respectively. The MSL APXS is capable of detecting major and minor geochemical elements from sodium upwards with PIXE responsible for exciting the lower Z elements and XRF responsible for exciting the higher Z elements. PIXE and

XRF processes are approximately equal in exciting titanium.

Resulting characteristic X-rays produced within the sample by these excitation processes are detected by the X-ray detector located in the center of the six source ring, and an energy dispersive X-ray spectrum is created (Figure 1.3). Fitting these spectra and dealing with matrix effects are already well developed in XRF and electron microprobe analyses of geological materials, and give the elemental compositions of the Martian samples.

1.2 APXS Spectrum Treatment Methods

The MSL APXS instrument has been calibrated using two unique methods: a semi-empirical approach that was also utilized to calibrate the MER APXS instruments [25], and a fun-

5 Figure 1.3: Example of an MSL flight equivalent unit (FEU) APXS spectrum. damental parameters approach [12]. Both approaches incorporate non-linear least squares fitting of the spectra to extract peak areas of the elements present. They differ, however,as regards to the method of conversion of these peak areas to elemental concentrations, i.e. in the treatment of matrix effects. By matrix effects we mean the interaction of the ionizing particles and radiation and the emerging characteristic X-rays within the sample. In this body of work the calibration of the MSL APXS via the fundamental parameters method is presented in detail. The underlying principles of both calibration methods will now be discussed.

1.2.1 Semi-Empirical Method Used on the Mars Exploration Rovers

Semi-empirical methods using 244Cm excitation sources were first described, rather qualitatively, by E. J. Franzgrote in 1971 [24]. He used several pure elements, simple compounds, and seventeen rock standards, noting that for the rock standards, homogeneity must be assumed despite their obvious heterogeneous nature. Only nine elements were studied: sodium, magnesium, aluminum, silicon, potassium, calcium, titanium, iron, and nickel. The

6 analysis of spectra was greatly simplified. The intensity of each peak was approximated and then corrected for absorption, only if considered appropriate. The heavier elements

(titanium, iron, and nickel) were corrected for absorption because their characteristic X-ray escape depths are much less than the penetration depth of the plutonium X-rays exciting the sample, thus approximating an infinitely thick sample. To correct for absorption, hefound an average mass absorption coefficient at each X-ray energy taken over all rock samples used in the calibration. He then ratioed the average mass absorption coefficient for each individual rock to the overall value to find a correction factor [24].

Franzgrote did not apply any absorption correction for potassium and calcium as he incorrectly assumed these elements are primarily excited by alpha particles (at this time, theoretical calculations of ionization cross sections of charged particles were not sophisticated). The characteristic X-ray escape depths for potassium and calcium would be much greater than the penetration depth of alpha particles. Therefore, the sample would approximate an “infinitely thin” target [24]. Absorption corrections are not made for thefour lightest elements either, although corrections were tested for magnesium. Franzgrote men- tions that the escape depths for these light elements are near the penetration depth of the alpha particles and that perhaps absorption should be considered. He then compared these absorption corrected intensities to the known concentration within the sample. He noted that overall there was good agreement between the intensities and known concentrations; however, there was large scatter observed that could have been due to the over-simplified nature of this analysis process.

The method used to produce the published MER and MSL concentrations employs a two-step process; first, peak areas are calculated from the spectrum via a non-linear least squares fitting routine, followed by conversion of these peak areas, through an iterative process, to elemental and then oxide concentrations [25]. All targets are assumed to be free of water and carbon. The conversion from peak areas to concentrations used in the calibration of this method is:

휇 퐶푃 푆 = 푂 + 휎푅푊 + (1 − 휎)푅푊 푚푒푎푛 (1.2) 휇

7 CPS represents the counts per second of the Z element peak and O is the background offset. The approximation is made that the PIXE induced light elements can be dealtwith as if they were excited by XRF. R is the response term, which represents the peak area per weight percent of element Z (CPS/wt%). W is the fractional concentration of element Z within the sample. 휇푚푒푎푛 is the average attenuation cross section for the Z characteristic X-ray line averaged over all samples, and 휇 is the calculated attenuation cross section of the characteristic elemental X-ray line for the sample in question. The ratio of these terms

is the matrix correction. The 휎 parameter ranges from zero to one and determines the

extent to which the matrix correction (휇푚푒푎푛/휇) is applied [25]. From the calibration a sensitivity curve is produced, which gives the response for each element. The response for

each element is an average over all response functions found using the calibration standards.

The averaging of the attenuation cross sections and response functions means the calibration

reflects the choice of geochemical reference materials. For the calibration of both theMER

and MSL APXS instruments, large and diverse calibration suites were used to minimize any

such bias.

Due to the complex processes involved in sample excitation by the 244Cm sources, some

simplifications are implied. The matrix term only considers excitation via XRF, evenfor

those elements excited primarily by PIXE. This is justified by the fact that the characteristic

X-ray escape depth of the light elements is considerably less than the alpha particle range.

1.2.2 GUAPX: A Fundamental Parameters Approach to Interpretation of APXS Spectra

The fundamental parameters method uses all the underlying physics involved in the exci-

tation and detection of the X-rays from the sample [28]. All relevant physics, geometry,

matrix considerations, and detector properties of this fundamental parameters method are

mathematically expressed by the complete X-ray yield equation (equation 1.3).

[︃∫︁ 0 15 ]︃ 푁퐴푣휔푍 푏푍 푇푍 (퐸)휎푍 (퐸) ∑︁ 푐푠푐휃 푌 (푍) = 퐻푇 퐶(푍) 푡 휀 푑퐸 + f 휎 퐴 푍 푍(푖푛푡) 푆(퐸) 퐿푖 푍푖 (휇 푐푠푐휑 + 휇 푐푠푐휃) 푍 퐸표 푖=1 1푚푖 푚푖 (1.3)

8 Y(Z) represents the experimental X-ray yield for a given element. This is the measured peak area (counts/second) and is a fixed value. H is the instrumental constant and was determined during the calibration procedure using a subset of calibration GRMs. It describes parameters that cannot be accurately determined, such as the solid angle and the source activity. T is the integration duration in seconds and C(Z) is the concentration of the given element, which is what GUAPX is ultimately calculating. N퐴푣 is Avogadro’s number and

A푍 is the atomic mass of the element in question. 휔푍 is the K-shell fluorescence yield, b푍 is the fractional contribution of the most intense K-line, t푍 is the transmission of the

X-rays through any absorber in the detector (eg. beryllium window), and 휀푍(푖푛푡) is the intrinsic detector efficiency, which is independent of the solid angle. The parameters within the square brackets represent the matrix of the sample. The energy integral term is the

PIXE component of the matrix. It includes the transmission of the characteristic X-rays in the sample on route to the detector (T푍 (E)), the alpha particle ionization cross section

(휎푍 (E)) and the alpha particle stopping power (S(E)). The sum term is the XRF portion of the matrix. It includes the ratio of X-rays to alpha particles (f 퐿), which like the H- value, was calculated during the calibration. 휎푍 is the photo-ionization cross section. It also includes the incoming and outgoing X-ray angles (휃 and 휑 respectively), the mass

attenuation coefficients of the plutonium X-rays within the sample (휇1푚), and the mass attenuation coefficient for the characteristic X-rays leaving the sample (휇푚). Equation 1.3 may be simplified to highlight the key components that will be discussed in this work. The matrix terms have been condensed within the square brackets and several physics terms have been combined into F퐴푃 (Z). The simplified yield equation is represented by equation 1.4.

푌 (푍) = 퐻푇 퐶(푍)퐹퐴푃 (푍)[푀푃 퐼푋퐸(푍, 푔푒표푚) + 푀푋푅퐹 (푍, 푔푒표푚)] 휀(푍) (1.4)

This equation, and all of the physics it entails, has been incorporated into a program

called GUAPX, which is based upon the widely employed PIXE-based GUPIX program [48].

GUAPX has the additional capability of XRF analysis. Through the yield equation, spec-

9 trum peak areas are converted to concentrations.

GUAPX can be run in two modes: fixed matrix (FM) and iterative matrix (IM). When

GUAPX is run in FM mode, the H-value is held constant at the value determined during calibration. When GUAPX is run in IM mode the program forces the oxide concentration sum to 100 wt% (the closure rule). To do this, it alters the predetermined H-value until the concentration sum is properly normalized. The matrix term is also treated differently, depending on the mode of operation. To use FM mode, the sample composition must be known beforehand, like in the case of the geochemical reference materials used in the calibration. The matrix term is determined using the certified concentrations, which are input directly to the program, including both visible elements (Z ≥ 11) and invisible elements in the sample (Z <11). For IM mode fits, the sample is entirely unknown; therefore, the matrix term is unknown and must be iteratively determined.

The matrix term, referred to as Y1 or the theoretical X-ray yield throughout this work, forces a major assumption upon sample analysis, which is also shared by the semi-empirical method and that of Franzgrote. Materials analyzed are assumed to be homogenous at the micron scale. Since the primary use of the MSL APXS instrument is to study the rocks and soils of Mars, which are by nature heterogenous, this assumption is presumably broken.

A primary goal of this thesis is to address this problem. The body of work presented here describes the calibration of the MSL APXS for both the pre-flight model (PFM), which is now on Mars, and the flight equivalent unit (FEU), which has remained on Earth.

The calibration utilized over sixty geochemical reference materials (GRMs). These

GRMs are well characterized powders of various terrestrial rocks, minerals, and sediments, augmented with chemical compounds. Most of these materials are also heterogeneous on the distance scale of the exciting radiations, which has allowed for the first in-depth study of the effects resulting from the homogeneity assumption. From the calibration, these effects showed well defined rock type dependence, leading to their classification as mineral phase effects (MPEs). Combining sample mineralogy, provided by X-ray diffraction (XRD)and

Rietveld refinement, and the bulk elemental chemistry for GRMs, the MPEs were foundto originate in the theoretical yields; they have been quantified for five simple GRMs. This work has led to the development of a computer program that simplifies the calculations re-

10 quired to quantify MPEs, allowing for a more extensive study of the effects in the calibration sample suite. New work has commenced on MPEs using the Guelph proton microprobe facility, with promising initial results. The application of MPE quantification is demonstrated with select MSL APXS targets where XRD has been provided by CheMin.

This work also covers the calibration of the plutonium L훼 scatter peaks (Figure 1.3). In the MER mission it was shown that additional light invisible components (ALICs), or in

other words elements and compounds composed of elements lighter than sodium (Z=11),

could be quantified using Compton and Rayleigh scatter peak ratios [9]. The K-value method

used for MER was reproduced here for MSL. A study of this method is presented, which

discusses new effects and considerations, advancing the understanding of ALIC calculation

in APXS spectra. Comparisons with the Sample Analysis at Mars (SAM) and Dynamic

Albedo of Neutrons (DAN) instruments on-board Curiosity have provided a recalibration to

the terrestrial calibration. This K-value calibration has been applied to select Martian MSL

APXS targets and ALIC values are presented.

11 Chapter 2

Elemental Calibration of the Mars Science Laboratory Alpha Particle X-ray Spectrometer by a Fundamental Parameters Approach

2.1 Introduction and Overview

The use of in situ scientific instrumentation in space exploration is essential for understanding the nature of planetary bodies. The greatest challenge with operating an instrument from such great distances is that if an issue arises, it is not easy to troubleshoot and correct for the issue. That is why it is essential to have two theoretically identical, operating instruments: one flight unit and one terrestrial laboratory unit. A thorough calibration of both instruments, as well as a cross calibration relating the two, will lay the foundation for a successful mission for that instrument.

The primary calibration of the MSL APXS for both the pre-flight model (PFM), the instrument that is now aboard the MSL rover on Mars, and the flight equivalent unit (FEU), which is the laboratory instrument that has remained on Earth, began in 2008, well before the MSL launch date in 2011. The primary calibration process outlined in this chapter spanned three years, after which it was felt that the instrument was sufficiently characterized for the mission. The second phase of characterizing the APXS instrument began shortly after the primary calibration, is ongoing today, and will continue in the future. The purpose of this second phase is to provide support for unique observations on Mars that were not

12 predicted prior to landing, to troubleshoot issues that arise with the PFM instrument on

Mars, and to increase our understanding of the details of the instrument.

The overarching mission goal of MSL is to determine the habitability of the landing region on Mars [29]. The Gale crater MSL landing site was selected because of its stratified central mound that, from orbit, appears to contain sedimentary materials with mineral as- semblages (including phyllosilicate signatures) that may record an early, wet environment that transitioned over time to a more acidic and eventually dessicated environment, characterized by sulfate and iron oxide minerals respectively [51]. The role of the APXS in the

MSL mission is to provide reliable, quantitative bulk chemistry of the rocks and soils of the landing site. It is able to identify key elements that indicate the presence of the clays, sulfates, and iron oxides that are important in accomplishing the mission goal. This chapter will provide a detailed account of the geochemical reference materials that were selected to calibrate the PFM and FEU instruments, the process of the calibration itself, and the cross calibration of the two instruments.

2.2 Geochemical Reference Materials

Approximately sixty terrestrial geochemical reference materials (GRMs) provided by suppliers around the world have been used to calibrate the MSL APXS (Table 2.1). They were selected to include and expand upon the GRMs used for the MER APXS calibration. This would ensure a thorough calibration covering expected rock types at Gale crater, as well as provide the ability to systematically cross-calibrate between the MER and MSL instruments at a future date. Elements ranging from sodium to yttrium, are well represented by this calibration suite and are of geologic importance on Earth and Mars. A wider variety of rock types is represented in this MSL calibration set, which has allowed for better low atomic number element calibration (sodium to silicon) and trace element calibration, as well as increase our database of matrix effects that the APXS instrument may encounter.

In particular, a large selection of basalts, sedimentary materials, and a few special reference materials have been collected for this calibration, as these materials were predicted to be the primary rock types at Gale Crater, Mars [29].

13 There is also a larger selection of GRMs with additional light invisible components

(ALICs). These have been useful in the calibration of the APXS L훼 scatter peaks, presented in Chapter 5, which may be used to estimate ALIC abundance. The calibration GRMs were grouped into nine main categories, which are summarized in Table 2.1. These categories have remained consistent with those used in Campbell et al. [12]. The igneous rock types were placed in categories based on their placement on the Total-Alkali-Silica (TAS) diagram.

Figures 2.1 and 2.2 plot the GRMs with both extrusive and intrusive regions respectively.

The calibration GRMs have been grouped by their rock type as follows: Minerals∙, granites

H, anorthosite ∙, andesites , trachytes ∘, high alkali rocks (HAR)∙, basalts H, Icelandic basalts (IB) ▽, ultra mafics (UM) ×, and sediments ∙.

Figure 2.1: TAS diagram with extrusive igneous classification. R represents the rhyolite region, D the dacite region, T the trachyte region, A the andesite region, B the basalt region, and UM the ultra mafic region. Combinations of these letters represent regions with similarities to both rock types.

The GRM classification in the extrusive TAS diagram appears incorrect for some cases.

When the TAS regions are adjusted to the intrusive igneous case,our GRM classification is correct. This adjustment is logical as the GRMs used in this calibration are, for the most

14 Figure 2.2: TAS diagram with intrusive igneous classification. Gr represents the granite region (equivalent to the extrusive rhyolites). D represents the diorites (equivalent to the extrusive andesite group). Ga represents the gabbro region (equivalent to basalts). S represents the syenite region (equivalent to trachytes). UM is the ultra mafic region. part, intrusive. Some GRMs appear to be categorized incorrectly in Table 2.1 based on their certificate description; however, they are correctly categorized based on the intrusive TAS classification.

GRM Supplier Detailed Classification Mineral AL-I SARM Albite DT-N SARM Disthene (Kyanite) FK-N SARM Potassium Feldspar GL-O SARM Glauconite Mica-Fe SARM Biotite Mica-Mg SARM Phlogopite UB-N SARM Serpentinite ZW-C SARM Zinnwaldite SRM70a NIST Potassium Feldspar Apatite WARD’S Apatite Clinochlore WARD’S Clinochlore High Alkali SY-4 CCRMP Diorite Gneiss GBW07109 CNACIS Syenite Anorthosite AN-G SARM Anorthosite

15 Granite/Rhyolite AC-E, GA, GH, GS-N SARM Granite GSP-2 USGS Granodiorite QLO-1 USGS Quartz Latite JG1a GSJ Granodiorite Trachyte ISH-G, MDO-G SARM Trachyte Andesite DR-N SARM Diorite AGV-2 USGS Andesite BCR-2 USGS Basalt JA-2, JA-3 GSJ Andesite VS2115-81 (MO1) AS-IGEM Diabase VS2118-81 (MO4) AS-IGEM Gabbro GBW07104 CNACIS Andesite Gabbro/Basalt PM-S SARM Microgabbro WS-E SARM Dolerite SRM688 NIST Basalt JGB-1 GSJ Gabbro BHVO-2, BIR-1a USGS Basalt DNC-1 USGS Dolerite 1045-94 (MO14), 1017-94 (MO15) AS-IGEM Basalt SARM5 SACCRM Pyroxenite Ultramafic BE-N SARM Basalt DTS-2b USGS Dunite SARM6 SACCRM Dunite SARM39 SACCRM Kimberlite VS2113-81 AS-IGEM Hornblendite Sediments and Sedimentary Rocks JSd-2 GSJ Stream Sediment JSl-1, JSl-2 GSJ Slate JLK-1 GSJ Lake Sediment JMS-1 GSJ Marine Sediment JSO-1 GSJ Soil MAG-1 USGS Marine Sediment GBW07315, GBW07316 CNACIS Marine Sediment Special Reference Materials GBW07296 CNACIS Polymetallic nodule SRM694 NIST Western Phosphate Rock GYP-D DOM Gypsum BX-N SARM Bauxite FER-3 GSC Iron Formation W-AP WARD’s Apatite

CaSO4 Calcium sulfate K2SO4 Potassium sulfate BaSO4 Barium sulfate Na2SO4 Sodium sulfate BHVO-2 + CuCl-1 P. L. King 1.5 wt% Cl mixture BHVO-2 + CuCl-2 P. L. King 3.0 wt% Cl mixture BHVO-2 + CuCl-3 P. L. King 4.5 wt% Cl mixture

16 Table 2.1: GRMs used in the calibration of the MSL APXS [12]. Suppliers are as follows: SARM: Service d’Analyse des Roches et des Mineraux, CRPG-CNRS, Nancy, France; USGS: United States Geological Survey,Denver, Colorado; NIST: National Institute of Standards and Technology, Gaithersburg, Maryland; GSJ: Geological Durvey of Japan; SACCRM: South African Bureau of Standards, Pretoria, South Africa; CCRMP: Canadian Certified Reference Materials Project, CANMET, Ottawa, Canada; CNACIS: China National Analysis Center for Iron and Steel, Beijing, China; AS-IGEM: Academy of Sciences: Institute for Geology of Ore Deposits, Petrography, Min- eralogy and Geochemistry, Moscow, Russia; DOM: Domtar Research Center, Senneville, Quebec, Canada; WARD’s: Ward’s Natural Science, USA; P.L. King: Mixtures supplied by Professor P.L. King

Some of the GRM suppliers could only provide provisional concentrations for trace elements and several of these values were over a decade old. Both of these facts suggest the usefulness of a highly sophisticated modern analysis. To be sure the “known” concentrations were as accurate as possible, all GRMs used in the calibration were sent to Activation

Laboratories Inc. in Ancaster, Ontario. Various analyses were performed (primarily metab- orate/tetraborate fusion with ICP/MS) to obtain accurate elemental and ALIC concentrations. These concentrations were compared to the certificate concentrations provided by the

GRM suppliers and are summarized in Table 2.2. In most cases the Activation Laboratories concentrations for both major and minor elements agreed with the supplier concentrations within error. Because of the similarities between these two “known” concentration datasets and because the Activation Laboratory data arrived after the primary calibration work was nearly completed, this calibration is based on certificate concentrations. We will later examine relevant differences between the two data sets.

2.3 Sample Preparation

A proper calibration requires that the sample material be prepared and analysed in a precise and repeatable way. Before preparing a sample of any GRM, each was sieved through a thoroughly cleaned 250 휇m sieve to ensure the sample was as homogeneous as possible. This step was precautionary, since most suppliers had sieved their material to approximately

17 Element Certificate/Act. Labs. Element Certificate/Act. Labs. Na 1.04 ± 0.03 Cr 1.00 ± 0.09 Mg 1.03 ± 0.03 Mn 1.01 ± 0.05 Al 1.02 ± 0.02 Fe 1.01 ± 0.02 Si 0.999 ± 0.009 Ni 1.1 ± 0.2 P 1.00 ± 0.04 Cu 1.0 ± 0.1 S 1.1 ± 0.1 Zn 1.1 ± 0.1 Cl 0.9 ± 0.2 Ga 1.0 ± 0.1 K 1.01 ± 0.02 Rb 0.98 ± 0.06 Ca 1.01 ± 0.03 Sr 1.01 ± 0.04 Ti 1.01 ± 0.05 Y 1.1 ± 0.2 Table 2.2: Comparison of certificate concentration means to concentration means produced by Activation Laboratories Inc. Errors are one standard deviation.

75 휇m in the process of homogenizing the reference material [22]. In some cases material was left in the sieve; this step was important and could not be omitted.

Once the reference material was sieved, each sample was prepared in the same fashion (see

Figure 2.3). Each sample was poured into a circular aluminum dish of 40 mm diameter and

13 mm height. The sample was poured over a piece of 1.6 mm FR4 circuit board material, which contains a high concentration of bromine (yellow layer in Figure 2.3). This bromine- rich material was key in making sure the sample was thick enough, since the fundamental parameters calibration method requires an infinitely thick sample. Observation of a bromine signal in excess of that expected is an indication that 240Pu X-rays have penetrated through the sample, excited the bromine in the FR4 material to produce characteristic X-rays, and those characteristic bromine X-rays have then escaped the sample and been detected by the

APXS. Felsic rock, mineral, and chemical compound reference materials are at greatest risk of not being thick enough, and two reference materials had to be made with aluminum dishes with a greater depth than 13 mm. In the few cases where excess bromine was observed, the sample was remade with greater thickness (e.g. SY-4). Thus, all samples used in the calibration may be considered infinitely thick. To reduce the amount of material required for mafic samples, where the escape depth ofthe 240Pu X-rays is very shallow, an aluminum

spacer of either 2.4, 3.4, or 4.4 mm thickness was placed under the FR4 material (grey layer

in Figure 2.3).

18 Figure 2.3: Cross section of the sample dish set up. The grey bottom layer is the aluminum spacer, the yellow layer is the bromine rich FR4, and the brown layer is the powdered reference material.

Once the sample was poured over the spacers in the aluminum dish, a thoroughly cleaned teflon press was used to manually smooth the surface of the sample. Teflon wasusedto prevent any contamination of elements greater than Z=11, since teflon is composed entirely of elements below those that the APXS can detect directly. The teflon press was designed to press the powdered sample 3.5mm below the lip of the aluminum dish so that each sample had uniform geometry. The sample mass was recorded and then each sample was heated

∘ − at 110 C for at least two hours to drive off any adsorbed water (H2O ), but prevent any

+ significant loss of bound water2 (H O ). After the sample had been heated, its mass was recorded again and it was placed in a desiccator to await analysis.

Samples with elevated concentrations of ALICs underwent the “explosion chamber” test.

This step ensured that the surface of the sample would not be disrupted when exposed to vacuum during analysis, which could potentially damage the APXS instrument. After heating, the sample was placed in a vacuum chamber, which was then evacuated. The sample was left in vacuum for at least an hour. After observation that no movement had occurred, the chamber was returned to atmospheric pressure, the sample was reweighed, and was placed in a desiccator to await analysis.

The reproducibility of the geometry using this sample procedure was tested with four

SiO2 samples. Figure 2.4 shows the repeatability of the silicon concentration produced by GUAPX in FM mode. The upper figure (Figure 2.4a) shows the relationship of the R-value

(ratio of the experimental concentration to the ideal concentration) to the fractional oxide sum for each SiO2 sample. A clear relationship between the silicon concentration and the

19 (a)

(b)

Figure 2.4: Reproducibility of sample geometry using the standard sample procedure. fractional oxide sum is demonstrated. The fractional oxide sum is not uniform among all four samples, indicating that the sample geometry was not identical in each case; two of the samples were closer to the detector than the ideal case, resulting in a higher detection rate and, therefore, oxide sum. This geometric variability of the sample can contribute about

±5% error in the elemental concentrations. Further investigations showed that heating the sample caused a very minor amount of surface relaxation and the “explosion chamber” test caused a slightly larger amount of surface relaxation. Since only samples with high ALIC content were tested in the vacuum chamber, most samples would not experience this larger

20 surface relaxation. Even if a sample was tested in the vacuum chamber before analysis, the geometric variability, represented by the deviations in the fractional oxide sum, would still be within the ±5% error. If we normalize the experimental silicon concentration to the total fractional oxide sum, the R-values all agree with the expected R-value of 1.0 within error (Figure 2.4b). At first sight this appears a trivial result, but it is useful to demonstrate the close agreement of SiO2 with the unity value imposed on the GRMs.

2.4 Instrument Parameters Required for the GUAPX Fitting

Program

The GUAPX fitting program utilizes the fundamental parameters approach to spectrum

fitting, which requires an extensive physics database, together with accurate knowledge of

the detector and setup geometry, to produce logical results. Unfortunately, many of the

FEU instrument parameters were not provided, so these gaps in our knowledge had to be

determined via other methods. The steps taken to properly quantify various properties of

the detector, sources, and geometry will now be addressed.

2.4.1 Defining the Transmissivity (푡푍 ) of the APXS Window

For the X-ray yield equation used in the GUAPX fitting program to be accurate, a well

characterized instrument is essential. The program uses a file that defines key detector

parameters; these detector parameters must be input correctly from provided information,

or deduced through other means if they have not been provided.

One parameter that is crucial to calculating correct concentrations, particularly for the

lighter elements, is the transmissivity (푡푍 ) of the incoming characteristic X-rays through any absorber before reaching the detector. The absorbers that characteristic X-rays must pass

through before reaching the detector are the beryllium window, nitrogen gas, an incomplete

charge collection (ICC) layer on the surface of the detector, and the detector contact [38].

The thickness of the beryllium window and density of the nitrogen gas directly in front

of the detector contribute the largest effect. Ketek provided some of these values, but

21 these parameters generally need refinement by users if the best accuracy is desired. Most

Ketek detectors of similar make and model to the MSL APXS instruments were made with nominally 8 휇m thick beryllium. This thickness was not sufficient to describe the light element concentration results in our preliminary analyses. Further research indicated that detector windows are Duraberyllium®, which comprises a beryllium foil coated by a “proprietary refractory material consisting of elements lighter than sodium” [21]. Using transmission curves provided by Moxtek and digitized by the author, it was found that the transmissivity of an 8 휇m Duraberullium® window is similar to the transmissivity of a 10.2

휇m beryllium window [8, 21]. This adjustment corrected the issues with the light element concentrations. Despite not knowing the specifics of the other absorbers, standard detector values were used in GUAPX and reasonable concentrations were produced. Any residual errors will show up in deviations of the light element concentrations from the certificate values.

2.4.2 Effective Angles for the MSL APXS

GUAPX is based on the well known GUPIX software program used for PIXE analysis [48].

PIXE experimentation is typically performed in a particle accelerator laboratory using a well defined incoming and outgoing beam configuration. In the case of the APXS, there aresix sources that emit radiation isotropically. A large area of sample can be irradiated by these sources. With a typical detector-to-sample standoff distance of 18 mm, characteristic X-rays from an approximately 17 mm diameter area can be observed by the detector. Omand et al. [57] describe a Monte Carlo method for determining effective angles that can be used by

GUAPX to simulate the well known geometry of beam line experimentation. The code has since been refined by S. Andrushenko [1]. Using this method, the FEU APXS detector and standard geometry (21.5 mm detector to sample standoff) produced an incoming radiation angle (to the normal of the sample surface) of 19∘ and an outgoing characteristic X-ray angle

(to the detector) of 24∘. In the laboratory, the geometry is consistent with a sample-to- detector standoff of 21.5 mm. On Mars, the standard in-contact sample-to-detector standoff is 18 mm. This in-contact placement is not always reliable, and often there must be a standoff between the instrument and target. S. Andrushenko calculated theoretical yields

22 with the MSL effective angles, varying the sample-to-detector standoff up to 40 mm.Only yields for the lightest elements showed any difference with increasing standoff; however, the differences are minimal. Magnesium to silicon showed only 1% difference in yieldsfrom in-contact to 40 mm standoff and sodium showed a slightly larger difference of 4% [12].This is not of concern as these element concentrations are subject to much larger influences that will be discussed in detail in Chapter 3.

2.4.3 훼/퐿푥 and H-value Determination

In addition to accurate detector and geometry parameters, GUAPX requires information about the sources, particularly their activity. The MSL APXS has three 10 mCi open

244Cm sources, covered by an approximately 3 휇m titanium cover foil, and three 10 mCi closed 244Cm sources. This was a deliberate improvement by Professor R. Gellert to the

MER rover APXS source configuration of six open 5mCi 244Cm sources with thin titanium cover foils [12, 25]. By sealing half of the 244Cm, only three of the six sources are able to emit alpha particles, thus increasing the detection of XRF excited elements relative to those elements excited by PIXE. The disadvantage of this change is that the 훼/L푥 ratio, which is known to high accuracy for the MER sources and agrees with the expected ratio to within 1.5% [16, 39], is no longer known. This ratio represents the fraction of 훼 particles relative to X-rays that are emitted by the curium sources. The sources are each nominally 10 mCi, however the initial source strength may vary from this nominal value. To complicate matters, some of the sources used were older than others, and it was not known if the older or younger sources were left open or closed. Thus, the 훼/L푥 ratio, along with the H-value, which includes the source activity and solid angle, must be experimentally determined.

The 훼/L푥 and H-value were experimentally determined by forcing both the silicon R- values (excited 99% by PIXE) and iron R-values (excited aproximately 95% by XRF) to 1.0 for a subset of GRMs. The GRMs, including some compounds, were different for both iron and silicon and were specifically selected to represent the best samples for each element. For the primary FEU calibration (with high energy cut off), where the sources were all nominally

10 mCi, the GRMs and oxides used to force the silicon R-value to 1.0 were: silicon powder, silicon disk, SiO2-1, SiO2-2, SiO2-3, SiO2-4, SiO, FeSi2, Si3N4, AL-I, DT-N, FK-N, GL-O,

23 Mica-Mg, Mica-Fe, NIST70a, and UB-N. The GRMs and oxides for iron were: Fe2O3, iron powder, FeS, iron disk, DT-N, FK-N, GL-O, Mica-Mg, Mica-Fe, and UB-N. This selection

of homogeneous materials averted any complications due to mineral phase effects (e.g. in

basalts). Several iterations of adjusting the H-value and 훼/L푥 were completed to minimize the difference between the Si and Fe R-values and to get Fe R-values as close to1.0as

possible. The R푆푖(푎푣푔) for the selected Si GRMs came to 1.003 and R퐹 푒(푎푣푔) for the Fe

GRMs came to 0.999. The H-value was 0.2246 and the 훼/L푥 ratio was 5.3. This FEU setup will be referred to as the original FEU calibration. Since this portion of the calibration

spanned approximately 3 years (May 29, 2009-December 31, 2011), the source decay was

accounted for by adjusting the H-value by the exponential decay equation (equation 2.1),

where the half life of 244Cm is 18.11 years, or 6610 days. Here, t is the time, in days, that

has passed since the initial set up and calibration of the H-value and 훼/L푥 ratio, which was

set for May 29, 2009. H(t) is the time adjusted H-value and 퐻∘ is the initial H-value of 0.225.

1 (푡/6610) 퐻(푡) = 퐻 (2.1) ∘ 2

In June 2011 the 3x10 mCi closed sources were removed and placed in the PFM to go to Mars. They were replaced with a 1x30 mCi closed source. The energy range of the

FEU instrument was extended in January 2012 to replicate the energy range of the PFM instrument, which included all of the L훾 scatter peaks. This adjustment to the sources and energy range required a recalibration of the H and 훼/L푥 ratio. Only GRMs were used for this calibration, as the oxides had not been re-run with the new source and energy configuration.

The GRMs used to calibrate both silicon and iron were AC-E, AGV-2, BCR-2, BE-N, DTS-

2b, Mica-Fe, Mica-Mg, QLO-1. We did not have to worry about any sample placement effects for this cross calibration since the same samples were used for both analyses. Each sample therefore had the same surface placement. To make sure we could easily cross calibrate these two FEU configurations, the H-value and 훼/L푥 ratio were adjusted to replicate the iron and silicon R-values obtained from the original calibration for this specific subset of GRMs. The

H-value for this FEU configuration was found to be 0.167 and the 훼/L푥 was 6.3. Running the

24 same sample a few years apart using the appropriate H and 훼/L푥 values produced excellent agreement for the major elements compared to the certificate concentrations (differences of

only 1% were observed). This set up must be used for all GRM analyses run since January 2012 up to summer 2014, when a new detector was inserted. It will be referred to as the

2012 FEU calibration. The H-value is time adjusted for the source decay as outlined for

the initial calibration based on equation 2.1, with the initial H-value of 0.167 representing

t = 0 on January 1, 2012. The original calibration values were used for the work done in

this chapter, as the primary calibration was performed with this configuration of the FEU

instrument.

2.4.4 FEU Low Energy Cut Off Correction

Not only did the FEU originally have a high energy cut-off that interfered with the proper

calibration of the scatter peaks, but the instrument also had a stepped cut off in the low

energy region that remained after the January 2012 adjustment. This low energy cut-off is

very close to the sodium peak, leaving too few channels to its left for the top-hat filter to

properly handle the background in this region. As a result, the sodium concentration given

by GUAPX was consistently low. To correct for this we manually flattened out the left hand

end of the spectrum, starting at the channel with the minimum count rate and extending

this minimum value out at least seven channels (Figure 2.5). This adjustment improved the

sodium R-value considerably.

The PFM instrument sent to Mars does not have this low energy cut-off issue; only FEU

spectra need to be adjusted to replicate the PFM low energy region and provide enough

channels to the left of the sodium peak for the background filter to operate properly.

2.4.5 Spectrum Calibration in GUAPX

The relationships between the Guassian peak position (Channel) and X-ray energy (keV),

and peak width and X-ray energy are given by equations 2.2 and 2.3.

퐶ℎ푎푛푛푒푙 = 퐴1 + (퐴2 * 퐸) + (퐴3 * 퐸2) (2.2)

25 (a)

(b)

Figure 2.5: BCR-2 spectrum with low energy cut off (a) and extended low energy region (b).

Width2 = A4 + A5 * E (2.3)

E represents the energy of the channel in question and A1 to A5 are variables determined

through calibration. We originally set and fixed A3 to zero for two reasons. First, we

had no reason to expect that equation 2.2 would be anything but linear for the APXS.

Second, this quadratic term was introduced into GUPIX to deal with electronic effects

observed by a few users using the previous generation of electronics. In recent PIXE work

by Dr. C.M. Heirweigh and Professor J.L. Campbell on geologic samples, the Ketek detector

employed displayed a very small non-linearity [33]. Some improvement resulted from use of

the quadratic term. For the present work we used the following starting values for these

26 parameters in the GUAPX least-squares fit:

A1 = -24

A2 = 48

A3 = 0 (fixed)

A4 = 8

A5 = 1

As mentioned, the FEU underwent a major change in the energy range and source configuration around January 2012, so the initial energy calibration of the FEUAPXS needed to be adjusted for any spectra collected after this time. The new A-values for any spectrum collected with the FEU after January 2012 are listed below:

A1 = -22

A2 = 44

A3 = 0 (fixed)

A4 = 9

A5 = 1

The PFM, being a different instrument with different sources, has different A-values than the FEU. The following are the A-values used to fit Martian spectra:

A1 = -13

A2 = 37

A3 = 0 (fixed)

A4 = 4.3

A5 = 0.6

27 2.5 GUAPX Calibration Procedure

The GUAPX fitting program can be used in two modes: fixed matrix (FM) modeand iterative matrix (IM) mode. FM mode is used when the matrix of the sample is known, as with the calibration GRMs. The known sample matrix is given to the program, including invisible components; GUAPX does not have to calculate the matrix on its own. The final oxide sum is not forced to 100 wt% by the program; however, it should be 100 wt% if everything has been correctly accounted for. In the case of standards with minimal ALICs, any deviation of the oxide sum from 100% is very likely due to placement variation and can be dealt with by normalization to 100 wt%.

On Mars we use IM mode since the matrix of the sample is unknown. In this case, the program goes through several iterations; in each iteration the concentrations are changed and the fit is optimized. The program repeats this fitting procedure until the chi squareis minimized and the concentrations for the elements included in the fit list remain consistent.

For this procedure to be effective, the closure rule must be implemented. This means that the oxide total must sum to 100 wt%. In this method the H-value is not held constant.

Any adjustment to the H-value is displayed in the output. This adjustment will be mostly due to placement discrepancies and source activity decay. IM mode is very reliable when the sample is measured in near standard geometry and when the sample does not contain

ALICs. If a sample contains a large quantity of ALICs, the normalized oxide sum, which only normalizes oxides that are detectable, will be wrong. This will be discussed in depth in Chapter 5. Several of our calibration GRMs contain significant ALICs; again FM mode, which does not enforce the closure rule, is the better method to use for the calibration.

GUAPX is set up using the parameters outlined in section 2.4, as well as the sample matrix, which includes invisible components (i.e. H O+, CO , F, NO−) provided in most cases 2 2 3 by the supplier certificates and in a few (mainly high ALIC) cases provided by Activation

Laboratories. If GUAPX is properly set up with accurate detector parameters, H and 훼/퐿푥 values, a properly calculated matrix, and a reliable database, the output concentrations should equal the concentrations provided by the GRM supplier; in other words the ratio of the GUAPX concentration over the certificate concentration (R-value) should be 1.0.

28 In the initial phase of the FEU calibration, some manual adjustments had to be made to the GUAPX concentrations before determining the final R-value. Since we are operating in FM mode, the H-value remains constant. We must first adjust our H-value to account for source decay, since our calibration standards were measured over three years, resulting in an approximately 7% decrease in source activity. This activity adjusted H-value is used in

GUAPX and concentrations are produced. From the SiO2 experiment discussed in section 2.3 we learned that concentrations need to be corrected for placement inconsistencies. Each element concentration was multiplied by the fractional total oxide sum, which normalized the total visible oxide sum from GUAPX to the certificate total visible oxide sum. This normalization takes care of any sample placement effects. Finally, element offsets are applied and the total visible oxide sum is renormalized to the certificate visible oxide sum. Error propagation was carried through all adjustments and the final GUAPX concentration and

R-value was recorded with a two sigma error.

2.6 Element Concentration Offsets in the FEU

We have described our best efforts to maintain cleanliness during the sample preparation, and thus prevent “contamination” in our spectra. However, extraneous contributions do occur from various processes within the APXS [12]. These are described in the following subsections and summarized in Table 2.3. The majority of this work was completed by

Professor J. L. Campbell and has been included here for completeness. In each case, a plot of measured concentrations of a given element in the GRMs versus certificate concentrations was linear with a finite vertical intercept. This intercept is the concentration offset inthe

FEU laboratory geometry.

29 Offset (ppm) P 500 ± 200 Ca 1720 ± 100 Ti 1600 ± 100 Cr 75 ± 20 Ni 7.5 ± 4 Cu 8.5 ± 3.5 Y 6.7 ± 2 Table 2.3: Instrument offsets applied to both the FEU and PFM MSL APXS analyses.

2.6.1 Aluminum Offset

Figure 2.6: Aluminum Offset

Figure 2.6 plots the measured aluminum concentration in the GRMs against the certificate concentrations. The intercept on the vertical axis corresponds to a concentration of

(1420 ± 100) ppm. This offset is likely due to excitation of aluminum components ofthe sample chamber, stage, and aluminum sample dishes. Since these items are not present on

Mars, we do not correct for an aluminum offset with the PFM.

30 2.6.2 Calcium Offset

The calcium offset is a result of scattered 240Pu M X-rays off the sample, which are then

detected by the APXS. The calcium K훼 X-ray energy is 3.31 keV and the K훽 X-ray energy

is 3.59 keV [69]. The energies for the two most intense plutonium M X-ray lines, M5N7 and

M4N6 are 3.35 keV and 3.54 keV respectively [69]. The relative intensities of these two lines are 1:0.67. The plutonium M peaks overlap the calcium K peaks. An attempt to include the

weak plutonium M lines as a feature in the fit failed, so an offset is applied to the calcium

concentration instead.

2.6.3 Titanium Offset

The offset for titanium is relatively large due to the thin titanium cover foils onthethree

open 244Cm sources. These foils are directly excited by the sources. Some of the emitted

characteristic X-rays from the titanium foils are able to scatter off the sample and return

back to the APXS instrument where they may be detected.

2.6.4 Chromium, Nickel, and Copper Offsets

The offsets for chromium, nickel, and copper result from the same process. Materials inthe

sensor head that are close to the detector and contain these elements are excited and emit

characteristic X-rays. Chromium, nickel, and copper are excited by plutonium X-rays that

have scattered from the sample surface back up to the detector.

2.6.5 Yttrium Offset

The yttrium offset is quite small and was not considered to be much of an issue until

calibration of the L훼 scatter peaks began. Since the yttrium K훼 peak (14.93 keV) directly

overlaps the rubidium K훽 peak(14.96 keV), whose K훼 peak (13.38 keV) resides on the left

hand side of the plutonium L훼 Compton scatter peak ( 13.5 keV), the offset in yttrium becomes important in ultimately producing the most reliable scatter peak ratios [69].

31 2.6.6 Phosphorous Offset

The phosphorous offset in the FEU APXS is due to the direct overlap of the zirconium Lpeak with the phosphorous K peak. The zirconium L peak is produced by three processes. The most dominant process is the excitation of the zirconium collimator by potassium, calcium, titanium, manganese, and iron characteristic X-rays that approach the detector from the sample. These characteristic X-ray energies are just above the zirconium L binding energy and are, therefore, very likely to excite L-shell electrons of the zirconium in the collimator.

As a result, the phosphorous offset has some dependence on the sample matrix. Zirconium

L lines are also produced through secondary excitation when an L-shell electron falls to fill the hole left by the ejected K-shell electron, leaving an L-shell hole that mustnowbe filled by electrons in higher order electron orbits. The most minor contribution to zirconium

L production is through the excitation of the collimator by plutonium X-rays that have scattered off the sample.

Since the majority of GRMs in the calibration suite contained too little phosphorous to apply the plotting method used to determine the other offsets, GRMs containing zero phosphorous were selected to determine the offset. These GRM spectra were fitted andthe average apparent phosphorous was taken to be the offset.

2.6.7 Final Remarks

We have assumed that these offsets apply to the PFM on Mars. The titanium may notbe highly accurate since there is no guarantee the foils used on the sources sent to Mars are the same thickness as those used on the sources that were used in the calibration here on

Earth.

All offsets are corrected for varying count rates due to increased standoff in theGUAPX program. For example, if an increased standoff results in a total oxide sum of 25 wt%, then the offset concentrations should be decreased to 25% of the listed values in Table 2.3.Once these reduced offsets have been applied, all concentrations are then renormalized toproduce an oxide sum of 100 wt%.

Work by Professor J. L. Campbell on the FEU has shown that the zirconium L count rate

32 falls, along with that of other elements, as the sample standoff is increased. In contrast, the zirconium K count rate falls very slowly. This indicates incomplete shielding of the detector from the sources. It also renders use of the zirconium L ratio to calculate the zirconium L offset in phosphorous questionable.

2.7 FEU Calibration Results and Discussion

The FEU calibration results, using FM mode for the detectable major (element is present in greater than 1.0 wt% abundance), minor (element is present between 0.1 and 1.0 wt%), and trace elements (less than 0.1 wt% abundance) [78], are summarized in Table 2.4 as the ratio of experimental concentrations to certificate concentrations, which we have called the

R-value. The only GRMs that do not utilize certificate concentrations in this calibration are the Icelandic basalts. Their concentrations were provided by Activation Laboratories.

Table 2.2 shows that in most cases Activation Laboratories concentrations agree well with the certificate values; therefore, the Icelandic basalts are valid as calibration GRMs.The mean R-values listed are the overall error-weighted means for all GRMs used to calibrate that specific element, irrespective of rock type. The “best” R-values represent the weighted mean R-values using only GRMs that do not display any innate issues, such as mineral phase effects (discussed in detail in Chapter 3). The errors associated with themeanR- values are purely statistical, i.e. represent precision, and are two sigma. Accuracy errors are summarized in section 2.9. Only GRMs with concentrations greater than the limit of quantitation (LOQ) have been considered in the calibration (see section 2.8 for details). The number of GRMs used to calibrate each element are listed in column two of table 2.4.

R-value calibration plots for all detectable elements are also presented in this section.

The colour scheme for all R-value calibration plots is consistent and is similar to the colour schemes previously used [10–12, 61]. The calibration GRMs have been separated into eleven groups based on their rock type: Minerals ∙, granites H, anorthosite ∙, andesites , trachytes

∘, high alkali rocks (HAR)∙, basalts H, Icelandic basalts ▽, ultra mafics (UM) ×, sediments ∙, and special cases △ or △. All errors in the following tables and figures are two standard deviations (95% confidence level).

33 Z Number of GRMs Mean R-value Best R-value Na 49 0.96 ± 0.15 0.95 ± 0.051 Mg 51 0.90 ± 0.15 0.98 ± 0.112 Al 59 1.06 ± 0.09 0.98 ± 0.063 Si 60 0.99 ± 0.02 P 15 1.03 ± 0.22 0.91 ± 0.034 S 24 0.91 ± 0.18 0.92 ± 0.045 Cl 23 1.09 ± 0.24 0.99 ± 0.036 K 48 1.00 ± 0.07 Ca 51 0.96 ± 0.06 Ti 41 0.99 ± 0.06 V 3 1.18 ± 0.03 Cr 14 1.01 ± 0.18 Mn 55 0.99 ± 0.09 Fe 56 0.97 ± 0.06 Ni 20 1.0 ± 0.1 Cu 25 0.97 ± 0.14 Zn 53 0.99 ± 0.11 Ga 9 0.97 ± 0.1 As 2 1.13 ± 0.09 Br 5 1.1 ± 0.2 Rb 9 1.07 ± 0.13 Sr 36 1.04 ± 0.11 Y 25 1.17 ± 0.2 Ba 2 0.81 ± 0.06 W 5 0.97 ± 0.08 Pb 18 1.17 ± 0.34 Table 2.4: Mean and “best” R-values for elements calibrated with the MSL FEU APXS. 1. exclude Icelandic basalts, basalts and sediments; 2. exclude Icelandic basalts, basalts, trachytes and andesites; 3. exclude Icelandic Basalts and basalts; 4. 2 Ca-phosphates NIST 694 and Ward’s Apatite; 5. weighted mean of 4 sulfates and GYP-D; 6. KCl chemical compound.

2.7.1 Major Elements

The most abundant major elements that can be detected by the MSL APXS are Na, Mg,

Al, Si, K, Ca, and Fe. Tables 2.5 and 2.6 summarize the average R-values for each major element by rock type. In the majority of geologic materials used for this calibration, the most abundant elements by wt%, other than oxygen, are silicon and iron. These elements should have consistent R-values of 1.0 and Figure 2.7 shows that for most GRMs the R-value is 1.0 within error. This is expected since the H-value and 훼/퐿푥 were determined by forcing

34 Type Si K Ca Fe Minerals 1.00 ± 0.02 1.01 ± 0.03 1.07 ± 0.06 1.03 ± 0.03 HARs 0.99 ± 0.01 1.1 ± 0.2 1.059 ± 0.001 0.96 ± 0.02 Granites 0.99 ± 0.01 1.02 ± 0.05 1.03 ± 0.02 0.97 ± 0.05 Trachytes 1.01 ± 0.01 1.02 ± 0.05 0.90 ± 0.04 0.89 ± 0.05 Andesites 1.00 ± 0.01 1.00 ± 0.03 0.98 ± 0.03 0.97 ± 0.02 Basalts 0.98 ± 0.01 1.01 ± 0.09 0.96 ± 0.02 1.00 ± 0.05 IBs 0.99 ± 0.01 0.88 ± 0.05 0.902 ± 0.006 0.94 ± 0.03 UMs 0.99 ± 0.01 1.01 ± 0.05 1.01 ± 0.02 0.98 ± 0.05 Sediments 1.00 ± 0.04 1.01 ± 0.05 0.93 ± 0.09 0.99 ± 0.08 Overall 0.99 ± 0.02 1.00 ± 0.07 0.96 ± 0.06 0.97 ± 0.06 Table 2.5: R-values by rock type for Si, K, Ca, and Fe.

the silicon and iron R-values in a subset of GRMs to 1.0. Since the majority of silicon and iron R-values are 1.0 within uncertainty, it supports the procedure used to determine these two quantities.

The excellent agreement of silicon and iron concentrations to the certificate concentrations suggests that the other major elements should also be well behaved. Figure 2.8 shows the excellent agreement between the GUAPX and certificate concentrations for potassium and calcium, whose X-rays are excited by both PIXE and XRF.

Type Na Mg Al Minerals 0.91 ± 0.03 0.98 ± 0.03 1.00 ± 0.04 HARs 0.98 ± 0.04 N/A 1.09 ± 0.02 Granites 0.96 ± 0.03 1.1 ± 0.2 1.05 ± 0.03 Trachytes 0.96 ± 0.02 0.64 ± 0.02 1.077 ± 0.009 Andesites 1.00 ± 0.07 0.8 ± 0.2 1.06 ± 0.06 Basalts 1.04 ± 0.09 0.8 ± 0.1 1.17 ± 0.09 IBs 1.36 ± 0.04 0.89 ± 0.08 1.24 ± 0.05 UMs 1.0 ± 0.1 0.97 ± 0.05 1.08 ± 0.08 Sediments 0.81 ± 0.06 1.0 ± 0.1 1.06 ± 0.05 Overall 0.96 ± 0.16 0.9 ± 0.2 1.06 ± 0.09 Best1 0.95 ± 0.05 1.0 ± 0.1 0.98 ± 0.06 Table 2.6: Major element R-values based on rock type: Na, Mg, and Al. (1. See ‘best’ description in text and Table 2.4).

The three lightest major elements detectable by the APXS (sodium, magnesium, and aluminum) do not behave as expected, despite their proximity in atomic number to the well

35 Figure 2.7: Si and Fe R-values. behaved silicon. Figures 2.9, 2.10, and 2.11 show two plots for each of these elements. In each case, the first plot shows the R-value of the element against its certificate concentration.

There appears to be a rock-type dependence, however it is difficult to decipher what the dependence may be when shown in this format. The second plot shows the R-value against the iron certificate concentration, following a suggestion by both Professor R. Gellert and

Professor P. L. King. It is clear for sodium and aluminum that as the iron concentration in the GRMs increases, the measured GUAPX concentration increases as well, relative to the certificate concentration. This is represented by the increasing R-value. Magnesium shows the opposite effect; as the iron content in a GRM increases, the R-value decreases. These

36 Figure 2.8: K and Ca R-values.

effects are the focus of Chapters 3 and 4 where they are discussed in greatdetail.

2.7.2 Minor and Trace Elements

The minor and trace elements that the APXS detects with some confidence are P, S, Cl,

Ti, Cr, Mn, Ni, Cu, Zn, Ga, Br, Rb, Sr, Y, Ba, W, and Pb. Tables 2.7, 2.8, 2.9, and

2.10 summarize the mean R-values for these elements, based on rock type, where at least two GRMs are above the LOQ. The elemental R-values for each GRM have been plotted, showing the scatter and concentration range that was covered for each element. The overall

37 Figure 2.9: Na R-value plotted against Na and Fe certificate concentrations.

weighted mean for each element is represented by the red line.

2.7.2.1 Elements Significantly Excited by PIXE: P, S,Cl

Phosphorous, sulfur, and chlorine are important elements in determining the alteration history of Mars [27, 31, 42]. Most GRMs in our calibration set contain low concentrations for phosphorous, sulfur, and chlorine, and are primarily found as minor and trace elements

(less than 1.0 wt%). Only a few GRMs contain phosphorous, sulfur, and chlorine in greater than 1.0 wt% abundance. Because of the overall lack of calibration GRMs for these three elements, we included several phosphates, sulfates, sulfides, a chloride, and a few mixtures

38 Figure 2.10: Mg R-value plotted against Mg and Fe certificate concentrations.

provided by Professor P. L. King, to the standard GRM suite. These chemical compounds are ideal standards and comparing their R-values with those of the low concentration GRMs helps us to discuss calibration of the instrument for the intermediate concentration ranges detected on Mars.

The “best” values for these three elements were determined through the use of chemical compounds and monomineralic GRMs with large quantities of either phosphorous, sulfur, or chlorine. The R-values for phosphorous, sulfur, and chlorine for these chemical compounds were calculated differently than for the GRMs, however this method is excellent forthese cases. The experimental concentration of the other dominant element in the compound,

39 Figure 2.11: Al R-value plotted against Al and Fe certificate concentrations. other than oxygen, was normalized to the expected certificate concentration. The factor required to match the experimental to certificate concentration, which is primarily a placement correction, is then applied to the experimental concentration for phosphorous, sulfur, or chlorine. These normalized experimental concentrations were compared to the chemical formula value to produce an R-value. Their R-values are not expected to be 1.0 due to the incomplete charge collection (ICC) layer on the silicon drift detector [14, 68]. The ICC layer on the surface of the silicon detector causes attenuation of the incoming characteristic

X-rays with energies very near the K-absorption edge of silicon, primarily for phosphorous, sulfur, and chlorine. As a result, we do not detect as many counts for these three elements,

40 Type P S Cl Minerals (0.8 ± 0.1)1 (0.9 ± 0.2) 1.3 ± 0.1 Igneous 1.27 ± 0.1 0.9 ± 0.2 1.0 ± 0.1 IBs 0.73 ± 0.06 N/A 0.7 ± 0.5 Sediments 1.0 ± 0.1 0.9 ± 0.2 0.9 ± 0.1 Special 0.91 ± 0.032 0.92 ± 0.043 0.99 ± 0.034 Mixtures 0.93 ± 0.01 1.017 ± 0.009 Table 2.7: Minor and trace element R-values based on rock type: P, S, and Cl. 1. Values in brackets indicate cases where only one GRM contains the element in question above the LOQ 2. Special reference materials are NIST 694 and Ward’s apatite

3. Special reference materials are CaSO4,K2SO4, BaSO4, SrSO4, and GYP-D 4. Special compound is KCl.

forcing the R-value below 1.0. In Figure 2.8 the R-values for both potassium and calcium

are essentially 1.0, indicating the ICC layer no longer has an effect.

Two phosphate GRMs were used to calculate the “best” R-value for phosphorous: NIST

694 and Ward’s apatite. These two GRMs produced an average R-value for phosphorous of

0.91 ± 0.03. Three sets of chemical compounds were used to determine the “best” R-value

for sulfur. First, the mean R-value for five sulfates (CaSO4,K2SO4, BaSO4, SrSO4, and GYP-D) was found to be 0.92 ± 0.04. Four basalt-plus-iron sulfide mixtures kindly provided by Professor P. L. King (BHVO2 + FeS2 (2.1% and 6.8% S) and BHVO2 + FeS (2.1% and 7.0% S) produced a mean R-value of 0.93 ± 0.01, which supports the mean R-value found with the sulfates. Three sulfides (FeS, ZnS, and CuS△ ( )) were then analyzed and their mean R-value is 0.86 ± 0.05. There is a clear difference between the sulfides and the sulfate and mixture R-values. This low R-value may be indicating that there is some effect due to the attenuation coefficient of sulfur X-rays in heavy metals. The sulfate R-value, supported by the mixture results, was chosen as the “best” R-value to characterize the behaviour of the instrument ICC layer. The “best” R-value for chlorine was determined with one compound,

KCl. The R-value for this compound is 0.99 ± 0.03. The NaCl compound was not included in this calculation; the sodium R-value has significant uncertainty due to uncertainty in detector window transmissivity. To support this “best” chlorine R-value, the mean R-value of three BHVO2 + CuCl mixtures provided by Professor P. L. King, with 1.5%, 3.0%, and

4.5% Cl, was calculated. These two components were an excellent mixture choice since the

41 Figure 2.12: P R-values. The top figure shows all GRMs. The bottom figure shows aclose up of the lower P concentration GRMs.

attenuation coefficient of CuCl for chlorine X-rays is very similar to that of BHVO2.The mean R-value for these mixtures is 1.017 ± 0.009, which does support the KCl R-value. The “best” R-value for these three elements provides a reasonable description of the ICC layer for the instrument. Phosphorous falls low by nearly 10% from the expected R-value of 1.0, sulfur is slightly less depressed, falling low by 8%, and chlorine shows a return to an R-value of 1.0.

When attention is switched from simple chemicals to rocks, these elements become susceptible to mineral phase effects, which are demonstrated with the multiminerallic GRMs.

42 Figure 2.13: S R-values. The top figure shows all GRMs. The bottom figure shows aclose up of the lower S concentration GRMs.

In our APXS calibration paper [12], igneous GRMs containing phosphorous above the LOQ had a mean R-value of 1.26 ± 0.2, which has since been revised to 1.20 ± 0.2. In the present work, four Icelandic basalts have been added, having significantly higher phosphorous content than the GRMs. Their inclusion changes the mean igneous rock R-value to 1.0

± 0.2.

Chlorine is high at 1.1 ± 0.2; this is supported by another set of mixtures provided by Professor P. L. King. NaCl is more likely to be detected on Mars than the CuCl mixtures previously discussed. A BHVO2 + NaCl (1.5wt% chlorine) mixture gives a high R-value 1.35

± 0.08. At 10 wt% chlorine the R-value decreases to 1.18 ± 0.07, and at 21 wt% chlorine,

43 Figure 2.14: Cl R-values. The top figure shows all GRMs. The bottom figure shows aclose up of the lower Cl concentration GRMs. where NaCl is likely a dominant component in the matrix, the R-value has returned near the expected value of unity to 1.03 ± 0.06.

The mean GRM R-value for sulfur is 0.9 ± 0.2, which could be a reflection of what was observed with the sulfides. There could also be mineral phase effects pulling thesulfur concentration down or up, depending on what the sulfur is bound to. This is a difficult case.

The best way to represent these three elements on Mars should be a compromise between the “best” R-value for the instrument and the overall GRM R-value. The average phosphorous concentration at Gale crater is approximately 0.3 wt% and this concentration range is well represented by our GRM suite, with the majority of the phosphorous GRMs

44 containing less than 0.8 wt%. This suggests that on Mars the best R-value to represent phosphorous is the overall R-value of 1.0 ± 0.2. On Mars the average sulfur concentration is approximately 2 elemental wt% and can get up to nearly 12 elemental wt%, if analyzing a sulfate target [25]; this is much greater than the sulfur content in our calibration suite.

Since sulfates are widely detected on Mars, we suggest that the sulfate R-value be used to characterize the sulfur concentrations on Mars. On Mars, the average chlorine is around

1.0 wt% [25], however, most chlorine bearing GRMs contain less than 0.1 wt% chlorine.

Considering only sediment GRMs and mixtures between 1-5 wt% chlorine, the R-value is

1.0 ± 0.2. If the chlorine in the sample is determined to be in a mineral that is likely to exhibit mineral phase effects, such as halite (NaCl) or perchlorate (ClO−), then an empiri- 4 cal correction factor (ECF) could be applied at a later time. ECFs are discussed in greater detail in Chapter 3.

2.7.2.2 Ti: Approximately Equal Excitation by PIXE and XRF

Type Ti Minerals 1.0 ± 0.1 HARs N/A Granites 0.95 ± 0.04 Trachytes 0.93 ± 0.05 Andesites 0.98 ± 0.04 Basalts 1.00 ± 0.05 IBs 0.95 ± 0.05 UMs 1.020 ± 0.002 Sediments 1.04 ± 0.09 Overall 0.99 ± 0.06 Table 2.8: Minor and trace element R-values by rock type: Ti.

Titanium is an interesting element because it not only has an offset due to the titanium foils over the open 244Cm sources (see section 2.6.3), but it is also excited approximately equally by PIXE and XRF. Figure 2.15 shows the R-value plot for titanium with the mean

R-value of 0.99 ± 0.06. This mean value is excellent, showing the titanium offset and balance between PIXE and XRF have been well characterized in this calibration.

45 Figure 2.15: Ti R-values.

2.7.2.3 Elements Excited Predominantly by XRF

Type Cr Mn Ni Cu Zn Ga Minerals 0.98 ± 0.08 0.98 ± 0.03 1.0 ± 0.1 N/A 1.01 ± 0.07 0.97 ± 0.04 HARs N/A 0.99 ± 0.03 N/A N/A 1.06 ± 0.05 1.0 ± 0.2 Granites N/A 0.95 ± 0.07 N/A N/A 0.9 ± 0.1 N/A Trachytes N/A 1.04 ± 0.02 N/A N/A 0.90 ± 0.09 N/A Andesites N/A 0.97 ± 0.05 N/A 0.88 ± 0.07 1.0 ± 0.1 N/A Basalts 1.2 ± 0.2 0.98 ± 0.08 1.1 ± 0.1 1.0 ± 0.2 0.9 ± 0.1 N/A IBs N/A 0.85 ± 0.07 N/A 0.97 ± 0.03 1.01 ± 0.08 N/A UMs 1.02 ± 0.04 1.0 ± 0.1 1.0 ± 0.1 1.1 ± 0.1 0.92 ± 0.09 N/A Sediments N/A 1.07 ± 0.09 1.1 ± 0.1 0.98 ± 0.08 1.04 ± 0.06 N/A Overall 1.0 ± 0.2 0.99 ± 0.09 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1 1.0 ± 0.1

Table 2.9: Minor and trace element R-values based on rock type: Elements from 24≤Z≤31.

Chromium and manganese (Figure 2.16), nickel and copper (Figure 2.17), and zinc and gallium (Figure 2.18), have mean R-values of essentially 1.0 in this calibration. None of these elements show major variability in experimental concentration based on rock-type (see Table

2.9). These excellent results indicate that instrument offsets have been properly accounted for in nickel, copper, and chromium, and the proximity of these elements to the dominant iron K peaks has no significant effect on these trace elements.

Note, in Table 2.2 the Activation Laboratory concentrations for zinc depart from the

46 certificate concentrations systematically by 10% relative. The GUAPX concentrations for zinc agree better with the certificate concentrations.

Figure 2.16: Cr R-values (top) and Mn R-values (bottom).

Bromine, rubidium, strontium, and yttrium are not only more challenging to calibrate because they are trace elements, but they also overlay the plutonium L훼 scatter peaks (Figure 5.1). Bromine, rubidium, and yttrium mean R-values are all higher than 1.0; the R-value for strontium is 1.0 with a 95% uncertainty level of 10%. The K훽 of bromine overlaps the K훼 of rubidium and the K훽 of rubidium overlaps the K훼 of yttrium. Bromine K훼 line is situated between the Compton and Rayleigh L푙 scatter peaks. Rubidium K훼 occurs at the left hand

47 Figure 2.17: Ni R-values (top) and Cu R-values (bottom).

side of the large L훼 Compton scatter peak, and the strontium K훼 resides on the right hand side of the L훼 Rayleigh scatter peak. Yttrium is found on the L훽 Compton tail and is near the large parasitic zirconium K훼 peak. Clearly, the APXS spectrum is complicated in this vicinity. Another complication is that the 0.45 mm thickness given by Ketek for the silicon detector wafer is only a nominal value. If the wafer is thinner or thicker, the efficiency of the detector will be over- or under-estimated. Even very small departures from the 0.45 mm thickness will cause changes in the concentrations of these elements. On top of these issues, bromine is complicated by the fact that it is not present as a free element, as is assumed by the GUAPX program, but rather as a bromide or bromate. As a result, its matrix is never

48 Figure 2.18: Zn R-values (top) and Ga R-values (bottom).

properly treated. If one of these features is not properly characterized, there is potential for influencing the calibration of the surrounding elements.

The majority of the materials being examined by MSL at Gale crater appear to be sedimentary in origin [50, 52] and many also contain elevated concentrations of bromine.

Because sedimentary materials were predicted at the Gale crater landing site, an emphasis was placed on characterizing sedimentary reference materials with the APXS for this calibration. As a result, there are five bromine-rich GRMs with concentrations almost to300 ppm used in the calibration. This contrasts with the MER mission, which had only a single

49 Type Br Rb Sr Y Pb Minerals N/A 1.10 ± 0.08 N/A N/A 1.1 ± 0.1 HARs N/A N/A 1.16 ± 0.03 N/A N/A Granites N/A 0.9 ± 0.1 1.00 ± 0.08 1.14 ± 0.06 1.14 ± 0.08 Trachytes N/A N/A 1.18 ± 0.07 N/A N/A Andesites N/A N/A 1.02 ± 0.06 N/A N/A Basalts N/A N/A 0.99 ± 0.08 1.0 ± 0.2 N/A IBs N/A N/A 0.86 ± 0.05 1.3 ± 0.2 N/A UMs N/A N/A N/A N/A 1.23 ± 0.04 Sediments 1.1 ± 0.2 N/A 0.93 ± 0.06 1.2 ± 0.2 2.0 ± 0.6 Overall 1.1 ± 0.2 1.1 ± 0.1 1.0 ± 0.1 1.2 ± 0.2 1.4 ± 0.5 Table 2.10: Minor and trace element R-values based on rock type: Elements greater than Z=31.

bromine-containing GRM. The mean MSL R-value for bromine is 1.1 ± 0.2. In Figure 2.19 there is large scatter below 0.1 wt% concentration for rubidium; however, a couple of mineral GRMs contain more than 0.1 wt% concentration. These GRMs verify that the mean R-value for rubidium is greater than 1.0. On Mars, rubidium is often found below the LOQ (130 ppm). The low abundance of rubidium on Mars simplifies this busy region of the spectrum, which is excellent for the scatter peak analyses described in Chapters

5 and 6.

Many GRMs, spanning the majority of rock types covered by the calibration, contain strontium above the LOQ (150 ppm). The strontium mean R-value is 1.04 ± 0.11. From MSL APXS Martian measurements, the average strontium detected is around 165 ppm, with concentrations reaching nearly 400 ppm. This concentration range is well populated by our calibration suite.

The weighted mean R-value for yttrium is 1.17 ± 0.21, after the yttrium offset has been applied. At Gale crater, the average yttrium concentrations are approximately 30 to 40 ppm; the maximum yttrium concentration detected by the MSL APXS is around 90 ppm.

Several GRMs contain yttrium above the LOQ (20 ppm) and range up to 250 ppm. The yttrium range of concentrations measured at Gale Crater is thoroughly represented by our calibration suite.

Tungsten and lead have not been detected on Mars using the APXS instrument, although

50 Figure 2.19: Br R-values (top) and Rb R-values (bottom).

these elements are detectable by the APXS and were present in a few of the calibration

GRMs. Five GRMs contained tungsten in quantities above the LOQ (80 ppm) and the weighted mean is near the expected R-value of 1.0 at 0.97 ± 0.08. Many GRMs contain lead in quantities greater than the LOQ of 35 ppm. The weighted mean R-value is 1.17 ± 0.34. The L peaks are the only visible lead peaks, and the L훼 exactly overlaps the K훼 peak of arsenic. However, the lead L훽 peak is free from overlap. In some cases, if arsenic was not added to the fit list, the fit was visibly poor and thelead

concentration was over estimated. Small quantities of arsenic below the LOQ are likely the

culprit for the elevated lead mean R-value. Moreover, the L X-ray database is significantly

51 Figure 2.20: Sr R-values (top) and Y R-values (bottom). less reliable than the K X-ray database [8].

2.8 Limits of Detection and Limits of Quantitation

In spectrometry it is important to define a lower concentration limit, below which one cannot detect the element in question. These limits are primarily dependent on the instrument, set up, and matrix of the sample being analysed. The GUAPX program output includes the limit of detection (LOD) for each fit element. The LOD defines a statistical lower limitof detection for each element. If an element’s concentration is determined to be greater than the LOD, it implies that there is a peak that can be statistically differentiated from the

52 Figure 2.21: W R-values (top) and Pb R-values (bottom). background. For our purposes, we prefer to use the more conservative limit of quantitation

(LOQ). The LOQ is defined by the American Chemical Society “as the level above which quantitative results may be obtained with a specified degree of confidence” [40]. When an element is reported to have a concentration at least three times the LOD listed by GUAPX, a quantitative use of that result is justified. If the concentration measured by GUAPX is between the LOD and LOQ, the result must be treated with caution. If the concentration is less than the LOD we can conclude the element is not present in the sample.

Table 2.11 summarizes the maximum, minimum, and average LOQs for detectable elements from sodium to yttrium. The average LOQ for each element is found from all GRMs

53 that contain that element, not just the average of the maximum and minimum value. The average LOQ values listed should be the limit that is used to determine if a sample contains the element in question. The reader should note that these LOQs are for long duration measurements (approximately 5 hours or greater). For shorter measurements the LOQs will change approximately in proportion to the inverse of the square root of integration duration.

This is not a huge issue for the calibration since all GRMs were analysed for at least five hours; indeed in most cases for twenty-four hours.

Obviously, the LOQ values in Table 2.11 must be treated with great caution. They are indicative of the situation of the set of GRMs used in this study. For example, they would not apply in the special case of 0.1 wt% aluminum in 99.9% SiO2. If an element (A) hitherto seen had low concentrations suddenly appears at high concentrations, and its lines overlap

those of a second element (B), then the LOD of element B will have to be determined for

that particular case. This is why GUAPX provides the LOD for every element in every

spectrum.

2.9 Errors

There are two sets of errors that must be examined for any analyses: precision and accuracy.

Precision gives a sense of how reproducible the results are when measuring the same sample.

GUAPX has three listed uncertainties in the program output. It was decided that the

fit error was the best representation of the precision of the GUAPX fit. The fiterroris

determined from the final linear least-squares fit parameters given by the diagonal elements

of the error matrix [48].

Accuracies give the average deviation from known concentrations and are based on the

calibration GRMs listed in Table 2.1. The accuracy of the APXS instrument is dependent

on several factors: sample heterogeneity, instrument geometry, temperature, integration

duration, surface roughness, and large quantities of ALICs within the sample. Sample

heterogeneity appears to be the largest determinant of accuracy for the APXS. Table 2.12

lists the accuracies for the detectable elements of the calibration GRMs with long duration

measurements (greater than 5 hours). The preliminary column lists the accuracies for each

54 Z Minimum LOQ Maximum LOQ Average LOQ (ppm) (ppm) (ppm) Na 200 800 500 Mg 200 500 350 Al 200 600 400 Si 200 500 250 P 300 600 450 S 100 200 150 Cl 100 200 150 K 150 250 200 Ca 200 450 400 Ti 150 350 250 V 300 450 400 Cr 100 200 150 Mn 100 200 150 Fe 50 150 100 Ni 30 50 40 Cu 20 50 30 Zn 15 40 25 Ga 10 30 20 Br 10 23 16 Rb 120 140 130 Sr 100 180 150 Y 15 25 20 W 15 125 80 Pb 20 87 35

Table 2.11: LOQ values determined for the FEU (measurement duration greater than 5 hours) for major, minor, and trace elements, in GRMS.

55 element averaged over all rock types. This column should be used when rock type is unknown

(e.g. for all Martian samples where there is no corresponding X-ray diffraction analysis).

The next eight columns list the element accuracies for specific rock types. The accuracy values improve for several elements in these columns because they are averaging over less heterogeneous compositions. When the rock type is known, the corresponding accuracies may be used. As with the LOQs, as the integration duration decreases, the precision and accuracy errors will increase. This is not an issue for the calibration.

2.10 Cross Calibration with the PFM

The majority of the MSL APXS calibration work has been performed on the FEU instrument and everything discussed in this chapter has been directed towards calibrating that specific instrument. The PFM and FEU instruments have been designed to be identical; however, in reality, there are slight differences between the instruments that need to be understood for a proper calibration to be complete.

The three major differences between the FEU and PFM instruments that will prevent the

FEU calibration being directly applicable to the PFM are their different sets of 244Cm sources and slight differences in the titanium foil thickness and detector absorbers (Be window and nitrogen gas thickness). These effects were originally dealt with by Professor J.L.

Campbell [12] and adjustments are made here by the present author.

The PFM sources that were sent to Mars were temporarily set into the FEU instrument to compare source effects. Campbell et al. [12] examined the peak areas of the PFM source spectra and compared them to the FEU source spectra. Ratios of predominantly PIXE excited elements to XRF excited elements indicated that the 훼/L푥 needed to be adjusted up by 3.1%. In addition, professor J.L. Campbell introduced CuS and ZnS, compounds where the sulfur is essentially 100% excited by PIXE and the metallic element is excited by

XRF; these provided the same result [7]. This indicated that alpha excitation was slightly greater in the PFM sources, likely due to different source strengths for PFM and FEU alpha sources. More thorough adjustment of the 훼/L푥 ratio and H-value has since been completed by the present author. The H-value needed adjustment as well, since the source activity

56 of the open and closed sources are unknown, leading us to absorb this unknown quantity into the H-value. The increase of 훼/L푥 was not as great as was previously cited (only 2% increase in the 훼/L푥) [12]. It has likely been offset by the adjusted H-value, which decreased by 10%. The average PFM/FEU concentrations were compared using the appropriate 훼/L푥 and H-values for each configuration, for eight common GRMs. The mean silicon andiron

PFM/FEU concentration ratio came to 1.035. This implies the PIXE and XRF portions of the spectra have been balanced properly by the 훼/L푥 ratios. The 훼/L푥 ratio has been adjusted in the PFM excitation file in GUAPX.

To determine any variations between detectors (as opposed to sources), a subset of GRMs

analyzed with both instruments were compared. Differences between instruments will likely

be found in the absorber thicknesses and will have the greatest effect on the three lightest

elements in the spectra. Campbell et al. [12] ratioed the peak areas of sodium, magnesium

and aluminum to silicon for both the FEU and PFM. The ratio of (Na/Si)푃 퐹 푀 /(Na/Si)퐹 퐸푈 was low at 0.92 ± 0.02. The equivalent ratios for Mg and Al were 0.96 ± 0.04 and 0.98 ± 0.1 respectively. This decrease indicates that these light elements experience more absorption in

the PFM. Campbell et al. [12] demonstrated that an increase in beryllium window thickness

of only 1 휇m is sufficient to cause this decrease. The thicker beryllium window hasbeen accounted for in the PFM detector file in GUAPX.

2.11 Summary

The calibration of the MSL APXS was rigorously completed and both the FEU and PFM

instruments are well characterized. The majority of concentrations derived from the GUAPX

program agreed with the expected certificate concentrations of the calibration GRMs within

error. However, some element concentrations did not agree with expected values, and from

this calibration emerged the mineral phase effect issue. This issue has been shown to affect

the light element concentrations by up to 40%. Sodium, magnesium, and aluminum are

important major elements, which are useful in identifying rock types, the potential presence

of various clay minerals [37], and other key minerals that are important for determining the

habitability of Mars. It is very important that we understand the cause of these mineral

57 phase effects and are able to quantify them. Chapter 3 will discuss the cause of themineral phase effects and will quantitatively describe these effects for the light elements basedon their mineralogy.

58 Preliminary Homogeneous Basalts Andesites Rhyolites Trachytes Ultra Mafic High Alkali Rocks Sediments Na 21 6 17 13 5 5 27 7 7 Mg 33 7 25 38 32 7 11 33 20 Al 15 7 14 12 6 5 15 5 9 Si 5 5 5 5 5 5 5 5 7 P 22 5 22 22 22 22 22 22 22 S 35 5 35 35 35 35 35 35 35 Cl 35 5 35 35 35 35 35 35 36 K 12 12 12 12 12 12 12 12 12 Ca 13 13 13 13 13 13 13 13 13 Ti 12 12 12 12 12 12 12 12 12 Mn 13 13 13 13 13 13 13 13 13 Fe 9 9 9 9 9 9 9 9 9

59 Ni 19 19 19 19 19 19 19 19 19 Cu 32 32 32 32 32 32 32 32 32 Zn 21 21 21 21 21 21 21 21 21 Ga 20 20 20 20 20 20 20 20 20 As 12 12 12 12 12 12 12 12 12 Se 26 26 26 26 26 26 26 26 26 Br 20 20 20 20 20 20 20 20 20 Rb 26 26 26 26 26 26 26 26 26 Sr 26 26 26 26 26 26 26 26 26 Y 26 26 26 26 26 26 26 26 26 W 16 16 16 16 16 16 16 16 16

Table 2.12: Relative accuracies (average two standard deviations in percent) for detectable elements and various rock types. Measurements durations were at least 5 hours. Chapter 3

Mineral Phase Effects

3.1 Introduction

The proposed mineral phase effects (MPEs) are not only evident in the fundamental parameters calibration discussed in Chapter 2 and Campbell et al. [12] for MSL, but they have also been observed in the fundamental parameters calibration for the MER APXS instruments

[10, 11, 16]. They were assumed to be a result of the heterogeneity of the reference materials, since both calibration methods are built upon the same assumption that all materials analysed are atomically homogeneous at the sub-micron scale. The use of terrestrial GRMs with innate heterogeneity was determined to be a preferable method for calibrating the APXS to using glass standards, which would remove any complexity due to heterogeneity. Glass standards of geologic materials, which are usually heated above 1000∘C, may have lost a portion of their volatile elements [4]. This would mean the calibration of alkali elements and sulfur would not be as well characterized as with powdered materials. Moreover, the APXS instrument will not be examining glassified geologic materials on Mars; natural, heterogeneous rocks and sediments are the primary targets for the APXS. Calibrating the APXS instruments with glass standards would have been misleading in that the MPEs would not have been observed prior to the mission.

The use of terrestrial GRMs for the calibration of the APXS implies that each sample is composed of several different minerals that have their own unique elemental chemistry.

Figures 3.1 and 3.2 show a sample of a simple powdered rock with two mineral species.

These minerals will be distributed randomly throughout the powdered sample, which implies

60 Figure 3.1: The escape depth of higher Z elements detected by the APXS is larger than the particle size. These elements are less susceptible to mineral phase effects.

there is a chance of grouping. In XRF, if the particle size becomes small relative to the interrogation depth of an element within a sample, X-ray intensities stabilize [56]. This implies the volume of material being interrogated is representative of the whole sample. For this scenario to be applicable, the grain size must be small enough to allow for excitation and detection over a depth that includes many grain diameters. This will account for random distribution and possible grouping of mineral grains. GRMs used in this calibration are mostly sieved to a grain size of 75 휇m by the suppliers. For example, the escape depth of iron in trachytes is approximately 82 휇m. This is slightly larger than the largest grain size, so iron will be detected from a couple to a few layers of grains, as is shown in Figure 3.1.

In the PIXE case, the interrogation depth is on the order of a few to approximately 10

휇m, which is likely much smaller than the typical grain size in the sample. As a result, the X-ray intensities measured by the APXS will be generated from only one surface grain.

This violates the GUAPX homogeneity assumption, which requires the exciting particles and radiation to sample many grains. This makes the elements primarily excited by PIXE highly susceptible to MPEs.

Despite previous knowledge of their presence, MPEs in the APXS have not been thoroughly studied. The greatly expanded calibration suite for the MSL APXS affords the opportunity for an extensive and quantitative study. With a larger data set to study these effects and better constraints on the range of the effects, which can produce erroneouscon- centrations by up to 30% for some elements, it became possible to undertake a quantitative

61 Figure 3.2: Elements predominantly excited by PIXE have interrogation depths likely much smaller than the typical grain size. These elements are therefore much more susceptible to mineral phase effects. examination of these effects. In this chapter the use of empirical correction factors forthe temporary correction of MPEs, based on rock type, is discussed. X-ray diffraction and

Rietveld analyses of thirty-nine GRMs in our calibration suite are summarized. The quantitative examination of MPEs for the four lightest elements that the APXS can detect (sodium to silicon) is described in detail through the examination of theoretically predicted X-ray yields for five GRMs. To support these results, a pressed pellet of BT-2, the same material that was sent to Mars as the MSL APXS calibration target, was analysed in the Guelph proton microprobe facilty. This experiment is an excellent analogue to the PIXE component of the MSL APXS and should replicate the MPEs measured by the APXS instruments.

62 Overall Homog. HAR Gran. Trach. And. Bas. UM Sed. Na 0.95 0.92 1 1 0.96 1 1.04 1 0.82 Mg 0.94 0.98 1 1 0.64 0.88 0.84 1 1 Al 1.08 1 1.09 1 1.08 1.09 1.19 1.11 1 Si 1 1 1 1 1 1 1 1 1 P 1.16 0.88 1.16 1.16 1.16 1.16 1.16 1.16 0.97 S 0.9 0.93 0.9 0.9 0.9 0.9 0.9 0.9 0.9 Cl 1.13 0.99 1.13 1.13 1.13 1.13 1.13 1.13 0.93 Ca 1 1 1 1 0.9 1 1 1 1 Ti 1 1 1 1 0.93 1 1 1 1 Fe 1 1 1 1 0.9 1 1 1 1

Table 3.1: ECFs by rock type (homogeneous, high alkali rock, granite, trachyte, andesite, basalt, ultra mafic, and sediment). These values have been updated from the Campbell et al. MSL APXS calibration paper [12].

3.2 Empirical Correction Factors

In Chapter 2 the calibration of the MSL FEU instrument was presented. The GRMs used in the calibration were grouped into several major rock types and the ratio of calculated concentration over certificate concentration (the R-value) was found for each element present in each GRM. The R-value, which should be 1.0, was not in several cases; the R-value appeared to be rock type dependent. The mean R-values grouped by rock type for the major and several minor and trace elements were summarized in Tables 2.5, 2.6, 2.7, 2.8,

2.9 and 2.10. Any major deviation from the expected R-value mean of 1.0 for a rock type was listed as a potential MPE and included in the rock dependent empirical correction files

( Table 3.1).

The “overall” ECF is to be used when the sample rock type is unknown. These ECF values are the overall mean R-values (listed in Table 2.4) that show significant deviations from 1.0. Sodium, magnesium, and aluminum differ from an expected R-value of 1.0 due predominantly to MPEs, which are discussed in detail later in this chapter, and instrument effects as discussed in Chapter 2 (primarily affecting sodium). The phosphorous, sulfur,and chlorine R-values, and therefore ECF values, are not 1.0 either, due to a mixture of the ICC layer effects discussed in Chapter 2, as well as MPEs.

The sodium, magnesium, and aluminum values for the “homogeneous” ECF are the mean

R-values from the mineral GRM group. The phosphorous, sulfur, and chlorine ECF values

63 are the special R-values using chemical compounds and minerals with large quantities of these particular elements. These particular values represent the best R-values we can obtain with the MSL APXS instrument; these R-value deviations are purely instrument effects.

When the APXS instrument is measuring homogeneous material this ECF should be used.

The remaining ECF values listed in Table 3.1 are derived from the mean R-values determined using GRMs in the calibration. If there were too few GRMs to produce a mean

R-value in a particular group, the ECF value was taken as the “overall” ECF.

The trachytes were unique in that they displayed R-value deviations from 1.0 for calcium, titanium, and iron, as well as the standard six elements. This could be a result of only having two GRMs in this category, or these R-value deviations may be real mineral phase effects.

These will be discussed later on in this chapter.

3.3 Mineral Phase Effects: Qualitative Description

As described in Chapter 2, an R-value of 1.0 is expected, in principle, for all elements. The

R-values for silicon and iron show that this calibration does produce an R-value of 1.0 for most GRMs, despite their range in rock type and composition. An R-value of 1.0 was of course expected for these elements because these two particular R-values were forced to

1.0 to determine proper H and 훼/L푥 values required for the calibration. It is encouraging that potassium and calcium, which are major elements, also have mean R-values of 1.0 (see

Figure 2.8). These elements demonstrate the reliability of this calibration method in cases

where both PIXE and XRF contribute strongly. It might be expected that the other major

elements (sodium, magnesium, and aluminum) should also have mean R-values of 1.0. This

is not the case. Figures 2.9, 2.10, and 2.11 display a rock type dependence for these lightest

detectable elements. These R-value discrepancies were attributed to the heterogeneity of

the GRM samples for MER [11] and MSL [12]. To calculate elemental concentrations via

the fundamental parameters approach, the sample is assumed to be homogeneous; in other

words, the elements are assumed to be equally distributed throughout the sample, like in

a glass. Since the GRMs are naturally occurring terrestrial samples, they are composed

of several minerals, which often have variable chemical compositions, depending on their

64 environment of formation. They have been finely ground and sieved to less than 250 휇m grain size prior to sample preparation, and most reference materials have been previously sieved to less than 75 휇m [22]. This is still insufficient to homogenize the GRMs at the micron scale. Detailed qualitative descriptions of these MPEs in sodium, magnesium, and aluminum will now be addressed.

3.3.1 Aluminum

Figure 2.11 demonstrates one of the largest mineral phase effects observed in the calibration of the MSL APXS. This is clearly shown when the aluminum R-value is plotted against the certificate iron concentration. The more felsic GRMs (granites and trachytes) havean

R-value near 1.0, as expected. For pure mafic minerals and nearly monomineralic mafic

GRMs, such as the ultramafic rocks primarily composed of pyroxene, amphibole, or olivine, the R-value is also well behaved. The “best” mean R-value for aluminum is 0.98 ± 0.06. For the intermediate GRMs, such as andesites and especially basalts, the R-value increases from this “best” value by up to 30%. This effect has been explained as a result ofthe distribution of the elements in the minerals [11, 12]. The basalts, which display the greatest deviation from the “best” R-value, are primarily composed of plagioclase and clinopyroxene, as determined via Rietveld analyses (Section 3.4) completed on a majority of these GRMs.

Most of the aluminum in the basalts is located in the plagioclase, which does not contain any iron. Only a minor amount of aluminum will be located in the clinopyroxene, which is the dominant carrier of iron. In other words, only a minority of aluminum atoms within the sample are found in the presence of iron atoms, due to the target mineralogy.

The GUAPX fitting program, which assumes a homogeneous matrix, will produce theoretical X-ray yields for each element. It will not take into consideration the differentiation among different minerals. For example, in a plagioclase the characteristic X-rays emitted from the excited aluminum will not be attenuated by coexisting iron, since there is no iron in the plagioclase. However, through its assumption, GUAPX will enforce significant attenuation on the aluminum, resulting in a lower theoretical X-ray yield. To compensate for the lower theoretical X-ray yield, the concentration term, which is the only other variable term in the yield equation, will be overestimated to match the experimental yield (Y), a fixed

65 value derived directly from the spectrum (equation 1.4).

In Campbell et al. [12], the aluminum R-value versus iron plot had very few data points between 10-14 wt% iron. It was unclear if the trend in increasing R-value continued upwards or if it plateaued before decreasing for the more homogeneous ultramafic and high-iron monominerallic GRMs. This region is of particular interest because Martian samples contain greater iron concentrations than terrestrial materials, with the majority of targets containing between 12-14 wt% iron [25]. Five Icelandic basalts with iron between 10-12 wt%, provided by the Royal Ontario Museum in Toronto, Ontario, were added to the calibration to help fill the gap where calibration samples were lacking. They too produce a high R-value with an average of 1.24 ± 0.05. These basalts show that the aluminum R-value discrepancy tends to plateau for iron containing heterogeneous samples.

3.3.2 Sodium

Sodium is a more complicated case compared to aluminum; however, Figure 3.3 [61] shows that sodium does follow a very similar R-value trend to aluminum. This makes sense in terms of the mineralogy. The basalt GRMs, showing the greatest sodium R-value discrepancies, are dominated by plagioclase and clinopyroxene. Sodium is found in both of these minerals.

However, based on the Rietveld analyses of a few of the basalt GRMs, most contain a greater portion of plagioclase than clinopyroxene. More sodium will be found in plagioclase, which is devoid of iron. As with aluminum, to compensate for the low theoretical X-ray yield, the sodium concentration term in the yield equation is forced high. Unfortunately, sodium is the lightest element detected by the APXS, which makes the final results somewhat dependent upon spectrum fitting issues, attenuation effects in the detector window materials thatare not known to 100% accuracy, and sample surface roughness, to name a few.

3.3.3 Magnesium

Magnesium has R-value deviations in several GRMs, but they are opposite to what was observed for sodium and aluminum. The trachyte GRMs demonstrate the largest mineral phase effect in magnesium. They are comprised mostly of plagioclase and clinopyroxene,

66 Figure 3.3: Correlation between Na and Al “best” R-values [61] much like the basalts. In this case, however, magnesium is located primarily in the iron- bearing clinopyroxene and is essentially absent in the iron-free plagioclase. This means the magnesium characteristic X-rays will be more strongly attenuated by iron than in an assumed homogeneous matrix. In this case, the theoretical X-ray yield calculated will be greater than the actual yield for the trachyte matrix and the concentration term will be forced low to compensate for this high theoretical yield. The mean Mg R-value for the trachytes is 0.64 ± 0.02, which is significantly lower than the “best” mean R-value of1.0 ± 0.1.

BCR-2, an andesitic GRM, has proven to be a valuable sample in that it demonstrates

R-value deviations for all three light elements. These deviations were observed in the MER

calibration. The magnesium R-value for BCR-2 is low at 0.69 ± 0.02, while the aluminum

R-value is high at 1.17 ± 0.04. Table 3.4 shows that the mineralogy of BCR-2 is quite simple, with 69 vol% of the GRM being comprised of plagioclase feldspar, the main location

of sodium and aluminum, and 31 vol% is made of clinopyroxene, where magnesium and iron

67 are primarily located. Here we have both upward and downward MPEs and the descriptions given for magnesium in the trachytes, and sodium and aluminum in basalts, hold for this case as well.

These qualitative descriptions appear reasonable when considering the physics of the situation; however, a quantitative representation will be more effective at proving the root cause of these mineral phase effects.

3.4 Mineralogy of GRMs

To shift the above arguments from qualitative to quantitative, we must first know their mineral make-up. Information on mineralogy was available for only a few of the calibration

GRMs. X-ray diffraction (XRD) and Rietveld analysis was performed on thirty-nine GRMs at Laurentian University in Sudbury, Ontario, by the author and Professor A. McDonald.

All of the GRMs were prepared in the same fashion. The powdered materials were pressed into an aluminum holder with a diameter of 25 mm. The surface was then smoothed to be in line with the lip of the aluminum holder using a frosted glass slide. Each sample was run using a Philips PW 1710 휃 to 2휃 Bragg-Brentano system with a fine-focus cobalt훼 K X-ray tube operated at 40 kV and 30 mA. The counting time was set to six seconds per step with a step size of 0.02∘ ranging from 5 to 75∘ for 2휃. Seventeen of the thirty-nine GRMs were analysed by Professor A. McDonald at Laurentian University (denoted by a ⋆) and twenty-two were analysed by the author. All Rietveld analyses were performed using the same software: Panalytical HighScore Plus, version 2.2. The diffraction peaks were modelled with a pseudo-Voigt profile and the background was modelled with a six-order Chebyschev polynomial. Preferred orientation, zero error, asymmetry, peak shape, and cell parameters were corrected or refined for all mineral phases present. For abundant minerals witha calculated abundance greater than 5 modal%, the atomic coordinates and site-occupancy factors were adjusted. Otherwise, these values were left as is. The goodness of the fit was assessed in all cases by examining the residuals (difference between the calculated and experimental diffraction patterns), as well as the statistical values accompanying the overall

68 Mineral DT-N⋆ NIST-70a SY-4⋆ GBW07109 Type mineral mineral HAR HAR Analcime 2 Biotite 4 Calcite 10 Chlorite 1 K-feldpsar 61 38 Kyanite 94 Magneitite 1 Muscovite 1 28 Natrolite 10 Nepheline 2 Plagioclase 36 60 Quartz 2 2 Rutile 2 Scapolite 21 Tourmaline 5

Table 3.2: Mineral abundance (wt%) for mineral and HAR GRMs.

Mineral AC-E⋆ GA⋆ GH⋆ GS-N⋆ GSP-2 Type granite granite granite granite granite Biotite 3 16 Chlorite 6 1 2 3 Clinoamphibole 5 K-feldpsar 18 26 24 14 Muscovite 7 3 Plagioclase 87 60 42 46 52 Quartz 7 14 27 19 18

Table 3.3: Mineral abundance (wt%) for granite GRMs.

fit and each mineral [11].

The mineral abundances for the thirty-nine GRMs are summarized in Tables 3.2, 3.3,

3.4, 3.5, 3.6, 3.7, and 3.8. For minerals present in greater than 10% modal abundance, the relative error is 5%. For minerals between 5 to 10% modal abundance, the relative error is between 10 to 15%. For minerals present below 5% modal abundance, the errors increase rapidly. Around 0.5% modal abundance the relative error is essentially 100%, indicating minerals present in a sample near or below this quantity cannot be detected, nor quantified, with certainty [11].

69 Mineral AGV-2⋆ BCR-21 DR-N⋆ GBW07104 JA-2 JA-3 Type andesite andesite andesite andesite andesite andesite Apatite Biotite 5 Chlorite 12 8 Clinoamphibole 15 Clinopyroxene 12 27 2 45 Elemental Fe 4 K-feldspar 7 2 33 Magnetite 1 Plagioclase 70 69 51 68 50 52 Quartz 11 13 22 5 16

Table 3.4: Mineral abundance (wt%) for andesite GRMs. 1. BCR-2 Rietveld analysis was provided by Dr. S. Wilson of the USGS [76].

Mineral VS2115-81 VS2118-81 BE-N⋆ PM-S⋆ WS-E⋆ Type andesite andesite basalt basalt basalt Apatite 2 2 Biotite 5 1 1.5 Chlorite 12 3.5 14 Clinoamphibole 4 4 2.5 2 Clinopyroxene 4 3 49 20 17 Gonardite 11 K-feldspar 5 Magnetite 6 1 Nepheline 19 Olivine 12 8 Orthopyroxene 4 Plagioclase 73 73 64 58 Quartz 13 6

Table 3.5: Mineral abundance (wt%) for andesite and basalt GRMs.

3.4.1 Detailed Mineralogy of Five Selected GRMs

Knowledge of the mineralogy for the five GRMs of focus in this study (GA, GH, ISH-G,

MDO-G and BCR-2) was a crucial component of this work. Since the elements that display the greatest R-value discrepancies are excited almost solely by PIXE, they are only sampled from a few microns at the surface of the sample. Table 3.9 summarizes the interrogation depth from which 90% of the characteristic X-rays are generated and reach the detector for

BCR-2 [61]; the greatest depth reached is approximately 6 휇m. In the discussion on sample preparation in Chapter 2, it was mentioned that the GRMs were all sieved to a grain size of less than 250 휇m and the majority had been sieved previously to a grain size of 75 휇m. The maximum penetration depth of the four lightest elements is still

70 Mineral BHVO-2 BIR-1a DNC-1 MO14 MO15 NIST688 JGB-1 Type basalt basalt basalt basalt basalt basalt basalt Biotite 4 Chlorite 3 10 Clinoamphibole 8 40 Clinophyroxene 27 25 29 K-feldpsar 1 Magneitite 4 Olivine 13 2 1 1 Orthopyroxene 44 6.5 Plagioclase 56 60 70 54 83 99 60

Table 3.6: Mineral abundance (wt%) for basalt GRMs.

Mineral DTS-2b SARM6 SARM5 VS2113-81 ISH-G⋆ MDO-G⋆ AN-G⋆ Type UM UM UM UM trachyte trachyte anorthosite Chlorite 34 Clinoamphibole 52 5 Clinopyroxene 14 2 13 13 1 Glauberite 3 Magnetite 1 Olivine 95 86 Orthopyroxene 93 Plagioclase 1 86 86 93 Quartz 1 1 1 Serpentine 5 Vesuvianite 14

Table 3.7: Mineral abundance (wt%) for UM, trachyte, and anorthosite GRMs.

Mineral JSD-2⋆ MAG-1⋆ JSL-1 JSL-2 SARM39 GXR-1 Type stream sediment marine sediment slate slate kimberlite jasperiod Apatite 6 Biotite 9 Calcite 1 Chlorite 8 31 23 20 Clinoamphibole 6 Clinopyroxene 34 1 Corundum 5 Goethite 27 Hematite 11 Muscovite 13 34 5 30 Orthopyroxene 20 Plagioclase 39 18 41 17 34 Quartz 16 57 Serpentine 30

Table 3.8: Mineral abundance (wt%) for sediment and special GRMs.

71 D90 (휇m) Element Pyroxene (Augite) Labradorite Na 1.3 2.7 Mg 2.1 3.8 Al 2.75 5.3 Si 3.8 5.9

Table 3.9: Interrogation depth for the major minerals of BCR-2

much smaller than the average grain size. In essence, these elements are exciting only the individual surface grains and do not represent an average. Rietveld determined mineralogy is provided in wt%, so the results needed to be converted to percent area abundance to equate the bulk chemistry for these lightest elements and the XRD data sets.

To convert the mineral abundances area abundances, the density of the minerals in each

GRM were needed. More precise mineral compositions were required for proper densities, as well as for determining the elemental concentration of each mineral. More precise mineralogy, with area adjusted abundances for the five GRMs, is listed in Table 3.10. During the adjustment, all minerals with 1.0 modal % were excluded to simplify further calculations.

Mineral Density (g/cm3)1 GA GH ISH-G MDO-G BCR-2 Albite 2.62 42 Oligoclase 2.65 61 Andesine 2.67 28 Labradorite 2.69 29 75 Quartz 2.65 14 27 Pigeonite 3.38 10 11 Clinohypersthene 3.40 23 Chamosite 3.00 2 K-feldspar 2.56 18 26 62 60 Muscovite 2.80 7 3 Elemental Fe 7.87 2

Table 3.10: Areal abundance of the 5 study GRMs. 1. Densities from Perkins [60].

72 3.4.1.1 Mineralogy of GA and GH

The only minerals that required further classification for both GA and GH were plagioclase feldspar and chlorite. Because chlorite in GA was approximately 1 wt%, it was excluded from future calculations and the mineral abundances were renormalized back to 100 wt% for the remaining minerals. Since both GA and GH are granites, albite, the sodium rich end member of the plagioclase series (calcium element fraction range 0 to 0.1), is the most likely plagioclase mineral. However, when distributing the elements into the mineralogy, the bulk chemistry placed the plagioclase of GA in the oligoclase range (calcium element fraction range from 0.1-0.3). Chlorite is such a large class of minerals that it was more challenging to deduce the proper composition of this mineral. It only comprised 2 % (area) of the sample and, therefore, does not have a large influence over the composition. Chamosite

2+ ((Fe ,Mg)5Al(AlSi3O10)(OH)8), a common chlorite mineral, fit well with the remaining bulk chemistry, once the other minerals had been accounted for.

3.4.1.2 Mineralogy of ISH-G and MDO-G

Despite listing the majority of the trachyte mineralogy as plagioclase feldspar in Table 3.7, the bulk chemistry suggests that the feldspar is more likely alkali feldspar. This is supported by Gillot et al. [26], which lists the groundmass mineralogy, determined via modal analysis

(vol%) with thin sections, to be dominated by alkali feldspar and pyroxene. The pyroxene is either pigeonite ((Ca,Mg,Fe)(Mg,Fe)Si2O6) or augite ((Ca,Na)(Mg,Fe,Al,Ti)(Si,Al)2O6) for

both MDO-G and ISH-G, with minor amounts of plagioclase feldspar and magnetite (Fe3O4). Phenocrysts were removed from both trachyte samples during the original preparation of the GRMs in France because they would interfere with argon dating, which was the primary purpose of these samples [26]. This explains their overly simple mineralogy.

After comparing the trachyte Rietveld mineralogy to their bulk chemistry, the alkali mineral was determined to be sanidine ((Na,K)AlSi3O8) and the clinopyroxene was determined to be pigeonite. The plagioclase mineral in ISH-G was found to be andesine (calcium

element fraction range 0.3 to 0.5) and in MDO-G was found to be labradorite (calcium

element range 0.5 to 0.7).

73 3.4.1.3 Mineralogy of BCR-2

BCR-2 was quarried from the Bridal Veil Flow Quarry near Portland, Oregon [77] and is part of the Columbia River Basalt Group (CRBG) [17]. It is most likely composed of the

Grande Ronde Basalt unit of the CRBG, as this deposit makes up nearly 80% of all the

CRBG. Despite being quarried from a basalt, the geochemistry, when plotted on a TAS diagram, classifies BCR-2 as a basaltic-andesite; it has been placed in the overall andesite class for the MSL APXS calibration. Caprarelli and Reidel [17] provide an extensive study of the clinopyroxene and plagioclase minerals in the Grande Ronde member. All clinopyroxenes measured were either augite or pigeonite, with the majority being augite. The plagioclase feldspar ranged from An11 to An60 (11-60% anorthite, the Ca rich plagioclase end member).

Supporting this research is the XRD and Rietveld analysis of BCR-2, kindly provided to the author by Dr. S. Wilson of the USGS. He found BCR-2 to be comprised of 69 wt% anorthite, 27 wt% augite and 4 wt% elemental iron [76]. This agrees very well with the

Rietveld analysis performed by Professor A. McDonald in Table 3.4, except for the addition of elemental iron. BCR-2 was not prepared by the USGS, which typically uses a corundum milling set-up. The elemental iron is likely an addition from the milling process performed by the outsourced company [76]. Despite being quite a large addition, this elemental iron detected by Dr. S. Wilson is supported by the high iron in the bulk chemistry, which could not be entirely accounted for by the clinopyroxene mineral (it only accounted for 7.44 wt% iron out of the total 9.65 wt% iron in BCR-2).

When apportioning the bulk chemistry elements into the fractional mineral areas, the plagioclase mineral fell very near the labradorite/andesine boundary, just within the labradorite region. All sodium and calcium from the bulk chemistry were used in labradorite, leaving none for the clinopyroxene. The calcium reported by Caprarelli and Reidel [17] for the

Grande Ronde CRBG is also nearly double that of the BCR-2 certificate, implying that the clinopyroxene in BCR-2 may not be primarily augite. Clinohypersthene ((Mg,Fe)2Si2O6) was selected as the clinopyroxene mineral in BCR-2, since it does not contain sodium or calcium. When pyroxene minerals are formed with little available calcium, clinohyperthene is often formed [3]. Thus, this replacement for augite by clinopyroxene agrees with the bulk

74 chemistry, and results in excellent theoretical X-ray yields, as shown in Section 3.5.

3.5 Mineral Phase Effects: Quantitative Determination

3.5.1 APX-Yield

If the homogeneity assumption is in fact the root cause of the observed mineral phase effects, we must examine and compare the elemental theoretical X-ray yields for the bulk

GRM chemistry and individual minerals within the GRM. The X-ray yields will be different between the heterogeneous bulk chemistry and the individual homogeneous minerals if this assumption is incorrect. To study the theoretical yields the APX-Yield program is used. APX-Yield is analogous to the widely-used GUYLS utility in the GUPIX software package [48]. APX-Yield relies on the same instrument-specific effective angles used by

GUAPX, the 훼/L푥 ratio deduced in the calibration, the alpha particle energy after passing through the titanium foils, and the fifteen most intense Pu-L X-ray energy lines and their corresponding intensities. It also assumes a homogeneous sample matrix.

3.5.2 Determining Elemental Compositions of Minerals

For this work the APX-Yield program was run on the bulk elemental composition of a GRM

(concentrations provided by the supplier) as well as the elemental composition derived here for each mineral composing that GRM. The modal abundance for each mineral was also provided to the program. APX-Yield then output theoretical X-ray yields for each element in the bulk chemistry (i.e. in the homogeneous glass-like composition assumed by GUAPX), as well as for each mineral.

Finding the elemental compositions for each mineral was the most complicated portion of this work. To calculate the elemental concentration of each mineral, the bulk elemental concentration of that GRM, the abundance of all minerals present, as well as the standard mineral formulae, were all required. The GRM bulk concentrations and Rietveld analysis were fixed. Standard chemical formulae for the minerals were allowed to vary within defined parameters. For example, calcium in the plagioclase feldspar, labradorite, was only allowed to vary between 0.5 and 0.7. Optimal concentrations for each mineral were found, such

75 that the combination of all mineral concentrations, considering the mineral abundances, resulted in little to no excess or depletion of any particular element in the bulk chemistry of the GRM. Of course, only elements present with ideal mineral formulae could be considered, and all trace elements in the bulk chemistry were ignored. As a result, the elemental concentrations summed between all minerals were slightly higher or lower than the bulk chemistry. These discrepancies were minimized as much as possible. Due to the complexity of the calculations required to deduce the mineral elemental concentrations, which were done by hand for this phase of the work, only GRMs with two or three minerals with variable formulae were considered. Even when the complexity was minimized, some assumptions still had to be made to ease the calculations. For example, in all cases where augite ((Ca,Na)(Mg,Fe,Al,Ti)(Si,Al)2O6) was present, aluminum was only partnered with silicon, not with magnesium, iron, and titanium. These restrictions on mineralogy greatly limited the GRMs that could be studied. Fortunately, the GRMs that display some of the largest R-value effects have simple mineralogy, and five simple GRMs were selected forthe initial phase of quantifying the MPEs: two well-behaved granites (GA, GH), two trachytes

(ISH-G, MDO-G), and one andesite (BCR-2).

Table 3.11 provides an example of the procedure for determining the mineral chemistry for BCR-2. Elemental iron was first subtracted from the original bulk chemistry because its composition is fixed. Next, a balance between the common elements in labradorite and clinopyroxene, assumed to be augite, was found. This procedure indicated that all of the sodium and calcium was distributed in labradorite only. This led to the adjustment of the clinopyroxene from augite to the calcium and sodium free clinopyroxene mineral, clinohypersthene. This mineral composition produced the well balanced chemical compositions summarized in Table 3.11.

A computer program has been written for the author by J. A. Maxwell [47] to replace the extensive by-hand calculations that were required for this work. The program greatly simplifies computation of mineral elemental concentrations for GRMs composed of several complex minerals, which allows for a more detailed study of MPEs. This is the focus of

Chapter 4.

76 Conc. Fe Labradorite Clinohypersthene Remaining Conc. (wt%) (wt%) (wt%) (wt%) (wt%) O 45.05 35.53 9.13 0.39 Na 2.34 3.06 -0.72 Mg 2.16 1.39 0.78 Al 7.14 8.99 -1.84 Si 25.28 21.83 5.34 -1.89 P 0.15 0.15 K 1.49 1.49 Ca 5.09 5.79 -0.70 Ti 1.35 1.35 Mn 0.15 0.15 Fe 9.65 1.50 7.44 0.71 Zn 0.01 0.01 Sr 0.03 0.03 Zr 0.02 0.02 Ba 0.07 0.07 SUM: 100.00 0.00

Table 3.11: Distribution of elements into BCR-2 minerals, constrained by the bulk chemistry and mineral abundances (areal %).

3.5.3 Calculating Weighted Mineral Y1 Values

Only the PIXE yield output from APX-Yield was considered for this work, as sodium to silicon are excited almost entirely by PIXE, and these elements display the largest MPEs.

The elemental theoretical yields from the program were first normalized to their concentration to produce yield per unit concentration values (Y1). These Y1 values allow for direct comparison between samples.

In several cases, one of the light elements was found in multiple minerals. A weighted

Y1 for each element had to be calculated for the mineral component, assuming the minerals that contained the element in question summed to 100% of the sample. For example, sodium was found in both potassium feldspar (KAlSi3O8) and labradorite in the trachyte MDO-G. These minerals sum to 89 area %. Augite composes the last 11 area %; however, it does

not contain sodium. Normalizing the sodium containing minerals to 100 area % gives an

area abundance of 67% for K-feldspar and 33% for labradorite. These renormalized mineral

abundances were then used to calculate the average yield for all sodium containing minerals

77 in MDO-G, as demonstrated by equation 3.1.

NaTotal Mineral Yield = (67 area%)(205.1 yield/conc) + (33 area%)(201.4 yield/conc) (3.1) NaTotal Mineral Yield = 203.9

3.5.4 Calculating R(Y1)

R(Y1) is simply the ratio of the weighted mineral Y1 value over the assumed homogeneous bulk chemistry Y1 for a given element. APX-Yield was run with the bulk chemistry of the

GRM, as well as with the chemical composition of each mineral, producing theoretical yields for each visible element (Z ≥ 11) present in the target. Before the program was run on the five specially selected GRMs, it was first tested for consistency using a mineral GRM,Mica-

Fe. Its bulk chemistry was first run through the program as a normal GRM. Three minerals with compositions of Mica-Fe at abundances of 33 area %, 33 area %, and 34 area % were then run through the program. The weighted Y1 for the minerals matched the GRM Y1.

Therefore, any differences between mineral theoretical yields and GRM theoretical yields must be due to actual deviations in their yields, not due to any effect of the program.

Only the four lightest elements have been considered for MPE quantification because they are the most susceptible to matrix attenuation effects, as was discussed qualitatively in section 3.3. The R(Y1) values have been compared to R-values determined in the calibration, which have been renormalized. Table 3.12 shows how sensitive aluminum is to MPEs.

The mineral group, which is the only purely homogeneous rock type listed, shows that the experimental concentrations match the certificate concentrations to give a weighted mean

R-value of 1.0. Therefore, the aluminum R-values have not been renormalized. Since sodium is more complicated due to its proximity to the low energy cut-off of the instrument and less than ideally characterized instrument parameters, and since it appears to display very similar mineral effects to aluminum, the sodium R-values have been renormalized to the weighted mean mineral R-value 0.91 ± 0.03. The “best” magnesium R-value listed in Chapter 2 and the magnesium mineral R-value are the same at 0.98; The magnesium R-values have been

78 Rock Type R-value (± 1 휎) Minerals 1.00 ± 0.04 Granites 1.05 ± 0.03 Andesites 1.06 ± 0.06 Basalts 1.17 ± 0.08 Icelandic Basalts 1.26 ± 0.06 Table 3.12: Error weighted mean R-values for aluminum by rock type.

GRM Na Mg Al Si RR(Y1) RR(Y1) RR(Y1) RR(Y1) GA 1.05±0.1 1.07 N/A N/A 1.09±0.06 1.03 0.98±0.01 1.04 GH 1.06±0.1 1.08 N/A N/A 1.06±0.06 0.99 0.99±0.01 1.04 ISH-G 1.05±0.1 1.08 0.8±0.1 0.81 1.08±1.07 1.07 1.01±0.07 1.02 MDO-G 1.07±0.1 1.11 0.7±0.1 0.77 1.09±0.07 1.09 1.02±0.07 1.03 BCR-2 1.24±0.1 1.22 0.71±0.05 0.79 1.20±0.04 1.21 0.99±0.03 1.01 Table 3.13: Renormalized R-values and calculated R(Y1) values for Na, Mg, Al, and Si [61].

renormalized by the “best” magnesium R-value. The mean mineral R-value and the “best”

R-value for silicon are both 1.0 so the silicon R-values were not renormalized.

AR(Y1) value of 1.0 would indicate that the mineral matrix and the GRM matrix, both treated as homogeneous, are the same. If this theoretical yield ratio does not equal

1.0, it indicates that one of the matrices, either the mineral matrix or the bulk chemistry matrix, is not being treated correctly by the program. It is unlikely that the naturally homogeneous mineral matrix is being treated incorrectly, since the APX-Yield program produces theoretical yields based on the major assumption that the sample is homogeneous on the sub-micron scale. Therefore, if the R(Y1) value is not 1.0, it strongly indicates that the bulk chemistry matrix is being mishandled; this mistreatment due to the homogeneity assumption is the root cause of the MPEs observed in the APXS calibration. Table 3.13 summarizes the calibration R-values and the theoretical R(Y1) values.

79 3.6 Discussion of Results

3.6.1 Silicon

The silicon R(Y1) values for these five GRMs are higher than the mean R-value of 1.0by3% on average. The two granite GRMs show larger deviations between the calibration R-values and calculated R(Y1) values than the trachytes and BCR-2. This might be an indication that there could be a minor MPE observed for silicon. Despite being a major element in most minerals, there may be a slight imbalance between the upward and downward trends of the theoretical yields for silicon due to heterogeneity.

3.6.2 Aluminum

The two granites have a mean R(Y1) near unity, which is in agreement with the “best”

aluminum R-value of 0.98 ± 0.06; however, the calibration R-values are higher than the “best” R-value by approximately 9%, indicating that there is some mineral effect in play for

these two GRMs. The inaccuracy between these two values for aluminum in the granites

is probably reflecting the difficulty in accurately distributing aluminum across allofthe

minerals present.

The two trachyte GRMs have elevated aluminum R(Y1), which agrees with the calibra-

tion R-values.

BCR-2 is the most important GRM for aluminum. Out of the five GRMs studied, it

displays the greatest MPEs and is high by 21%. Since the basalt GRMs were too complex

mineralogically and we do not have XRD and Rietveld analyses for the Icelandic basalts,

BCR-2 is the only GRM that was useful in testing MPEs in aluminum. The calculated

R(Y1) for BCR-2 is identical to the R-value, supporting the mineral selection and element

distribution performed by hand, as well as the qualitative description for MPEs.

3.6.3 Sodium

Sodium follows the pattern observed for aluminum, and all R(Y1) values agree well with the

calibration R-values. GA and GH R-values are elevated by 5-6%, once renormalized to the

best sodium R-value of 0.91 ± 0.03, which was derived from the pure mineral GRMs. The

80 R(Y1) values for both GRMs are also elevated and are only slightly higher than the R-values.

Both the R-values and R(Y1) values for the trachytes are slightly elevated above unity and they agree. In these four granite and trachyte GRMs sodium is found in approximately

90% of the minerals, all of which are iron-free. This causes an underestimation of Y1 in the matrix term of the yield equation, since the GUAPX program overestimates the absorption of sodium based on a homogeneous matrix. Since Y1 is underestimated, the concentration term must compensate and comes out high.

BCR-2 is the most useful GRM for proving MPEs in sodium, as it was for aluminum; its R-value is the most elevated out of these five cases at 24%. The calculated(Y1) R value came out to 1.22, which is in good agreement. For the case of BCR-2, sodium is found in only 75% of the sample, while the highly attenuating iron is found in the remaining 25%.

Since sodium is found in a smaller fraction of minerals than for the granite and trachyte cases, the mineral phase effect is emphasized.

Despite the complications with sodium (i.e. close proximity to the FEU low energy cut- off and larger uncertainty due to the imperfectly characterized window transmissivity), the results are excellent.

3.6.4 Magnesium

All three magnesium-containing GRMs show MPEs with their significantly low R-values. In the case of ISH-G and MDO-G, magnesium is only present in the clinopyroxene, which is also the sole location of iron in these GRMs. It would be expected, based on the discussion of magnesium in section 3.3, that the R-value, and therefore the R(Y1) value, would be low.

The R(Y1) value for ISH-G is in excellent agreement with its R-value. The R(Y1) value for

MDO-G is slightly less depressed than the R-value for MDO-G; however, it does show the large mineral phase effect.

Once again, BCR-2 is an excellent GRM for demonstrating the mineral phase effect in magnesium. Its R-value is low like the trachytes, and it shows the largest effect for magnesium out of all GRMs in the andesite group. Magnesium in BCR-2 is located in the clinopyroxene mineral as is iron. The R(Y1) value is not as low as the R-value, but it does indicate that there is an effect greater than 20% on the magnesium yield.

81 All three of the magnesium R(Y1) values are slightly greater than the calibration R-

values. This is likely a reflection, once again, of the difficulty in distributing the elements

perfectly within the minerals of the GRM.

3.6.5 Potassium, Calcium, and Iron in ISH-G and MDO-G

The R-values for calcium, titanium and iron in the two trachyte GRMs were anomalous

enough to suggest the presence of MPEs and imply a non-unity value in the trachyte ECF.

The same procedure used to calculate the R(Y1) values for sodium to silicon above was applied to potassium, calcium, and iron in ISH-G and MDO-G. Titanium was assumed to be absent from all minerals and, therefore, was excluded from any further calculations. The one adjustment to the procedure was the use of the total theoretical yield from PIXE and

XRF.

ISH-G MDO-G RR(Y1) RR(Y1) K 0.97 ± 0.07 0.89 1.035 ± 0.08 0.96 Ca 0.88 ± 0.06 1.09 0.83 ± 0.05 1.07 Fe 0.90 ±0.06 1.01 0.81 ± 0.06 1.03 Table 3.14: Theoretical yield comparison to calibration R-values for K, Ca and Fe in trachyte GRMs.

Table 3.14 summarizes the R(Y1) values and the experimentally determined R-values

from the calibration. From these results, it is clear that the mean R-value discrepancies

for both calcium and iron are not caused by the same heterogeneous effects that have been

influencing the R-values for sodium, magnesium, aluminum, and silicon. These effects could

be a result of having only two GRMs within this category. In addition, the majority of

elemental concentrations were classified as provisional for both of these GRMs. To fully

comprehend the higher Z R-value deviations in these two trachytes, more GRMs of this rock

type should be collected and examined.

82 3.6.6 Summary

How a sample’s matrix is treated, results in different Y1 values computed by GUAPX. Itis clear that the assumption and qualitative descriptions of Campbell et al. [11, 12] for both

MER and MSL calibrations were correct: sample heterogeneity is the source of divergence of the GUAPX experimental concentrations for sodium, magnesium, and aluminum. If a sample is naturally heterogeneous, which is the case for most geologic materials, then the

GUAPX program will produce erroneous concentrations.

3.7 PIXE Emulation of the APXS to Test for Mineral Phase

Effects

The majority of MPEs occur in elements that are primarily excited by PIXE. To further test

MPEs, we used the Guelph microprobe facility, which uses 3 MeV protons as an excitation source.

The MSL APXS uses approximately 5 MeV alpha particles to excite the light elements within its samples, which are quite different from 3 MeV protons. It was important to establish that the use of the accelerator would be a suitable analogue for the APXS PIXE component. Table 3.15 [7] summarizes the 90% escape depths for sodium to silicon with both 5 MeV alpha particles and 3 MeV protons. Despite having different ranges within the sample (see Table 3.16 [7]), the escape depths for the characteristic X-rays produced by the alpha particles and protons are very similar. When considering that the average grain size is much greater than 10 휇m, the differences between the escape depths are negligible. Therefore, the accelerator set-up is an excellent analogue to the PIXE component of the

MSL APXS.

The particular beam line used for this work was accurately calibrated by Dr. C. M. Heir- wegh using various USGS and other rock standards reduced to a glassy state [32]. A pressed pellet, thin section, and solid rock slab of the BT-2 calibration target material were provided by Professor P. L. King. A carbon coat was applied to these three targets using an Edwards

Auto 306 machine at the University of Guelph Electron Microscopy Unit. This conductive

83 5 MeV Alpha Particles 3 MeV Protons Augite Labradorite Augite Labradorite Na 1.3 휇m 2.7 휇m 1.35 휇m 2.75 휇m Mg 2.1 휇m 3.8 휇m 2.15 휇m 4.1 휇m Al 2.75 휇m 5.3 휇m 2.95 휇m 6.4 휇m Si 3.8 휇m 5.9 휇m 4.5 휇m 6.8 휇m Table 3.15: 90% escape depth for characteristic X-rays of sodium to silicon for 5 MeV alpha particle and 3 MeV proton emitters.

carbon coating must be applied to insulating materials to prevent charge build up on the surface, which may potentially cause sample charging and consequent spectrum distortion.

The details of this process are described in the PhD thesis of Dr. C. M. Heirwegh [32]. The carbon coating process was repeated until the voltage drop across the surface was less than

10%, indicating the thickness of the coating was approximately 80 nm.

5 MeV alpha particles 3 MeV protons Augite Labradorite Augite Labradorite 18 휇m 20휇m 64 휇m 72 휇m Table 3.16: Penetration depth (range) of 5 MeV alpha particles and 3 MeV protons in an iron rich mineral (augite) and an iron free mineral (labradorite).

The initial test of the BT-2 material was performed on the pressed pellet. The pressed pellet was placed in the sample chamber in standard geometry (surface is 45∘ to the beam line and 45∘ to the detector). Each spectrum was collected until 0.5 휇C was reached. The beam was set to raster a 500 휇m by 500 휇m area, and eighty-one spectra were taken in a 9x9 grid pattern to cover a total area of 4 mm by 4 mm. Spectra were then added sequentially to produce spectra of increasing area coverage. 1, 2, 5, 10, 20, et cetera spectra were summed together and analysed with GUPIX. Figure 3.4 summarizes the R-values obtained for the four lightest elements with increasing surface coverage.

The typical surface area analyzed by the MSL APXS is approximately 240 mm2, which

is significantly greater than the 216 mm achieved in the PIXE work. Figure 3.4 shows that

despite the smaller area coverage of the proton beam, the elemental concentrations become

consistent around forty spectra or 8 mm2 for the pressed pellet target. Therefore, the 9x9

84 Figure 3.4: R-values as area coverage increases through spectrum summation.

grid obtained is sufficient for replicating MSL APXS results when using powdered materials.

3.7.1 Results for the BT-2 Pressed Pellet

We have shown that the proton accelerator is an excellent analogue for the PIXE component of the MSL APXS. Therefore, the R-values obtained from the sum of the 4mm by 4mm grid should replicate the R-values obtained for BT-2 analyzed by the APXS. R-value averages were calculated for eight BT-2 samples with the APXS. Six of these samples were powdered

BT-2 material and two were pressed pellets; all were provided by Professor P. L. King. Mean

R-values were calculated for sodium, magnesium, aluminum, silicon, and phosphorous from the eighty accelerator runs on the single pressed pellet. The last spectrum was of the sample holder and was removed from analyses. Table 3.17 summarizes the mean R-values obtained for these elements by the MSL APXS and the proton accelerator. With the exception of phosphorous, these results are in very close agreement. This is a very promising outcome.

Only two basalt GRMs in the calibration suite contained phosphorous above the LOQ.

The mean R-value for these two GRMs was elevated, indicating there may be a mineral

85 Element APXS R-value Accelerator R-value Na 1.2 ± 0.1 1.2 ± 0.2 Mg 0.79 ± 0.06 0.8 ± 0.2 Al 1.21 ± 0.04 1.2 ± 0.1 Si 1.00 ± 0.02 1.025 ± 0.04 P 1.5 ± 0.4 1.9 ± 1

Table 3.17: BT-2 element R-values from APXS and proton beam analyses (2휎 error.)

phase effect for phosphorous. This is also replicated in both the APXS and accelerator analyses of BT-2; however, the error for this element is quite large. This is likely due to the fact that phosphorous is present primarily in apatite (Ca5(PO4)3(F,Cl,OH))in BT-2, which comprises less than 1% of the sample [41]. The majority of mineral grains excited by PIXE

are unlikely to be a phosphorous bearing apatite. The odd mineral grain examined will be

apatite, however, and this will generate the large scatter observed for phosphorous.

3.7.2 Results for the BT-2 Solid Rock Slab and Thin Section

The work of this sub-section is purely exploratory and the goal is to ascertain if individual

grains might be examined by micro-PIXE to aid in further studies of mineral phase effects.

The BT-2 thin section and solid rock slab were analysed in the same configuration as the

pressed pellet; however, random spots were chosen instead of collecting a systematic rastered

grid of spectra like what was done with the pressed pellet. In theory, thin sections and solid

rock targets could be analysed in a rastered grid pattern. Due to the increased heterogene-

ity of these targets over the pressed pellet, a much larger area would need to be covered

to stabilize the bulk concentrations. These targets were useful in that they allowed us to

measure the chemistry of individual minerals with a fine focused beam of 7 by 15 휇m2 spot size. This would take tedious calculations out of the procedure for determining the mineral

compositions. The solid rock target was more useful than the thin section, but unfortunately

a mineral map of the surface of the BT-2 rock target could not be completed before carbon

coating. Since the surface composition was unknown at the time of analysis, spots were

selected at random for examination. The mineralogy of BT-2 is now known. A QEMSCAN

of the calibration target was determined by Digitalcore in Canberra, Australia, and is listed

86 in Table 3.18 [41, 72]. Certain minerals present in BT-2 have diagnostic elements, such as phosphorous in apatite, which was useful in identifying the mineralogy at some of the loca- tions that were measured. In the future, it would be crucial to have a Raman or QEMSCAN mineral map of the surface, for example, for this technique of targeting individual minerals to be of use. This way, the chemical compositions of targeted minerals can be measured by micro-PIXE.

Mineral abundance (%) Mineral abundance (%) Ca-plagioclase 28.25 Analcite 2.36 Albite 20.11 Fe-Ti spinel 1.57 Alkali feldspar 16.70 Apatite 0.63 Pyroxene 14.47 Biotite 0.51 Olivine 8.58 Illmenite 0.39 Unclassified 3.68 Sodalite 0.09 Chlorite 2.66

Table 3.18: Modal mineralogy of the BT-2 calibration target.

3.7.3 Summary

The Guelph PIXE microprobe facility has proven to be an excellent analogue for the PIXE component of the MSL APXS. In future work, it would be beneficial to have both a pressed pellet and previously mapped surface of a solid rock target for any sample analysis to have high accuracy. This will allow for the measurement of the bulk chemical composition of the entire sample using the pressed pellet, as well as the mineral chemistry of the individual minerals in the sample using the solid target. In most cases, however, a carbon coated pressed pellet of the calibration GRMs will be more than sufficient to re-examine the observed MPEs in the light elements of the MSL APXS calibration.

3.8 Conclusion

The quantification of the MPEs, together with the success of the pressed pellet analysis using the proton accelerator, has opened several doors for examining greater varieties of reference materials and as yet unobserved MPEs. The work summarized in this chapter has proven

87 previous hypotheses that sample heterogeneity is the cause of erroneous concentrations in

APXS fitting methods, which assume all samples are homogeneous. The matrix termis mistreated based on this key assumption in both the semi-empirical and the fundamental parameters approaches to the APXS calibration. These effects, due to sample heterogeneity

(mineralogy), had been described qualitatively in the past; however, it was not previously possible to prove this hypothesis. With the aid of X-ray diffraction and Rietveld analyses on our GRMs, along with the theoretical X-ray yield calculation program, APX-Yield, it is now possible to quantitatively compute the effects of sample heterogeneity. This method requires accurate mineral compositions, which requires in depth calculations that become too complex to deal with manually once several variable-composition minerals are introduced.

GRMs with simple mineralogy that still displayed MPEs were selected for this work. This meant that the more complex basalt GRMs, which display some of the greatest MPEs for sodium and aluminum, were not usable. To be able to quantify the MPEs in more

GRMs, particularly the basalts, a computer program has been developed to deal with the complex calculations for determining the elemental compositions of the minerals, based on the sample’s bulk chemistry and mineral modal abundances. The next chapter discusses this program in detail, highlighting its effectiveness and presents quantitative MPEs for GRMs with more complex mineralogy.

88 Chapter 4

APXRD: A Computer Program to Determine Mineral Composition

4.1 Introduction

To quantify mineral phase effects for light elements detected by PIXE, the bulk elemental chemistry, modal mineralogy, and element distribution within each mineral, must be known.

In Chapter 3 the process for calculating the theoretical X-ray yields (Y1) for both the bulk chemistry and independent minerals within a GRM was described in detail. The largest challenge in this process was calculating the element distribution within the minerals.

Determining the Y1 values for the minerals in a GRM is dependent upon the bulk elemental chemistry of the minerals, as well as the modal abundance of all minerals within the sample.

Rietveld analyses of X-ray diffraction patterns provide the modal mineral abundances, and in cases where the mineral is present in greater than 5 wt% abundance, the site occupancy factors could be refined to provide more information about the elemental chemistry ofthe minerals. When Rietveld analyses are presented, these site occupancy factors are often not provided; only the minerals and their abundances are typically given. This means that the elemental distribution within each mineral is unknown and must be calculated. Due to the complexity of the calculations required to determine the mineral elemental concentrations by hand, a program was developed to do these calculations.

The purpose of this program is twofold. One goal is to replace the manual approach of the previous chapter when working with GRMs. The second goal is to produce mineral elemental concentrations using mineral abundances determined via XRD and spectrum peak

89 areas provided by APXS. In the first of these cases, the resulting mineral concentrations can be run through the APX-Yield program to obtain elemental Y1 values for each mineral, which are then compared, as before, to produce R(Y1) values (ratios of mineral weighted

theoretical yield to homogeneous GRM theoretical X-ray yields). This ratio of theoretical

yields can then be compared to the R-value obtained from the MSL APXS calibration.

The calculations to determine the mineral elemental concentrations by hand had limited

the GRMs that could be studied to only those with simple mineralogy. This excluded the

examination of some of the most important cases that display MPEs (i.e. basalts). In this

chapter, the program, called APXRD, is tested using the five GRMs used in the Chapter 3

calculations of MPE quantification. Further study of MPEs in mineralogically more complex

GRMs is also presented. The use of this program opens the door to further understanding

the observed MPEs in the MSL APXS calibration and provides the opportunity to predict

MPEs for expected rock and mineral combinations at the MSL Gale Crater landing site on

Mars.

4.2 Details of the APXRD Program

APXRD was written for the author by John A. Maxwell using the Nelder-Mead downhill

simplex method. This method was chosen because it produced the best results in the most

timely fashion. The downhill simplex method is designed to optimize n variables of a func-

tion without using derivatives [55, 70]. It is simplex based; the n variables are placed on

the vertices of an (n+1) polytope. For example, a two variable simplex in R2 would be

represented by a triangle and a three variable simplex in R3 would be represented by a

tetrahedron. The method works by first constructing the initial (n+1) simplex. The sim-

plex then transforms itself to adapt to the local landscape by reflecting vertices, contracting,

expanding or shrinking. Once the simplex has reached a defined endpoint, by either com-

pleting a set number of iterations or by reaching a situation where the function is no longer

changing, the simplex is terminated and the final values for each of the n variables are

given [55, 70].

Before APXRD begins the downhill simplex optimization, minerals with fixed chemical

90 compositions, such as quartz (SiO2), are removed from the procedure. The theoretical X- ray yields are calculated for these minerals and these yields are then subtracted from the experimental yields provided by the GUAPX fitting code. Only minerals with variable chemistry are left to be optimized by the program, and the newly adjusted experimental yields are now the benchmark that the program is attempting to reproduce.

Once only minerals with variable composition remain, APXRD runs four simplexes with loose to restrictive constraints on the variables being determined. In our case, the variables that are being optimized are contained in and constrained by the mineral formulae. We refer to these variables as p-values. For example, olivine ((Mg,Fe)2SiO4) has variable magnesium and iron in its formula and the sum of the magnesium and iron fractions, or p-values, must

be one in theory. The accepted variability within the p-value sum is defined differently

for each of the four simplex runs performed by APXRD. Simplex 1 has 100% variability.

For olivine, this means that the p-value sum can range from zero to two. This may seem

unnecessarily loose, but this simplex iteration is useful because it determines p-values that

converge based only on the experimental X-ray yields. Mineral abundances are ignored [49].

In some circumstances, simplex 1 does a good job of distributing the proper concentrations

into the minerals, but in most cases more rigid restrictions are required for the p-value sums.

Simplex 2 allows the p-value sums to vary by ± 10% (i.e. the p-value sum may vary from

0.9 to 1.1) and simplex 3 increases the restrictions on the p-value sum range to ± 1% (i.e. 0.99-1.01 p-value sum range). At the other extreme, simplex 4 allows only 0.1% variability

on the p-value sum. In other words, if the p-value sum is expected to be one, which is the

case for magnesium and iron in olivine, the sum can range from 0.999 to 1.001. These tighter

restrictions require both the experimental X-ray yields and mineral abundances to optimize

the p-values in all of the minerals.

APXRD requires specific information to compute the mineral elemental concentrations.

An IM mode “mega file” output from GUAPX is needed to supply the experimentally de-

termined yields. The mineral abundances and general chemical formula for each mineral

present above 1.0 wt% are provided in a *.min file. The starting p-values, p-value sum

restrictions, and number of loops for each simplex are defined in the *.PV file. The ultimate goal of APXRD is to produce “theoretical” X-ray yields for each element

91 that match the experimental yields for the GRM in question. In the by-hand work completed in Chapter 3, the sum of mineral elemental concentrations was compared to the certificate concentration sums, which do not contain MPEs. This program was designed to be used for both calibration GRMs and Martian samples. The samples analysed by the APXS on

Mars obviously do not come with certificate concentrations. Since GUAPX relies onthe homogeneity assumption to produce concentrations, the final concentration results will be distorted by MPEs. To work around this issue, the experimental X-ray yields for each element are used, which are determined before the homogeneity assumption is implemented by GUAPX.

Mineral element concentrations are the final output of APXRD. Each simplex gives the p-values in the mineral formulae and lists element concentrations in each individual mineral. APX-Yield has been incorporated within APXRD, and PIXE, XRF and total theoretical yields are calculated for each mineral based on the APXRD determined elemental concentrations. When working with GRMs, this affords a major reduction in the work required to produce elemental R(Y1) values by GUAPX and eliminates the possibility of user error.

4.3 Comparison of APXRD to By-Hand Results

4.3.1 Mineral Elemental Compositions

4.3.1.1 BCR-2

To determine if APXRD can successfully distribute elements within the minerals, R(Y1)

results for sodium to silicon produced using the APXRD mineral concentrations were com-

pared to the R(Y1) values found using hand-calculated mineral element concentrations for

the five GRMs examined in Chapter 3. BCR-2 was first used to test the program inthree

ways:

Test 1: Labradorite, augite, and elemental iron were used. In the literature, augite was

found to be the most prevalent pyroxene in the Columbia River Basalts [17]. The by-

hand calculations found clinohypersthene to be a better match with the bulk chemistry.

92 This test will show if augite is a reasonable mineral choice but was just too complex to

calculate with by-hand calculations. If, on the other hand, the APXRD R(Y1) values

using augite are not as good as the by-hand values (which used clinohypersthene), it

suggests augite is not the pyroxene mineral, or the errors inherent in the methods have

conspired to make this case intractable.

Test 2: Labradorite, hypsersthene, and elemental iron were used. In this test, the pyroxene

mineral has been defined to what was used in the by-hand calculations. IfAPXRD

works, these results should agree well with what was presented for BCR-2 in Chapter

Test 3 : Here the starting p-values in the APXRD program were altered to the by-hand

final results originally found. This test will show if the program has found stable

results for the mineral compositions. All starting parameters were previously set to

the mid point of the accepted range. In this case, they are set to the by-hand values.

The program should not deviate from this greatly if these values are correct.

Each GRM run through APXRD produces four simplex results with increasing restrictions upon the standard deviation of the variable parameters. Each simplex has increasing constraints on the p-value sum, therefore, the by-hand mineral concentrations are compared to the fourth simplex mineral concentrations where the p-value sum can vary by only 0.1%.

Table 4.1 compares the mineral concentrations for the three BCR-2 tests outlined above to the previously determined by-hand mineral concentrations. The APXRD derived concentrations should agree closely to the by-hand concentrations, as these results gave excellent

R(Y1) values when compared to the calibration R-values.

Test 1 used augite as the clinopyroxene mineral. The resulting concentrations for both plagioclase and augite did not agree well with the by-hand calculations. When the clinopyroxene mineral was adjusted to clinohypersthene (test 2), both the plagioclase and pyroxene concentrations matched closely to the by-hand concentrations. Test 3 set the starting parameters to the by-hand values. The concentrations produced by APXRD were nearly identical to the test 2 concentrations. This indicates that the program found a stable solution for

93 Labradorite By-Hand Test 1 Test 2 Test 3 wt% wt% wt% wt% O 47.25 46.88 47.15 47.17 Na 4.07 3.25 3.90 3.90 Al 11.95 6.96 10.85 10.82 Si 29.03 33.91 30.13 30.15 Ca 7.69 9.00 7.96 7.96 Augite/Clinohypersthene O 39.19 35.41 39.92 39.93 Na 8.45 Mg 5.96 3.37 7.50 7.52 Al 19.90 Si 22.93 23.36 23.37 Ca 0.01 Ti 3.57 Fe 31.92 29.29 29.22 29.18 Elemental Fe Fe 100 100 100 100

Table 4.1: Three test results comparing by-hand and APXRD mineral elemental concentrations using BCR-2. Simplex 4 results have been used in all cases.

BCR-2 and can reproduce solutions even with reasonably varied starting parameters.

4.3.1.2 ISH-G and MDO-G

ISH-G and MDO-G were run through APXRD using the accepted mineralogy that was used for the by-hand calculations in Chapter 3. The mineral elemental abundances are listed in

Table 4.2 for both GRMs.

The APXRD mineral compositions for ISH-G and MDO-G do not agree with the by- hand calculations as well as they did for BCR-2. Anorthoclase in both GRMs showed the best agreement. Sodium, aluminum, silicon, and calcium are all very different from the by-hand calculations to the simplex 4 result in both MDO-G and ISH-G. This, in turn, has affected the plagioclase simplex concentrations for those four elements. However, the by-hand calculations placed the plagioclase in the albite region (0 to 0.1 calcium element fraction), and the simplex results also placed the plagioclase in this region, or very near it.

94 ISH-G MDO-G Plagioclase By-Hand APXRD Plagioclase By-Hand APXRD (wt%) (wt%) (wt%) s(wt%) O 47.70 47.08 O 47.07 46.60 Na 5.23 3.67 Na 3.38 2.28 Al 15.08 10.08 Al 14.88 10.06 Si 26.17 30.83 Si 25.82 30.44 Ca 5.82 8.34 Ca 8.82 10.62 Augite Augite O 41.14 36.29 O 39.80 35.76 Na 8.69 Na 8.50 Mg 8.33 5.86 Mg 6.05 4.69 Al 5.78 20.34 Al 5.59 20.06 Si 18.05 Si 17.46 0.04 Ca 5.15 Ca 3.32 0.10 Fe 21.54 28.75 Fe 27.78 30.83 Anorthoclase Anorthoclase O 47.12 47.07 O 47.75 47.49 Na 3.17 3.00 Na 4.92 4.38 Al 9.93 9.92 Al 10.00 10.01 Si 31.02 30.99 Si 31.43 31.26 K 8.75 9.01 K 5.83 6.87

Table 4.2: Comparison of by-hand and APXRD mineral elemental concentrations for ISH-G and MDO-G.

4.3.1.3 GA and GH

Only the plagioclase, muscovite (KAl2(AlSi3O10)(F,OH)2), and chlorite (chamosite) minerals had variable compositions for GA and GH; the mineral formulae for quartz and potassium

feldspar are fixed, resulting in identical concentrations for the by-hand and APXRD cases.

Out of the three variable minerals, plagioclase was the most abundant and, therefore, the

most influential mineral on the theoretical yields. For both GA and GH, the mineral con-

centrations deduced by APXRD are very similar to what was calculated by-hand. In both

cases, muscovite was assumed to have a fixed chemical composition in APXRD using OH

and excluding fluorine. This same configuration in muscovite for GA was selected forthe

by-hand calculations and the two results are quite similar. However, fluorine was chosen in

the by-hand calculations for GH. This affected the concentrations only slightly. Chamosite

was only present in GH and the concentration results for the by-hand calculations differed

95 GA GH Plagioclase By-Hand APXRD Plagioclase By-Hand APXRD (wt%) (wt%) (wt%) (wt%) O 47.84 48.05 O 48.53 48.53 Na 6.02 6.63 Na 8.05 7.92 Al 8.57 8.57 Al 8.44 10.20 Si 33.07 33.28 Si 33.81 31.97 Ca 4.49 3.48 Ca 1.17 1.38 Quartz Quartz O 53.00 53.26 O 53.00 53.26 Si 47.00 46.74 Si 47.00 46.74 K-feldspar K-feldspar O 46.00 45.99 O 46.00 45.99 Al 9.70 9.69 Al 9.70 9.69 Si 30.30 30.27 Si 30.30 30.27 K 14.00 14.05 K 14.00 14.05 Muscovite Muscovite H 0.50 0.26 H 0.26 F F 9.45 O 48.20 46.16 O 39.77 46.16 Al 20.32 21.23 Al 20.12 21.23 Si 21.16 22.10 Si 20.95 22.10 K 9.82 10.26 K 9.72 10.26 Chamosite Chamosite H N/A N/A H 1.12 0.17 O N/A N/A O 40.37 29.61 Mg N/A N/A Mg 0 0.01 Al N/A N/A Al 7.56 9.08 Si N/A N/A Si 11.81 14.18 Fe N/A N/A Fe 39.14 46.95

Table 4.3: Comparison of by-hand and APXRD mineral elemental concentrations for GA and GH.

significantly to the APXRD concentrations. It is unlikely that these variations in muscovite and chamosite for GH will result in large theoretical yield deviations, since these elements comprise roughly 5 area% of the sample.

4.3.2 APXRD Derived R(Y1) Values for the Five Test GRMs

In the previous section, the concentrations for each mineral produced by APXRD were compared to the concentrations calculated by hand. There were some differences between the two data sets, but the program was shown to be capable of calculating the mineral

96 elemental concentrations. By incorporating APX-Yield within APXRD, the R(Y1) values were calculated automatically. These values have been compared to the by-hand R(Y1)

values, as well as the R-values from the calibration (Table 4.4), as a final test to see if

the APXRD program is capable of producing results that agree with the experimentally

measured MPEs.

Na Mg Al Si R 1.05 N/A 1.05 0.99 GH manual R(Y1) 1.08 N/A 0.99 1.04 simplex 4 R(Y1) 1.08 N/A 0.98 1.00 R 1.04 N/A 1.08 0.98 GA manual R(Y1) 1.07 N/A 1.03 1.04 simplex 4 R(Y1) 1.08 N/A 1.02 1.02 R 1.05 0.79 1.08 1.01 ISH-G manual R(Y1) 1.08 0.79 1.07 1.02 simplex 4 R(Y1) 1.04 0.69 1.12 1.01 R 1.07 0.70 1.09 1.03 MDO-G manual R(Y1) 1.11 0.77 1.09 1.02 simplex 4 R(Y1) 1.05 0.69 1.07 1.03 R 1.24 0.71 1.21 0.99 BCR-2 manual R(Y1) 1.22 0.79 1.21 1.01 simplex 4 R(Y1) 1.21 0.82 1.21 1.01

Table 4.4: Comparison of APXRD R(Y1) values to the by-hand R(Y1) and calibration R values.

4.3.2.1 BCR-2

Test 2 mineral concentrations (using hypersthene rather than augite) were input to APX-

Yield to produce the R(Y1) values. The APXRD R(Y1) values are very nearly the same as the by-hand derived R(Y1) values (refer to Table 4.4). The element with the largest deviation between these two sets of results is magnesium. In both cases, silicon R(Y1) values agree very closely and these results agree with the calibration R-value within error.

The magnesium R(Y1) deduced via the APXRD method is very near the R(Y1) value found by-hand. These two values are not quite low enough to replicate the low magnesium R-value from the calibration. This does prove that the low magnesium R-value is a real effect and the results show that the low magnesium R-value is predominantly a mineral phase effect.

97 R-value Test 2 R(Y1) Test 1 R(Y1) Na 1.24 1.21 1.11 Mg 0.71 0.82 0.74 Al 1.21 1.21 1.10 Si 0.99 1.01 1.02

Table 4.5: Effect on R(Y1) values for BCR-2 when pyroxene is run through APXRD as augite (test 1) and hypersthene (test 2).

Spectrum fitting issues may be causing the difference between R and R(Y1), giventhat the weak magnesium peak is sandwiched between intense aluminum and silicon peaks in the BCR-2 spectrum. Use of accelerator-based PIXE with superior energy resolution (see

Chapter 3) may help resolve the observed difference. The sodium and aluminum R(Y1)

values for both the APXRD and by-hand cases are in good agreement with the calibration

R-values, demonstrating that the high sodium and aluminum R-values are real mineral phase

effects due to the heterogeneity of the sample.

One valuable use for the APXRD program is that different mineralogy can be easily

tested to see how sensitive the theoretical yields are to element distribution. For example,

what R(Y1) values would be produced if the BCR-2 test 1 mineralogy was used? Table 4.5

compares BCR-2 with augite (test 1) to BCR-2 with hypersthene as the pyroxene (test 2) and

the calibration R-values. Magnesium is more accurate for the augite case, but sodium and

aluminum differ greatly from the hypersthene(Y1) R and R-values. This demonstrates that

the program is sensitive to user input and requires that the user either have very accurate

mineralogy and bulk chemistry beforehand, or they have an a priori understanding of the

mineralogy and are able to produce various scenarios before selecting the correct mineral

combination.

Another test for the APXRD program with BCR-2 would be to see what effect removing

elemental iron from the mineral list would have on the theoretical yields. Plagioclase and

pyroxene were renormalized to 100% of the mineral composition and new mineral composi-

tions and theoretical yields were calculated. Table 4.6 compares the “test 2” BCR-2 simplex

4 concentrations and R(Y1) values to the “no elemental iron” results. The only difference

98 Test 2 concs No Fe concs Test 2 concs No Fe concs Test 2 No Fe plagioclase plagioclase hypersthene hypersthene R(Y1) R(Y1) (wt%) (wt%) (wt%) (wt%) O 47.15 46.21 39.92 36.37 Na 3.90 2.51 1.21 1.18 Mg 7.50 0.03 0.82 0.67 Al 10.85 0.60 1.21 1.23 Si 30.13 40.56 23.36 21.28 1.01 1.23

Table 4.6: Comparison of the “test 2” simplex 4 results to the “no elemental iron” simplex 4 results for BCR-2.

between these two data sets is the removal of the 1.5 area% elemental iron.

Overall the “no elemental iron” results are worse than if elemental iron is included. Be- cause the elemental iron is not present, removal of the excess iron in the bulk chemistry has forced clinohypersthene to take up more iron in its matrix. As a result, the magnesium

R(Y1) has been significantly reduced. The plagioclase mineral now contains 0.7 fractional abundance of calcium, placing it on the labradorite-bytownite border. This is reasonable, although the major difference in the mineral chemistry resides in the silicon-aluminum ratio. Aluminum is almost entirely removed from plagioclase, likely a result of the program’s attempt to replicate the aluminum yield supplied by GUAPX. As a result, silicon has increased by 10 wt%, producing a silicon R(Y1) that is significantly worse for the “no elemental iron” case. The program was unable to distribute aluminum and silicon properly and silicon was grossly overused when compared to the bulk chemistry of BCR-2, while aluminum was hardly touched. The quick computation of mineral compositions by the APXRD program allows for testing of GRMs in various circumstances that would have otherwise been too time-consuming or too difficult to compute by hand. The ability to test GRMs with various alterations to mineral compositions and abundances quickly is an excellent resource in further studying MPEs observed by the APXS instrument.

4.3.2.2 ISH-G and MDO-G

As Table 4.2 shows, the mineral concentrations calculated by hand and by the APXRD program do not agree as well as in the BCR-2 case. However, comparing the R(Y1) val-

99 ues to the calibration determined R-values shows that the APXRD program was in better agreement with the R-values for MDO-G in all of the light element cases and was also better for sodium and silicon in ISH-G.

It is interesting to look at the iron calibration R-values for MDO-G and ISH-G, which were both very low. The iron R-value for MDO-G was 0.81 ± 0.06 and the iron R-value for ISH-G was 0.90 ± 0.06. These low values imply the presence of a mineral phase effect.

However, the interrogation depth for iron in trachytes is approximately 82 휇m. This is in the same order of magnitude as the maximum grain size provided by the GRM suppliers, and the majority are likely much smaller. Clearly, our assumption that the X-rays of interest are only produced within a single mineral grain does not hold for iron. It is most probable that

X-rays have traversed through a volume of material rather than an area, as we correctly assumed for sodium, magnesium, aluminum, and silicon. Therefore, MPEs for higher Z elements are not necessarily confined to the near-surface region, and are dependent upon grain size. While attempts could be made to predict this, it is not clear that the results would have any value.

4.3.2.3 GA and GH

GA and GH were selected in the original work of Chapter 3 because they displayed minimal mineral phase effects. These GRMs were selected to show that when MPEs are not expected, the theoretical X-ray yields do not show any effects.

The by-hand and simplex 4 R(Y1) values agree with each other very well for all elements in these two granite cases. In both cases, simplex 4 produced better results for the silicon

R(Y1). In the by-hand calculations, silicon was 4% too high. With the simplex results the silicon R(Y1) is at or is near unity, which agrees with the R-values calculated in the calibration. The R(Y1) values for sodium and aluminum determined by APXRD are nearly identical for both GA and GH. Aluminum R-values determined by the calibration indicate that a very slight mineral phase effect may be present. The(Y1) R values calculated by- hand and by simplex 4 in APXRD show that there should only be a very slight, if any, effect for aluminum. In contrast, APXRD and by-hand R(Y1) values suggest that the slight mineral phase effect observed in the calibration R-values for sodium should be slightly

100 larger. Magnesium is not present in the standard mineral formula, nor in sufficient elemental quantities, to be studied.

The excellent agreement between the R(Y1) values calculated by hand and via APXRD indicates that the differences observed between the R(Y1) values and R-values in Chapter 3 are real. These differences are minimal, however, and demonstrate that APXRD is capable of producing reasonable results for cases where MPEs are expected to be small. This is a valuable test for consistency.

4.3.3 Summary

The good agreement between APXRD and the by-hand approach for both mineral compositions and the R(Y1) values successfully demonstrates that APXRD can be used to determine mineral concentrations and, therefore, realistic theoretical X-ray yields for samples where the bulk chemistry and mineralogy are known. The excellent agreement between the by- hand and APXRD R(Y1) values verifies the slight deviations observed in Chapter 3when the R(Y1) values were compared to the calibration R-values. This suggests that other subtle effects, not due to sample heterogeneity, exist within the APXS spectra that mayneedto be examined further.

4.4 New GRM R(Y1) Results and Discussion

The test of APXRD with previously studied GRMs showed that the program was just as good at determining the mineral elemental concentrations as the work completed by-hand and was even better in some cases. Now that it has been shown that APXRD produces credible results, MPEs of more complex GRMs may be studied. The basalt group of GRMs in the calibration suite demonstrated the greatest MPEs in the three lightest elements; however, they were mineralogically too complex to examine by hand. Three basalts (BIR-

1a, BHVO-2 and BT-2), which have not had R(Y1) values calculated previously, have been selected for analysis.

101 4.4.1 Basalt GRMs

Two basalt GRMs, BHVO-2 and BIR-1a, have been selected to test the APXRD program for mineralogically more complex GRMs. BHVO-2 is an excellent candidate since it is the primary basalt base for Professor P. L. King’s mixtures, some of which were discussed in

Chapter 2. BIR-1a was another key basalt since it expressed the MPEs for the three lightest elements out of all of the calibration GRMs. Tables 4.7 and 4.9 compare the simplex 4

R(Y1) values to the calibration R-values for BHVO-2 and BIR-1a, respectively. Simplex

4 was once again selected due to its high restrictions on the mineral p-value sums, which result in more accurate mineralogy.

In Table 4.7, simplex 4 R(Y1) values are compared to the renormalized calibration R- values. Recall that the four lightest elements were renormalized to a “best” R-value. In the case of aluminum and sodium, the normalization value was not the “best” R-value listed in

Table 2.4, since all rock groups display MPEs. For these two elements the mean R-value of the monominerallic GRMs was used as the normalization value.

The mineralogy for BHVO-2 is relatively simple with two major minerals: plagioclase

(63 area%) and clinopyroxene (37 area%). No restrictions were placed on the general plagioclase formula so the sodium and calcium concentrations could vary as the program saw fit.

The pyroxene input, after some thought, was chosen to be hypersthene. The final mineral compositions given by simplex 4 are listed below. The program gave a plagioclase composition within the bytownite range (0.7 to 0.9 calcium fraction), which is a very reasonable plagioclase composition for a basalt.

plagioclase: (Ca0.8Na0.2)(Al1.3Si2.7)O8

hypersthene: (Mg1.1Fe0.9)Si2O6

Before calculating the R(Y1) values, the element residue was calculated to make sure

the program distributed the elements reasonably within the minerals. Some plus/minus

variability in the element concentration residuals is acceptable, and the distribution for

BHVO-2 is reasonable (see residue column in Table 4.7).

102 R-value APXRD R(Y1) Residue (wt%) Na 1.24 ± 0.05 1.17 +0.59 Mg 0.81 ± 0.02 0.89 +0.13 Al 1.27 ± 0.02 1.27 -1.04 Si 0.96 ± 0.02 1.01 -2.91 Table 4.7: R(Y1) values for major elements and calibration R-values for BHVO-2.

The APXRD determined R(Y1) values are in good agreement with the calibration R- values overall. The only major differences between the two data sets is that sodium and magnesium R(Y1) values do not show quite as large MPEs as the calibration R-values suggest. Despite this, the R(Y1) values for sodium and magnesium do indicate the presence of MPEs for these elements, and they do follow the same directional trends as the calibration

R-values.

BIR-1a provides a more mineralogically complex case with more extreme mineral phase effects. BIR-1a is a coarse grained olivine tholeiite, sampled from the Reykjavik dolerite lava flows [23]. It contains 66.4 area% plagioclase, 23.6 area% clinopyroxene, and10.0 area% olivine. It was first run through APXRD with no restrictions upon the mineralogy to produce the “Round 1” mineral compositions listed in Table 4.8.

Plagioclase CaAlSi3O8 Round 1 Clinopyroxene Ca0.1Na0.9FeAl2O6 Olivine Mg2SiO4 Plagioclase Ca0.9Na0.1AlSi3O8 Round 2 Clinopyroxene Ca0.2Na0.8FeSi0.1Al1.9O6 Olivine Mg2SiO4 Plagioclase Ca0.9Na0.1Al1.2Si2.8O8 Round 3 Clinopyroxene Ca0.3Na0.7Mg0.5Fe0.5SiAlO6 Olivine Mg1.4Fe0.6SiO4 Table 4.8: Final APXRD-derived mineral compositions for BIR-1a.

The plagioclase was classified as anorthite (calcium fraction ranges from 0.9 to1.0), which is more calcic than typical feldspars in basalts. There is also no silicon in the augite mineral, which is unlikely. A second round of minerals was run through APXRD, where augite was forced to contain silicon with a fractional abundance ranging from 1.0-2.0 and

103 calcium in plagioclase was forced to remain within the labradorite and bytownite region (0.5 to 0.9 fractional abundance). The simplex 4 mineralogy determined by APXRD for “Round

2” gave a reasonable plagioclase composition at the bytownite-anorthite boundary. The silicon restriction was not enforced by the program; however, a small portion of silicon was given to augite. Finally, a third round of restrictions on the mineralogy was run through APXRD to give a final set of mineral compositions. For “Round 3” the same restriction was placedon calcium in plagioclase, olivine was forced to contain 1.0 to 1.8 fractional abundance of magnesium, and augite was forced to contain 0.1 to 0.5 fractional abundance of both silicon and magnesium. These restrictions were enforced based on Icelandic basalt mineral compositions published by Thy [73]. Olivine composition ranged from Fo55 to Fo90 (magnesium fractional abundance ranged from 1.1 to 1.8), plagioclase ranged from An50 to An90 (calcium fraction ranged from 0.5 to 0.9) and clinopyroxene ranged from En45Fs10Wo45 to En40Fs17Wo43. This clinopyroxene range placed the composition in the augite range, with a greater abundance of magnesium over iron, based on the greater enstatite (MgSiO3)composition over ferrosilite

(FeSiO3) [20, 73]. The final mineral compositions with the “Round 3” restrictions listedin Table 4.8 are reasonable.

R-value Round 1 Round 2 Round 3 Round 3 R(Y1) R(Y1) R(Y1) Residue Na 1.3 ± 0.1 1.04 1.05 1.09 -1.0 Mg 0.69 ± 0.02 0.94 1.39 0.88 +2.3 Al 1.35 ± 0.03 1.18 1.17 1.18 -2.9 Si 0.97 ± 0.01 1.13 1.02 1.02 -1.0 Table 4.9: Simplex 4 R(Y1) values for three rounds of mineralogy adjustments and calibration R-values for BIR-1a.

The simplex 4 R(Y1) values from the “Round 1” mineralogy gave poor values for most of the elements. This is not surprising considering the relatively poor element distribution into the mineral formulae. With increased restrictions on the mineral compositions the “Round

2” R(Y1) values improve for some cases. The silicon R(Y1) value is now in much better agreement with the calibration R-value, likely due to its inclusion into the augite mineral matrix. Magnesium is considerably worse for “Round 2”, which is a result of its exclusion

104 from the augite matrix. In “Round 3”, the R(Y1) values align most closely to the calibration

R-values. Sodium, magnesium, and aluminum R(Y1) values all verify that BIR-1a should show standard MPEs for these elements; however, the extremes recorded by the calibration

R-values are not reflected in the R(Y1) values. Silicon R(Y1) is in good agreement with the calibration R-value, as expected. Likely another round of minor adjustments to the mineral formulae restrictions would produce slightly larger MPEs for the three lightest elements. In this fourth round the key would be to minimize the element residues summarized in Table

4.9. In other words, adjusting the mineralogy to use more magnesium and less sodium and aluminum could increase the observed MPEs reflected in the R(Y1) values.

4.4.2 BT-2

BT-2 was another obvious test basalt, since it is the calibration target for the MSL APXS.

Modal mineralogy of this GRM was determined using quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN); results of this analysis were provided by

Professor P. L. King and they are summarized in Table 3.18. Sources were provided in the preceding chapter. The sample used was a polished rock slab of BT-2, which is now on the MSL rover on Mars. Following what was done for the XRD modal mineralogy, only minerals present in greater than 1.0 wt% abundance were considered. The modal mineralogy was renormalized to 100% after removal of the minor minerals. The mineral abundances from QEMSAN are given in terms of area abundance so no conversion was required. The mineralogy, together with the bulk chemistry, were input to APXRD and four simplex results were computed.

R-value APXRD R(Y1) Na 1.18 ± 0.04 1.18 Mg 0.80 ± 0.08 0.88 Al 1.20 ± 0.05 1.16 Si 1.02 ± 0.02 1.03 Table 4.10: R(Y1) values from simplex 4 of APXRD and R-values for the calibration target, BT-2.

Table 4.10 gives the simplex R(Y1) values and compares them to the calibration R-

105 values, which are taken as an average of eight different powdered BT-2 samples. The errors given are two standard deviations of the mean value. All of the lightest element R(Y1)

values agree with the calibration R-values, within the R-value error. Sodium, magnesium,

and aluminum show the typical basalt MPEs for these elements.

4.4.3 Summary

The results for BHVO-2, BIR-1a, and BT-2 show that APXRD can be used as a tool

to predict MPEs in various APXS targets with known mineralogy. APXRD does a more

accurate job of distributing elements into the mineral formulae for samples with simple

mineralogy than the by-hand approach. In cases where the mineralogy is more complex,

more information about the sample is required to generate realistic results. More information

allows the user to enforce logical restrictions on the mineral formulae, as was done for BIR-

1a, and even select more specific mineralogy when given a general mineral class, as wasdone

with BHVO-2. This in turn produces more reasonable results than if the program was left

to distribute elements without these logically enforced restrictions.

4.5 Conclusion

In this chapter, the use of a downhill simplex based program to optimize elemental distri-

bution into minerals of set abundances was tested. This program, titled APXRD, produces

four simplex results with increasing restrictions enforced upon the variables being optimized,

namely the element fractions for variable elements in given mineral formulae. The ultimate

goal of this program is to simplify the extensive calculations required to produce mineral el-

emental concentrations by-hand, which are essential for determining the mineral theoretical

X-ray yields used to calculate R(Y1) values. APXRD was tested using the five GRMs used

in Chapter 3: two granites with minimal mineral phase effects (GA and GH), two trachytes

with large magnesium mineral phase effects (ISH-G and MDO-G) and an andesite with large

mineral phase effects for sodium, magnesium, and aluminum (BCR-2). Overall, the APXRD

results agreed with the by-hand results and in some cases, like silicon in GA and GH, the

simplex 4 R(Y1) values improve upon the by-hand results, agreeing more closely with the

106 calibration R-values. The good agreement between the by-hand and APXRD determined

R(Y1) values verifies certain deviations that were observed from the calibration R-values.

One such example is aluminum in both GA and GH. This may indicate that other effects may be influencing the calibration data that are not due to sample heterogeneity.

Our calibration R-value results suggest that MPEs are present for iron in the trachytes.

Iron yields in these samples do not obey the basic condition that each X-ray producing event is confined to a single grain, due to the relatively large interrogation depth. One possible route to dealing with iron would be to replace areal abundance by volume abundance, but this change would not be appropriate for the lightest elements.

A benefit to using the APXRD program over by-hand calculations is that it allows more complex GRMs to be studied. New GRMs, particularly basalts that show the largest

MPEs from the calibration, were tested by the program. The program results agreed with the calibration R-values, but the user must make logical and relevant adjustments to the standard mineral formulae to help the program produce the best results. The R(Y1) results confirm the conclusions of Chapter 3, namely that the large R-value discrepancies fromthe calibration are due to the homogeneity assumption.

More work needs to be done to examine observed deviations in the calibration and this program will be an excellent resource in doing so. Not only will it help explain previously observed MPEs, but it will be useful in identifying new and as yet undiscovered effects.

Future consideration for improving this program will be to incorporate the amorphous component, particularly how to distribute elements into samples with a large unknown portion of its mineral composition. This program can then be run on mutual Martian APXS and

CheMin targets to potentially aid in constraining the amorphous and mineral compositions.

Future work combining proton microprobe analyses and APXRD will also be educational in further understanding MPEs. Proton microprobe analyses on solid rocks with a 2 x 2 micron beam can be used to determine the mineral chemistry for the minerals within the rock sample. The individual chemistry of each mineral, in combination with mineral maps of the surface, will be a valuable adjunct in future testing and application of the APXRD program.

107 Chapter 5

Calibration of the MSL APXS Scatter Peaks for Determining Light Element Abundance

5.1 Introduction

The initial purpose of the APXS instrument was to aid in determining the elemental composition of Martian rocks and soils on the Mars Pathfinder mission [5, 67]. The Pathfinder

APXS instrument, along with the two MER APXS instruments, contained a charged particle detector to measure backscattered alpha particles (and protons from alpha-induced nuclear reactions for the Pathfinder APXS). The objective of using these two processes was todetect the presence of otherwise invisible elements (elements below Z = 11) within the sample. The alpha mode data of the MER detectors has not been as widely used as the PIXE and XRF mode data, and no additional light invisible component (ALIC) data using this mode have been presented for MER APXS targets.

It was not until several years into the MER mission, when intriguing sulfate deposits were discovered, that the plutonium L훼 X-ray scatter peaks were considered as an alternative tool to quantify ALICs within the sample [9]. The combination of Compton (inelastic) and

Rayleigh (elastic) scattering can be used to infer the presence of elements below sodium in the energy region outside which the APXS operates [43]. The quantity employed is the intensity ratio between the Compton and Rayleigh peaks, denoted C/R. In our method, a quantity K is defined as follows:

108 퐶/푅 퐾 = 푠푖푚 (5.1) 퐶/푅푒푥푝 The simulated C/R is computed by a Monte Carlo based program and requires the concentrations of the visible oxides [46]. The K-value should be 1.0 in theory, provided the sample is homogeneous and the concentrations (both visible and invisible) provided to the simulation program are accurate. For a geostandard, ALICs are considered in the simulation program, but for an unknown sample, ALICs are not considered. To determine if a sample contains ALICs, a K “calibration” line is produced using GRMs (details on producing such a calibration line are discussed below). If the K-value of the target in question falls below the calibration line, it suggests that the sample contains ALICs. This K-value method was first described by Mallet et al. [46]; a detailed description of the Monte Carlo simulation program was also given. The simulation program, given the name Marsgeom, has been improved over the years and has been shown to be robust [44].

The paper by Campbell et al. [9] was the first to publish quantitative ALIC values from

APXS data. In this paper, it was deduced from the scatter peaks that the interesting high- sulfate soils discovered in the Columbia Hills, Gusev Crater, by Spirit, contained up to 18 wt% ALICs. This limited work on ALICs with MER suggested that the method should be refined for potential use on MSL.

MSL is now studying the rocks and soils of Gale Crater, Mars. This crater has been hypothesized to have once been a large lake with an active hydrothermal system [6]. Evi- dence for hydrated samples and minerals that on Earth require water for their formation are some of the important discoveries that would help Curiosity complete its goal of determining the habitability of Gale Crater [29]. Phyllosilicates, clay minerals formed in the presence of water [75], and hydrated samples [52] have already been detected at Gale by MSL; however, the CheMin and SAM instruments that made these findings are used infrequently. It would be ideal for the APXS to be able to quantitatively determine the ALIC content of its targets, which far outnumber those of CheMin and SAM. This chapter discusses in detail the calibration of the MSL APXS L훼 scatter peaks, the testing of the K-value calibration, and the limits of detection and uncertainties of this method.

109 5.2 L훼 K-value Method Calibration

The MSL APXS GRM calibration suite, discussed in Chapter 2, was expanded to improve the elemental calibration, and special attention was paid to the collection of GRMs with high

ALIC concentrations. Extensive work has been done in examining the MSL scatter peaks and calibrating the instrument through various techniques [13, 62]. The calibration presented in this chapter differs from the previously published MSL work in that it uses spectra with full L훼,L훽 and L훾 scatter peaks (Figure 5.1). Various effects that have not been studied previously are also reported here that are crucial to understanding the Compton-Rayleigh

scatter method for ALIC determination.

Figure 5.1: High energy cut-off (pre-2012 MSL FEU) versus full energy range (equivalent to the MSL PFM) spectra. Both spectra are shown from channels 500 to 1024.

110 5.2.1 Selection of GRMs and Data

For a reliable calibration of the APXS scatter peaks, some restrictions on the GRM data set needed to be enforced.

Only spectra with the full energy range are considered. Calibration of the cut-off energy spectra has been studied and is being prepared for publication [62].

Only GRMs with rubidium and strontium concentrations less than 500 ppm were selected

(Table 5.1). This reflects the strong peak overlaps, which are clearly seen in Figure 5.1.

These overlaps challenge the spectrum fitting routine. When rubidium is present inthe sample with large enough concentrations, rubidium counts may be registered in the L훼 Compton scatter peak, or vice versa, depending on the abundance of rubidium. The same situation occurs for strontium with the L훼 Rayleigh scatter peak. Yttrium complicates matters because the K훼 line of yttrium overlaps directly with the K훽 line of rubidium. If enough yttrium is present it may affect the rubidium count rate and subsequently alter the

Compton scatter peak count rate. Very few GRMs contain yttrium in quantities that may affect rubidium, and these GRMs have already been excluded from the calibration dueto elevated rubidium or strontium concentrations. All GRMs used in this full spectrum L훼 calibration follow the same rubidium and strontium criteria that were used for the cut- off MSL L훼 calibration [13, 62]. The GRMs used for the calibration presented here were supplemented by several minerals from Boreal (Wards) Science. The element concentrations for these minerals were provided by Activation Laboratories.

Only spectra with durations of at least five hours were used to produce the K-value calibration. Section 5.3.2 discusses the effect of spectrum duration on the C/R value by comparing two hour to roughly twenty-four hour integrations. It was initially thought that the majority of the MSL APXS spectra acquired on Mars would be around two hours in duration so a significant amount of work was done with two hour spectra for the 2010 energy cut-off calibration. After over two (Earth) years of MSL operations on Mars, it isnowclear that many APXS integrations are taken overnight with long durations, so we may be more selective with respect to spectrum duration. The AL-I spectrum run on the full energy range FEU instrument was excluded from this calibration because the spectrum was only

111 two hours in duration. This resulted in an elevated experimental C/R. The discrepancy observed between short (two hour) and long (approximately twenty-four hours) duration L훼 C/R values discussed in section 5.3.2 for the reduced energy region spectra is verified by this full energy region AL-I spectrum.

Only GRMs with ALIC content less than 2 wt% were selected for the calibration suite.

This ensures that the bound oxygen fraction, F(O), is by far the dominant invisible component, which simplifies the calibration. Selecting GRMs with low ALIC content minimizes the risk of bound water loss during the pre-heating of each GRM to 110∘C during sample

− preparation (this was done to drive off adsorbed water,2 H O ). Certain GRMs contain-

− ing phyllosilicate minerals have lost not only the expected adsorbed water (H2O ), but

+ also some of the mineralogically bound water (H2O ) [62]; the uncertainty in their ALIC content makes them unsuitable materials for the K-value calibration. Activation Labora-

tory concentrations for the GRMs were preferred for this calibration because their reported

ALIC information was generally more detailed than in the suppliers’ certificates. For most

GRMs, ALIC content between the certificates and Activation Laboratories agreed; however,

there were some exceptions. These discrepancies were greatest in GRMs with ALIC content

greater than 2 wt% and are thus not an issue in this calibration. Another reason why only

GRMs with less than 2 wt% ALICs were considered, arises from the recalibration of the

FEU APXS after the extension of its energy range, which eliminated the truncation shown

in Figure 5.1. The GRMs used in this recalibration had been stored in a glove box for an

extended period of time. GRMs with significant ALICs could have lost, or gained, adsorbed

water, and perhaps even mineralogically bound water, depending on the composition of

− the GRM. Only GRMs that initially reported low ALIC content (primarily low H2O and

+ H2O ) could be considered stable when exposed to atmosphere over long periods of time.

5.2.2 The Marsgeom Simulation Program

A Monte Carlo program has been used to produce simulated L훼 C/R ratios. This program was based on Professor J. O’Meara’s code, originally designed for in vivo X-ray fluores-

cence [58]. It was adapted by C. L. Mallet for analyses of Martian rocks and soils by the

112 GRM Rb Concentration Sr Concentration ALICs (ppm wt) (ppm wt) (wt%) ACE 151 2 0.43 ANG 0 76 0.44 AUGITE 4 96 0.74 BCR-2 48 344 0.88 BHVO-2 10 393 0.25 BIR-1a 0 108 0.10 DIOPSIDE 0 96 0.41 DNC-1 4 144 0.86 DT-N 5 32 1.25 DTS-2b 0 0 0 GSP-2 256 242 1.02 JA-2 69 242 1.37 JA-3 35 289 0.32 JG-1a 171 179 0.68 JGB-1 6 322 0.70 MO-15 49 481 1.96 NIST688 2 167 0.22 OLIVINE 0 0 0.05 PM-S 0 274 0.89 SARM6 0 3 0.65 VS2113-81 4 336 1.06 WSE 25 405 1.49

Table 5.1: GRMs and minerals selected for the L훼 K-calibration line. Their Rb and Sr concentrations are below 500 ppm weight and their ALICs are all below 2 wt%.

MER APXS instruments [46]. M. Lee made improvements to this program, now named

Marsgeom, and expanded it to include MSL APXS geometry and excitation files [44]. The version of Marsgeom used for this work is MG10e2.

Marsgeom requires the input of the sample concentrations as oxides (Na2O, MgO, Al2O3,

SiO2,P2O5, SO3,K2O, CaO, TiO2, Cr2O3, MnO, FeO, Fe2O3, ZnO, Rb2O, SrO, ZrO2,H2O,

CO2, OH), with a few exceptions dealt with as elements (F, Cl, Ni, Br). It also requires the instrument-to-sample geometry, atomic data, and the number of photons to be generated in the simulation. 40 billion photons was the chosen number of photons generated, producing approximately 1% statistical error (two standard deviations) on the simulated C/R [46].

Activation Laboratories concentrations were used for the calibration. As discussed in

Chapter 2, the Activation Laboratories and certificate concentrations of major and minor oxides do not deviate greatly from one another so the use of the Activation Laboratories con-

113 centrations here is legitimate. The known ALICs, specifically2 H O, CO2, OH, and fluorine, were included in these sample files.

Before photon simulation begins in Marsgeom, various sample-dependent parameters are

calculated. The program then produces and tracks each photon through random excitation

events. The program only records an event if the photon undergoes Compton or Rayleigh

scattering, is able to escape the sample, and is then able to reach the detector through

the collimator [44]. Each simulation takes on average twelve hours, after which a detector-

efficiency-adjusted L훼 C/R ratio and uncertainty are produced. This value is then used to produce the K-value as described in the next section.

5.2.3 L훼 K-value Calibration for the MSL FEU

The K-value method calibration used here is very similar to what was described by Mallet et al. [46] for MER. The spectra of the selected GRMs were fit in GUAPX using IM mode, producing experimental C/R ratios. The Activation Laboratories concentrations were input to

Marsgeom. Activation Laboratories provides all iron oxide as Fe2O3-Total. In certain cases where the iron is present primarily as FeO (as indicated by the certificate concentrations),

the simulated C/R, which has now overestimated the invisible component of the sample, is

too low. GRMs with FeO/Fe2O3 ratios greater than 10.0 have had their iron adjusted to the certificate iron oxide value. The only such GRM for this calibration was SARM6,which

had an FeO/Fe2O3 ratio of 20.6. The simulated and experimental C/R values were then ratioed to produce the K-value

(equation 5.1). The K-value is plotted against the fraction of cation bound oxygen (F(O))

within the sample. Because of the very low ALIC concentrations in the chosen GRMs, the

average F(I)/F(O) ratio is 1.004. It follows that F(O) and F(I) can be used interchangeably

with this data set.

The errors on the K-values are two standard deviations and take into consideration both

the simulated and experimental uncertainties, as well as the average difference between

simulated C/R values using Activation Laboratories concentrations, and the C/R values

using certificate concentrations. A weighted linear fit to the K-values was produced (black

line) and two standard deviation uncertainties on the linear fit were calculated. The region of

114 uncertainty for the K-value calibration line is bounded by a maximum (blue) and minimum

(red) error line. The full energy range K-value calibration with linear fit is shown in Figure

5.2.

Figure 5.2: K-method calibration for the full energy MSL L훼 scatter peaks.

This L훼 calibration line is similar to both the MSL cut-off [62] and the MER-A (Spirit)훼 L calibration [9, 46]. Figure 5.3 shows both the MER-A and cut-off MSL K-value calibrations.

The MER-A calibration slope is -0.5 ± 0.4 with a y-intercept of 1.15 ± 0.2 [7]. The MSL cut-off calibration slope is -0.7 ± 0.2 with a y-intercept of 1.24 ± 0.08 [62]. The slopes of the three calibrations agree within error, as do their y-intercepts. The reduced scatter of both

MSL calibration lines attests to the effort given to improving the calibration GRM suite for

the ALIC calibration.

5.3 Effects on C/R

Throughout the K-calibrations for the MSL APXS, various studies have been made to in-

crease our understanding of the L훼 scatter peaks and the effects various parameters have on

115 (a) MER-A [9]

(b) Cut-off MSL [62]

Figure 5.3: MER (a) and cut-off MSL (b) K-value calibrations훼 forL scatter peaks the experimentally determined (GUAPX) C/R values.

5.3.1 Effect of Energy Region on Experimental C/R

The FEU APXS instrument originally had a shorter energy range than the PFM, which cut out half of the L훾 scatter peaks and made it impossible to assess the background beyond the scatter region. The MSL scatter peak calibration papers are based solely on the cut-off MSL

FEU spectra [13, 62]. This energy region discrepancy between instruments was rectified by

116 the manufacturer in 2012 to replicate the energy range of the PFM instrument on Mars.

The K-calibration presented in this chapter uses the PFM-equivalent energy range FEU spectra. The experimental C/R values for both sets of data were determined via IM mode in GUAPX and the simulated C/R values were determined using Activation Laboratories concentrations; simulated C/R values are, therefore, identical in both cases.

The mean ratio between the cut-off and full spectra K-values is 0.903 ± 0.006. In other words, the cut-off experimental C/R values are smaller than the full energy range spectra

C/R values. This suggests that either the cut-off Compton peak area is being underestimated and/or the cut-off Rayleigh peak area is overestimated compared to the full energy spectra.

5.3.2 Effect of Spectrum Duration on Experimental C/R

Spectrum duration has been found to be a factor in accurate experimental C/R determination. This was studied with thirty-five cut-off spectra. The mean ratio between twohour and twenty-four hour experimental C/R values is 1.016 ± 0.002. Figure 5.4 demonstrates how the experimental C/R changes with changing spectrum duration for the BT-2 calibration target (collected on sol 179), and a Martian target (Wer- necke, collected on sol 173). These spectra were collected with the PFM instrument, which does not have the energy cut-off issue shown in Figure 5.1. Only spectra with a manganese

FWHM less than 180 eV were selected for this analysis. The FWHM for Wernecke ranges from 139 eV to 175 eV and from 139 eV to 163 eV for BT-2.

The C/R error bars in Figure 5.4 decrease with increasing spectrum duration (and therefore increasing scatter peak intensity). This is expected. Also note that for BT-2 the shortest duration spectra show some scatter in the C/R values, and at around three hours the C/R values behave more predictably. The C/R value for Wernecke is fairly constant. The errors for both BT-2 and Wernecke decrease rapidly at first, then decrease more slowly around three hours.

The trends, or lack thereof, in C/R value for the MSL spectra, differ from the slight increase in C/R for shorter duration GRM C/R values; the upward trend for the short duration GRM C/R values could be due to the shortened energy region. It is clear that for

117 Figure 5.4: Effect of spectrum duration on experimental C/R for BT-2 (sol 179) andWer- necke (sol 173).

both laboratory and Martian C/R values, spectra with durations shorter than three hours are not stable. Only spectra with durations of three hours or greater should be used for the calibration and ALIC determination on Mars.

5.3.3 Effect of Marsgeom ALIC Type on Simulated C/R

The type of ALIC simulated by Marsgeom affects the C/R that the program produces. It is not surprising that the simulated C/R value is different when the same weight fraction is defined for various ALIC types. The differences between simulated C/R values for various light elements and compounds arises from the differential cross sections for Compton and

Rayleigh scattering. Using the median scattering angle of 160∘ for the APXS instrument,

Table 5.2 gives the ratio of Compton to Rayleigh differential cross sections for the light elements at 14.26 keV. These values were deduced using the tables of Hubbell et al. [35, 36].

Table 5.2 shows that as Z increases, the ratios decrease. A smaller ratio of Compton to Rayleigh differential scattering cross sections produces smaller simulated C/R values. In

118 ALIC d휎/dΩ ratio H 36051 B 7.50 C 5.21 N 4.38 O 3.95 F 3.82

Table 5.2: Ratio of the Compton and Rayleigh differential scattering cross sections휎 (d /dΩ) for select light elements at 14.26 keV.

turn, this gives a smaller K value, which results in higher calculated ALIC content. H2O should, therefore, produce a larger simulated C/R and K-value than CO2, which will in turn give smaller calculated ALIC content.

The differences between ALIC type and resulting ALIC concentration are important to consider when determining the ALIC content of unknown samples. The effect of calculated

ALIC content when H2O and CO2 are used in the simulations is addressed in greater detail in section 5.4.3.

5.4 Accuracy of the K-value Method and Calibration

To test the accuracy of the MSL full energy L훼 K-value calibration, GRM ALICs were calculated as though they were Martian samples. Since Martian sample composition and

ALIC content are obviously unknown prior to analysis, no a prori knowledge was assumed

for the GRMs. The GRMs in the calibration line, as well as a few other GRMS with high

ALICs, were tested. The only requirement that was upheld from the initial calibration

restrictions was that the spectrum duration be at least five hours. The calculated ALICs

were then compared to their known ALIC content to verify how accurate the calibration

line and method are. Only plutonium L훼 scattering has been considered for this work.

5.4.1 Method for Calculating ALICs

Our starting point is the K versus F(O) calibration line established above. Recall from above that F(I)/F(O) for the calibration GRMs is on average 1.004. As a result, the calibration

119 line can be used with either F(O) or F(I) values and will be relevant in either case. We use the F(I) value here.

Each sample is first fit with GUAPX in IM mode.훼 TheL C/R, its error, and the F(O) from the GUAPX output, were recorded and the oxide concentrations for all major and several minor and trace elements were provided to Marsgeom. The reader might recall that IM-GUAPX normalizes all oxides to 100 wt%; apart from this step, no light element component is considered.

Four Marsgeom input files were then made for each GRM with the IM GUAPX oxide concentrations. Historically, H2O was the ALIC used in Marsgeom simulations [9, 46]. In each of the four Marsgeom sample files, the ALIC portions simulated were arbitrarily selected to be 0, 5, 10, and 15 wt% H2O. These simulated values are ratioed to the GUAPX

L훼 experimental C/R to produce four K-values, representing the sample with four different simulated ALIC contents. The addition of ALICs increases the simulated C/R, thus increasing the K value. The initial GUAPX bound oxygen concentration represents the F(I) for the zero wt% ALIC case. This F(I) is adjusted to consider the additional ALICs added, so each K value has its own unique F(I). This enables us to fit these four K-value points with a line, which will be referred to as the sample line, onto the calibration plot (see Figure 5.5).

The point of intersection between the calibration line (with the GRM under study removed if it was originally present in the calibration line) and the sample K-line represents the percentage of ALICs in the sample. For this work the ALIC is assumed to be H2O. The error associated with this ALIC value is calculated by finding the minimum and maximum

F(I) values based on the errors of the two lines (these maximum and minimum positions are

marked on Figure 5.5 by black stars). Using the upper and lower F(I) values, maximum and

minimum ALIC values can be calculated. The average difference between the maximum and

the minimum ALIC values relative to the calculated ALIC is taken as the error estimate.

To convert these three F(I) values into an ALIC value with an associated error, another

graph relating F(I) to ALIC is used. ALIC content is given on the horizontal axis and F(I)

is on the vertical axis (see Figure 5.6). Each sample will have a unique F(I) versus ALIC

plot; however, a generalized formula may be used for all cases to simplify calculations. This

120 Figure 5.5: Point of intersection and associated errors for the K-calibration line and the GRM (UB-N) K-line with 0, 5, 10, and 15 wt% simulated ALICs. formula is derived as follows:

First, we must recognize that the F(I) is calculated using the original F(O)퐺푈퐴푃 푋 value (where no ALIC concentration is included), renormalized to account for the ALIC con-

centration that is being added. In a sample where some ALIC has been added, the total

components can be summarized by equation 5.2.

Total F(O) F(V) ALIC ALIC (5.2) = ( 퐺푈퐴푃 푋 + )(1 − ) + = 100푤푡%

The fraction of visible elements (F(V)) and bound oxygen (F(O)퐺푈퐴푃 푋 ) have been renormalized to account for the ALIC that is present. To account for only the invisible fraction

of the sample, both bound oxygen and ALICs, the F(V) is removed. This is represented by

equation 5.3.

F(I) F(O) ALIC ALIC (5.3) 퐴퐿퐼퐶 = 퐺푈퐴푃 푋 (1 − ) +

F(I)퐴퐿퐼퐶 in our calculations is the point of intersection value and ALIC represents the

121 fractional ALIC concentration to be calculated.

Rearranging equation 5.3 to isolate for the ALIC term gives:

F(I) − F(O) 퐴퐿퐼퐶 퐺푈퐴푃 푋 (5.4) 퐴퐿퐼퐶 = F(O) 1 − 퐺푈퐴푃 푋 ALIC values can be calculated for the minimum, maximum, and middle point of inter-

section F(I)퐴퐿퐼퐶 values found from the K-value versus F(I) plot. Averaging the difference between the maximum to middle and minimum to middle ALIC values gives the error on

the calculated (middle) ALIC value.

Figure 5.6: Graph to convert F(I) to ALIC content for UB-N. F(O) values are also represented to demonstrate the discrepancy between these values.

If F(O) had been used to calculate the ALIC value, then the two hydrogen atoms in water would not be considered in the calculation of the ALICs (when Marsgeom is run using

H2O as the additional ALIC). The slower increase in F(O), in relation to F(I) (see Figure

5.6), will result in a larger equivalent H2O content. Since the quantitative detection of H2O on Mars is a topic of interest for the Mars community, and since the APXS instrument

was not initially intended for such measurements, it is reasonable that the F(I) is used to

122 calculate ALICs. This properly represents the assumption that all invisible content in the sample is H2O. This will ensure the equivalent water content of the Martian APXS targets are not over-estimated.

As mentioned earlier, GRMs used in the K-value calibration line (containing low rubidium, strontium, and ALIC content) were used individually to test the calibration. We examined the effects of rubidium and/or strontium concentrations exceeding 500 ppmin select GRMs. Before in-depth examination of the calculated ALICs is performed, the high rubidium and strontium cases have been examined to see if they should be considered in future analyses.

Figure 5.7: Calculated ALICs of high rubidium and strontium GRMs, compared to their expected ALIC content.

Figure 5.7 indicates that rubidium concentrations greater than 500 ppm are indeed prob- lematic. Two of the three cases with high rubidium generate calculated ALIC values much greater than expected. The other gives an ALIC value 50% too low. Strontium does not cause as much of an issue as rubidium; however, the 500 ppm limit has not been adjusted and thus remains consistent with the original guidelines. Due to the issues caused by high

123 rubidium and strontium, GRMs with elevated rubidium and strontium concentrations will not be used in further analyses.

Several additional high ALIC samples were added to test the K-method. Among these high ALIC samples are mixtures of DTS-2b plus H3BO3, which were made to test if it was

possible to simulate high ALIC GRMs without adding volatile H2O to the sample. Water added in relatively large quantities would be difficult to characterize and would likely be

unstable for analysis with the FEU APXS under vacuum. The dominant ALIC of the siderite

mineral sample is CO2, which is a useful test to see how well this method can reproduce

ALICs when the composition is not primarily H2O.

5.4.2 H2O Simulations

Table 5.3 summarizes the experimentally calculated ALICs for each GRM in the calibration

line, as well as the various high ALIC GRMs. Water was used as the ALIC in the Marsgeom

simulations. In the cases where ALICs were calculated for a GRM that was used in the cali-

bration line, that GRM was removed from the calibration, and slightly adjusted calibration

parameters were used in the calculations.

JMS-2, a high ALIC GRM (containing approximately 8.4 wt% ALICs) was run twice;

one JMS-2 sample was prepared in 2009 and was held in the glove box at standard laboratory

atmospheric pressure and humidity until 2013 when it was analysed. Another sample was

prepared in March of 2014 and was analysed immediately afterwards. Their experimental

C/R values differ by only 2% and their calculated ALIC values agree within error. Thefresh

JMS-2 calculated ALIC is slightly less compared to the JMS-2 sample that was housed in

the glove box, indicating that a small amount of water was likely picked up. JMS-2 contains

over 8 wt% ALICs and is likely to be more susceptible to hydration changes over time than

GRMs containing smaller quantities of ALICs. Therefore, the low ALIC GRMs used in the

calibration are likely unaffected by their storage in the glove box. Only the “fresh” JMS-2

has been used in the following plots.

Hectorite (Na0.3(Mg,Li)3Si4O10(OH)2), a clay mineral, contains lithium, which has not been measured by Activation Laboratories, nor is it included in the Marsgeom simulations.

124 GRM Act. Lab. ALIC Calculated ALIC error (2휎) AC-E 0.43 -2 2 ANALCIME 11.74 11 4 AN-G 0.44 1 2 AUGITE 0.74 3 2 BCR-2 0.88 0 2 BHVO-2 0.25 -2 2 BIR-1a 0.10 -1 3 DIOPSIDE 0.41 3 5 DNC-1 0.86 0 2 DR-N 2.44 0 2 DT-N 1.25 -2 3 DTS-2b 0.00 5 2

DTS-2b, 1g H3BO3 6.30 6 2 DTS-2b, 2g H3BO3 12.80 10 3 DTS-2b, 3g H3BO3 19.40 17 4 DTS-2b, 6g H3BO3 40.20 33 7 GSP-2 1.02 0 2 HECTORITE 22.98 33 7 JA-2 1.37 4 3 JA-3 0.32 3 3 JG-1a 0.68 2 3 JGB-1 0.70 1 2 JLK-1 9.09 7 3 JMS-2 8.41 7 2 JMS-2 (fresh) 8.41 6 2 JSD-2 3.43 -5 2 JSL-1 5.93 5 3 JSL-2 6.27 5 3 MO14 2.63 2 2 MO15 1.96 3 2 NIST688 0.22 -1 3 OLIVINE 0.05 1 3 PM-S 0.89 -2 3 SARM6 0.65 -1 3 SIDERITE 36.3 31 2 UB-N 11.35 9 3 VS2113-81 1.06 4 2 WS-E 1.49 0 2

Table 5.3: Calculated ALICs using H2O based simulations.

The lithium content in hectorite may range from 0-6 wt%. If it is present in the sample studied in this research, it would decrease the mean Z value, thus increasing the simulated

C/R. This would in turn increase the K value, resulting in a decrease in the calculated ALIC

125 value. Since the hectorite calculated ALIC is higher than the Activation Laboratories ALIC value, it suggests there is lithium present in this sample, which has not been accounted for in the known ALIC value.

Figure 5.8: Experimentally determined ALICs (H2O simulations) compared to Activation Laboratories ALICs.

ALICsexp = (0.85 ± 0.05)ALICsAL − (0.1 ± 0.5) (5.5)

Figure 5.8 graphically represents the calculated ALICs summarized in Table 5.3 in relation to the known Activation Laboratories ALIC content. Equation 5.5 gives the overall fit for this data. The green line represents the mean, the pink and blue lines representthe maximum and minimum boundaries or error limits on the data, and the black line represents the 1:1 line, or in other words, the line where the calculated ALICs would be equivalent to the known ALIC content. When all GRMs are considered, the overall fit lies below the 1:1 line, indicating that the method is underestimating ALIC content. Based on the scatter in the low ALIC portion of Figure 5.8, quoting results below 5 wt% will carry considerable uncertainty and should be treated with great caution.

126 Figure 5.9: Experimentally determined ALICs (H2O simulations) compared to Activation Laboratories ALICs for GRMs with known ALIC content greater than 5.0 wt%.

ALICsexp = (0.86 ± 0.05)ALICsAL − (0.3 ± 1) (5.6)

Figure 5.9 summarizes the relationship between calculated ALICs and known ALICs

where GRMs with known ALIC content less than 5.0 wt% have been excluded. The overall

trend given by equation 5.6 shows a very similar trend to the relationship described when all

GRMs are considered. Equation 5.6 describes the high ALIC GRMs, and it also translates

well to the region below 5.0 wt% known ALICs. When no ALICs are expected to be present

in the sample, no ALICs should be calculated, within error. Despite calculating ALICs

slightly below the expected ALIC content, this method and calibration is reliable when the

known ALIC content is greater than 5.0 wt%.

5.4.3 CO2 Simulations

In this section, CO2 was considered as the additional ALIC in Marsgeom simulations. This test was performed to see how the final calculated ALIC values differ from the standard

127 H2O Marsgeom simulations. Only GRMs with Activation Laboratories ALICs greater than

5.0 wt% were considered for this test, since the H2O results showed that the ALIC content of GRMs with less than 5.0 wt% known ALICS cannot be reliably calculated. The 500 ppm cap was also enforced for rubidium and strontium.

GRM Act. Lab. ALICs Calculated ALICs error (2휎) ANALCIME 11.74 14 6

DTS-2b, 1g H3BO3 6.30 7 3 DTS-2b, 2g H3BO3 12.80 12 4 DTS-2b, 3g H3BO3 19.40 19 5 DTS-2b, 6g H3BO3 40.20 38 9 HECTORITE 22.98 37 8 JLK-1 9.09 9 4 JMS-2 8.41 8 3 JMS-2 (fresh) 8.41 6 3 JSL-1 5.93 6 4 JSL-2 6.27 5 3 SIDERITE 36.3 33 3 UB-N 11.35 10 3

Table 5.4: Calculated ALICs from CO2 based simulations.

Figure 5.10: Experimentally determined ALICs (CO2 simulations) compared to Activation Laboratories ALICs for GRMs with known ALIC content greater than 5.0 wt%.

128 ALICsexp = (0.93 ± 0.07)ALICsAL + (0.2 ± 1) (5.7)

Table 5.4 summarizes the calculated ALICs where the additional ALICs in Marsgeom

were given as CO2. Figure 5.10 compares the Activation Laboratory ALICs to the CO2 equivalent calculated ALICs. The calculated ALICs are slightly less than the known ALIC

content of the samples once again, as demonstrated in Figure 5.10, with the 1:1 line located

on the upper error boundary for the majority of the fit.

Figure 5.11: Difference between calculated ALIC content with CO2 and H2O Marsgeom simulations.

Using CO2 as the ALIC in Marsgeom simulations increases the calculated ALICs by approximately 10% consistently (see Figure 5.11). The difference in calculated ALICs is based on what ALIC type is used in Marsgeom simulations. This difference is due to the

Z-dependence of the ratio of Compton to Rayleigh scattering cross sections, which is shown in Figure 5.12 [62]. The mean Z for CO2 is 7.5 and 6.6 for H2O. Figure 5.12 shows that as the mean Z increases, the simulated C/R value decreases. This in turn decreases the

K-value. In the case of CO2, more added ALIC will be required to bring the K-value to

129 the calibration line than would be required for H2O, resulting in a greater calculated ALIC content.

Figure 5.12: Demonstration of the inverse square relation of C/R on mean Z.

Historically, water was the ALIC considered in Marsgeom simulations [9, 46], and is one of the most desired ALICs to be detected on Mars (the MER mission slogan was “Follow the

Water” [71]). Despite slightly better agreement between calculated and known ALIC content with CO2 based simulations, future work will continue to use H2O as the added ALIC in Marsgeom and all calculated ALIC values will be given as “water equivalent” ALICs.

5.5 Detection Limit and Error Estimate

As was shown in section 5.4.2, the method used to calculate ALICs for samples containing less than 5.0 wt% ALICs becomes unreliable. Figure 5.13 emphasises the discrepancy between the calculated and known ALIC values for the low ALIC GRMs.

Figure 5.14 demonstrates that around 2.0 wt% Activation Laboratory ALICs the associ-

130 Figure 5.13: Ratio of calculated to Activation Laboratories ALICs compared to known ALIC values. Around 5 wt% the calculated to Activation Laboratories ratio becomes consistent.

Figure 5.14: Ratio of calculated to Activation Laboratories ALICs for GRMs with known ALIC values less than 5.0 wt%.

131 ated uncertainties begin to decrease and then stabilize around 5.0 wt% known ALICs. This enforces the conclusion made in section 5.4.2 that ALIC values at or above 5.0 wt% can be calculated with some accuracy; below this value the results are not reliable.

Figure 5.15: Ratio of calculated to Activation Laboratories ALICs for GRMs with known ALIC values greater than 5.0 wt%.

Figure 5.15 shows results above 5.0 wt% known ALICs. The weighted mean for GRMs with known ALIC values greater than 5.0 wt% is 0.87 ± 0.06. This value is lower than the expected ratio of 1.0, and it replicates the low calculated ALIC trend observed previously

in Figure 5.8. The calculated ALIC values are almost consistently low by approximately

13% when H2O is used in the simulations. The two standard deviation uncertainty for the calculated ALICs above 5wt% known ALICs is 14%.

It now follows that on Mars, where the ALIC values are not known before measurement, any sample with ALICs calculated below 5.0 wt% should either be said to contain an unde- tectable amount of ALICs, or have 100% uncertainty associated with the calculated ALIC.

If the calculated ALIC content in an unknown sample is at or above 5.0 wt% equivalent

H2O, then it can be said with some certainty that the ALIC content is real.

132 5.6 Conclusion

This chapter builds upon previous work that has calibrated the MSL FEU L훼 scatter peaks with a shortened energy region [62]. The calibration line presented in this chapter improves upon this previous work by using only MSL FEU spectra with the full energy resolution.

The L훼 calibration line has a negative slope and is described by the equation in Figure 5.2. The slope of this calibration line agrees with both the MSL FEU cut-off calibration line [62] and the MER calibration line [9].

The K-value calibration was tested using several GRMs. The low-ALIC, rubidium, and strontium GRMs of the calibration line, as well as several high-ALIC and low rubidium and strontium cases were considered, simulating ALIC content as purely H2O. In cases where the known ALIC content was less than 5.0 wt%, the calculated ALIC values were unreliable.

Above this threshold the K-value calibration works well, although it produces ALIC content approximately 13% below the expected values. High-ALIC GRMs were also examined using

CO2 as the simulated ALIC. The CO2-equivalent ALICs agree with expected results within error; however, these calculated values were also slightly underestimated compared to the actual ALICs present. H2O will remain the simulated ALIC of choice and all future calculated ALIC values will be given as H2O equivalent, due to historic practices and importance of this compound. A correction factor of 13% increase in calculated ALIC content may be applied to Martian ALICs to account for the underestimation of ALICs found from this work.

The practical LOD for calculating ALICs with H2O simulations has been determined to be 5.0 wt%. If the calculated ALIC content is below this value then the associated errors are 100% and these samples should be treated with great caution in future analyses. In the region above 5.0 wt% calculated ALICs, the associated two standard deviation uncertainty of the method is 14%, despite being systematically low. With this calibration and thorough analysis, the ALIC content of suitable Martian targets analyzed by the MSL APXS may now be studied.

133 Chapter 6

Application of the Elemental and K-value Method Calibrations to MSL APXS Targets and First Alterations of Martian APXS Results for Mineral Phase Effects

6.1 Introduction

The previous chapters of this thesis have focused on element and scatter peak calibration of the MSL APXS instrument, as well as an in-depth study of the mineral phase effects observed in the GRM calibration suite resulting from sample heterogeneity. The purpose of this research has been to produce accurate analyses of Martian targets at Gale Crater,

Mars. In this chapter sample-specific MPEs have been calculated using APXRD for three

APXS targets that have accompanying XRD data from the CheMin instrument. ECFs have been applied to these cases as well, to demonstrate the differences between sample-specific corrections and the calibration-determined ECFs. The effects of pressure and sample-to- detector standoff are studied, as well as manganese full width half maximum (FWHM) effects on elemental concentrations and experimental C/R values. ALIC content of select Martian materials is presented, and various issues that increase complexity in ALIC determination are discussed.

134 6.2 Extension of the Elemental Calibration to Mars

Before presenting MSL APXS concentrations using GUAPX for Martian spectra, it is important to account for variables that affect APXS spectra. In the laboratory, the calibration spectra were collected under vacuum and at a constant “in-contact” geometry, which gives a standoff between the flat sample surface and the detector of 21 mm. The “in-contact” standoff on Mars is 18 mm, which is a negligible difference in “in-contact” standoff positions.

The biggest difference between spectra collected in the laboratory and on Mars is duetothe atmosphere. On Mars, the atmosphere is composed primarily of CO2, which varies in pressure from approximately 6.9 to 9.0 mbar depending on the time of day and time of year [18].

The temperature is also widely variable, which influences the FWHM of the APXS data.

Effects due to instrument standoff and atmospheric pressure changes in the FEU laboratory spectra and PFM spectra on Mars, are addressed below.

6.2.1 FEU and PFM Cross Calibration

After landing, it was important to measure the BT-2 calibration target as soon as possible to verify that the APXS had not been damaged in any way and confirm the instrument was behaving as expected. The calibration target had not been analyzed on Earth with the PFM using the same source configuration that the PFM now holds on Mars. Fortunately, both

FEU and PFM instruments have been characterized, and each major variation in source configuration and adjustment to the FEU and PFM instruments has been calibrated in

GUAPX by adjusting the instrument constant (H) and 훼/L푥 value with a common set of GRMs. If a sample has been analyzed several times in various configurations, applying the appropriate 훼/L푥 ratio and instrument excitation files in GUAPX should produce equivalent concentrations. Table 6.1 gives the BT-2 calibration target concentrations produced by IM-

GUAPX. The 2008 concentrations were collected on Earth using the PFM spectrum of the pristine calibration target, and the sol 34 and sol 179 concentrations were collected on Mars using the PFM instrument.

MAHLI (MArs Hand Lens Imager) images showed that the calibration target was dust- covered after the landing and the elemental concentrations reflect this. The detection of

135 2008 sol 34 sol 179 (wt%) (wt%) (wt%) Na 3.8 ± 0.1 3.6 ± 0.2 3.8 ± 0.1 Mg 2.8 ± 0.1 3.1 ± 0.1 3.1 ± 0.1 Al 10.0 ± 0.2 9.19 ± 0.3 9.2 ± 0.2 Si 24.0 ± 0.3 23.85 ± 0.4 24.3 ± 0.4 P 0.17 ± 0.02 0.09 ± 0.02 0.075 ± 0.01 S N/A 0.55 ± 0.03 0.32 ± 0.02 Cl N/A 0.17 ± 0.02 0.10 ± 0.01 K 1.14 ± 0.04 1.08 ±0.05 1.08 ± 0.04 Ca 5.9 ± 0.1 5.6 ± 0.1 5.6 ± 0.1 Ti 0.91 ± 0.02 0.90 ± 0.04 0.90 ± 0.03 Cr 0.013 ±0.004 0.007 ± 0.006 0.011 ± 0.005 Mn 0.113 ± 0.009 0.12 ± 0.02 0.12 ± 0.01 Fe 6.44 ± 0.08 7.0 ± 0.1 6.71 ± 0.09 Ni 0.022 ± 0.002 0.015 ± 0.003 0.011 ± 0.002 Sr 0.059 ± 0.003 0.067 ± 0.005 0.068 ± 0.004 O 44.68 44.68 44.75

Table 6.1: BT-2 calibration target concentrations on Earth, sol 34, and sol 179. Elevated Mg, S, Cl, and Fe indicate a layer of dusty material was deposited during landing.

sulfur and chlorine and decrease in sodium and aluminum are key indicators of a dust layer. The elevation of magnesium and iron concentrations suggest the dust is enriched in these elements. Based on the elemental concentrations, this dust layer on the surface of the calibration target seems to be thinner in the sol 179 analysis, as the lowest energy sodium X-rays are not as greatly attenuated, and the magnesium, silicon, chlorine, and iron concentration elevations are not as high. The excellent agreement between the silicon concentrations of the pristine BT-2 analysis in 2008 and the analyses on Mars post-landing, gives a good indication that the sources have not been covered by any material during landing. Campbell et al. [15] presented a detailed examination of the dust layer thickness and composition and it was verified that the contamination layer was isolated to the calibration target and not the sources. The calibration produced in the laboratory remains applicable, and elemental and C/R results may be trusted.

136 6.2.2 Martian ECF Files: Adjustment of Phosphorous and Chlorine

Once a significant number of spectra were collected by the MSL APXS instrument onMars, a general sense of the element concentration ranges at Gale Crater could be determined. The values used in the ECF files needed to be revisited and adjusted for cases where the Gale crater concentration ranges suggest adjustments are warranted. Table 6.2 gives the average concentration and the two standard deviation range in wt% for the GRM calibration suite and the Martian GUAPX results up to the Confidence Hills target on sol 790. Only elements that have been corrected in the ECF files are considered.

GRM oxide range Martian oxide range (wt%) (wt%)

Na2O 0.24 - 7.88 0.65 - 7.49 MgO 0.02 - 29.92 2.07 - 16.75

Al2O3 1.55 - 31.43 5.32 - 17.07 P2O5 0.16 - 0.62 0.21 - 3.91 SO3 0.02 - 1.32 1.04 - 28.58 Cl 0.02 - 3.86 0.21 - 2.84 CaO 0.26 - 15.90 3.26 - 18.08

TiO2 0.29 - 1.63 0.33 - 1.34 FeO 0.46 - 17.93 6.03 - 27.91

Table 6.2: Oxide ranges for the GRM calibration suite and MSL APXS targets up to sol 790. Only elements with ECF corrections are considered.

The Martian oxide concentration ranges have significant overlap with the oxide ranges used in the calibration. However, the Martian means differ from the terrestrial calibration suite concentration ranges, and for the oxides that have had ECFs applied, the Martian means are greater than those of the calibration. Calcium, titanium, and iron are only altered by MPEs in the trachyte ECF file and will not be considered further. Sodium, magnesium, and aluminum corrections have been shown to be rock-dependent (Chapters 2,

3, and 4). These corrections are therefore not altered in the ECF files either.

Sulfur and chlorine average concentrations are both greater on Mars than what was observed in the terrestrial calibration suite. The ECF value applied to sulfur in the ECF files is 0.9; however, there may be rock-dependent MPEs associated with sulfur. Sulfuris also a major component of the ubiquitous dust that covers most of the rocks analyzed by

137 the APXS. The sulfur story is not a simple one, and as a result, the ECF for sulfur was not adjusted.

The chlorine mean concentration on Mars is most similar to that of the sediment GRMs.

The ECF value for chlorine in the sediment ECF file is 0.93. Since the minimum chlorine value on Mars is greater than in most of the GRMs used in the calibration, it makes sense to adjust the chlorine value in all ECF files to the sediment chlorine correction value, which has a concentration range that more closely matches that measured on Mars. Chlorine values were adjusted from 1.13 to 0.93 in all Martian ECF files.

On first inspection, the Martian phosphorous mean concentration is very similar tothe overall GRM mean concentration. However, a more detailed inspection indicates that the

Mars phosphorous mean most closely matches that of the sediments used in the calibration.

The phosphorous correction value in all Martian ECF files has been adjusted from 1.16 to

0.97.

6.2.3 Effect of Sample-to-Instrument Standoff on Concentrations

The effect of increasing the sample-to-instrument standoff has been examined in thelabo- ratory. It is a common occurrence that during operations on Mars, the APXS instrument cannot be placed in contact (18.0 mm standoff) with the rock or soil it is analysing on Mars.

AN-G, BCR-2, JSD-2, SARM39, and BE-N were studied by the FEU APXS with increasing standoff under laboratory conditions (i.e. in evacuated chamber). Figure 6.1 summarizes the peak area ratios (standoff peak area/in-contact peak area) for silicon, iron, and sodium from the GUAPX fitting program.

Only spectra up to 20 mm standoff were considered. Above this standoff the aluminum sample dish enters the field of view of the APXS and the spectrum is no longer representative of the sample. As is expected, the peak areas for sodium, silicon, and iron decrease with increasing standoff, relative to the in contact position. This resembles the1/r2 relation (r represents the standoff in millimetres), which despite being an approximation, is a reasonable representation of the source-to-sample configuration. Sodium, silicon, and iron display the same trends with the same magnitude, suggesting that all elements behave uniformly. If all

138 Figure 6.1: Effect of instrument standoff on sodium, silicon, and iron peak areas.

element peak areas are depressed by the same relative amount for a given standoff, GUAPX should produce uniform concentrations for these laboratory spectra, regardless of standoff.

On Mars, the predominately CO2 atmosphere becomes an issue. GUAPX requires manual adjustment of sample-to-detector standoff when an atmosphere is present. Figures 6.2 and 6.3 show the effect of ignoring standoff in IM-GUAPX. Each Martian target wasana- lyzed with its actual standoff as well as the “in-contact” standoff; concentrations werefound for both cases. The pressure of the atmosphere was assumed to be 8.0 mbar and the error bars represent two standard deviations of the proper standoff to in-contact concentration ratio values.

It would be expected that sodium X-rays are most attenuated by the atmosphere, resulting in a decrease in deduced concentration at higher standoff. This is exactly what is depicted in Figure 6.2. From sodium to silicon, the effect of standoff decreases until there is very little effect on silicon, even up to 50 mm standoff. In the worst case(50mm standoff) the concentrations for sodium differ by greater than 20%, with the proper stand-

139 Figure 6.2: Effect of standoff in IM mode GUAPX analyses of Martian spectra uptosol 400 for Na to Si.

Figure 6.3: Effect of standoff in IM mode GUAPX analyses of Martian spectra uptosol 400 for select elements from P to Fe.

140 off concentrations produced by GUAPX coming out larger than the incorrect in-contact concentrations.

Figure 6.3 shows the opposite trend for the major elements above silicon. As Z increases, the effect of standoff becomes greater; the concentrations determined with proper standoff are smaller than those measured when considered in-contact. In the worst case (50 mm standoff), the iron concentration found using the incorrect in-contact position inGUAPX is approximately 7% greater than the properly considered standoff concentration. This see- saw effect in the concentrations is not surprising, since the closure rule is enforced forall

IM-GUAPX analyses.

The peak areas for spectra taken at increasing standoff in the laboratory decrease uniformly for all elements, resulting in no overall effect on the final computed concentrations.

The presence of the CO2-based atmosphere of Mars skews the computed concentrations due to the closure rule’s normalization of all concentrations to 100%. These results show that it is important to fit Martian analyses with the proper standoff. The approximate measurement standoff can be taken from the planned rover planner standoff, or for greater accuracy, from the present geometric normalization or from that of Professor R. Gellert. The last of these options was used.

6.2.4 Geometric Normalization and Standoff

As was previously mentioned, the APXS instrument is not always placed in the ideal “in- contact” position on Mars when a measurement is taken for various reasons. During operations, the rover planners do their best to get the APXS instrument to this planned standoff, and the planned standoff for each measurement is always recorded. Unfortunately, the actual standoff can vary significantly from what is targeted, sometimes byuptoone centimetre. It is therefore useful to find another method for determining at what standoff each measurement was collected.

In the APXS analysis method used by Professor R. Gellert, the standoff of the instrument is obtained through the use of a geometric normalization factor. The GUAPX method described in this thesis also produces a geometric normalization factor, which in essence is a correction factor that is applied to our instrument value (H) when GUAPX is run in IM

141 mode. The geometric normalization is given after the fit and from this value, with sufficient data and calibration, the standoff of the APXS on Mars may be deduced.

The standoff versus GUAPX geometric normalization has been found for the laboratory

GRM standoff data as well as for Martian targets (up to sol 707). Figure 6.4 summarizes the laboratory results and equation 6.1 gives the fit to the trend presented. The same initial

H-value (0.2246) was used for all fits.

Figure 6.4: GUAPX geometric normalization factor versus standoff using GRM FEU data.

Lab. Geo. Norm. = (0.953 ± 0.009) − (0.064 ± 0.003)푥 + (0.0019 ± 0.0002)푥2 − (2.0 ± 0.5)푒−5)푥3 (6.1)

On Mars, the standoff versus geometric normalization will most likely be different from what was measured on Earth (Figure 6.4). There is an atmosphere to contend with (see section 6.2.5), irregular sample surfaces, arm placement issues, source decay et cetera. Fig- ure 6.5 summarizes the geometric normalization versus standoff trend on Mars with these additional factors, and equation 6.2 gives the relationship of the fit to Figure 6.5.

142 Figure 6.5: GUAPX geometric normalization factor versus standoff using APXS data collected at Gale Crater, Mars.

Geo. Norm = (0.95 ± 0.02)푒(0.061±0.007)푥 (6.2)

Spectra with FWHM less than 180 eV were selected for this exercise. All were analysed with a starting H-value of 0.2246, the planned standoff, and the standard element fit list

(Na2O, MgO, Al2O3, SiO2,P2O5, SO3, Cl, K2O, CaO, TiO2, Cr2O3, MnO, FeO, NiO, Cu2O,

ZnO, Ga2O3, GeO2, As2O5, SeO2, Br, Rb2O, SrO, Y2O3, PbO). Geometric normalization factors from GUAPX were renormalized to account for the source decay to decrease some of the scatter observed. There is still a significant amount of scatter in Figure 6.5; however, it resembles an exponential curve and is reasonably well represented by equation 6.2. The geometric normalization curve found in the laboratory was best modelled by a third order polynomial; an exponential function was tried with these data, however it did not fit nearly as well. To test this Martian standoff function, standoff values were calculated from equation

6.2 using the GUAPX geometric normalization values. These calculated standoff values were compared to those obtained by Professor R. Gellert (Figure 6.6).

143 Figure 6.6: Calculated standoffs using the GUAPX geometric normalization factor versus standoffs calculated by Professor R. Gellert.

The black line in Figure 6.6 represents the 1:1 line. The GUAPX-based calculated standoffs are very slightly smaller than those calculated by Professor R. Gellert. Despite producing slightly smaller standoffs, this function produces reliable results. If Et_Then, measured at 54 mm standoff, was not considered in this fit, the agreement between these two methods would be even better.

6.2.5 Effect of Pressure Variations on Element Concentrations

All calibration data were analysed by the FEU under vacuum, with the consequence that pressure effects on element concentrations can only be studied using Martian data. Spectra collected up to sol 400 have been fit in IM-GUAPX with 6 mbar, 8 mbar, and 10mbar pressure, representing the average, minimum, and maximum pressures we may observe at

Gale crater. Figure 6.7 shows the effect of the pressure on the four lightest elements: sodium, magnesium, aluminum, and silicon. Trends of 6 mbar/8 mbar concentrations and 10 mbar/8 mbar concentrations have been graphed on the same figure. As expected, sodium was most greatly affected by pressure changes; its concentration range varied by ±7% at the extreme

144 standoff of 50 mm. In contact, the sodium concentrations change by ±2%. The effect of pressure on the silicon concentrations is very minimal up to 50 mm standoff.

Figure 6.7: Effect of pressure on Martian APXS concentrations for sodium up to silicon. MSL APXS spectra up to sol 400 were used and were fit with IM mode GUAPX.

Figure 6.8 shows the results of pressure on select higher Z elements. These show the opposite trend to the light elements. As the pressure increases, the concentrations decrease.

Phosphorous is very minimally affected by pressure; however, as Z increases, so does the effect on concentration. In contact, iron is affected byonly ± 2%; in a worst case scenario of 50 mm standoff, iron is only affected by ± 3%. This see-saw effect in concentrations is again a result of the closure rule enforced by

IM-GUAPX analyses. As with standoff, silicon and phosphorous appear to be the tipping point between decreased/increased concentrations.

145 Figure 6.8: Effect of varying pressure on Martian APXS concentrations for high Zelements beyond silicon. MSL APXS spectra up to sol 400 were used and were fit with IM mode GUAPX.

6.2.6 Optimal Manganese Full Width Half Maximum for Concentration Determination

In the laboratory, the full width half maximum (FWHM) values of the peaks in APXS spectra are fairly stable, since they depend on the ambient temperature of the detector.

Temperature fluctuations, although present in the lab, are much less than what theAPXS experiences on Mars, where the temperatures commonly fluctuate by ∘80 C in one day [65].

Several long-duration Martian spectra were broken into their sub-spectra (all MSL APXS spectra are a collection of short-duration spectra that have been summed together). Cases in which the manganese FWHM varied greatly over time were studied. Each element was examined, and low manganese FWHM (less than 180 eV) results were compared to both mid range manganese FWHM (between 180 to 200 eV) and high range manganese FWHM

(greater than 200 eV). Figures 6.9, 6.10, and 6.11 show the effect FWHM has on the concentration in six MSL APXS targets (Ekwir_1_postbrush (sol 150), Persillon (sol 154),

Yukon (sol 161), APXS_Drill_Site_Raster_Integration_Site7 (sol 231), McGrath_center

146 (sol 270), and Cumberland_A_adjusted_Target1 (sol 287)). Only the Cumberland, Ek- wir, and Persillon targets have high FWHM spectra. Table 6.3 summarizes the average middle/low FWHM and high/low FWHM trends for these six MSL targets.

Figure 6.9: Na concentration comparison among six MSL targets with low, middle, and high Mn FWHM values.

Sodium is the element most affected by the increase in FWHM. The concentration for sodium is 20% greater in the middle range FWHM spectra compared to the low FWHM range and it is 30% greater for the high FWHM spectra. The effect is not as large for magnesium, with an 8% and 13% effect for mid/low and high/low comparisons respectively.

Aluminum and silicon have even smaller effects for the mid/low comparison; the results agree within two standard deviations. The high/low comparison for aluminum is high by

18% and is low by 5% for silicon. The relative magnitudes of these effects are expected.

Increasing the FWHM of a spectrum results in a broadening of all peaks within the spectrum.

Silicon will be the least affected, as it is the dominant peak in the spectrum. Its negative trend in comparison to the surrounding elements is reasonable, given that aluminum and phosphorous, in particular, appear to be ‘stealing’ counts from the silicon peak as the FWHM

147 Mid/Low High/Low Na 1.20 ± 0.08 1.3 ± 0.2 Mg 1.08 ± 0.07 1.13 ± 0.04 Al 1.06 ± 0.07 1.18 ± 0.03 Si 0.98 ± 0.02 0.95 ± 0.01 P 1.4 ± 0.8 2 ± 1 S 0.99 ± 0.05 1.0 ± 0.1 Cl 1.0 ± 0.1 0.98 ± 0.04 K 1.0 ± 0.1 1.0 ± 0.2 Ca 1.00 ± 0.05 0.96 ± 0.06 Fe 0.97 ± 0.05 0.91 ± 0.05 C/R 1.00 ± 0.04 1.00 ± 0.02 Table 6.3: Ratios of the average elemental concentrations for middle or high Mn FWHM over the average low FWHM. Major and select minor elements are shown, along with the

L훼 C/R values.

increases. Magnesium and aluminum are found in relatively large quantities on Mars and peak broadening will cause only small apparent concentration increases. It is not surprising that sodium, being the lightest element detectable by the APXS, as well as being found in smaller concentrations, displays the greatest effect out of the four light elements. As the

FWHM increases and the silicon peak broadens, it gets closer to the left hand spectrum cut-off and will overlap more with magnesium and aluminum, causing counts to be ‘stolen’ from these larger peaks.

Phosphorous, a minor element in all six of these targets, sits on the right hand slope of the large silicon peak. When the FWHM increases, the small phosphorous peak becomes overwhelmed by peak broadening and the apparent phosphorous concentration increases.

Sulfur and chlorine are far enough removed from silicon to be well resolved, and are present in large enough quantities.

Potassium and calcium are also relatively unaffected by increasing FWHM. The region around iron is another convoluted area with many overlapping element peaks. At higher

FWHM values the iron peak will broaden, increasing overlap with surrounding elements.

Table 6.3 shows that the approximately 65 wt% abundance of the four light element oxides is increased by approximately 1 wt%. The closure rule dictates that the remaining 35 wt%,

148 Figure 6.10: Si concentration comparison among six MSL targets with low, middle, and high Mn FWHM values.

Figure 6.11: Fe concentration comparison among six MSL targets with low, middle, and high Mn FWHM values.

149 primarily composed of potassium, calcium, and iron oxides, must, therefore, be reduced by

1 wt%, or a realtive decrease of 3%. This effect must account for some of the observed decrease in the iron concentration.

From this investigation it is apparent that spectra with manganese FWHM values greater than 180 eV should be handled with caution. For further analyses, such as ALIC determination, or concentration improvement to account for mineral phase effects, only spectra with manganese FWHM values less than 180 eV should be used.

The observation of concentration dependence on FWHM prompted a re-examination of the calibration spectra. The majority of the GRM calibration spectra had manganese

FWHM values near or slightly above 180 eV. Most of the GRMs have been reanalyzed with the full energy region FEU instrument, which also has improved capabilities for reducing the FWHM of the spectra. This reanalysis was outside the scope of this thesis. It will be presented by R. Pardo in his MSc thesis [59].

6.2.7 Consideration of Mineral Phase Effects: Collaboration of APXS Bulk Chemistry and CheMin Mineralogy

To improve the MSL APXS concentrations by accounting for MPEs, the mineralogy, or at least the general rock type of the material being analyzed, must be known. The XRD instrument (CheMin) included in Curosity’s scientific payload can provide this information. Very few MSL APXS targets have accompanying mineralogy due to the complexity of drilling, sieving, and getting the material to CheMin, which resides in the body of the rover. Special effort is made to ensure that an APXS measurement is taken on every CheMin sample. This means that when a sample is delivered to CheMin for analysis, we will be able to apply unique MPEs to that particular target. For the targets that are related to the material analyzed by CheMin, but do not have XRD results, our more generalized ECFs may in principle be applied, where MPEs have been averaged over all effects observed for various rock types used in the calibration.

Early in the mission, three CheMin analyses were performed on material that had been examined by the APXS. These targets were the Rocknest soil (sol 102), John Klein mudstone drill tailings (sol 231), and Cumberland mudstone drill tailings (sol 287). Note that the

150 term Martian soil is used here to denote any loose, unconsolidated materials that can be distinguished from rocks, bedrock, or strongly cohesive sediments. No implication of the presence or absence of organic materials or living matter is intended, nor is the genesis of the deposit. Vaniman et al. [75] published the mineralogy of these three targets, as well as the unit cell parameters for the plagioclase, augite, pigeonite, and Fe-forsterite minerals, which give the mineral elemental concentrations. This allowed for predictions of the MPEs through examination of the theoretical X-ray yields, as was done in Chapters 3 and 4. Rocknest, a scooped sample from an aeolian sand shadow, was found to be derived from basaltic material [2]. John Klein and Cumberland targets also appear to have basaltic mineralogy, with the addition of phyllosilicates and sulfate material, which implies weathering of the parent material [75]. This suggests that the basalt ECF defined in Chapter 3, with adjusted chlorine and phosphorous, would be appropriate for these three targets.

A few assumptions were made during this process since the unit cell parameters for some minerals with variable chemistry were not provided. In all cases, mineralogy was converted from the given modal abundance to area coverage. Only minerals with concentrations greater than 1.0 % area abundance were considered in further calculations, as was done in Chapters 3 and 4. The orthopyroxene detected in John Klein and Cumberland targets was assumed to be enstatite (MgSiO3) and the smectite was assumed to be saponite

(Ca0.25(Mg,Fe)3(Si,Al)4O10) [75]. This mineral was distributed into the bulk chemistry after all other minerals had been removed. Magnesium and iron were assumed to be present in

equal fraction and aluminum and silicon were distributed so that the remaining aluminum

was used up entirely. Akaganeite (FeO(OH,Cl)) was only present in the Cumberland sam-

ple above 1.0 % area abundance. The chlorine concentration in this target was greater

than for the Rocknest and John Klein targets so the chemical formula was assumed to be

FeO(Cl). The remaining bulk chemistry of the samples was assumed to be the composition

of the amorphous component. The density of the amorphous component was assumed to be

2.9 g/cm3, which is the average density of a basaltic rock [20]. Table 6.4 summarizes the

predicted MPEs from the R(Y1) values.

151 Rocknest R(Y1) John Klein R(Y1) Cumberland R(Y1) basalt ECF Na 1.05 1.07 1.03 1.04 Mg 0.96 0.93 0.93 0.84 Al 1.12 1.13 1.03 1.19 Si 0.99 0.99 1.00 1.00

Table 6.4: Predicted mineral phase effects in Rocknest, John Klein, and Cumberland targets. R(Y1) for these three targets have been renormalized to the “best” R-value found from the calibration. These “best” values are inherent to the ECF values.

Figure 6.12: Rocknest (circle), John Klein (square), and Cumberland (triangle) targets plotted on a TAS diagram to demonstrate mineral phase effects. Standard fit concentrations were used to get the black points (grey box represents region of uncertainty on this point), basalt ECF corrected concentrations were used to get the red points, and sample-specific MPE-corrected concentrations were used to get the green points. The labels marking specific regions are as follows: Rhyolite (R), dacite (D), trachyte (T), andesite (A), basalt (B), and ultra mafic (UM).

Figure 6.12 depicts each target on an extrusive igneous TAS diagram. The concentrations were taken from the full list of concentrations in Table 6.5. The element fit list for these analyses were adjusted to only consider elements present in the sample. The standoff and pressure were also properly considered. Uncorrected concentrations, basalt ECF corrected concentrations, and sample specific MPE corrected concentrations are shown. The

152 Rocknest ECF MPE % err John Klein ECF MPE % err Cumberland ECF MPE % err (wt%) (wt%) (wt%) (2s) (wt%) (wt%) (wt%) (2s) (wt%) (wt%) (wt%) (2s) Na 3.38 3.13 3.07 13 2.20 2.01 1.96 7 2.41 2.20 2.23 8 Mg 3.05 3.44 3.14 12 4.67 5.22 4.94 4 5.05 5.64 5.32 4 Al 5.90 5.43 5.21 6 4.72 4.33 4.12 4 4.68 4.29 4.46 5 Si 21.55 21.67 21.94 2 20.40 20.46 20.70 2 21.60 21.63 21.62 2 P 0.72 0.73 0.72 32 0.24 0.24 0.24 17 0.30 0.30 0.30 21 S 1.48 1.50 1.49 10 2.23 2.23 2.24 4 1.01 1.01 1.01 7 Cl 0.66 0.66 0.66 15 0.41 0.41 0.41 9 1.17 1.17 1.17 6 K 1.53 1.54 1.54 8 0.51 0.51 0.51 8 0.43 0.43 0.43 12 Ca 4.08 4.11 4.11 5 5.15 5.16 5.17 3 4.01 4.01 4.01 3 Ti 0.50 0.51 0.51 12 0.56 0.56 0.56 6 0.50 0.50 0.50 8 153 Cr 0.14 0.14 0.14 25 0.25 0.25 0.25 10 0.22 0.22 0.22 13 Mn 0.33 0.33 0.33 14 0.24 0.24 0.25 11 0.24 0.24 0.24 14 Fe 13.92 14.07 14.03 2 15.90 15.93 15.97 1 16.40 16.37 16.42 2 Ni 0.0433 0.0439 0.0436 30 0.0712 0.0714 0.0715 11 0.0819 0.0820 0.0820 13 Zn 0.0907 0.0920 0.0914 10 0.0873 0.0875 0.0877 7 0.0852 0.0853 0.0853 8 Ge 0.0099 0.0101 0.0100 33 0.0083 0.0083 0.0083 25 0.0084 0.0084 0.0084 31 Br 0.0057 0.0057 0.0058 40 0.0037 0.0037 0.0038 40 0.0115 0.0115 0.0115 19 Sr 0.0105 0.0112 0.0106 78 0.0133 0.0133 0.0134 44 0.0123 0.0123 0.0123 52 Y 0.0038 0.0039 0.0038 66 0.0042 0.0042 0.0042 43 0.0057 0.0057 0.0057 39 O 42.60 42.58 42.93 42.30 42.27 42.49 41.80 41.78 41.84

Table 6.5: General concentrations, basalt ECF corrected concentrations, and target specific mineral phase effect corrected concentrations for Martian targets Rocknest, John Klein, and Cumberland. black points represent the sample fit in GUAPX under standard fitting conditions withno consideration for rock type. The grey box represents the uncertainty associated with this general point. The red points represent the adjustment that would occur if the basalt ECF was used and the green points represent the adjustment due to the application of individually determined MPEs. In most cases, the corrected points lie within the uncertainty region; however, for Rocknest and John Klein, the sample-specific corrections lie on the error region boundary. These corrections, although minor in most cases, may be larger than the original uncertainties and may even position a target into a new classification.

6.3 Application of the K-value Method to MSL Spectra

The K-value method described in Chapter 5 has been applied to select MSL APXS targets to calculate their ALIC content. The experimental C/R ratio is sensitive to various parameters as described in Chapter 5, such as the measurement duration and FWHM. Spectra with less than three hour durations have larger uncertainties and insufficient time to reach a stable

C/R value. Only spectra with durations greater than three hours have been considered.

Targets with a manganese FWHM below 180 eV have been selected for this study; all others have been excluded.

Other issues arise on Mars and have been addressed above for the elemental concentrations. The effect of standoff on C/R is shown in Figure 6.13. Given that the energies ofthe

Compton and Rayleigh features are large and almost equal, no CO2 pressure effects would be expected on the C/R ratio.

Despite good behaviour for C/R values at varying standoff, only MSL targets planned in contact have been included in ALIC calculations. Table 6.6 lists standoff values for

MSL APXS targets that satisfy all of our requirements. These standoff values have been calculated by Professor R. Gellert, and as the table shows, two targets were collected at greater than 5.0 mm standoff. These need to be treated with some caution, as any errorin elemental concentrations will feed into the simulated C/R values. No restrictions have been enforced on pressure as the average REMS pressure for the sol in question was considered in the GUAPX fits. Out of the 138 APXS targets collected up to and including sol707,

154 Figure 6.13: Effect of standoff on the C/R value in Martian spectra.

thirty-four fit these requirements.

6.3.1 Preliminary ALIC Results

Table 6.6 gives the calculated ALICs for all MSL APXS targets that satisfy our conditions, as outlined in this chapter. Targets with measured standoffs greater than 5.0 mm have been marked with a ⋆ to indicate that these ALICs must be treated with greater caution. Only Stephen, measured on sol 627, has a calculated ALIC greater than, or equal to, the 5 wt% limit of detection that was determined from the calibration data in Chapter 5.

When the 13% relative increase in ALIC content is applied to correct for the deduced ALIC under-estimation when H2O is used in the simulations, the Glossopteris Gully ALIC content also increased to the 5 wt% LOD. This target must be treated with caution, however, since its spectrum was obtained at an increased standoff from in-contact. Stephen must also be treated with caution as it is likely a Windjana substrate rock coated with a high-manganese material. The C/R calculated by GUAPX for Stephen is average in comparison to the other targets measured at The Kimberley (the magenta points in Figure 6.14). Since the C/R

155 Target Type Sol Duration Standoff ALICs ALICs (h:m:s) (mm) (2휎 error) (13% adjusted) Flaherty R,D 129 5:00:00 0 -3 ± 3 -3 ± 3 Ekwir R,D 149 6:18:16 0 -2 ± 3 -2 ± 3 Ekwir_brushed RB 150 5:11:19 0 -4 ± 3 -3 ± 3 Persillon R,D 154 3:31:25 2.6 -4 ± 3 -3.5 ± 3 Nastapoka R 158 4:27:55 0 -5 ±3 -4 ± 3 Wernecke R,D 168 8:34:00 0 -2.5 ± 3 -2 ± 3 Wernecke_brushed RB 169 3:13:53 0 -4 ± 4 -4 ± 3 Wernecke_brushed_3 RB 173 6:25:24 0 -4 ± 3 -4 ± 3 McGrath center R,D 270 3:45:16 2.8 -3 ± 4 -3 ± 3 Cumberland_brushed RB 291 4:33:13 2.1 -4 ± 3 -4 ± 3 Equalulik R 323 4:00:00 1.7 -5 ± 4 -4 ± 3 Matthew R 360 4:00:00 3.9 -1 ± 3 -1 ± 2 Ruker R 387 8:02:23 2 2.5 ± 2 3 ± 2 Bardin Bluffs Matrix R 395 6:01:11 3 ± 2 3 ± 2 Glossopteris Gully⋆ R 399 4:00:00 8.3 4.5 ± 3 5 ± 3 Shackleton R,D 400 7:20:15 2.9 4 ± 2 4 ± 2 Morehouse R 503 4:00:00 0 0 ± 2 0 ± 3 Larrabee R,D 510 5:00:00 1.2 1 ± 2 1 ± 2 Kodak R 512 7:43:09 2 0.5 ± 2 1 ± 2 King S 523 8:00:00 4.6 -2 ± 3 -2 ± 3 Argyle S 531 4:00:00 2.4 -1 ± 3 -1 ± 3 Halls⋆ R,D 537 4:00:00 7.5 3 ± 3 3 ± 4 Johnny Cake S 558 7:32:23 2 2 ± 2 3 ± 2 Square Top Overnight R 583 4:00:00 0 -5.5 ± 4 -5 ± 4 Pandanus Yard R,D 584 7:00:00 2.6 -2 ± 3 -1 ± 3 Virgin Hills R,D 585 7:30:00 1.5 -1 ± 3 -1 ± 3 Liga R 601 6:18:41 1.5 -1 ± 3 -1 ± 3 Lagrange S 605 7:30:00 1.3 -2 ± 3 -1 ± 2 Windjana_brushed RB 612 4:01:12 0 -2 ± 4 -1 ± 3 Stephen RB 627 6:30:00 2.5 5 ± 3 5 ± 4 Wift R,D 633 8:02:24 0 0 ± 3 0 ± 2 Sourdough S 673 7:39:13 2.3 -1 ± 3 -1 ± 2 Windjana fines F 704 8:00:00 3.7 1.5 ± 3 2 ± 4 Stirling R 707 4:20:00 2.2 -3 ± 3 -3 ± 3 ALIC Mean -1.05 -0.66

Table 6.6: Calculated ALICs for select MSL APXS targets. Adjusted ALICs have had a correction factor of 1.13 applied. Several target types are included, such as rocks (R), brushed rocks (RB), soils (S), and drill fines (F). The standoff values are taken from Professor R. Gellert’s calculations. Targets labelled with a ’D’ indicate those with dusty surfaces.

value is similar to the Windjana material, it is likely that Windjana is the rock type beneath the high-manganese coating. using the Windjana simulation C/R values gives a calculated,

156 water equivalent, ALIC of 4 wt%, which is below the limit of detection for this method.

Figure 6.14: L훼 C/R values for select MSL targets organized by sol. Black: targets along the rapid transit route; Red: Yellowknife Bay; Green: Darwin; Magenta: The Kimberley

The majority of the targets have negative ALICs, much more so than when the GRMs were treated as unknown samples. This suggests that there may be an instrument offset between the FEU, which was used for the K-value calibration, and the PFM on Mars.

Although very similar, the FEU and PFM APXS instruments are not identical and slight variations cannot be ruled out. Very few GRMs were analyzed on both the PFM and FEU instruments, so a cross comparison of the C/R values cannot be reliably completed. Table

6.7 shows the average calculated water-equivalent ALIC content for targets at Yellowknife

Bay, Darwin, and The Kimberley. Yellowknife Bay, which is the only location with published

SAM ALICs at this time, has a negative average ALIC. Darwin and The Kimberley are both positive, but below our LOD. These results suggest that if there is a real offset between the

FEU and PFM instruments, it is minor.

A possible interim solution is to renormalize the calibration K-line using targets that have

ALICs calculated by SAM (Sample Analysis at Mars). At this time only SAM ALIC values

157 Number of targets approximate sol range ALIC average Yellowknife Bay 11 121 - 330 -3.5 ± 3.3 Darwin 4 396 - 400 3.3 ± 1.6 The Kimberley 5 597 - 630 0.50 ± 5.3 Table 6.7: Average ALICs for targets at Yellowknife Bay, Darwin, and The Kimberley waypoints. Uncertainties are 2휎 standard deviations.

have been published for Yellowknife Bay. The average of eight H2O + CO2 SAM analyses of Yellowknife Bay samples is 2.6 ± 0.8 (uncertainty given as two standard deviations). This value is below the limit of detection for the K-value method and any re normalization

at this time would be unreliable. In the interest of completeness, DAN water equivalent

hydrogen (WEH) values have been compared to APXS measurements. The Dynamic Albedo

of Neutrons (DAN) instrument on board Curiosity measures sub-surface hydrogen up to a

meter in depth. The DAN results should be taken as a general guideline when compared to

the calculated APXS ALICs, since DAN is sampling a much larger volume of material and is

likely observing material from beneath the rover and not the APXS target material in front

of the rover. In addition, the WEH values that were collected at the same location as the

selected APXS targets ranges from 1.78 ± 0.1 wt% to 3.37 ± 0.41 wt% [53]. These values are also less than the 5 wt% LOD for the K-value method. Further work will need to be

done to compare SAM and APXS ALICs on other mutual targets to ascertain if adjustments

to the calibration line are necessary.

6.3.2 Dust Effects

A complication to ALIC quantification in MSL targets is the dust layer that covers thema-

jority of targets. This layer alters the elemental concentrations that are input to Marsgeom,

which affects the simulated C/R ratios. From previous missions, it has been shown thatthe

dust is elevated in sulfur and chlorine. This layer also attenuates the X-rays of the lightest

visible elements from the sample; as a consequence, these characteristic X-rays detected by

the APXS instrument are either generated in the dusty layer or significantly influenced by

the dusty layer. Table 6.8 shows that, for brushed targets, sodium and magnesium are most

greatly affected by the dust with their concentrations increasing after brushing. Brushing

158 Target Na Mg Al Si SO3 Cl L훼 C/R (wt%) (wt%) (wt%) (wt%) (wt%) (wt%) Ekwir 2.01 4.73 4.801 20.44 5.95 1.14 1.66 ± 0.02 Ekwir_brushed 2.14 5.29 4.804 21.74 2.57 1.45 1.65 ± 0.02 Wernecke 2.05 4.739 4.913 20.76 5.32 1.01 1.67 ± 0.03 Wernecke_brushed 2.35 5.311 5.091 22.49 1.04 0.93 1.65 ± 0.02 Wernecke_brushed_3 2.28 5.299 5.092 22.77 1.16 1.00 1.68 ± 0.02

Table 6.8: Concentrations for select elements and L훼 C/R values in targets that have been measured with unbrushed and brushed surfaces.

does not have as great an effect on the aluminum and silicon concentrations. SO3 and chlorine typically have the opposite trend to the light elements; after brushing their concentrations generally decrease, particularly SO3 in these two cases. The experimental C/R should not be affected by the assumed thin dust layer typically observed on MSL targets, since the scatter peaks are generated within the sample up to 300 to 400 microns. Table

6.8 shows that the C/R values are constant within error. It is interesting to note that

Wernecke_brushed_3 was the same brushed target as Wernecke_brushed; however, it was analysed four sols after. Sodium and magnesium concentrations are slightly decreased and the SO3 and chlorine values are slightly increased, indicating that dust had begun to cover the brushed surface already.

Figure 6.15 shows the effect of dust on the calculated ALIC value. In these two cases, brushing decreases the SO3/Cl ratio (ratioed to that of the soil Portage), as well as the calculated ALICs. The calculated ALICs for Ekwir decreased by 33% and they decreased by 50% on average for Wernecke.

It is clear that brushing results in a decrease in ALIC content; however, these two examples are not sufficient to quantify this effect with high accuracy. For these two cases, the effect of brushing results in an approximately 2% decrease in ALIC value. Witha thicker dust cover this downward correction will clearly increase. The dust effect will need to be studied more thoroughly as more high quality brushed versus unbrushed cases become available throughout the mission.

159 Figure 6.15: Effect of surface dust on calculated ALIC content in MSL APXS targets. The data points represented by the circles are unbrushed targets and the square data points are brushed targets.

6.4 Conclusion

This chapter has amalgamated the material presented in the previous chapters and applied it towards the MSL APXS data. Preliminary work showed that the calibration of the APXS instrument produced in the laboratory for visible element concentrations, primarily performed on the FEU instrument, holds for the PFM on Mars. Only slight adjustments to the

ECF files were needed to account for the elevated phosphorous and chlorine detected atGale

Crater. The effects of atmosphere and instrument-to-sample standoff have been addressed.

The application of ECFs and sample-dependent MPEs, discussed in Chapters 3 and 4, have been applied to the Rocknest, John Klein, and Cumberland scoop and drill targets, where quantitative mineralogy has been provided by CheMin. These examples demonstrate that

MPEs can be large enough to potentially alter the igneous TAS classification of the material being examined (Figure 6.12).

MSL data have been useful for improving the understanding of the calibration data set presented in Chapter 2. From spectra taken over several hours and varying temperatures, it

160 was found that spectra with a manganese FWHM greater than 180 eV produced incorrect concentrations, particularly for minor elements situated near major element peaks. This has prompted a re-examination of the calibration data, which will be presented in the thesis of

R. Pardo [59].

The K-value method described in Chapter 5 has been applied to select MSL APXS targets. The calculated ALICs appear to be skewed so that the majority of the values are negative (-0.66 wt% average when values are corrected by the 1.13 factor). This suggests that there may be an offset between the FEU instrument that the K-value method was based upon, and the PFM instrument on Mars. Further work in comparing SAM and APXS ALICs should be completed. If SAM measures ALICs greater than 5 wt% those targets should be used for possible adjustments to the calibration line.

Finally, the effect of dust on the calculated ALIC value was addressed. The ubiquitous dust is one of the largest complications the APXS must face on Mars, and understanding the effects this dust may have on elemental concentrations and ALIC calculations is important.

Ekwir and Wernecke, the only two unbrushed versus brushed examples in this data set, showed that ALIC content decreases with brushing. Therefore, dust appears to be fabricat- ing ALIC content through its alteration of the elemental concentrations. Examination of more brushed versus unbrushed spectra of high quality (duration greater than three hours, manganese FWHM less than 180 eV, in contact, et cetera) may be helpful in potentially quantifying the magnitude of concentration and ALIC effects caused by the dust.

This chapter has connected the preceding chapters of this thesis and provided a practical application of each topic previously presented. The preliminary work with Martian APXS data on mineral phase effect application, ALIC calculation, and the study of dust effects, for example, demonstrates the validity of the elemental and C/R calibrations and value of the mineral phase effects work. As Curiosity continues its scientific investigations ofGale

Crater, more APXS data will become available to further address these topics.

161 Chapter 7

Concluding Remarks

This body of work provides a comprehensive calibration of the MSL APXS instrument for both elemental concentrations and additional light invisible components using a fundamental parameters approach to spectrum analysis. This method has allowed for an in-depth examination of the effects, resulting from the immutable homogeneity assumption onthe geochemical reference materials used in the calibration. The observed effects, particularly for sodium, magnesium, and aluminum, were given the name “mineral phase effects”, as all natural rocks and sediments are composed of various minerals, which make them innately heterogeneous. Despite being finely ground, the grain size of the calibration GRMs isnotfine enough to render them homogeneous on the micron scale, which corresponds to the interrogation depth of alpha particles. Theoretical elemental X-ray yields from several GRMs were generated using certified homogeneous bulk chemistry and properly considered mineralogy.

Results from this study yielded the first quantification of MPEs for the APXS instrument.

The selected GRMs for this work were chosen for their simple mineralogy, however the by- hand calculations were still arduous. The program, APXRD, was created to automate the calculation process. APXRD has opened the door to further MPE quantification in more complex materials, where both quantitative mineralogy and bulk chemistry are known.

An avenue for furthering our understanding of MPEs will be to examine more complex

GRMs with the APXRD and APX-Yield programs, refining the process for calculating sample-specific MPEs. This procedure was applied to three MSL APXS targets where

CheMin quantitative mineralogy was available. These sample specific MPEs show similar trends to the ECFs, but provide superior accuracy. This procedure should be applied to

162 more MSL targets where APXS and CheMin data are provided on a mutual target, beyond

Yellowknife Bay. The refined MSL APXS concentrations will be useful to members of the MSL science team who utilize MSL APXS data to determine conclusions regarding the geochemistry at Gale Crater. The refined concentrations may also be useful to other instrument teams; for example, users of CheMin data may be able to use these results to refine the composition of their amorphous component.

New work using the Guelph proton microprobe facility, introduced in this thesis, has shown that accelerator based PIXE is an excellent APXS analogue for studying MPEs.

A pressed pellet of BT-2, the material used for the MSL APXS calibration target, was examined using the proton microprobe. The MPEs observed for the light elements in several powdered and pressed pellet spectra of BT-2 collected with the MSL APXS were replicated using micro-PIXE. For further MPE study, this approach offers many benefits. Energy resolution is much superior. The Guelph micro-PIXE system is very accurately calibrated, the geometry is well defined, and data acquisition is five to ten times faster than withthe

APXS. In addition, this will allow for the study of both solid and powdered material from the same rock; this averts the risk of sample contamination due to the 244Cm sources in the

APXS. The benefit is that any observed differences must be attributed to MPEs andnot

variations between samples. Another benefit of using micro-PIXE for MPE examination is

that accurate mineral compositions on the surface of solid rock targets can be measured

and used for MPE quantification. With mineral mapping of the surface of a solid rock

target before PIXE, various minerals may be targeted and accurate elemental compositions

of those individual minerals may be collected. More accurate theoretical X-ray yields can

be easily found using the direct compositions measured in this manner. The realization of

these opportunities has led to a redesign of the newer Guelph proton microprobe, focussed

on handling rock samples.

Further study of basalt GRMs, the materials that demonstrated the greatest MPEs in

the calibration results, will be a good place to start quantification of MPEs using micro-

PIXE, and work has already begun on this project. If these results replicate those observed

with the APXS instrument, new experiments, such as the solid versus powdered rock target

experiment described above, should be made as they will yield new and valuable results that

163 will further our understanding of MPEs.

A thorough calibration of the L훼 scatter peaks for additional light invisible component (ALIC) determination was presented. This method, called the K-value method, was originally developed for the MER APXS instruments. In 2008, it was used to quantify ALICs in bright, high-sulfate soils discovered by the Spirit rover at Gusev Crater, Mars [9]. The

K-value method has been refined for the MSL APXS using a more extensive GRM suite than that used previously for the MER APXS instruments. The calibration presented here is for the full energy region FEU instrument, which is now nearly equivalent to the energy region of the PFM instrument on Mars. The parameters of this calibration are close to those of MER and of the MSL FEU prior to rectification of the energy cut-off. The calibration line was tested by treating GRMs as unknown samples and simulating their ALIC content as H2O and CO2. The resulting water equivalent ALIC values showed that the limit of detection for this method is approximately 5 wt% calculated ALIC content. Above this LOD, water equivalent calculated ALIC content is underestimated by approximately 13%. The

CO2 equivalent calculated ALIC content agrees with the known ALIC content within error. Despite underestimating the ALIC content, water will remain the simulated ALIC because it is the more important ALIC on Mars, and has been measured in MSL targets by both

SAM and DAN [52, 53]. A correction factor of 1.13 may be applied to all water equivalent calculated ALICs to correct for this underestimation.

Finally, C/R and K values of MSL APXS targets were studied in detail. Effects that could not be studied in the laboratory were addressed, such as atmospheric pressure and instrument to sample standoff. It was discovered from the MSL data set that elemental concentrations and C/R ratios are susceptible to changes resulting from increased full width half maxima (FWHM) above a certain threshold. This threshold was found to be approximately 180 eV at 5.9 keV energy (manganese K훼 line). This led to a re-examination of the calibration data set, which has been completed by R. Pardo, and will be presented in his masters thesis [59]. Understanding these effects allowed for the selection of appropriate

MSL APXS targets for the application of MPEs and ALIC determination.

Using mineralogy provided by CheMin and the bulk chemistry provided by the APXS, sample-specific MPEs were determined for the Rocknest soil sample and John Kleinand

164 Cumberland drilled samples at Yellowknife Bay. These sample-specific MPEs were larger than the standard basalt ECFs that were applied to each case. The concentrations resulting from the application of the sample-specific MPEs may be large enough to change the geochemical classification of a sample in the TAS diagram, for example. This demonstrates that although ECFs are useful at adjusting concentrations in the right direction, the ability to apply sample specific corrections to APXS results, with the support of CheMin Rietveld mineralogy, is more accurate and informative.

ALICs were also calculated for MSL APXS targets taken in contact with manganese

FWHM values less than 180 eV and spectrum durations greater than three hours. The average pressure for the sol on which the spectrum was collected (obtained from REMs data) and a refined element fit list were used in each case. The majority of ALIC values came out negative when the FEU K-value line was used, even after the 1.13 correction factor was applied. This may indicate that there is a difference between the FEU and PFM instruments that is producing a vertical offset to the calibration line. If SAM measures

ALICs greater than the K-value LOD of 5 wt% it would be interesting to apply a vertical renormalization to the K-value calibration line to align the APXS ALIC with the SAM results. Why there appears to be an offset between the FEU and PFM instruments is presently unknown. Further study into the possible cause(s) of this difference would be beneficial.

Dust is one of the largest challenges the APXS faces in determining reliable concentrations of a sample. Results were presented in Chapter 6 that suggested dusty targets have falsely increased ALIC content, regardless of the calibration line used. Only two targets were available in the data set, so this effect could not be reliably quantified. As more brushed versus unbrushed targets become available throughout the mission, this effect should be revisited. More samples will help quantify the false ALIC content that the dust contributes to a sample’s calculated ALIC.

The main goal of this thesis was to quantify the observed concentration anomalies found in geologic materials that resulted from the intrinsic homogeneity assumption. The mineral phase effects observed in several APXS results were quantified for the first time,withthe aid of X-ray diffraction and Rietveld analysis. The secondary goal of this thesis wasto

165 produce a K-value calibration for the MSL APXS L훼 scatter peaks to determine water equivalent ALIC content of samples. This was completed and various properties of this technique were studied in greater detail than they have been before. The procedure used to quantify MPEs in terrestrial geochemical reference materials may be applied to Martian targets with accompanying X-ray diffraction and Rietveld analyses provided by CheMin, which will produce the most accurate bulk geochemistry on Martian materials to date.

The extensive calibration and our increased understanding of the K-value ALICs method for the MSL APXS, along with supporting ALIC information provided by SAM and DAN instruments, means the ALIC data provided by the current APXS is more reliable than for previous APXS instruments.

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