GEOCHEMISTRY AND PROVENANCE OF CHERT STONE TOOLS, COSHOCTON COUNTY, OHIO
A thesis submitted To Kent State University in partial Fulfillment of the requirements for the Degree of Master of Science
by
Diana Simone
December 2019 © Copyright All rights reserved
Thesis written by Diana Simone B.S., Kent State University, 2017 M.S. Kent State University, 2019
Approved by
, Advisor Jeremy C. Williams , Chair, Department of Geology Daniel K. Holm , Dean, College of Arts and Sciences James L. Blank
Table of Contents TABLE OF CONTENTS------iii
LIST OF FIGURES------iv
LIST OF TABLES------vi
AKNOWLEDGMENTS------vii
CHAPTERS
1. PROPOSAL OF RESEARCH------1
1.1 INTRODUCTION------1
1.2 SAMPLES------4
1.2.1 Site------4
1.2.2 Collection------6
1.3 METHODS------6
1.4 PREPARATION FOR XRF------7
1.4.1 Pulverizing------7
1.4.2 Loss on Ignition------7
1.4.3 Beading------9
1.5 X-RAY FLUORESCENCE------9
1.6 VISUAL DERIVATIVE SPECTROSCOPY------10
1.7 REFERENCES ------13
2. GEOCHEMICAL ANALYSIS------15
2.1 INTRODUCTION------15
2.2 METHODS------20
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2.3 PREPARATION FOR XRF------20
2.3.1 Pulverizing------20
2.3.2 Loss on Ignition------20
2.3.3 Beading------21
2.4 RESULTS------22
2.4.1 Na2O------22
2.4.2 TiO2------23
2.4.3 CaO------24
2.5 TERNARY DIAGRAMS------25
2.6 STATISTICAL ANALYSIS------27
2.6.1 All Three Sample Sets: The Artifacts, The UM,
and the Other N.A. Outcrops------27
2.6.2 The Artifacts versus the Upper Mercer------29
2.6.3 The Artifacts versus the Other North American Outcrops------30
2.7 PROVENANCE------31
2.7.1 Silica Content.------31
2.7.2 Statistical Analysis------32
2.7.3 Conclusions------33
2.7.4 Future Work------34
2.8 CONCLUSIONS------35
2.9 REFERENCES CITED------36
3. SPECTROSCOPY ANALYSIS------37
3.1 INTRODUCTION TO SPECTROSCOPY------37
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3.2 METHODS------39
3.2.1 Spectroscopy------39
3.2.2 Verimax Principal Component Analysis------39
3.3 RESULTS------40
3.3.1 Spectroscopy------40
3.3.2 Verimax Principal Component Analysis------43
3.3.3 Statistical Analysis------47
3.4 DISCUSSION------49
3.4.1 Spectroscopy------49
3.4.2 VPCA and Statistical Analysis------49
3.5 CONCLUSIONS AND FUTURE WORK------51
3.6 REFERENCES CITED------52
REFERENCES------53
APPENDIX------56
I. Data------56
II. SPSS Kruskal-Wallis Results------61
III. Geochemical Standard Operating Procedure------74
IV. Visual Derivative Spectroscopy on SPSS Standard Operating Procedure------115
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LIST OF FIGURES Figure 1.1: Conchoidal Fracture on a Stone Tool Artifact------1 Figure 1.2: Flow Chart for Formation of Chert Possibilities------3 Figure 1.3: Google Map of Welling Archaeological Site------5 Figure 1.4: Map Coshocton County in Ohio------5 Figure 1.5: Picture of a Konica Minolta Spectrophotometer and Set Up------11 Figure 2.1: Welling Site Artifact------15 Figure 2.2: Upper Mercer Chert Color Variation------16 Figure 2.3: Polished Flint from Alibates Flint Quarries------17 Figure 2.4: Map of Welling Archaeological Site in Ohio------19
Figure 2.5: Na2O Scatter Plot------23
Figure 2.6: TiO2 Scatter Plot------24 Figure 2.7: CaO Scatter Plot------25 Figure 2.8: Ternary Diagram------26
Figure 2.9: SiO2 Box and Whisker Plot------28 Figure 2.10: CaO Box and Whisker Plot------29 Figure 3.1: Spectral Data Before and After Taking the 1st Derivative------38 Figure 3.2: Spectral Wavelengths of the Artifacts------41 Figure 3.3: Spectral Wavelengths of the Upper Mercer------42 Figure 3.4: Spectral Wavelengths of the Other North American Artifacts------42 Figure 3.5: General Reference of Graph Quadrants------43 Figure 3.6: Artifacts and Upper Mercer, VPCA1 versus VPCA2 ------44 Figure 3.7: Artifacts and Upper Mercer, VPCA2 versus VPCA3------45 Figure 3.8: Artifacts and the Other NA Outcrops, VPCA1 versus VPCA2------46 Figure 3.9: Artifacts and the Other NA Outcrops, VPCA2 versus VPCA3------47
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LIST OF TABLES Table 2.1: Kruskal-Wallis Hypothesis Chart for All Three Sets of Samples------27 Table 2.2: Kruskal-Wallis Hypothesis Chart for Artifacts versus UM------30 Table 2.3: Kruskal-Wallis Hypothesis Chart for Artifacts versus Other NA Outcrops------30 Table 3.1: Comparison of the Means ------48 Table 3.2: Comparison of the Standard Deviations------48
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ACKNOWLEDGEMENTS
I would like to take time to thank the many that have helped me through the process of getting my masters in geology. First and foremost, I want to recognize all of you at Kent State that were there for support, extra knowledge, or just someone to talk to. To Dr. Jeremy Williams, you gave me the opportunity to continue my education and taught me indispensable knowledge.
To Dr. Palmer, you were always there to answer my numerous questions and lead me in the right direction to become a good scientist. To Dr. Metin, you walked me through the archaeological portion of my research and showed me how fun it can be. To Dr. Ortiz, you taught me how interesting and useful spectroscopy is. To Kelly Thomasson, you were always there to chat and to help me print, copy, or fax. And to Merida, I’m so glad you were able to consistently supply technical help in the lab, with field supplies, and with PC issues. I also want to thank all my friends at KSU that were there to lighten the mood and get lunch every Friday. Bryan Ice, Max
Barzoch, Zack Loffer, Eddy Ferguson, and Ashley Haas, you guys are the best.
Next, I want to acknowledge my family and close friends that were there for me during this last 2 years. Josh, you were patient with me during this time, and I’m so glad you supported me and was always there for Alex and me. Bob, you were there throughout the whole 5 years of me continuing my education, and I thank you for that. My parents, Ralph and Louise, you always supported me and encouraged me to continue even when times were tough. My personal trainer,
Prince, you were always there to supply a extra motivation at the gym to remind me our bodies were made to move, not sit at a computer desk all day. And my magical mathematician friend,
Ryan Hastings. I may not have understood everything you said, but you still rock.
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To Michael Simone, if it were not for you, I would have never started my educational journey 5 years ago. You are the best brother a girl can ask for and I love it when you send me pictures of rock formations or a box of rocks in the mail. Please, never stop.
And lastly, I am going to dedicate all my work to my son, Alex. You sacrificed too many nights in front of the TV instead of getting to play with mommy. My goal now is to surround you with love and knowledge and watch you grow to be magnificent. You mean the world to me and
I love you millions.
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Chapter 1: Proposal of Research
1.1: Introduction
The prehistory and technology of Native Americans prior to European colonization is regularly studied by archaeologists. One of the first groups to colonize North America is represented by the Clovis culture, appearing on the continent at the end of the Pleistocene, approximately 13,500-12,800 years ago (Boulanger, et al., 2015). After the Clovis culture,
Native Americans diversified culturally over the next several millennia. Throughout the Pleistocene and the
Holocene, people produced tools and arrowheads through the craft of flintknapping. These artifacts are easily found and are well preserved since they were manufactured primarily from chert and flint (Eren, 2016). Chert and flint are valued as excellent rocks to make tools and arrowheads due to their conchoidal fracturing (King, 2018), and hardness. This fracture pattern creates a sharp edge that can easily be used as a knife or arrowhead and ancient Figure 1.1: Conchoidal fracture on a stone tool artifact. Picture curtesy of humans took advantage of this trait. The tools vary in size Dr. Metin Eren. from small micro blades to larger hand axes. Figure 1.1 is one of the artifacts used for the geochemical analysis. Despite the small size of this tool, the conchoidal fracture pattern is still very noticeable.
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Chert is a siliceous sedimentary rock, it can be formed in diverse environments in either marine or non-marine origins (Raymond, 1995). Currently, there are still some uncertainties on how chert is formed, but there are some clues on its diagenesis (Clayton, 1984). It is formed through recrystallization or in situ replacement of other materials. Often, diatoms or radiolarian skeletal fragments will be the precursor to forming a chert. These microorganisms use silica in forming their shells or skeletal structure (Harper, 2009). Once their life cycle ends, their remains sink to the bottom of the sea to contribute to the biogenic silica oozes present on the ocean floor
(Hesse, 1988). It is the siliceous ooze that is the precursor to biogenic chert. The ooze is eventually buried and becomes opal-CT or opal. As the opal-ct or opal is buried deeper it will experience an increase of pressure and temperature allowing for chertification as sea water mobilizes the silica (Hesse, 1988). The first stage will turn the biogenic silica ooze into a hydrated form of quartz (opal-CT) and as it is buried deeper, the pressure will expel the water and converting the opal to chalcedony and eventually chert. In the final stage, it can be considered a true quartz despite having some unfamiliar features (Figure 1.2; Raymond, 1995).
Like quartz, its hardness on the Mohs Hardness scale is still a 7 and the color can vary drastically
(Raymond, 1995). However, unlike quartz, it has a waxy opaque appearance and fractures in a conchoidal manner (Luedtke, 1970). The hardness and the fracture traits are the reason why our human ancestors were able to create excellent tools from this rock.
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Figure 1.2: Flow chart illustrating possible pathways for how chert is formed in marine environments. The skeletal remains of diatoms and/or radiolarians are most likely is the siliceous precursor to the chert that is prominent in this study. The flow chart is derived from Raymond, et al., 1988.
The nature of chert can cause color and composition to vary greatly even within the same formation (Luedtke, 1970). The variance within the formation displays the differences in major and trace elements throughout the rock. It is the trace elements that give chert its color fluctuations and the major elements will influence the quality since they prohibit the rock from being 100% quartz (Luedtke, 1970). These two characters are important for determining provenance and quality of ancient artifacts.
The provenance of the source rock used to make these tools will give valuable insight to the economy and culture of the prehistoric people. One way archaeologists decide origin of these arrowheads and other artifacts is through qualitative comparison of the color of the arrowheads to known outcrops of chert in the area in which they are found (Stevenson, et al., 2007).
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However, in mineralogy, color cannot be relied on to determine qualities or type of rock or minerals due to the large color range. Thus, using qualitative analysis of color to determine the provenance of chert artifacts is not reliable (Cackler, et al., 1999). Therefore, we should rely on geochemistry and quantitative physical properties to indicate the true chemistry with respect to provenance. Additionally, the quality of the tool may indicate the toolmaker’s ability to choose a distinct rock. I used major geochemical analysis along with visible derivative spectroscopy to identify the provenance and quality, which, in turn, will allow us to reconstruct trade routes.
1.2 Samples
1.2.1: Site
The artifacts were collected from the Welling archaeological site in Nellie, Ohio. Nellie is a very small farming town located in Coshocton County in central Ohio (Figures 1.3 and 1.4) and lies just northwest of the Upper Mercer chert outcrop and sits on an ancient chert quarry. The quarry is on the edge of Mohawk Creek that joins Opossum Hollow Run before flowing into the
Wahonding River. It appears the site has not been disturbed by agriculture like in the surrounding areas.
During the 1960s, there was an effort to excavate the Welling site and other sites within the area (Prufer and Wright, 1970). Specifically, at the Welling site, there were abundant debitage and artifacts ranging from numerous time periods that were undisturbed due to a large amount of railroad cut fill providing a barrier (Prufer and Wright, 1970). From this excavation, a large number of artifacts were collected and catalogued.
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Figure 1.3: Welling archaeological site (red star) and surrounding area. The area is close to the town of Nellie, Ohio and it is right on the Walhonding River.
Figure 1.4: A map of Ohio showing the Coshocton county boundaries, where the town of Nellie is located. Modified image from worldatlas.com
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1.2.2: Collection
The collection process of artifacts and cherts were completed by the Kent State
University Anthropology department. The outcrops were collected by numerous people over the years including professors and private collectors (see appendix).
The artifacts were all collected during a series of excavations by Drs. Olaf Prufer and Dr.
Norman Wright in the 1960s (Prufer, et al., 1997). The chert artifacts found at the site represent nearly the entire prehistory of North America, from the Clovis period throughout the Holocene
(Prufer, et al., 1970). The artifacts I received range from Early Archaic to Late Woodland (Ohio
Archaeologist Vol. 20 No. 4, 1970).
I measured 59 artifacts that matched the color variations of the Upper Mercer with the intent to provide evidence they were, indeed, from this outcrop. They were photographed and fully documented before laboratory measurements. This research project is the beginning phase of ongoing investigation to compare the geochemistry of chert artifacts to the current archaeological determination methods.
1.3: Methods
The primary method used to determine the provenance of each artifact is with an X-Ray fluorescence (XRF). This method establishes the elemental compositions within the chert. The process to prepare each artifact and outcrop sample takes no more than a week to complete, thus making it a quick method to produce reliable results. To ensure reliability, replication of the technique was completed approximately every 5th sample for the artifacts and on nearly every sample of the Upper Mercer outcrop.
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Among the process to prepare for the XRF, loss on ignition (LOI) is one of the most important steps. Not only is it necessary for the XRF, but it also provides data on how much organics and volatiles make up each rock.
Next, I performed visual derivative spectroscopy tests on the samples to determine the mineralogy of each piece of chert
1.4: Preparation for XRF
1.4.1: Pulverizing
To start, all 89 samples (outcrops and artifacts) were cleaned, and any writing was removed with sandpaper. Bulk samples were broken down into small pieces by hammer, next 3-
5g of samples were pulverized by an 8000M SPEX SamplePrep ball mill for ten minutes, if there were still chunks of chert present in the powder, we did another r 10-minute run. This insured homogeneity amongst the powder and would allow the future beading process to be more successful. The samples were ashed and transformed into a borosilicate glass bead.
1.4.2: Loss on Ignition
Once the samples were completely pulverized, we performed the loss on ignition (LOI) process. LOI is preformed specifically to eliminate any organics or volatiles within the samples by placing all samples in a muffle furnace and heating them at extreme temperatures. The samples go through three runs to complete the elimination. The elimination of organics and volatiles are done in the first two runs. The temperature is set for 550°C to ensure any organics are burned off. The third and final run is used to eliminate any volatiles and carbonates, at a
7 temperature of 850°C. Once this process was completed, a calculation was done to determine how much of each were lost during the ignition process. This calculation is as follows:
(푚 − 푚 ) 푤 = 푥 100 (푚 − 푚 )
where
wv represents the percentage of the total loss on ignition of the dry mass
ma represents the mass of the crucible with lid, in grams
mb represents the mass of the crucible with lid and original sample in grams
mc represents the mass of the crucible with lid and ignited sample, in grams
Considering the nature and hardness of chert, the LOI was expected to be a low value for any of the samples that were truly chert, obsidian, or other volcanic rocks. If there are other types of rocks present, such as limestone, then the LOI will be much higher due to the high content of carbonates.
The LOI process required only three days to complete and the majority of the time was consumed by the muffle furnace igniting the samples. The samples are weighed out and placed into ceramic crucibles of which can withstand the high temperatures of the muffle furnace. Here we ashed samples at temperature reaching 550°C twice for 60 mins. The samples were left to cool to room temperature in between each treatment. These two 550°C runs are intended to eliminate any volatiles associated with organics present within the samples. The third and final run eliminated any volatiles associated with carbonates or gases, within the samples. This treatment required samples to be treated at 850°C for two hours (see appendix for LOI values), .
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1.4.3: Beading
Next samples were transformed into beads for major element analysis with a Malvern
LeNeo Fusion Fluxer. We used a 1:10 ratio ashed sample to lithium tetraborate to create the glass bead under manufacturers method at 1050°C.
1.5: X-Ray Fluorescence
X-Ray fluorescence (XRF) uses x-rays to analyze the elemental composition of samples.
This method provides reliable results that are available immediately. United States Geological
Survey (USGS) standards are run along with the samples to ensure the measurements are accurate and precise. USGS standards of GSP2, a granodiorite, and BRC2, a basalt, were measured with samples as an unknown, these standards were within 10% error of the certified values.
The XRF works by emitting short bursts of intense x-rays at the sample. The atoms within the sample will become exited and either scatter or absorb the x-rays depending on the chemical makeup (Wirth & Barth 2018). The x-rays that are absorbed will affect electrons in the inner valiances causing the electrons in the outer shell to be pulled towards the nucleus filling the lower shells. This produces an energy signature read by the instrument and it can then determine the element (Bruker, 2018). The XRF is capable of measuring elemental concentration within a given specimen. In this project I used the Malvern Panalytical Epsilon 3XLE Energy Dispersive
X-ray Fluorescence instrument to measure the major elemental concentration of artifacts and geological outcrop samples. This instrument is detection limit is 1ppm (Wirth & Barth 2018).
There are limitations that exist with this instrument as well. For concentrations below 1 ppm, it
9 may not be able to determine the amount and will be assumed it is below detectable amount.
Also, the XRF cannot determine isotopes or ions in different valence states (Wirth & Barth
2018). For this study, there is no need to be concerned about the listed limitations. I am primarily looking at the bulk composition of the chert and determining its variance. The major elements toto determine provenance of an artifact.
1.6: Visual Derivative Spectroscopy
Visible derivative spectroscopy (VDS) is a method that deciphers a definitive color of an object or substance. The technique employed use of near infrared to differentiate between similar colors of each of the samples in wavelength form. Visible derivative spectroscopy utilizes portions of the electromagnetic spectrum to determine the true color of the artifacts. This method uses an optical spectrum ranging from the visible wavelength of 400-900nm, for this study, the range is between 400-700nm. The mineralogy of the artifacts will depend on the absorbance of the wavelengths (Davies, 2017). This method quickly identifies the minerals present in my artifacts (Hubbard, et al., 2004).
One of the benefits to this method is the measurement can be taken from either an intact or pulverized sample. The artifacts and outcrops had already been pulverized for the XRF preparation, so this is what we used for VDS. Another benefit to this method, is that a very small amount can be measured (<0.5g). Four artifacts were not measured due to lack of sample.
The only concern with VDS, may stem from the condition of the surface of a sample, this may lead to a false reading. However, in Cackler, et al. 1999, they found that weathering of the
10 surface of chert had no geochemical impact. Despite the surface being a darker color than a freshly broken chert exposing the inside, chemically they were the same. They concluded the difference must have been either pH or Eh differences due to wetting and drying periods.
In order to carry out this method, I used the portable Konica
Minolta Spectrophotometer cm2500d (Figure 1.5). Along with the spectrophotometer, a metal plate for the powdered sample, a brush and metal spatula/scoopula, plastic wrap, writing utensils, and the samples. Figure 1.5: Konica Minolta Spectrophotometer cm2500 setup. This included the instrument, a paint brush for ushering the sample into the metal plate, and plastic wrap to create a barrier between the An important aspect for powdered sample and the instrument. this method to work properly, is to calibrate the instrument every 10 samples to ensure the measurements are accurate and precise.
Once the instrument is calibrated, a small amount of powdered sample was used to fill the small hole of the metal plate. Then covering it with plastic wrap to protect the lens of the spectrophotometer. Using viewfinder, the spectrophotometer is then placed over the sample, and a total of 5 measurements are taken per sample to accurately characterize the visual wavelength of the sample.
11
In order to determine if there was instrument interference, I measured the steel plate and a piece of white copy paper that the sample and steel plate were placed on. Once any interference was documented, I looked at the values present between the visible wavelength, 400-700nm of each sample. The full standard operating procedure for the Konica Minolta and data processing can be found in the appendix.
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1.7: References Cited
Boulanger, M.T., Buchanan, B., O'Brien, M.J., Redmond, B.G., Glascock, M.D. and Eren, M.I., 2015. Neutron activation analysis of 12,900-year-old stone artifacts confirms 450–510+ km Clovis tool-stone acquisition at Paleo Crossing (33ME274), northeast Ohio, USA. Journal of Archaeological Science, 53, pp.550-558.
Cackler, Paul R., et al. “Effects of Weathering on the Coloration of Chert and Its Implications for Provenance Studies.” Lithic Technology, vol. 24, no. 2, 1999, pp. 81–90.
Clayton, Christopher J. The geochemistry of chert formation in Upper Cretaceous chalks (1984) King’s College London, PhD thesis.
Davies, A M.C. “An Introduction to near Infrared (NIR) Spectroscopy.” IM Publications, 2017, www.impublications.com/content/introduction-near-infrared-nir-spectroscopy.
Eren, M.I. and Buchanan, B., 2016. Clovis technology. eLS.
Glenn, Stephanie. “Welcome to Statistics How To!” Statistics How To, Andale Publishing, 2018, www.statisticshowto.com.
Hesse, Reinhard. "Diagenesis #13. Origin of chert: Diagenesis of biogenic siliceous sediments." Geoscience Canada [Online], 15.3 (1988): n. pag. Web. 2 Jan. 2018
Hubbard, J., M & Waugh, David & Ortiz, Joseph. (2004). Provenance determination of chert by VIS/NIR diffuse reflectance spectrometry. The Compass. 78. 119-129.
King, Hobart M. “Flint: A Hard, Tough Material That Humans Have Used to Make Tools for Millions of Years.” Geology.com, 2018, geology.com/rocks/flint.shtml.
Luedtke, Barbara E. “Chert Sources and Trace-Element Analysis.” American Antiquity, vol. 43, no. 3, 1978, pp. 413–423.
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Müller, German. “Chapter 4 Diagenesis in Argillaceous Sediments.” Developments in Sedimentology Diagenesis in Sediments, Geoscience Canada, vol. 15, no. 3, 26 July 1967, pp. 127–177.
Raymond, Loren A. Petrology: the study of igneous, sedimentary, and metamorphic rocks. Wm. C. Brown, 1995, pp. 311-312
Schuessler, Zachary. “Delta E 101.” Learn Delta E, 2016, zschuessler.github.io/DeltaE/learn/.
Stevenson, Christopher M., et al. “Investigations into the European Provenance of Historic Gunflints from Fort Cristanna, Virginia, through Trace Element Chemistry.” Archaeology of Eastern North America, vol. 35, 2007, pp. 49–62.
Williams, J.C., Basu, A.R., Bhagarva, O.N., Ahluwalia, A.D., Hannigan, R.E. 2012. Resolving original signature from a sea of overprint-The geochemistry of the Gungri Shale (Upper Permian, Spiti Valley, India). Chemical Geology, 324: 59-72.
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Chapter 2: Geochemical Analysis: X-Ray Fluorescence 2.1: Introduction
Ohio has an abundance of chert outcrops that were utilized by humans for millennia. Despite the brittleness of chert, it is one of the hardest stones found and can be shaped into many types of sharp-edged tools (Andrefsky, 2008). By studying artifacts fabricated from these the outcrops, archaeologists are able to determine how cultures survived and traveled (Smithsonian Institute, 2019). The tools made from chert are highly resistant to weathering making them suitable for a sourcing study (Smithsonian Institute, 2019).
The conchoidal fracture of chert is imperative to create a quality stone tool. It is this quality that creates a sharp cutting or piercing edge for the tool. The conchoidal pattern is Figure 2.1: Welling site artifact. The conchoidal fracture can be created when the rock is struck by another object, such as seen clearly in this artifact. Photo courtesy of Dr. Metin Eren. another rock, antler, or piece of wood. The manufacturer of stone tools knew how to precisely strike to toolstone to create the shape of a point (Figure 2.1).
Prehistoric Ohioans at the beginning of the Holocene were nomadic hunter/gathers much like the Paleoindians of the Pleistocene. With the glaciers receding, these cultures were able to stay in one place longer and eventually became less nomadic as agriculture became common
(Gupta, 2004). During this time, they used natural raw materials, like chert, to create tools for
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hunting, gathering, and farming. The raw materials and the tools were also traded from other commodities between tribes (Andrefsky, 2008).
The artifacts I am studying are from the Early Archaic to Late Woodland, approximately
9000 – 400 years ago (Prufer, et al., 1970). The artifacts vary in shape, color, and style and are no larger than 6 cm in length. The colors of the tools also vary but stay within the range of colors from the Upper Mercer (UM) chert outcrop. The most common colors found are light gray, gray, and blue-black, but can vary from a marble white to a dark blue-black (Figure 2.2).
Figure 2.2: Various points taken from the Welling archaeological site. The colors vary from a marble white to a dark blue-black color. All points have either inclusions or streaks of different colors. Some of which are red or gold (pyrite)
As of right now, when archaeologists find a stone tool, they often use color comparison to determine its provenance (Luetke, 1992). Although this method is common in archaeology, it is not in geology. Using color as an identifier of minerals is an unreliable method since many minerals can present the same color, so it is rarely used. Considering the color variation in chert, comparing an artifact to an outcrop may be applicable at times. Similar to other geologic specimens, chert can be represented in multiple colors, such as red, blue, black, yellow, white,
16 and purple (Luedtke, 1994). These colors can be noted within the same formation and within small samples. Chert from the Labiates Flint Quarries National Monument, in Texas shows a drastic color variance within just one sample (Figure 2.3). Although chert may be easy to identify due to the variance of color, other chert outcrops can vary throughout a large formation.
Within a chert specimen and outcrop, the color variation can greatly differ. This can lead to discrepancies in provenance if one relies on the color identification method.
Figure 2.3: Polished flint from Alibates Flint Quarries National Monument, Fritch, Texas. Extreme color variations of browns, reds, blues, and whites can be seen clearly in this sample taken from the quarry. Pyrite is even present in this sample. Photo courtesy of the National Parks Conservation Association.
Since color can pose an issue with determining provenance, a more dependable way should be considered. Provenance of artifacts can also be determined through geochemical techniques. Comparing the geochemical composition of the artifacts to known chert outcrops may create a more definitive way to determine the source rock.
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One geochemical technique we can use to understand the provenance of artifacts is major elemental geochemistry, by way of energy dispersive x-ray fluorescence instrumentation (ED-
XRF).
In this study, the archaeological visual method was put to the test by using the XRF method (see Appendix). I measured the major elemental composition of 59 artifacts found at the
Welling archaeological site and compared them to 89 outcrop samples within the USA to determine provenance of the artifacts. Of these artifacts and outcrop samples, duplicated tests were run on 23 of them to ensure reproducibility on the XRF instrument. Out of the 89 outcrop samples, 12 were known UM chert. The remaining samples are primarily chert with some exceptions of limestone, obsidian, and other igneous rocks from the Mountain West, Midwest, and Northeast areas of the USA. If one, or more, of my samples did not match the UM’s geochemical signature, it would be deemed a non-UM artifact.
The artifacts are from an archaeological site called Welling (33CO2), in Nellie, Ohio. It is located in the eastern portion of Ohio along the Walhonding River. Areas 4 and 5 denote the extent of the Upper Mercer chert (UM) outcrop. The UM covers 3 counties: Coshocton,
Hocking, and Perry. Nellie is denoted by the large yellow star, situated on the edge of the
Walhonding River and the northwestern corner of the outcrop (Figure 2.4).
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Figure 2.4: Location of the Welling Archaeological site in Nellie, Ohio. The lower left hand corner is an outline of Ohio and the red box is the approximate location of Nellie. The red crosshatched areas give a depiction of the chert outcrops in the area. The one labeled with a 4 is the Upper Mercer Chert outcrop and 5 is the Vanport chert outcrop. The large yellow star at the north western portion of the Upper Mercer is the approximate location of the Welling site. Figure modified from Boulanger, et al., 2015.
The site yields a tremendous number of artifacts ranging from the Early Archaic to the
Late Woodland, approximately 1600-400 BP. There has only been one excavation done by Dr.
Olaf Prufer, of Kent State University, in the 1960s. During the excavation, there were numerous artifacts collected from this site and there is still a great deal more in situ (Prufer, et al., 1970).
All the artifacts collected during the excavation are housed in the archaeology department at
Kent State University.
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2.2: Methods
The primary method used to determine the provenance of each artifact utilizes an X-Ray fluorescence (XRF). This method establishes the elemental compositions within the chert.
Outcrop and artifacts were powdered transformed into glass bead for major elemental analysis by
XRF. To ensure reliability, replication of the technique was completed approximately every 5th sample for the artifacts and on nearly every sample of the UM outcrop.
Among the processes to prepare for the XRF, loss on ignition (LOI) is one of the most important steps. Not only is it necessary for the XRF, but it also provides data on how much organics and volatiles make up each rock. This provides valuable data by providing a percentage of how much of the artifact is pure rock.
2.3: Preparation for XRF
2.3.1: Pulverizing
To start, any writing or paint was removed with sandpaper and then the samples were wiped clean using milli-q water. They were broken into pieces with hammer, ground down with an augite mortar and pestle, and powdered with an 800M Sample prep Spex Ball Mill. After powdering was complete, I performed Loss on Ignition (LOI).
2.3.2: Loss on Ignition
LOI is preformed specifically to eliminate any impurities or volatiles within the samples by placing all samples in a muffle furnace and heating them at extreme temperatures. The samples go through three runs to complete the elimination. The elimination of impurities is done
20 in the first two runs. The temperature is set for 550°C to ensure any volatiles are burned off. The third and final run to eliminate any volatiles, such as gases, is set at a temperature of 850°C.
Once this process was completed, a calculation was done to determine how much of each were lost during the ignition process. This calculation is as follows:
(푚 − 푚 ) 푤 = 푥 100 (푚 − 푚 )
Where wv represents the percentage of the total loss on ignition of the dry mass, ma represents the mass of the crucible with lid, in grams, mb represents the mass of the crucible with lid and original sample in grams, and mc represents the mass of the crucible with lid and ignited sample, in grams.
Considering the nature and hardness of chert, the LOI was expected to be a low value for any of the samples that were truly chert, obsidian, or other volcanic rocks. If there are other types of rocks present, such as limestone, then the LOI will be much higher since there are more substances to eliminate.
2.3.3: Beading
After LOI, samples were transformed into glass beads utilizing a LeNeo Fusion Fluxer.
This involved combining the sample with the flux agent, lithium tetraborate, at a 10.5 g of flux to
1.5 g of sample. Then using the LeNeo Fluxer to heat the sample to 1050°C to fully incorporate the two into a molten mass. After the fusion process, the beads are analyzed on the XRF.
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2.4: Results
The following graphs exhibit the outcrops that gave the best representation when comparing the artifacts to the Upper Mercer (UM) and all other North American (NA) outcrops.
I removed the outcrop samples that were obsidian, limestone, or any other igneous non-chert stone. These samples were deemed to be outliers. Currently, samples from the chert outcrops are the only samples of concern.
The scatterplots display a relationship between two major oxides. Silica is the parameter that is constant on all graphs to show the relationship of the two categories and to exhibit the concentration differences. Silica is used as the constant since it is the most abundant mineral present, while the others would fall under a minor or trace amount. The graphs shown below compare SiO2 to Na2O, TiO2, and CaO. The comparisons presented are for the artifacts with the
UM outcrop (A), and the artifacts with various NA chert outcrops (B). It is important to note that all the artifacts have high silica values of at least 89%, with the majority plotting around
95% and above.
2.4.1: Na2O
Na2O concentrations are between the artifacts and the UM are very similar while the artifacts and the NA outcrops show some similarities, but also some differences. The range that the UM has is much tighter and typically has less concentration of Na2O and the artifacts fall well within this range and contain no outliers, 0-0.32% (Figure 2.5). The artifacts versus the other N.A. outcrops depict most of the samples plotting in the same area as the artifacts, but also it also contains many outliers. The values for the NA outcrops range between 0-3.5%.
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Figure 2.5: Sodium Oxide (Na2O) scatter plots of the artifacts versus the Upper Mercer (A) and the other North American chert outcrops (B). The Upper Mercer and the artifacts all plot in a similar place while there are many more outliers in the North American graph.
2.4.2: TiO2
TiO2 also provides an illustration of the differences between the outcrops. When plotted
against SiO2, it exhibits a similar plotting behavior as Na2O (Figure 2.6). The artifacts and the
Upper Mercer (Figure 6A) are loading in the same area with a similar slope with the only
difference being the amount of SiO2 present. When plotted against the other chert outcrops
(Figure 6B), the dispersion of data depicts a similar trend as Na2O. Most of the artifacts and
outcrops plot in the above 87% signifying a large amount of SiO2 present.
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Figure 2.6: Titanium dioxide (TiO2) scatter plots of the artifacts versus the Upper Mercer (A) and the other North American chert outcrops (B). The Upper Mercer and the artifacts all plot in a similar place while there are many more outliers in the North American graph.
2.4.3: CaO
CaO is the final oxide that depicts a similarity between the artifacts and the UM and a difference between the artifacts and the other outcrops. This oxide has more of a variation than the others do thus far. Both graphs show a larger range in abundance. CaO in the artifacts loads similar to the UM outcrop with a similar trend and have a range of 0 -7%. The artifacts displayed a more drastic portrayal of the differences when plotted against the other outcrops, however
(Figure 2.7). The range for the other outcrops is larger, between 0-12%.
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Figure 2.7: Calcium Oxide (CaO) scatter plots of the artifacts versus the Upper Mercer (A) and the other North American chert outcrops (B). The Upper Mercer and the artifacts all plot in a similar place while there are many more outliers in the North American graph.
2.5: Ternary Diagrams
Ternary diagrams were created to show the relation between K2O, Al2O3, and
CaO*+Na2O to understand provenance (CaO* is CaO associated with silicate fraction, isolated from LOI) The artifact ternary diagram characterizes the artifacts into 2 distinct clusters. One shows a larger amount of CaO*+Na2O with a very small amount of Al2O and K2O. While the other cluster displays nearly no CaO*+Na2O, a small amount of K2O and a large amount of
Al2O. The similarities can be seen between the artifacts and the Upper Mercer outcrop (Figure
2.8). Both diagrams show a similar trend where some of the samples plot with higher
25
CaO*+Na2O and the others with a very high Al2O3. K2O has very little influence on the samples, but it is present in both and contributes to the visible trend.
Al O Al2O3 2 3
100
90
80
70
60
50
40
30
20
10
CaO* + Na2O 0 0 50 100 K2O
Upper Mercer Atrifacts
Figure 2.8: Both ternary diagrams set side by side. The artifacts and the Upper Mercer samples are plotting in the same areas on both of the diagrams.
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2.6: Statistical Analysis
The data from the experiments was not normally distributed. In order to provide statistical data for my project, I chose the nonparametric hypothesis Kruskal-Wallis test. The results from the Kruskal-Wallis tests were then presented in box and whisker plots for a visual representation. I chose to compare variations of the data sets against each other. It follows the typical hypothesis testing format with a 0.05 significance level and a null and alternative hypothesis. The null is there is no significant difference between the sample sets, and the alternative is there is a significant difference and to reject the null. For all results and plots from statistical testing, please see the appendix.
2.6.1: All three sample sets: the artifacts, the Upper Mercer, and the other N.A. outcrops
The Kruskal-Wallis hypothesis test results for all three sample sets show there are only two of the oxides where the null can be rejected (Table 2.1). SiO2 and CaO show a significant difference. The box and whisker plots for both oxides give a visual representation of the differences between the samples sets as well (Figures 2.9 & 2.10).
Oxide Significance Decision
SiO2 0.017 Reject the null hypothesis
TiO2 0.401 Retain the null hypothesis
CaO 0.004 Reject the null hypothesis
Na2O3 0.246 Retain the null hypothesis
Table 2.1: Chart of the Kruskal-Wallis hypothesis test results for all three sets of samples run to determine if there is a difference. SiO2 and CaO are the only two that show a significant difference where the null hypothesis can be rejected.
27
SiO2
Figure 2.9: Box and whisker plot of how SiO2 plots with the artifacts (1), the UM (2), and all other NA outcrops (3). The SiO2 outliers for the N.A. outcrops are very noticeable while the difference between the artifacts and UM are very similar.
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CaO
Figure 2.10: Box and whisker plot of how CaO plots with the artifacts (1.00), the UM (2.00), and all other NA outcrops (3.00). There are more CaO outliers for the N.A. outcrops than the other two while the outliers for the artifacts still fall close to the range of the UM.
2.6.2: The Artifacts versus the Upper Mercer
When running a Kruskal-Wallis test for the artifacts and the UM, the nulls are nearly all retained. Three out of the four of the oxides are above the 0.05 significance level so the difference between the two sets are not significant (Table 2.2). The only one that is not above the
0.05 significance level is CaO, 0.033, thus the null is rejected. However, this could indicate a possible type 1 error, a false positive, due to the significance being close to the 0.05 threshold.
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Oxide Significance Decision
SiO2 0.066 Retain the null hypothesis
TiO2 0.229 Retain the null hypothesis
CaO 0.033 Reject the null hypothesis
Na2O3 0.346 Retain the null hypothesis
Table 2.2: Chart of the Kruskal-Wallis hypothesis test results for the artifacts versus the UM. The only oxide where the null hypothesis is rejected is CaO. However, the significance value is 0.033 and is near the 0.05 threshold, indicating a possible type 1 error.
2.6.3: The Artifacts versus the other North American Outcrops
When comparing the artifacts to the other N.A. outcrops, the same null hypotheses are rejected; SiO2 and CaO. All of the significance values are more drastic than the values for running all three of the sample sets together (Table 2.3). The CaO is even farther from the 0.05 significance threshold, so there is no possibility of a type 1 error for this set.
Oxide Significance Decision
SiO2 0.008 Reject the null hypothesis
TiO2 0.803 Retain the null hypothesis
CaO 0.002 Reject the null hypothesis
Na2O3 0.331 Retain the null hypothesis
Table 2.3: Chart of the Kruskal-Wallis hypothesis test results for the artifacts versus the other N.A. outcrops. As with the Kruskal-Wallis test for all three of the sample sets together, SiO2 and CaO have significance values of below 0.05, indicating to reject the null hypothesis. Both of these values are much lower than the ones in Figure 2.9.
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2.7: Discussion
2.7.1: Provenance
Analysis of the artifacts on the XRF suggest they were manufactured from chert taken from the Upper Mercer chert outcrop. The oxides discussed previously illustrate how the UM has a signature that varies from other outcrops around North America, despite there being some similarities between them all. The similarities are not significant considering chert is typically high in silica and highly variable with minor and trace elements depending on the chemical composition of the host rock in which it is deposited (Cressman, 1964). When the artifacts are plotted along with the UM outcrop, the correlation is strong and provides evidence they may have come from this outcrop. As seen in the graphs above (Figures 5-8) there is still some variation within the UM, but the artifacts fall within the variation.
One limitation of the analysis is that for each NA outcrop, there were only 1-2 samples to process and analyze. Because of this, it may not be ab accurate representation of the individual outcrop. A bigger set of samples (n=5), would given a stronger analysis of every outcrop making a better comparison to the many artifacts and UM samples. The outliers presented in the plots may also be outliers for that specific outcrop analysis. Geochemical variation is consistent with other major element analysis experiments. Luedtke, (1978) presents results that chemical variation exists within an outcrop wherever physical differences occur. Considering physical properties depend on the geochemical make up of a rock, and within a small nodule as well, the difference within the UM seems to be small enough to dictate a chemical signature for the outcrop, and the artifacts fit within the broad variation.
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2.7.2: Silica Content
All the outcrops and artifacts have high silica content, which is consistent with the composition of all cherts. One study found cherts can range in silica content from around 60% to over 99% depending on the original silica source (Cressman, 1964). Silica can either be from siliceous sediment made up of the skeletal remains of sea creatures, or it could have been dissolved silica from surrounding rocks. There are a few outliers with lower than 80% silica, such as S6 or S11, that could indicate the point of where it is within the diagenetic process or the origin of the siliceous sediment. Perhaps these outliers are not mature or formed earlier in the diagenetic process, resulting in lower SiO2. There is also the possibility the pore waters that carry the dissolved silica are not as silica rich as the others, thus silica will not precipitate out in as much abundance. The UM seems to be consistently high in silica and the artifacts are on the higher end of the silica abundance. The UM chert chosen to construct the tools were of higher silica content, at least 89%, indicating a higher quality of tool (Williams, et al 2019.).
When examining the Upper Mercer ternary plots (Figure 8), there are two very distinct signatures shown. About half of the samples are loading near the Al2O3 area, while the others are loading near CaO* and Na2O areas. Indicating there could have been two factors at play during the creation of the Upper Mercer chert outcrop. Since chert is primarily silica, it is through these constituents that we can see the differences within the composition of the chert. Creating a ternary diagram where silica has been eliminated removes the overburden of Si, allowing us to assess other factors making up the chert. The first is loading between CaO*+Na2O and Al2O3 and the second is between Al2O3 and K2O, but very close to Al2O3. Perhaps, there are two diagenetic sources responsible for the formation of the Upper Mercer, one being influenced by an Al source and the others by Ca and Na. It has been noted that Si will be replaced by Al in chert giving way
32 to the transformation of clay minerals as the chert is undergoing constant alteration. Other influences of Ca, could be link to the source of the chert from limestone beds, resulting in the alteration being less mature than cherts with a heavier component of Al. Nonetheless, the UM outcrops artifacts are still above 80% SiO2, indicating that is not in the late transformation stage of a chert changing into clay.
Both the artifacts and the UM ternary diagrams exhibit the same loading areas as described above (Figure 8). For the UM, half loads between CaO*+Na2O and Al2O3 and the other half is between Al2O3 and K2O. For the artifacts, there are more that load between Al2O3 and K2O, but there is still a good deal of them loading in the other area as well. Because of this consistency between the two, it provides evidence the artifacts were manufactured using chert from the Upper Mercer outcrop. Considering the collection site of the artifacts, this finding would make sense. It also corresponds to the original assumption by archaeologists that based the provenance of the artifacts on color comparison.
2.7.3: Statistical Analysis
A Kruskal-Wallis hypothesis test was the best statistical approach for the data to provide support of the findings since the data was not normally distributed, due to its extremely high
SiO2 content. This type of hypothesis testing is an excellent way to assess if there is any significant difference between the independent groups (N.A., 2019). The outcome of the analysis showed there is no significant difference between artifacts to the UM. Despite rejecting the null hypothesis for CaO, the value is 0.033. I consider this to possibly be a type 1 error, since the p
33 value is relatively close to 0.05 level and could possibly be resolved by having a larger sample set of UM outcrops
The comparison of all three of the sample sets showed significant difference for two of the oxides presented, SiO2 (p= 0.017) and CaO (p=0.004). This finding could be interpreted that these two oxides could help differentiate between the UM and the North American outcrops.
Since SiO2 is showing a significant difference when comparing all three sets of samples, this could imply the UM is a higher quality stone, it was preferentially chosen over other nearby outcrops to create tools. As mentioned previously, the higher the silica content, the more durable the tool will be.
2.7.4: Future Work
Some of the considerations that should be taken for future work would be collecting and analyzing more NA outcrop samples, comparing the trace and rare earth elements (REEs) within all the sample sets, and comparing the UM with other nearby outcrops, such as the Vanport Chert member. As far as the NA outcrops, there are only one to two samples of each of the outcrops. It would be of utmost importance to gather more samples of these outcrops and create a mean signature to compare artifacts to. This would give more validity to the UM being the best provenance fit for the artifacts.
Lastly, comparing of the trace and REEs would hopefully pinpoint the exact chemical signature of the UM and other outcrops. If a chemical signature of the UM was determined to be different from the NA outcrops, then it would provide more of a concrete way to compare any artifacts to determine provenance.
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2.8: Conclusions:
The evidence provided from the geochemical analysis of the Welling Site chert tool artifacts are most likely derived from the Upper Mercer chert outcrop from which the site sits on.
This is due to the following:
Major element data shows the artifacts have a similar geochemical composition to the
UM.
The silica content of the artifacts is in the higher portion of the average for chert. The UM
also shows this characteristic. This also indicates that individuals creating the tools may
have been more enticed to knap UM chert due to its durability.
The evidence is strengthened through the statistical analysis by providing no significant
difference between the artifacts and the UM.
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2.9: References Cited
Andrefsky, W. (2008). The Analysis of Stone Tool Procurement, Production, and Maintenance. Journal of Archaeological Research, 17(1), pp.65-103.
Cressman, Earle R. “Nondetrital Siliceous Sediments.” USGS Data of Geochemistry, Sixth Edition, 1962.
Gupta, Anil K. “Origin of Agriculture and Domestication of Plants and Animals Linked to Early Holocene Climate Amelioration.” Current Science, vol. 87, no. 1, 10 July 2004, pp. 54– 59. Luedtke, Barbara E. An Archaeologist's Guide to Chert and Flint. Institute of Archaeology, University of California, 1994, pp. 61-63
N.A. “Kruskal-Wallis Test.” Statistics Solutions, Statistics Solutions, 2019, www.statisticssolutions.com/kruskal-wallis-test/.
The Smithsonian Institute. “Stone Tools.” The Smithsonian Institution's Human Origins Program, Smithsonian Institution National Museum of Natural History, 14 Mar. 2019, humanorigins.si.edu/evidence/behavior/stone-tools.
Williams, Jeremy C., et al. “Nine-Thousand Years of Optimal Toolstone Selection through the North American Holocene.” Antiquity, vol. 93, no. 368, 2019, pp. 313–324., doi:10.15184/aqy.2018.187.
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Chapter 3: Spectroscopy Analysis
3.1: Introduction to Spectroscopy
Spectroscopy is a technique that employs visual wavelengths to decipher the color of a substance. It has the potential to provide a quantitative representation of the color of substances by utilizing wavelengths in the electromagnetic spectrum. The full wavelength range is between
350-2500nm, but for this project, I only used between 400-700nm, which are the visual wavelengths detected by the human eye in which is compared to the color method that archaeologist use to identify chert (Mason, et al., 2008). This method works by measuring the wavelengths produced by a spectroscopy instrument that are absorbed by the sample. The mineral composition of the samples will depend on the absorbance of the wavelengths (Davies,
2017). By comparing the readings of the artifact samples to the Upper Mercer (UM) samples and the other North American (NA) chert outcrop samples, I aim to determine any differences that would provide evidence the artifacts did come from the UM. Since the color of the artifacts is due to trace elements within the rock, I suggest the instrument would measure the visible reflectance spectrum that can be used to determine the mineralogy of each sample (Hubbard, et al. 2004).
There have been few studies on sourcing chert and chert artifacts using Visible/Near-
Infrared Spectroscopy (VNIRS). The two most significant were completed by Michael J.
Hubbard of Kent State University, and Ryan Michael Parish of the University of Memphis.
These studies were successful, with at least 98% accuracy, by using the full spectral wavelength
37 range between 350-2500nm. Both of the studies have distinctive patterns for each chert and artifact sample between approximately 1350-2000 nm (Hubbard, et. al 2004; Parish, 2011). Since these studies used the extreme infrared, past 700 nm, they can only be a general reference for the visual range tests I performed.
In order to obtain workable data from the measurements, the first derivative of the raw data was determined. This is known as visual derivative spectroscopy (VDS). This method of taking a derivative allows the data to transform into varying wavelengths. The difference between the two types of graphs, raw data and first derivative data, is very noticeable. The raw data graph shows all positive sloping lines that have very unnoticeable characteristics, while the graph containing the first derivative of the data depicts lines with various peaks and troughs and more discernable characteristics (Figure 3.1).
Figure 3.1: The spectral data before and after the 1st derivative was taken. The raw data (A) depicts upward sloping lines with no discernable pattern, while after the first derivative was taken (B), patterns emerge that can be used to compare the artifacts to the numerous outcrops.
The first derivative also allowed for me to analyze the data by completing a Varimax
Principal Component Analysis (VPCA) on each of the data sets; the artifacts, the UM, and the other NA outcrops. VPCA allows for easier interpretation of the data by producing a small
38 number of important variables that can be compared to other data sets (Glen, 2018). Evaluating the data in a planar graph gives a clearer picture of any extreme outliers and differences between the data sets.
3.2: Methods
3.2.1: Spectroscopy
The instrument used to perform the spectroscopy measurements on the samples was a handheld Konica Minolta Spectrophotometer CM-2500d. The instrument measured in sets of five replicates per sample to minimize the noise that could be present. The average of the five the measurements generates an accurate depiction of the color reflectance spectrum for each sample.
To ensure the accuracy of the readings, the instrument was calibrated every ten samples to maintain accuracy and precision. Data was put into Microsoft Excel workbook where it could be analyzed. As mentioned, the raw data did not produce enough information when putting it into a graph, so the first derivative of each set of data was determined, which rendered values that produced a visualization of the wavelengths when added onto the graph.
3.2.2: VERIMAX PRINCIPAL COMPONENT ANALYSIS (VPCA)
The Verimax Principal Component Analysis (VPCA) was completed by using IBM’s
SPSS statistical software. Data was pulled from the Microsoft Excel workbook containing the first derivative of the VDS data to perform the analysis. By using SPSS, it enabled a quick
39 determination of the principal components of each of the data sets. A full detailed standard operating procedure (SOP) for this analysis is in the appendix.
There were two combinations of the sample sets used to evaluate the data. From these two, the first and second components were chosen to be compared. The first combination was the artifacts and the UM, and the second combination was the artifacts and the other NA outcrops.
The third combination, the UM and the other NA outcrops.
Another statistical approach I used to examine the significant difference between the components were to examine the mean and the standard deviation of the two combinations of samples sets; the artifacts and the UM, and the artifacts and the other NA outcrops. The mean will provide the average value for the two combinations. Depending on how close the two means are, I can determine if they are comparable to each other. Comparison of the standard deviation between the two combinations will yield a general representation of any variability they are in reference to the mean. Considering the general properties of chert, such as the high silica content and distinct impurities, I expect these values to be close but still distant enough to make a notable difference.
3.3: Results
3.3.1: Spectroscopy
Results from the VDS give a general representation of the visual wavelengths recorded from each of the samples. There are three sample sets, the artifacts, the UM, and North American
(NA) outcrops (Figures 3.2 - 3.4). The spectral wavelengths that the artifacts presented vary from sample to sample. Rarely are there 2 samples following a similar pattern throughout the whole of
40 the wavelength limits (Figure 3.2). There is no discernable pattern present to make it clear the artifacts all exhibit a similar wavelength.
Artifacts 0.2 0.18 0.16 0.14 0.12 0.1 0.08
Measurment 0.06 0.04 0.02 0 -0.02 400 450 500 550 600 650 700 Visual Wavelength (nm)
Figure 3.2: Spectral wavelengths of the artifacts derived from the first derivative of the raw data. There does not seem to be a distinct common wavelength pattern that can help identify the artifacts having a unique spectral wavelength. The only commonality of these wavelengths is the around 600 nm, where the wavelengths decrease. However, this tends to be the same for all the sample sets.
The UM also had varying wavelengths, and because there are less of these samples, the variance can be seen a little more clearly (Figure 3.3). As with there are some samples that follow a general pattern together, but do not follow the pattern throughout the span of the wavelengths chosen.
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The Upper Mercer 0.25 0.2 0.15 0.1
Measurment 0.05 0 400 450 500 550 600 650 700 Visual Wavelength (nm)
Figure 3.3: Spectral wavelength readings of the Upper Mercer derived from the first derivative of the raw data. The UM has the smallest sample set and it shows a few possible patterns. Unfortunately, these patterns are also visible for the NA outcrops as well. The hope of having a very distinct pattern or patterns to define the UM does not seem to be possible within the visible wavelength spectrum.
The NA outcrops have the most samples, it shows less variance than the artifacts and the
UM samples (Figure 3.4). There are more groupings of similarities as the number of samples increase. There are still variances between the groupings however, but more patterns are emerging within the sample set.
Other North American Outcrops 0.4
0.3
0.2
0.1
Measurment 0 400 450 500 550 600 650 700 -0.1 Visual Wavelenght (nm)
Figure 3.4: Spectral wavelength for other North American chert outcrops between 400-700nm. More samples provide more instances of grouping throughout the sample set. There are patterns emerging, yet since these are samples taken throughout North America, it could indicate general patterns for the rock type of chert. Considering there are similarities between the NA outcrop wavelengths and the artifacts wavelengths, there is no discernable indication of variance between the two sets.
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3.3.2: Varimax Principal Component (VPCA)
The results from completing the VPCA allowed the components to be compared to each other. For the results of the VPCA I chose to compare components 1 and 2, and components 2 and 3account for 96.3% for the artifacts and the UM. Similarly, 94.9% of the total variance is explained within the first three components for the artifacts and the NA outcrops.
The following Figures depict where the components load on a traditional coordinate plane. The graphs have the mathematical four-quadrant format, and I will refer to them using that language to describe where the data points fall. Although there are other ways to present the data in graph format, this format allows us to understand the influences of the different components
(Figure 3.5).
Figure 3.5: A general reference of the quadrants of a typical graph.
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The VPCA1 vs VPCA2, for the artifacts and the UM, fall within similar boundaries. The outcrop tends to have more outliers, but the artifacts still fall within the limits. The largest concentration of both artifacts and the UM is in the 3rd and 4th quadrant. The 3rd quadrant represents that the artifacts are less influenced by VPCA1 and more influenced by VPCA2, and vice versa in the 4th quadrant (Figure 3.6).
Figure 3.6: VPCA1 vs VPCA2 for the artifacts and the UM chert outcrops. The UM shows a more scattered pattern appearing in every quadrant; however, the artifacts seem to fall within the limits of the UM. This provides evidence the artifacts are showing similar characteristics to the UM. The large number of artifacts loading in the 3rd and 4th quadrant could indicate some elemental component or possibly any alteration during heat treating of the artifact.
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Although the data points load in a different area, VPCA2 versus VPCA3 yields somewhat similar results to the VPCA1 versus VPCA2. This time, the 2nd and 3rd quadrants are where the artifacts are primarily loading and the UM primarily loads in the 3rd (Figure 3.7).
Figure 3.7: The second versus the third principal components for the artifacts and the UM. The artifacts are primarily clumping in the 2nd and 3rd quadrant. There are some scattered elsewhere, but these are few. The majority of the UM is also loading in the same area with a few exceptions. All the artifacts are loading within the boundaries that the UM has.
The principal components for the artifacts & the NA outcrops have a different loading pattern. The NA outcrops show a much stronger variation for where they load. The have a more
45 scattered layout whereas the artifacts tend to fall all in a similar area. More precisly, the majority of them fall within the 3rd quadrant with some scattering in the other quadrants (Figure 8).
Figure 3.8: VPCA1 vs VPCA 2 for the artifacts and the North American chert outcrops. The artifacts have a much tighter cluster in the 3rd quadrant with some scattering in the other quadrants. The NA chert seems to be scattered in every quadrant with more outliers appearing in the 4th quadrant.
Lastly, the results for the comparison between the second and third components show a much more scattered pattern. The concentration of artifacts still load together, but more variance is observed (Fig. 3.9).
46
Figure 3.9: The second versus the third components for the artifacts and the NA outcrops. Both sets of data points are more scattered compared to the first versus the second components, but there still is a large number of artifacts loading in a specific area whereas the NA outcrops tend to be scattered more.
3.3.3: Statistical Analysis
The means and the standard deviations for the two sample sets, the artifacts, and the UM; and the artifacts and the NA outcrops, were compared to determine any differences. Despite all calculations being close in value, there still was a significant amount of variance to determine a difference in the sample sets. The mean for the three principal components of the two sample sets displays an increase for the first two components and then a decrease for the third component (Table 3.1).
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Mean Artifacts & UM Artifacts & NA
PC1 0.530323 0.54694
PC2 0.507839 0.51955
PC3 0.439097 0.17819
Table 3.1: The mean for all three components for the artifacts & the UM and the artifacts & the NA outcrops. The first two components were as expected where the mean is lower between the artifacts & the UM, but the third component showed the opposite. The mean is much lower for the artifacts & the NA outcrops. This could be because there are many more NA outcrop samples to drive the lower mean.
As with the mean, the standard deviations for the two samples sets also show the same type of pattern of an increase in value for the first two components and a decrease for the third
(Table 3.2).
Standard Deviation Artifacts & UM Artifacts and NA
PC1 0.31303 0.40298
PC2 0.289389 0.39063
PC3 0.240346 0.21406
Table 3.2: The standard deviation for both of the sample sets and their principal components. For the first two components, the artifacts & the NA outcrops have higher values than the artifacts & the UM outcrops indicating there is more distance between the data points. The third component shows a decrease for the artifacts & and the NA outcrops.
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3.4: Discussion
3.4.1: Spectroscopy
The VDS measurements depict very similar wavelengths across the sample sets. One of the hopes prior to running these spectroscopy tests, was to show a definitive similarity between the artifacts and the UM and a difference between the UM and the NA outcrops. Although there are some similarities, there does not seem to be distinct wavelength patterns that can be determined as the UM or the artifacts.
The previous studies done by Hubbard, et al., and Parish using spectroscopy to determine provenance were able to see a finite difference in the higher wavelengths, between 1350-2000 nm. Since I only used the visible wavelength, 400-700 nm, I was not able to see a distinctive pattern for the UM or the artifacts. There are a few patterns that could be considered distinctive; however, they do not appear to be noticeably characteristic. These results closely resemble the geochemical analysis done in the previous chapter, in that there were many cherts which all loaded in the same area the Upper Mercer and the artifacts did. However, since it is the impurities that drive the visible derivative signal, not silica signal, it explains the variability in the results.
3.4.2: VPCA and Statistical analysis
The principal component analysis was able to give a better description on the similarities and differences between the sample sets. The first sample set, the artifacts & the UM, showed how similar the components are by plotting so closely to each other on the graph. The artifacts even stayed within the boundaries the UM presented. This was the initial hope that the components of the artifacts would plot similarly to the components of the UM.
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The second sample set, the artifacts, and the other NA outcrops, depicted more variance compared to the first sample set. Considering the sporadic nature of where all the outcrop samples were collected (across the continental US and a few from Canada). The artifacts did fall within the boundaries of the NA outcrops, but there was a much bigger spread and it does not show any kind of pattern that the artifacts were from any other specific place other than the UM.
One item to note is majority of the artifacts and UM tend to load on the graphs. There must be a mineral driving the clumping for both. I chose points for each of the artifacts and the
UM and cross referenced them to the X-Ray Fluorescence (XRF) data from Chapter 2.
Unfortunately, there does not seem to be a pattern. I had hoped it was silica that drove the clumping, but it does not seem to be the case. Using XRF data may not be the greatest of methods since it is elemental data, whereas other mineralogical data would be more accurate to compare the VDS. Analyzing the samples on an X-Ray Diffraction (XRD) instrument is a better alternative in order to compare these two types of results.
The mean and standard deviation of the first two components are also showing favor towards the artifacts and UM, being more similar than the artifacts and the other NA outcrops.
This suggests the mean for the artifacts and UM to be closer in value since they are from the same outcrop, having similar elemental composition (see Chapter 2). Although the first two components collaborated with the expectation of having a smaller mean and standard deviation for the artifacts and the UM, the third component was opposite and displayed a lower value for the artifacts & the other NA outcrops. The third component is only approximately 7.6% of the
94.9% total variance explained while the other are above 40%. Since it is such a small percentage, it does not have as big of an impact on the overall evaluation. The mean appears to have the largest difference between the two samples sets.
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3.5: Conclusion and Future Work
I believe I have captured the visual wavelength patterns for chert as a rock type however, the spectral range was not large enough to secure a unique pattern in identifying any artifacts constructed from the UM against the other NA outcrops. The recommendation for future work would be to use the full spectral spectrum to see what the patterns of wavelengths are in the higher limits. This will hopefully yield results that can be analyzed quickly to identify the likeness of the artifact or rock without the need for further testing.
Analysis of the VDS data using VPCA offered a better outlook on the data. The breakdown of components showed the provided more hindsight that the VDS data could not.
This method was similar to the major element results in chapter 2, where the artifacts are closer to the UM than the other NA outcrops. VPCA was able to verify the artifacts most likely came from the UM.
The next step to further concreting these results would be to run all the samples through an X-Ray Diffraction instrument to determine the minerals present. This could lead to a more detailed explanation of the components present in each sample set. It could also help to determine what element/mineral is causing the artifacts and UM to load so closely in a specific area of the graphs.
Another consideration of future work would be to identify the NA outcrops that loaded close to the artifacts, gather more data to understand the similarities, which would lead to the formation of these outcrops. One hindrance of the analysis is there are only one to two samples of some of the outcrops. It does not give a good representation of the whole outcrop. Perhaps there is more to these other outcrops than we can see in these analyses thus far.
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3.6: References Cited
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Glen, Stephanie. “Varimax Rotation: Definition.” Statistics How To, 2 Nov. 2018, www.statisticshowto.datasciencecentral.com/varimax-rotation-definition/.
Hubbard, M & Waugh, David & Ortiz, Joseph. (2004). Provenance determination of chert by VIS/NIR diffuse reflectance spectrometry. The Compass. 78. 119-129.
Mason, Kenneth A., et al., Biology, ninth edition, McGraw Hill, 2008, p. 930
Parish, Ryan Michael. “The Application of Visible/Near-Infrared Reflectance (VNIR) Spectroscopy to Chert: A Case Study from the Dover Quarry Sites, Tennessee.” Geoarchaeology, vol. 26, no. 3, 11 Apr. 2011, pp. 420–439.
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All References Andrefsky, W. (2008). The Analysis of Stone Tool Procurement, Production, and Maintenance. Journal of Archaeological Research, 17(1), pp.65-103.
Boulanger, M.T., Buchanan, B., O'Brien, M.J., Redmond, B.G., Glascock, M.D. and Eren, M.I., 2015. Neutron activation analysis of 12,900-year-old stone artifacts confirms 450–510+ km Clovis tool-stone acquisition at Paleo Crossing (33ME274), northeast Ohio, USA. Journal of Archaeological Science, 53, pp.550-558.
Cackler, Paul R., et al. “Effects of Weathering on the Coloration of Chert and Its Implications for Provenance Studies.” Lithic Technology, vol. 24, no. 2, 1999, pp. 81–90.
Clayton, Christopher J. The geochemistry of chert formation in Upper Cretaceous chalks (1984) King’s College London, PhD thesis.
Cressman, Earle R. “Nondetrital Siliceous Sediments.” USGS Data of Geochemistry, Sixth Edition, 1962.
Davies, A M.C. “An Introduction to near Infrared (NIR) Spectroscopy.” IM Publications, 2017, www.impublications.com/content/introduction-near-infrared-nir-spectroscopy.
Eren, M.I. and Buchanan, B., 2016. Clovis technology. eLS.
Glenn, Stephanie. “Welcome to Statistics How To!” Statistics How To, Andale Publishing, 2018, www.statisticshowto.com.
Gupta, Anil K. “Origin of Agriculture and Domestication of Plants and Animals Linked to Early Holocene Climate Amelioration.” Current Science, vol. 87, no. 1, 10 July 2004, pp. 54– 59. Hesse, Reinhard. "Diagenesis #13. Origin of chert: Diagenesis of biogenic siliceous sediments." Geoscience Canada [Online], 15.3 (1988): n. pag. Web. 2 Jan. 2018
Hubbard, J., M & Waugh, David & Ortiz, Joseph. (2004). Provenance determination of chert by VIS/NIR diffuse reflectance spectrometry. The Compass. 78. 119-129.
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King, Hobart M. “Flint: A Hard, Tough Material That Humans Have Used to Make Tools for Millions of Years.” Geology.com, 2018, geology.com/rocks/flint.shtml.
Luedtke, Barbara E. An Archaeologist's Guide to Chert and Flint. Institute of Archaeology, University of California, 1994, pp. 61-63
Luedtke, Barbara E. “Chert Sources and Trace-Element Analysis.” American Antiquity, vol. 43, no. 3, 1978, pp. 413–423.
Mason, Kenneth A., et al., Biology, ninth edition, McGraw Hill, 2008, p. 930
Müller, German. “Chapter 4 Diagenesis in Argillaceous Sediments.” Developments in Sedimentology Diagenesis in Sediments, Geoscience Canada, vol. 15, no. 3, 26 July 1967, pp. 127–177.
N.A. “Kruskal-Wallis Test.” Statistics Solutions, Statistics Solutions, 2019, www.statisticssolutions.com/kruskal-wallis-test/.
Parish, Ryan Michael. “The Application of Visible/Near-Infrared Reflectance (VNIR) Spectroscopy to Chert: A Case Study from the Dover Quarry Sites, Tennessee.” Geoarchaeology, vol. 26, no. 3, 11 Apr. 2011, pp. 420–439.
Raymond, Loren A. Petrology: the study of igneous, sedimentary, and metamorphic rocks. Wm. C. Brown, 1995, pp. 311-312
Schuessler, Zachary. “Delta E 101.” Learn Delta E, 2016, zschuessler.github.io/DeltaE/learn/.
Stevenson, Christopher M., et al. “Investigations into the European Provenance of Historic Gunflints from Fort Cristanna, Virginia, through Trace Element Chemistry.” Archaeology of Eastern North America, vol. 35, 2007, pp. 49–62.
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The Smithsonian Institute. “Stone Tools.” The Smithsonian Institution's Human Origins Program, Smithsonian Institution National Museum of Natural History, 14 Mar. 2019, humanorigins.si.edu/evidence/behavior/stone-tools.
Williams, J.C., Basu, A.R., Bhagarva, O.N., Ahluwalia, A.D., Hannigan, R.E. 2012. Resolving original signature from a sea of overprint-The geochemistry of the Gungri Shale (Upper Permian, Spiti Valley, India). Chemical Geology, 324: 59-72.
Williams, Jeremy C., et al. “Nine-Thousand Years of Optimal Toolstone Selection through the North American Holocene.” Antiquity, vol. 93, no. 368, 2019, pp. 313–324., doi:10.15184/aqy.2018.187.
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Appendix I Major Element Data for North American Outcrops
Lab ID LOI SiO2 Na2O Al2O3 SO3 CaO TiO2 MnO Fe2O3 (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt %)
S1_ST 0.635 96.830 0.000 0.953 0.034 0.073 0.002 0.010 0.512
S2_ST 0.956 96.687 0.062 1.024 0.036 0.073 0.006 0.000 0.528
S3_ST 1.275 89.600 0.000 0.861 0.031 0.073 0.000 0.000 7.735
S4_ST 2.209 90.297 0.136 3.585 0.000 0.073 0.134 0.244 2.406
S5_ST 0.199 97.805 0.054 0.944 0.000 0.073 0.010 0.000 0.090
S6_ST 0.577 71.705 3.476 13.091 0.022 1.368 0.240 0.081 4.234
S7_ST 0.373 98.044 0.059 0.791 0.045 0.073 0.000 0.000 0.411
S8_ST 1.692 95.140 0.000 0.958 0.243 0.226 0.000 0.009 0.957
S9_ST 3.481 91.149 0.000 1.027 0.242 3.054 0.008 0.008 0.613
S10_ST 21.083 33.231 0.000 4.136 0.016 0.929 0.346 0.244 18.922
S11_ST 7.321 79.809 0.058 1.601 0.047 10.146 0.043 0.000 0.399
S12_ST 2.876 94.100 0.074 1.419 0.123 0.128 0.038 0.000 0.502
S13_ST 2.299 92.279 0.106 1.439 0.177 0.656 0.033 0.197 2.231
S14_ST 1.215 78.822 0.584 3.454 0.041 0.107 0.189 0.713 13.539
S15_ST 5.783 91.178 0.000 0.969 0.101 0.892 0.007 0.000 0.200
S16_ST 0.65 97.757 0.024 0.998 0.044 0.008 0.000 0.000 0.143
S17_ST 0.922 97.076 0.039 0.902 0.033 0.054 0.000 0.000 0.026
S18_ST 0.307 75.001 3.655 12.834 0.026 0.000 0.056 0.030 2.298
S19_ST 1.403 95.915 0.054 0.785 0.144 1.335 0.000 0.000 0.091
S20_ST 0.461 89.074 0.062 BDA 0.065 0.019 0.001 BDA 0.068
S21_ST 0.944 96.627 0.038 1.102 BDA 0.033 0.014 0.000 0.777
S22_ST 10.706 74.304 0.000 0.990 0.094 8.017 0.004 0.014 0.395
S23_ST 0.698 97.810 0.076 0.948 0.038 0.086 0.007 0.000 0.044
S24_ST 2.079 93.575 0.000 1.344 0.133 1.523 0.026 0.000 0.456
S25_ST 2.215 94.830 0.081 1.075 0.035 0.743 0.025 0.000 0.377
S26_ST 2.714 89.221 0.109 2.289 0.365 1.354 0.044 0.020 3.243
S27_ST 3.585 89.246 0.093 1.044 0.228 2.571 0.000 0.058 2.274
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Lab ID LOI SiO2 Na2O Al2O3 SO3 CaO TiO2 MnO Fe2O3 (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt %)
S28_ST 1.336 97.184 0.078 0.775 0.039 0.071 0.000 0.014 0.259
S29_ST 1.755 65.205 3.003 12.008 0.034 3.693 0.770 0.438 10.310
S30_ST 0.923 94.557 0.047 1.618 0.065 0.059 0.045 0.013 2.260
S31_ST 1.46 96.012 0.040 0.811 0.086 0.715 0.004 0.013 0.562
S32_ST 7.024 81.940 0.056 0.884 0.130 9.072 0.003 0.000 0.439
S33_ST 1.231 95.121 0.052 0.983 0.207 0.286 0.004 0.011 1.695
S34_ST 6.257 92.135 0.000 1.074 0.028 0.027 0.002 0.000 0.411
S35_ST 3.842 89.100 0.065 0.914 0.034 5.606 0.000 0.000 0.139
S36_ST 0.863 98.154 0.000 0.793 0.034 0.000 0.000 0.000 0.081
S37_ST 42.792 4.967 0.000 1.034 0.331 29.726 0.036 0.031 1.591
S38_ST 2.065 94.343 0.084 1.027 0.044 1.717 0.015 0.042 0.405
S39_ST 1.352 96.268 0.000 1.073 0.249 0.190 0.022 0.000 0.406
S40_ST 1.094 93.677 0.069 1.173 0.036 0.051 0.025 0.082 3.491
S41_ST 2.156 92.100 0.086 1.635 0.041 1.943 0.035 0.220 1.009
S42_ST 3.577 90.197 0.069 0.729 0.531 3.164 0.000 0.053 1.392
S43_ST 3.998 87.818 0.062 0.965 0.150 6.063 0.015 0.042 0.528
S44_ST 1.73 94.963 0.046 1.641 0.0639 0.052 0.049 BDA 0.906
S45_ST 0.1 74.834 4.379 13.549 0.000 0.422 0.013 0.050 1.740
S46_ST 1.398 95.650 0.068 1.559 0.059 0.032 0.058 0.006 0.815
S47_ST 1.805 92.809 0.053 1.619 0.362 0.735 0.025 0.035 1.536
S48_ST 13.868 39.805 0.033 36.775 0.016 0.020 1.927 0.004 6.952
S49_ST 0.05 73.206 4.592 11.196 0.035 0.000 0.158 0.110 5.243
S50_ST 2.192 95.771 0.047 1.141 0.036 0.036 0.015 0.000 0.277
S51_ST 0.895 97.761 0.000 0.785 0.036 0.009 0.000 0.000 0.251
S52_ST 0.594 98.113 0.053 0.834 0.035 0.013 0.000 0.000 0.073
S53_ST 0.864 97.430 0.075 1.066 0.035 0.019 0.017 0.000 0.080
S54_ST 1.05 94.015 0.000 1.857 0.033 0.036 0.079 0.016 2.335
S55_ST 1.002 97.444 0.070 1.056 0.000 0.071 0.013 0.000 0.114
S56_ST 11.147 71.747 0.069 1.052 0.333 14.339 0.017 0.044 0.683
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Lab ID LOI SiO2 Na2O Al2O3 SO3 CaO TiO2 MnO Fe2O3 (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt %)
S57_ST 0.552 98.279 0.000 0.794 0.039 0.010 0.000 0.000 0.101
S58_ST 1.39 94.678 0.081 2.304 0.052 0.047 0.070 0.000 0.723
S59_ST 0.95 96.629 0.063 1.278 0.087 0.233 0.034 0.000 0.196
S60_ST 0.95 95.980 0.045 0.804 0.103 0.251 0.000 0.029 1.495
S61_ST 0.299 98.258 0.069 0.863 0.038 0.052 0.000 0.000 0.107
S62_ST 3.718 90.177 0.070 1.275 0.347 2.121 0.028 0.013 0.715
S63_ST 1.201 95.451 0.117 1.907 0.000 0.027 0.099 0.000 0.640
S64_ST 3.528 90.915 0.080 1.417 0.112 1.795 0.036 0.016 0.545
S65_ST 1.303 95.365 0.000 1.500 0.072 0.097 0.032 0.013 1.089
S66_ST 1.513 95.333 0.093 1.839 0.000 0.027 0.101 0.051 0.609
S67_ST 0.597 98.110 0.035 0.791 0.000 0.011 0.000 0.000 0.087
S68_ST 0.874 95.376 0.073 1.207 0.094 0.223 0.004 0.027 0.971
S69_ST 0.457 98.409 0.054 0.806 0.000 0.009 0.000 0.000 0.053
S70_ST 1.395 96.533 0.095 1.385 0.034 0.087 0.032 0.000 0.136
S71_ST 0.427 97.566 0.030 1.048 0.000 0.009 0.004 0.000 0.109
S72_ST 1.455 93.483 0.043 2.385 0.174 0.050 0.043 0.000 1.464
S73_ST 1.369 96.100 0.047 1.471 0.000 0.016 0.024 0.006 0.485
S74_ST 1.106 95.886 0.070 1.750 0.000 0.014 0.042 0.000 0.781
S75_ST 48.806 48.579 0.028 0.508 0.052 1.429 0.002 0.000 0.185
S76_ST 3.922 87.679 0.089 1.234 0.120 4.329 0.024 0.012 0.537
S77_ST 44.306 5.281 0.000 1.051 0.380 28.959 0.039 0.029 1.469
S78_ST 25.591 36.475 0.000 0.543 0.183 36.293 0.000 0.006 0.155
S79_ST 4.844 89.153 0.073 1.237 0.159 2.355 0.030 0.000 0.392
S80_ST 1.447 97.489 0.051 0.762 0.046 0.010 0.000 0.000 0.016
S81_ST 0.698 97.991 0.000 0.758 0.044 0.138 0.000 0.000 0.033
S82_ST 2.512 90.438 0.048 1.383 0.330 0.427 0.017 0.153 4.130
S83_ST 4.539 72.049 0.579 5.549 0.391 0.350 0.205 3.277 10.380
S84_ST 2.68 91.973 0.000 0.991 0.662 2.417 0.003 0.000 0.991
S85_ST 1.194 97.244 0.057 0.936 0.034 0.026 0.000 0.000 0.250
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Lab ID LOI SiO2 Na2O Al2O3 SO3 CaO TiO2 MnO Fe2O3 (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt %)
S86_ST 11.05 73.671 0.000 1.166 0.065 10.908 0.017 0.017 0.449
S87_ST 1.846 91.054 0.000 0.881 0.074 0.178 0.006 0.075 5.424
S88_ST 3.812 89.533 0.107 1.783 0.044 3.371 0.040 0.048 0.691
S89_ST 2.342 93.146 0.090 2.300 0.098 0.069 0.101 0.000 1.174
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Major Element Data for Artifacts
Lab ID LOI SiO2 Na2O Al2O3 SO3 CaO TiO2 MnO Fe2O3 (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt %) ST1 1.82 90.65 0.07 1.28 0.04 0.58 0.03 0.10 4.58 ST2 1.40 96.39 0.06 0.94 0.08 0.47 0.01 0.00 0.27 ST3 1.01 96.41 0.07 1.42 0.00 0.03 0.05 0.00 0.35 ST4 1.11 95.57 0.09 0.95 0.03 0.31 0.00 0.00 1.20 ST5 4.39 92.03 0.06 0.85 0.13 1.71 0.00 0.01 0.22 ST6 1.22 95.82 0.05 1.27 0.03 0.40 0.03 0.00 0.24 ST7 1.31 96.19 0.10 1.46 0.00 0.04 0.04 0.00 0.29 ST8 1.31 93.85 0.10 2.09 0.00 0.40 0.10 0.00 1.33 ST9 1.91 94.92 0.08 1.07 0.03 0.10 0.01 0.00 1.15 ST10 1.64 95.55 0.07 1.24 0.03 0.03 0.03 0.00 0.21 ST11 1.39 95.82 0.09 1.45 0.03 0.01 0.04 0.00 0.34 ST12 0.66 95.12 0.05 0.95 0.05 0.16 0.01 0.00 1.59 ST13 1.27 98.05 0.00 0.00 0.03 0.01 0.00 0.00 0.06 ST14 1.24 96.56 0.07 1.04 0.00 0.03 0.01 0.00 0.10 ST 15 1.41 96.09 0.06 1.52 0.04 0.02 0.05 0.00 0.10 ST16 2.76 95.68 0.05 0.97 0.03 0.02 0.01 0.00 0.05 ST17 1.84 95.97 0.12 1.25 0.00 0.02 0.03 0.00 0.15 ST18 3.67 91.01 0.10 1.63 0.08 2.11 0.03 0.01 0.51 ST19 1.36 96.89 0.00 1.04 0.03 0.01 0.02 0.00 0.06 ST20 3.04 91.02 0.05 1.11 0.13 0.07 0.02 0.03 3.64 ST21 1.56 95.62 0.11 1.70 0.00 0.02 0.08 0.00 0.38 ST22 1.68 95.63 0.07 1.63 0.03 0.02 0.05 0.00 0.18 ST23 1.36 94.25 0.08 0.85 0.00 0.09 0.00 0.00 0.47 ST 24 1.97 95.32 0.00 1.56 0.00 0.02 0.06 0.00 0.15 ST 25 1.29 96.00 0.07 1.07 0.03 0.05 0.01 0.00 1.09 ST 26 1.23 96.94 0.08 1.05 0.00 0.02 0.01 0.00 0.07 ST 27 1.31 96.20 0.08 1.40 0.04 0.05 0.07 0.00 0.11 ST 28 1.33 95.52 0.07 1.44 0.05 0.12 0.05 0.01 0.57 ST 29 0.93 97.13 0.07 0.93 0.00 0.05 0.00 0.00 0.40 ST 30 0.51 96.13 0.10 1.84 0.00 0.04 0.07 0.00 0.22 ST31 0.60 93.74 0.26 0.00 0.05 0.02 0.00 0.00 0.08 ST32 1.62 94.94 0.13 1.08 0.04 0.03 0.00 0.00 0.08 ST33 1.02 97.33 0.00 0.90 0.00 0.07 0.01 0.00 0.04 ST34 9.36 89.98 0.00 0.00 0.00 0.01 0.00 0.00 0.04 ST35 0.75 96.63 0.00 0.90 0.04 0.12 0.00 0.07 0.91
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Lab ID LOI SiO2 Na2O Al2O3 SO3 CaO TiO2 MnO Fe2O3 (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt %) ST36 1.39 95.12 0.06 0.92 0.03 0.15 0.00 0.00 1.52 ST37 0.99 96.88 0.06 1.12 0.00 0.02 0.02 0.00 0.34 ST38 1.08 96.42 0.05 0.89 0.00 0.03 0.01 0.01 0.21 ST39 1.05 96.63 0.08 1.45 0.03 0.03 0.04 0.00 0.10 ST40 1.08 96.78 0.07 1.04 0.04 0.21 0.01 0.00 0.21 ST41 1.99 96.12 0.06 1.16 0.00 0.01 0.02 0.00 0.08 ST42 0.90 97.72 0.00 0.79 0.00 0.01 0.00 0.00 0.03 ST43 1.22 96.88 0.00 0.84 0.00 0.02 0.00 0.00 0.07 ST44 1.20 96.52 0.00 1.00 0.00 0.01 0.01 0.00 0.06 ST45 1.13 96.67 0.05 1.02 0.03 0.18 0.01 0.00 0.09 ST46 1.40 96.72 0.05 1.04 0.03 0.03 0.01 0.00 0.12 ST47 1.33 96.03 0.10 1.44 0.10 0.14 0.05 0.00 0.19 ST48 0.81 97.80 0.00 0.83 0.00 0.01 0.00 0.00 0.20 ST49 3.17 92.74 0.05 1.16 0.04 2.16 0.02 0.01 0.14 ST50 0.66 97.96 0.00 0.76 0.00 0.03 0.00 0.00 0.13 ST51 0.57 97.80 0.00 0.99 0.00 0.01 0.01 0.00 0.16 ST52 1.22 96.34 0.07 1.19 0.04 0.35 0.03 0.00 0.17 ST53 1.36 96.15 0.08 1.27 0.06 0.02 0.03 0.00 0.54 ST54 1.27 96.86 0.05 0.88 0.03 0.18 0.01 0.01 0.25 ST55 2.36 90.32 0.04 0.86 0.31 0.31 0.00 0.03 5.23 ST56 1.15 95.87 0.09 1.09 0.00 0.12 0.02 0.00 1.29 ST57 1.78 93.23 0.03 1.51 0.03 1.57 0.04 0.01 0.43 ST58 1.19 96.26 0.08 1.52 0.00 0.02 0.08 0.00 0.34 ST59 1.66 95.66 0.09 1.42 0.05 0.02 0.04 0.00 0.40
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Statistical Testing: Kruskal-Wallis Hypothesis Tests and Box and Whisker Plots
Kruskal-Wallis Tests for Artifacts, Upper Mercer, and the other North American outcrops.
Null Hypothesis: There is no significant difference between the artifacts, the UM, and the other
NA outcrops.
Table A1: Kruskal-Wallis test results from SPSS. SiO2 and CaO reject the null hypothesis indicating there is a significant difference between the three sample sets.
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Box and Whisker Plots:
SiO2
Figure A1: SiO2 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: UM, 3.00: NA outcrops.
TiO2
Figure A2: TiO2 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: UM, 3.00: NA outcrops.
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CaO
Figure A3: CaO box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: UM, 3.00: NA outcrops.
Na2O3
Figure A4: Na2O3 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: UM, 3.00: NA outcrops.
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Artifacts versus the Upper Mercer
Null Hypothesis: There is no significant difference between the artifacts and the Upper Mercer.
Table A2: Kruskal-Wallis test results from SPSS. Only CaO rejects the null hypothesis indicating there is a significant difference between the artifacts and the UM.
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Box and Whisker Plots
SiO2
Figure A5: SiO2 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: UM
TiO2
Figure A6: TiO2 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: UM
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CaO
Figure A7: CaO box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: UM
Na2O3
Figure A8: Na2O3 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: UM
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Artifacts vs NA Outcrops
Null: There is not a significant difference between the artifacts and the other NA outcrops.
Table A3: Kruskal-Wallis test results from SPSS. SiO2 and CaO reject the null hypothesis indicating there is a significant difference between artifacts and the other NA outcrops.
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Box and Whisker Plots
SiO2
Figure A9: SiO2 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: Other NA Outcrops
TiO2
Figure A10: TiO2 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: Other NA Outcrops
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CaO
Figure A11: CaO box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: Other NA Outcrops
Na2O3
Figure A12: Na2O3 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: Other NA Outcrops
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Upper Mercer vs North American Outcrops Null Hypothesis: There is no significant difference between the UM and the NA outcrops.
Table A4: Kruskal-Wallis test results from SPSS. SiO2 and CaO reject the null hypothesis indicating there is a significant difference between artifacts and the other NA outcrops.
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Box and Whisker Plots:
SiO2
Figure A13: SiO2 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: Other NA Outcrops
TiO2
Figure A14: TiO2 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: Other NA Outcrops
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CaO
Figure A15: CaO box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: Other NA Outcrops
Na2O3
Figure A16: Na2O3 box and Whisker plot for Kruskal-Wallis test. 1.00: Artifacts, 2.00: Other NA Outcrops
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Geochemical Standard Operating Procedure
by
Katherine Reuter
Daniel Phillipi
Diana Simone
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Milling SOP
Pulverizing and milling the rock samples into a fine powder is an important first step to prepare the samples for many of our tests. It will require some attention to detail to make sure that the powdered sample is fully homogeneous. Failure to properly follow these procedures could result in problems with future operations, such as cracking the glass bead.
1. Put gloves on prior to beginning to protect hands from samples. 2. Put on safety glasses to protect eyes. 3. Prepare the Sample Prep tumbler first. You will find these in the drawer under the Sample Prep miller. You will need two lids, two or more cork rings, the middle cylinder, and the ball.
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4. The lids require a cork ring to be inserted prior to screwing onto the cylinder. In the event that the lid is damaged and it does not screw properly onto the cylinder an additional one or two cork rings may be required to fill the gap. This is important, as if there is a gap then some of the sample will be lost during the milling process. 5. Insert cork rings as needed (each lid needs at least one) into the lids and screw one lid onto to one side of the cylinder.
6. Put tumbler aside.
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7. Make sure granite slab and mortar and pestle are clean. If not, wipe off with Kimwipe and Milli-Q water. 8. On the granite slab use a rock hammer to break up the sample into small pieces (around 30g worth).
9. Transfer the pieces to a mortar and use the pestle to further pulverize and homogenize the sample.
10. Once the sample is sufficiently broken down, transfer to a prepared Sample Prep tumbler (Spex?) and add a milling ball.
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l 11. Open the door of the (Spex?) and unscrew the arm lock on the long screw and loosen the vice by unscrewing it. Do so until you are able to place the tumbler into the vice that holds it into place. 12. Tighten the tumbler into place and then lock it by screwing the arm lock tightly into place.
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13. Close lid of instrument and lock. 14. Turn it by flicking the switch located behind the right side of the instrument. 15. Adjust length of milling depending on the type of sample. For instance, with a soft sedimentary rock like shale 5 minutes will suffice, but with a harder rock like chert 15 minutes may be required. 16. Push the start button. 17. Once sample is done and the instrument unlocks itself, unlock the manual lock and lift the lid. Unscrew the arm lock and then unscrew the vice to release the tumbler. 18. Inspect the powdered sample to assure that the sample is homogeneous. Often times, shale may clump together to look like it is not powdered, but it may be. Harder rocks like chert will require a more thorough inspection. If the sample is not fully powdered, put back into ball mill and run again. Repeat as necessary.
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19. After the sample is fully powdered, empty contents into sample jar (may need to use funnel) or onto clean weigh paper to then transfer to desired storage container. Label any and all storage containers. 20. To clean the tumbler, unscrew both sides and remove the corks. Run the lids and tumbler under clean water to remove all residue. Use Milli-Q water to rinse thoroughly and then wipe out with alcohol. To clean the corks, only use clean water and a sponge to remove the residue. Then rinse with Milli-Q. DO NOT use alcohol on the corks. 21. Place all things on paper towels to dry. 22. Once dry, put away in drawer where found.
Loss on Ignition (LOI) SOP
The LOI process is important to burn off any organics and volatiles from the samples. This will provide a proper analysis by the XRF instrument. It also gives other useful information such as the amount of organic matter in a black shale (an important factor to determine the original conditions of deposition). This process can take up to 3 days to complete.
1. Put on gloves to protect your hands. 2. To begin, you will need your pulverized rock samples, a pen, a sharpie, lab notebook, and 12 ceramic crucibles. The ceramic crucibles are are located in a drawer near the windows on the far side of the lab (near pellet press).
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3. Bring everything to the balance area.
4. In your lab notebook, create a chart like the one shown below. This will be used throughout the entire LOI and beading process.
5. Turn on the balance and zero it.
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6. Label your first crucible with the sample number using the sharpie. 7. Weigh the crucible with lid on and record the weight.
8. Take lid off, leave crucible on, zero the scale.
9. Measure out at approximately 2g-4g of sample. Record the weight.
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10. Take the crucible off the scale and zero. 11. Replace the lid, and return the crucible with sample and lid on to the scale. Record weight. 12. Repeat with remaining samples. Make sure you record properly. If there is a lot of variance in the weight when weighing, close the doors to the balance. This will stop the variance on the display. 13. Once all crucibles are labeled, filled, and weights recorded, transfer them to the muffle furnace. Align them into 2 rows 6 deep.
14. On a piece of paper or lab notebook, make a legend/map of where each of the crucibles are. The sharpie will burn off in the process leaving the crucibles blank.
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15. Close door of furnace. 16. To turn on furnace: a. Flip breaker on front of instrument. Wait for display to activate.
b. To program the first run sequence, press and hold set/ent button for a few seconds until “node” appears.
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c. Tap set/end again until PrG shows i.Press △ (up arrow) to “1” ii.Hit set/ent
d. This will bring up a series of components that will need to be set. i.SSP 1. Set temp (550॰C for first 2 runs, 850॰C for 3rd run)
2. Press set/ent 2 times ii. SPI 0. Set same temp as on SSP
2. Press set/ent
iii. tn1 1. Set time (1.15 for first 2 runs, 2.15 for 3rd)
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2. Press set/ent
iv. Leave all others alone. No need to input any other information. v.Press and hold set/ent until menu goes away. vi.Press and hold set/end again until “node” appears. vii.Press △ (up arrow) until “run” appears.
viii. Press set/ent. ix.The muffle furnace will turn on and start heating. A small “run” light near the green temperature and an orange light under the breaker will turn on.
17. After the furnace starts, it will turn itself off after the run is complete. 18. Allow to cool to room temperature. Normally we wait until the following day to proceed. 19. After furnace and crucibles have cooled, open the door and carefully take each one out. Lightly shake them or stir them.
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20. Return them to the same spot in the furnace from where you took them (this is important!) 21. Once all have been stirred, start a second 550॰C. Furnace is already programed. a. Press and hold set/end until “node” appears. b. Press △ (up arrow) until “run” appears. 22. After second run is complete and furnace and samples are cool, take each sample out and weigh them in the crucible with lid on. Record each weight. 23. Return to the furnace and place them again in the same spots as prior and close the door. 24. Program the furnace for the 850॰C 2.15hr run (refer to step 16 for programming instructions.
25. Halfway during the 2 hour run, the samples will need to be stirred. a. Put on heat mitts to protect your hands and open the door of the furnace. b. Allow heat to escape for a minute. IT WILL BE HOT. c. With long crucible tongs, carefully pick up each crucible and lightly shake them. Put back in same spot. d. Close door and allow run to complete. 26. The following day once the samples and furnace are cool, take each crucible out and label them with the sharpie again. 27. Weigh each sample in the crucible with lid on. Record as final weight.
28. Samples are now ready to be processed into glass beads. Store any samples not ready to bead in the desiccator.
Glass Bead SOP
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Transforming the powdered and LOIed samples into glass disks is the final step of preparing the sample to be read in the XRF. This process is called beading, despite the flat disks that it produces.
1. Put on gloves to protect your hands. 2. Start by turning on the Claisse LeNeo Fluxer. a. Flip breaker switch on the left side of the instrument.
b. Once the screen activates and goes through it’s starting sequence, press the wrench on the interactive screen.
c. This will bring up a menu. Chose “system.”
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d. Chose Manual control and monitoring
e. Input numerical password 1169
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f. Chose heating unit
g. Press the 0°C box to input a temperature.
h. Input 1050°C and hit OK
i. Press “turn on” and the heating unit will start to warm up. j. Press the home button to bring you back to the original screen. 4. Once the instrument is heating, you can prepare a sample to be beaded.
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5. You will need a titanium crucible, mold, lithium tetraborate flux, your sample (that has gone through LOI), your lab notebook, a scale, and a pen. **Always wear gloves when dealing with lithium tetraborate, it can be an irritant. Please read the MSDS available in the lab for more information.
5. Put a piece of weigh paper on the scale and zero it out.
6. Measure out 1.5g your sample on the weigh paper. If you do not have enough sample to make 1.5g, then get as close as you can. 7. Record the weight and put sample aside. 8. Put the titanium crucible on the scale and zero it.
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9. Carefully fill the crucible to 10.5g of flux. In the event that you sample was not 1.5g, add 9g to it to get the amount of flux to be used. Example: 1.325g of sample, 10.325g of flux.
10. Record weight of flux in lab book. 11. Take crucible off of scale and add the sample into the flux. Mix well. *You may see “clumps” of the sample. As long as these are not hard solid chunks, the bead will be fine. If they are hard and solid, there is a chance that they will not melt and the bead may crack.
12. The sample is now ready to run in the fluxer. 13. Place the titanium mold and the crucible into the fluxer and close the door.
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14. On the interactive screen, press fusion (located on home screen).
15. Choose the appropriate run for your sample. Most samples are run on “Disk-general oxides.”
16. A synopsis of the run will come up. Press select.
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17. It will ask you to confirm you placed the mold into the machine, check it and then hit ok.
18. The instrument will start the process and display a timer until it will be done. 19. During this time, you can prep another sample to be run after the first one is done. If you do, then put the mixed flux and sample into the desiccator until the fluxer is ready for it. 20. It would be wise to allow the sample to sit after the door unlocks to cool longer. Another 3-5 minutes would be best.
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21. Once the sample is cool enough to handle, with gloved hands, take the mold with the bead out and flip it onto a Kim Wipe.
22. Do not touch any part of the disk except on the thin sides. If the flat sides are touched it the XRF may not get an accurate reading. 23. Place it into a plastic petri dish (found in the drawer under the desiccator).
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24. Label the petri dish with the sample number and wrap the dish with either a rubber band or tape the sides down.
25. Store in desiccator until it is ready to run on the XRF.
26. Clean the crucible and the mold using a small amount of citric acid and Milli-Q water after each use.
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27. Rinse thoroughly with Milli-Q water and let dry. If needed right away, dry with a Kim Wipe.
28. When done fluxing, go back to the home page and hit “turn off heat.” 29. Flip the breaker on the side to the off position.
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XRF Major Element Analysis SOP
Major element analysis via X-Ray diffraction should be used as the first step in analytical geochemistry when examining a sample. It provides a general overview of the constituent elements of a rock or other sample, down to parts per million precision. It cannot be used to look for rare earth elements or trace elements, or any organic or volatile constituent elements. This procedure can be used for a variety of rock samples, or other kinds of samples, including glass beads, pellets, powders, and bulk samples.
1. Put on gloves. 2. XRF should have at least 3 lights on and the Helium tank to the left of the machine should be no lower than 500 psi. If the XRF is off, press “power” button on the front of the machine and then turn the key.
3. Open XRF and place one sample in each slot. **Make a diagram noting the position of each sample AND only touch the sides of your sample as fingerprints or other materials could corrupt the sample.***
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4. Close XRF and open Epsilon 3 Software found on the lab computer. If multiple errors windows pop up, restart the computer and reopen the software. If the problem persists, call the support phone number on the lower right sticker of the machine.
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5. Click “Measure>>Measure Omnian” on the top toolbar.
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6. Choose “Omnian” for Measure Omnian application.
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7. Enter Sample identification and choose “Omnian” for Calibration used for quantification.
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8. Under Processing Parameters to be applied: enter the type of sample to be tested (i.e. glass bead, pellet, or powder). If you are processing oxides, LOI will have to be entered as well, so that the machine can correctly calibrate the results.
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9. Under TAG used for quantification: select “General”. 10. It is possible to run more than one sample at a time, but generally each sample should be of the same type. Simply change the sample ID for each sample you wish to measure, and the machine will automatically cycle through them during the analysis. *Note that it may be possible to run different kinds of samples, but this can cause problems with calibration.
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11. Select “Measure>>Okay” and the XRF will lock, and begin its analysis. This takes about 15 minutes per sample and a window will show estimated time remaining and progress. 12. When it has finished its analysis, the XRF will unlock and you will carefully retrieve your samples and put them in their designated petri dishes or other container.
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13. To review results click on “Results>>Last result”
14. Results can then be copied into Microsoft Excel or other spreadsheet software.
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Spectroscopy Standard Operating Procedure Visual Derivative Spectroscopy on SPSS Standard Operating Procedure
by Diana Simone
Bryan Ice
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VPCA SOP on SPSS
1) Start by opening the Microsoft excel file that contains the data from the Konica Minolota. 2) Select data that you are concerned with, press “Ctrl + C” to copy the data onto the clipboard. 3) Paste this data onto a new sheet in Excel and name it 4) Save the excel workbook, no need to exit out of it 5) Open SPSS 6) Choose new spreadsheet 7) Click on File, Open, Data 8) Click on the drop down box for “files of type” and choose Excel
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9) Choose your workbook, hit “open”
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10) Use the drop down arrow for “Worksheet” to choose the data you want to analyze. Hit OK
11) Once your worksheet is copied over, click on Analyze – Dimension Reduction – Factor
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12) Choose which variables you want to analyze and click on the arrow in between the 2 boxes.
13) Click on the buttons on the left to set up the analysis. The following is a list of things that should be checked/chosen a. Descriptive: only Initial Solution should be checked b. Exctraction: i. Method: Principal component ii. Analyze: Correlation Matrix iii. Display: Unrotated factor Solution and Scree plot iv. Extract: Based on Eigenvalue grater than 1 v. Maximum iterations for convergence 25 vi. Hit Continue c. Rotation: i. Method: Varimax ii. Display: Rotated Solution and Loading plot(s) iii. Hit continue d. Scores: Display factor score coefficient matrix, hit continue e. Options: leave as defult f. Hit ok 14) SPSS will bring up another window with the analyzed data within it. 15) Cut and paste the graph, plot, rotated component matrix, and factors 1,2 &3 into a new excel worksheet.
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16) Starting with the 1st component, copy the data onto the worksheet that contains the library
17) Back in SPSS, open the library data onto a new page. 18) Once loaded, click Analyze-Regression- linear 19) The dependent variable should be the first principal component and the independent should be the spectral library. 20) Click on the buttons on the left to set up the test. Everything listed below should be checked/chosen. a. Stats: i. Estimates ii. Confidence intervals 95% iii. Model fit iv. R squared change v. Descriptives vi. Collinearity diagnositics vii. Hit continue b. Plots: i. *ZPRED=Y ii. DEPENDNT = X iii. Method: Stepwise iv. Histogram v. Normal probability plot vi. Produce all partial plots vii. Hit continue c. Save: i. Predicted values 1. Unstandardized
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ii. Residuals 1. Unstandardized iii. Prediction Intervals 1. Mean 95% 2. Individual iv. Hit continue d. Options: default e. Click OK 21) Data will process
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