Characterization of Rare Earth Minerals Extraction Flowsheet With

Characterization of Rare Earth Minerals Extraction Flowsheet with

X-ray Microanalysis at High Spatial Resolution

Chaoyi Teng

Department of Mining and Materials Engineering McGill University Montreal, Quebec, Canada August 2015

A Thesis Submitted to the Faculty of Graduate studies and Research in Partial Fulfilment of the Requirements for the Degree of Master of Engineering

©Chaoyi Teng, 2015 Abstract

Rare earth elements (REE) become increasingly significant for renewable energy devices and high-tech electronic products because of their large-scale popularization and applications.

Nechalacho rare earth deposit is an important REE source in the world and an ideal setting to investigate the rare earth minerals (REM) evolution. Qualitative description of mineralogy and quantitative measurement of composition and texture are both important for the optimization of mineral processing. However, most of the widely industrially applied techniques have the problems of low spatial resolution and global analysis, which result in the omission and misidentification of small features. The aim of this work is to characterize REM in Nechalacho deposit with high spatial resolution and high count rate SEM/EDS system and assisting to find the optimal extraction flowsheet.

In this work, two field emission SEM and one tungsten emission VP-SEM were utilized to characterize the composition and distribution of REM. X-ray microanalysis was achieved by the conventional silicon drift detector (SDD) with 80 mm2 collecting area located on the side of specimen and an annular SDD inserted below the objective lens. f-ratio method and phase map transformed from EDS elemental map were applied to acquire the compositional information and to characterize the REM distribution. Two methods (manual and automatic) of processing phase map were presented and discussed, demonstrating the phase changes through REM separation stages.

The high spatial resolution and high count rate of field emission SEM allow phase identification at a small scale. The annular detector differentiated Zr-bearing phases and Y-bearing phases at a micron-scale successfully. Tungsten emission VP-SEM allows the characterization on a sample without a good conductivity, simplifying the mineral sample preparation. The standardless f-ratio method is proved to be a more effective way to judge the sample homogeneity and can provide higher compositional contrast in a quantitative element map. Python scripts and

Aztec software were used to obtain phase map for evaluating the effectivity of each REM separation stage and determining the best fraction for recovering REM. Compared with the automatic software, Python script method is more accurate, reliable, and applicable for satisfying different requirements.

Résumé

Les terres rares sont au centre de la fabrication de produits technologique de pointe, tel que des batteries, turbines, et autres appareils électriques. Le gisement de terres rares Nechalago est une importante source pour l’approvisionnement mondial de ces éléments, ce qui en fait un candidat idéal pour l’étude de l’évolution géologique de tels gisements de minéraux de terres rares.

Décrire la composition et la texture cristallographique de ces minéraux de façons qualitative et quantitative est nécessaire à l’optimisation du procéder d’extraction métallurgique. Cependant, la plupart des techniques de caractérisation actuellement employé en contexte industriel sont limités en termes de résolution spatiale et d’analyse globales. Le but de ce travail est de caractériser les minéraux de terres rares du gisement de Nechalago avec une grande résolution spatial et avec un grand nombre de rayon X avec un system d’analyse dispersive en énergie dans un microscope

électronique à balayage, dans le but d’optimiser l’extraction de minéraux de valeurs.

Au cœur de ce travail, deux microscopes équipé de cannons a émission de champ à froid, et un microscope a pression variable équipé d’un canon à émission thermoïonique au tungstène et leurs détecteurs d’analyse dispersive en énergie ont été mis à contribution. Deux méthodes de traitement de données, manuelle et automatisée, ont été utilisées pour produire les cartes de phases présentées dans ce travail.

La haute résolution spatiale et la rapidité de collection de microscopes à balayage à champs froid permettent l’identification de phases minéralurgique de façon très localisée. Le détecteur annulaire différencie les phases contenant du zircon de celles contenant de l’yttrium. Le

III microscope à émission thermoïnique au tungstène et à pression variable permet la caractérisation chimique d’échantillons peu conducteurs, ce qui simplifie la préparation d’échantillons. La caractérisation sans standards par f-ratio est une méthode efficace pour évaluer l’homogénéité d’un

échantillon. Des scripts Python ainsi que le logiciel Aztec sont utilisés pour générer des cartes de phases. Ces cartes permettent d’évaluer la performance de chaque stade de la séparation des minéraux à terres rares et d’évaluer la meilleure route d’extraction. Le script Python est plus efficace, fiable et flexible pour différents usages.

Acknowledgement

I would like to thank my supervisor, Professor Raynald Gauvin, for his excellent guidance and consistent encouragement throughout this entire research project.

My special thanks go to Dr. Hendrix Demers for his constructive guidance and critical evaluation of my research. I would like to thank Nicolas Brodush for his technical assistance. I would also like to thank Professor Kristian Waters and Adam Jordens for their patient guidance for the mining processes. I gratefully appreciate the financial support provided by NSERC and

Avalon Rare Metals Inc. (CRDPJ-445372 – 12).

I am deeply grateful to my roommate, Jing Su, for her help with my study and life. I would like to extend my since thanks to all my colleagues and my friends, who made my journey at

McGill University really special and memorable.

Table of Contents

Abstract ...... I Résumé ...... III Acknowledgement ...... V Table of Contents ...... VI List of Figures ...... VIII List of Tables ...... XI 1 Introduction ...... 1 2 Literature Review ...... 3 2.1 Rare earth minerals in Nechalacho deposit ...... 3 2.1.1 Currently exploited rare earth minerals ...... 4 2.1.2 Metallurgical processing of Nechalacho deposit ores ...... 9 2.2 Characterization method for REM ...... 14 2.2.1 SEM ...... 15 2.2.2 EDS and WDS ...... 19 2.2.3 EBSD ...... 25 2.2.4 Quantitative X-ray analysis in mineral processing ...... 26 2.3 Summary ...... 33 3 Experimental Method ...... 34 3.1 Sample preparation ...... 34 3.1.1 Nechalacho ore sample ...... 34 3.1.2 Nechalacho powder samples ...... 34 3.2 Sample characterization ...... 35 3.2.1 SEM ...... 35 3.2.2 f-ratio method ...... 39 3.2.3 Phase map ...... 40 4 Characterization of Nechalacho Ores ...... 41 4.1 Identification of REM ...... 41

4.2 Standardless f-ratio method ...... 45 4.2.1 Sample homogeneity ...... 45 4.2.2 Recovery of contrast ...... 51 4.3 Characterization with high spatial resolution ...... 53 4.4 Summary ...... 60 5 Characterization of REM at Different Physical Separation Stages ...... 61 5.1 Gravity separation and magnetic separation ...... 61 5.2 Characterization with a FE-SEM ...... 64 5.2.1 Characterization of Fe bearing minerals ...... 65 5.2.2 Characterization of RE bearing minerals ...... 71 5.2.3 Characterization of liberation size ...... 80 5.3 Characterization with a VP-SEM ...... 83 5.4 Comparison of the two phase map methods...... 85 5.5 Summary ...... 87 6 Conclusion ...... 89 7 Bibliography ...... 91

VII

List of Figures

Figure 2-1 Typical comminution products [28] ...... 10 Figure 2-2 Schematic drawing of Falcon Concentrator [31] ...... 12 Figure 2-3 Schematic drawing of Knelson Concentrator [38] ...... 13 Figure 2-4 Schematic drawing of the typical SEM column [44] ...... 16 Figure 2-5 Signals generated when a high-energy beam of electrons interacts with a thin specimen [49] ...... 18 Figure 2-6 Characteristics of conventional EDS detectors (a) the definition of the solid angle; (b) the relationship between solid angle and active area of the detector; (c) the relationship between the accuracy, productivity and active area of the detector [44, 58, 59] ...... 22 Figure 2-7 Characteristics of annular EDS detector (a) Annular silicon drift detector [61]; (b) Schematic drawing of the annular detector position [44]; (c) Variation of solid angle with detector distance for the conventional SDD and the annular SDD [60] ...... 23 Figure 3-1 Hitachi SU8000 ...... 36 Figure 3-2 Hitachi SU8230 ...... 37 Figure 3-3 Hitachi SU3500 ...... 38 Figure 4-1 Qualitative X-ray microanalysis of Nechalacho ore with an accelerating voltage of 20 kV (a) low magnification BSE image; (b) high magnification BSE image of the chosen area in (a); (c)(e)(f) EDS spectra of the three locations shown in (b); (d) EDS spectrum of standard bastnäsite spectrum...... 43 Figure 4-2 Qualitative X-ray microanalysis of the bright phase with an accelerating voltage of 20 kV (a) BSE image; (b) EDS spectra of the five locations shown in (a)...... 46 Figure 4-3 Calculation of sample homogeneity. (a)-(b) Scatter diagraph of 푓푖 ± 3휎푓푖 of La L lines and Ce L lines detected in a standard bastnäsite sample; (c)-(d) Scatter diagraph of 푓푖 ± 3휎푓푖 of La L lines and Ce L lines detected at the five locations shown in Figure 4-2...... 47 Figure 4-4 Standardless Quantitative X-ray microanalysis of standard bastnäsite sample with an accelerating voltage of 20 kV...... 48 Figure 4-5 Standardless Quantitative X-ray microanalysis of the Nechalacho ore with an accelerating voltage of 20 kV...... 50 Figure 4-6 Standardless Quantitative X-ray microanalysis of the Nechalacho ore with an accelerating voltage of 15 kV...... 52 Figure 4-7 Qualitative X-ray microanalysis with an accelerating voltage of 20 kV (a) BSE image; (b)-(d) EDS spectra of the six locations shown in (a)...... 55 Figure 4-8 Interaction volume of incident beam simulated by Monte Carlo simulation at 20 kV in fergusonite (a) and zircon (b)...... 56 Figure 4-9 Qualitative X-ray SDD-EDS maps of Nechalacho ore sample at 20 kV. (a) BSE image; (b) EDS qualitative map of Y L lines; (c) EDS qualitative map of Zr L lines...... 56

VIII

Figure 4-10 Qualitative X-ray EDS maps of zircon and fergusonite at 20, 10 and 5 kV obtained with an annular detector...... 57 Figure 4-11 Interaction volume of incident beam simulated by Monte Carlo simulation. (a) fergusonite at 5 kV; (b) fergusonite at 10 kV; (c) zircon at 5 kV; (d) zircon at 10 kV...... 58 Figure 5-1 Flowsheet of main gravity and magnetic separation steps applied to the Nechalacho ore. P80 of 40 μm means that passing size of 80 wt. % feed is 40 μm; KC Mag is labeled for the products selected by the low intensity magnetic separation stage from Knelson concentrate; FC Mag for the products selected by the low intensity magnetic separation stage from Falcon concentrate; KC RE Mag for the products selected by the medium intensity magnetic separation stage from Knelson concentrate; FC RE Mag for the products selected by the medium intensity magnetic separation stage from Falcon concentrate; KC Non-Mag for the non-magnetic fraction from Knelson concentrate; FC Non-Mag for the non-magnetic fraction from Falcon concentrate...... 62 Figure 5-2 Standerdless Quantitative X-ray microanalysis for iron bearing phases of the magnetic separation products from Knelson concentrate from Nechalacho deposit with an accelerating voltage of 15 kV. The left column displays BSE images; the middle column displays EDS standardless element maps; the right column displays phase maps...... 65 Figure 5-3 Standerdless Quantitative X-ray microanalysis for iron bearing phases of the magnetic separation products from Falcon concentrate from Nechalacho deposit with an accelerating voltage of 15 kV. The left column displays BSE images; the middle column displays EDS standardless element maps; the right column displays phase maps...... 66 Figure 5-4 Area fraction of Fe-oxide phases in the products at different separation stages calculated according to the phase maps displayed in Figure 5-2 and Figure 5-3...... 70 Figure 5-5 Phase map of main phases (the left column) and RE phases (the middle column displays the RE phases (yellow) and non-RE phases (blue); the right column displays different RE phases) of the magnetic separation products from Knelson concentrate...... 72 Figure 5-6 Phase map of main phases (the left column) and RE phases (the middle column displays the RE phases (yellow) and non-RE phases (blue); the right column displays different RE phases) of the magnetic separation products from Falcon concentrate...... 73 Figure 5-7 Area fraction of RE phases in the products at different separation stages calculated according to the phase map displayed in the middle column in Figure 5-4...... 75 Figure 5-8 Area fraction of LREM and fergusonite in the products at different separation stages calculated according to the phase map displayed in the right column in Figure 5-4...... 75 Figure 5-9 Phase map of fergusonite and zircon of the magnetic separation products from Knelson concentrate. The left column displays the two phases together, and the other two columns display the two phases separately...... 77 Figure 5-10 Phase map of fergusonite and zircon of the magnetic separation products from Falcon concentrate. The left column displays the two phases together, and the other two columns display the two phases separately...... 78 Figure 5-11 Area fraction of fergusonite and zircon in the products at different separation stages calculated according to the phase map in Figure 5-9 and Figure 5-10...... 79

Figure 5-12 SEM images and phase maps of three kinds of particles with different liberated classes (a) unliberted REM; (b) mid-liberted REM; (c) liberated REM...... 81 Figure 5-13 Phase maps layered with the BSE images of the products at different separation stages from Nechalacho deposit with a VP-SEM. The yellow phases refer to iron bearing phases; blue phases refer to zircon; red phases refer to LREM; purple phases refer to fergusonite...... 84 Figure 5-14 Comparison of the two phase map methods. (a) BSE image of the KC Non-MAG fraction acquired with a VP-SEM; (b) phase map produced by AZtec software; (c) phase map of main phases calculated by Python scripts. The yellow phases refer to iron bearing phases; blue phases refer to zircon; red phases refer to LREM; purple phases refer to fergusonite...... 86

List of Tables

Table 2-1 Main ore minerals present in Nechalacho deposit deposit [6] ...... 5 Table 2-2 Ore minerals present in Nechalacho deposit [4] ...... 6 Table 2-3 Three types of fergusonite [1, 18] ...... 8 Table 2-4 Comparison of Falcon and Knelson Concentrator [35, 36] ...... 11 Table 2-5 Comparison of electron sources at 20 kV [44] ...... 17 Table 2-6 Comparison between EDS and WDS techniques [44] ...... 20 Table 2-7 Standardless X-ray microanalytical accuracy of SEM/EDS-SDD system [57] ...... 31

1 Introduction

Rare earth elements (REE) refer to the fifteen lanthanide elements and yttrium, which were found in more than 250 different minerals [1]. The demand for these elements keeps continuously rising because of their large-scale applications in renewable energy devices and high-technology electronic fields, urging to seek techniques for evaluating potential REE reserve and to optimize the extraction process. Even though there is a proven reserve of about one hundred million tons of rare earth oxide in the world, only a few minerals are sufficiently concentrated to constitute economic deposits [2]. Under the present situation, Nechalacho deposit is an important REE source and an ideal setting to investigate the rare earth minerals (REM) evolution.

A variety of techniques have been developed to provide mineralogical data and are widely applied in the industry. Among the techniques, the quick and accurate analysis with SEM/EDS system has become a general-purpose instrument for element analysis and chemical characterization of minerals. Combined with softwares for acquiring and analyzing data, those techiniques are fully automated, but strongly dependent on the database, cause them not able to deal with unexpected situations and to satisfy different requests flexibly. Most quantitative evaluation is achieved by automated SEM (QEMSCAN) and Mineral liberation analyzer (MLA), but most the results have the problems of low spatial resolution and global analysis, which result in the omission and misidentification of small features. And long acquisition time is required due to the low collecting count rate during the X-ray microanalysis. The aim of this work is to

1 characterize REM in Nechalacho deposit with high spatial resolution and high count rate

SEM/EDS system, assisting to find the optimal extraction flowsheet.

In this work, three scanning electron microscopies were utilised, playing different roles in characterization REM. EDS analysis, phase map, and f-ratio intensity map were applied to acquire compositional information of the REM. A literature review on REM exploited in Nechalacho deposit and the widely applied characterization methods are presented in Chapter 2. Materials and experimental methods are described in Chapter 3. The characterization of a bulk Nechalacho ore is presented in Chapter 4. The main minerals were identified and the associations of adjacent phases were discussed. A high spatial resolution was achieved by an annular EDS silicon drift detector (SDD). f-ratio method was applied to calculate phase homogeneity and recovery contrast shown in EDS map. The characterization of products from different REM beneficent stages is shown in Chapter 5. A field emission SEM and a tungsten emission SEM were utilised, and two methods of obtaining phase map were discussed.

2 Literature Review

The demand for rare earth elements (REE) keeps continuously rising because of their significance in the electronic and electrical fields, urging to seek techniques to evaluate the potential REE reserve and to optimize the extraction process. The Nechalacho deposit is an important REE source in the world, and a variety of rare earth minerals (REM) are found in this deposit. In this work, the currently exploited REM and their common metallurgical processing is reviewed. Additionally, qualitative and quantitative X-ray microanalytical techniques based on

SEM will be discussed and compared.

2.1 Rare earth minerals in Nechalacho deposit

Rare earth elements refer to the fifteen lanthanide elements and yttrium, which are found in more than 250 different minerals [1]. They are divided into two groups, the light rare earth elements (LREE) (atomic number 57-64) and the heavy rare earth elements (HREE) (atomic number 65-71 and yttrium). Because of their chemical similarity, the efficient separation processes were not developed until the 20th century, when all the REE were finally identified [3]. Recently, the demand for REE is continuously rising because of their large-scale popularity and applications in renewable energy devices and high-technology electronic fields. Even though there is a proven reserve of about one hundred million tons of rare earth oxide in the world, only a few minerals are sufficiently concentrated to constitute economic deposits [2]. Because of the extraction difficulties

3 and the increasing applications of these elements, efficient analytical method are needed to evaluate the potential REE reserve and to optimize the extraction process.

2.1.1 Currently exploited rare earth minerals

The Thor Lake REE mine in Canada’s Northwest Territories is regarded as one the largest resources of HREE. Nechalacho deposit, which is located at the Thor Lake, is an ideal setting to investigate the evolution process of REE [4]. Rare earth minerals (REM) are difficult to exploit because of their complexity and high variability in chemical composition. There is a vast array of minerals found in this deposit, but only a few contain large rare earth concentration [5]. The objective of exploiting Nechalacho deposit is to beneficiate REM from gangues and to purify individual rare earth oxides form REM. The mineralization of this deposit is dominated by nepheline syenite, which is largely consisted of nepheline and alkali feldspar through extensively hydrothermal alteration. It has been indicated that the LREE are dominantly carried by monazite, allanite, bastnäsite, and synchysite; HREE and zirconium (Zr) mainly occur in zircon, fergusonite and rare xenotime [4, 6, 7]. Niobium (Nb) and tantalum (Ta) mainly exist in columbite

(ferrocolumbite), fergusonite, and zircon[6]. REE, Nb and Ta are more possible to occur in the areas which are rich in magnetite or zircon [6, 8, 9]. The main REM found in this deposit are listed in Table 2-1[6], and the complete list of minerals identified are presented in Table 2-2 [4]. Among the minerals, bastnäsite, fergusonite, and zircon are the three commonly extracted REM in this deposit.

Light Rare Earth-bearing Minerals Monazite-(Ce) (REE,Th)PO4 2+, 3+ Allanite-(Ce) Ca(REE,Ca)Al2(Fe Fe )(SiO2O7)O(OH) Bastnäsite-(Ce) REE(CO3)F Synchisite/Parissite-(Ce) Ca(REE)2(CO3)3F2 Heavy Rare Earth-bearing Minerals Zircon Zr(HREE)SiO4 Fergusonite-(Y) Y(HREE)(Nb,Ta)O4 Other Potential Ore Minerals Columbite (Nb,Ta)2O6

Table 2-1 Main ore minerals present in Nechalacho deposit deposit [6]

Silicates Aegirine NaFe3+(Si2O6) Aenigmatite Na2Fe2+5TiSi6O20 Albite Na(AlSi3O8) Allanite Ca(La,Y,Ce)(Al2Fe2+)Si3O12 Analcime NaAl(Si2O6).(H2O) Annite KFe2+3AlSi3O10(OH)1.5F0.5 Barkevikite Ca2(Fe,Mg,Al)-5(Si,Al)8O22(OH)2 Biotite K2(Mg,Fe2+)6-4(Fe3+,Al,Ti)0-2[Si6-5Al2-3O20](OH,F)4 Britholite Ca2[(Y,Ce)Ca]3[(OH,F)(SiO4,PO4)3] Catapleiite Na2Zr(Si3O9).2H2O Cerianite (Ce4+,Th)O2 Chamosite (Fe2+,Mg)5Al[(OH)8AlSi3O10] Chlorite (Mg,Fe2+,Fe3+,Mn,Al)12[(Si,Al)8O20](OH)16 Eudialyte Na15Ca6(Fe2+,Mn2+)3Zr3(Si25O73)(O,OH,H2O)3(OH,Cl)2 Ferro-bustamite Ca(Fe2+,Ca,Mn2+)Si2O6 Synchysite Ca(Y,Ce,La,Nd,Gd)[F(CO3)2] Ferroricherite Na[Ca,Na][Fe2+5][(OH)2Si8O22] Gittinsite CaZrSi2O7 Lepidolite K(Li,Al)3(Si,Al)4O10(OH,F)2 Mesolite Na2Ca2(Al2Si2O10)3.8H2O Natrolite Na2(Al2Si3O10).2H2O Oneillite Na15Ca3Mn3Fe2+3Zr3Nb(Si25O73)(O,OH,H2O)3(OH,Cl)2 Orthoclase K(AlSi3O8) Pectolite NaCa2(HSi3O9) Quartz SiO2 Scapolite Na4[Cl(AlSi3O8)3] – Ca4[CO3(Al2Si2O8)3] Sericite K2Al4(Si6Al2O20)(OH,F)4 Sodalite Na8(Al6Si6O24)Cl2 Thorite (Th,U)SiO4 Topaz Al2SiO4(OH,F)2 Uranothorite (Th,U)SiO4 Willemite ZnSiO4 Wollastonite CaSiO3

Zinnwaldite K(Li,Fe,Al)3(Si,Al)4O10(OH)F ZrSiO4 Zircon ZrSiO4 Sulfides Barite BaSO4 Chalcopyrite CuFeS2 Molybdenite MoS2 Pyrite FeS2 Pyrrhotite Fe7S8 Sphalerite ZnS Halides Fluorite CaF2 Phosphates Apatite Ca5(PO4)3(OH,F,Cl) Monazite (Ce,La,Th)PO4 Carbonates Ankerite Ca(Mg,Fe2+,Mn)(CO3)2 Bastnäsite (Ce,La,Y)F(CO3) Calcite CaCO3 Dolomite CaMg(CO3)2 Kutnahorite Ca(Mn,Mg,Fe)(CO3)2 Lanthanite (Ce,La,Nd)2(CO3)38H2O Parisite Ca(Ce,La)2(CO3)3F2 Siderite FeCO3 Oxides Aeschynite (Ce,Nd,Y,Ca,Fe,Th)(Ti,Nb)2(O,OH)6 Ashanite (Nb,Ta,U,Fe,Mn)4O8 Betafite (Ca,U)2(Ti,Nb)2O6(OH) Cassiterite SnO2 Ceriopyrochlore (Ce,Ca,Y)2(Nb,Ta)2O6(OH,F) Columbite (Fe,Mn)(Nb,Ta)2O6 Columbo-tantalite (Fe,Mn)(Nb,Ta)2O6 Fergusonite (Ce,La,Nd,Y)NbO4 Ferrocolumbite Fe++Nb2O6 Hematite Fe2O3 Ilmenite Fe2+TiO3 Ixiolite (Ta,Nb,Sn,Fe,Mn)4O8 Limonite FeO.OH.nH2O Magnetite Fe2+Fe23+O4 Nioboaeschynite (Y,Ca,Ce,Nd,Th)(Nb,Ta,Ta)2(O,OH)6 Polycrase (Y,Ca,Ce,U,Th)(Ti,Nb,Ta)2O6 Samarskite (Y,Ce,U,Fe,Nb)(Nb,Ta,Ti)O4 Specularite Fe2O3 Titanomagnetite Fe(Fe,Ti)2O4 Uraninite UO2 Yttrocolumbite (Y,U,Fe++)(Nb,Ta)O4

Table 2-2 Ore minerals present in Nechalacho deposit [4]

Bastnäsite

Bastnäsite is a carbonate-fluoride mineral, including bastnäsite-(Ce) ((Ce, La)CO3F), bastnäsite-(La) ((La, Ce)CO3F) and bastnäsite-(Y) ((Y, Ce)CO3F). In bastnäsite, rare earth oxides accounts for 75% weight fraction, and light rare earth oxides are the majority [10, 11]. Ce is the most common rare earth and bastnäsite-(Ce) is the primary mineral among the three types.

Bastnäsite was first described in 1838 and was well known since a large carbonatite deposit was found in 1949 in Mountain Pass, California [12]. Now, bastnäsite has been regarded as the primary source of rare earth in the world, mainly deposited in Baotou, Miannin, Weishan in China and

Mountain Pass in the United States [10]. In the world’s largest rare earth deposit, Bayan Obo in

Baotou, bastnäsite is associated with iron (Fe) bearing ore [1]. The major source of rare earth is the tailings produced by selecting iron bearing minerals. However, in the Mountain Pass carbonatite bastnasite deposit, bastnasite associates with ultrapotassic alkaline igneous rocks, which have a relative low content of iron [13, 14].

Fergusonite

Fergusonite was first identified as the mineral comprising of various rare earth oxides with the dominant components of yttrium and niobium by Haidinger in 1826. However, because of their complexity, systematic studies were not conducted until the chemical variations in fergusonite-formanite series were discussed [15]. From then, fergusonite refers to the

Niobium(Nb)-rich mineral in the fergusonite-formanite series, with the chemical formula defined

7 as ((Y,REE)NbO4) [16]. Fergusonite-(Y) (YNbO4), fergusonite-(Ce) ((Ce,La,Y)NbO4) and fergusonite-(Nd) ((Nd,Ce)(Nb,Ti)O4) are the three main types (listed in Table 2-3), and all of them have the tetragonal-dipyramidal structure, where Yttrium and REE can substitute for each other in the solid solution [17]. In Nechalacho deposit, zircon and fergusonite-(Y) are regarded as the main carriers for HREE, and ferrocolumbite and fergusonite-(Y) are the main hosts for Nb [4, 8].

Mineral name Chemical formula Density (g/cm3) Magnetic Crystal properties system Fergusonite-(Y) YNbO4 5.60-5.80 Tetragonal- Fergusonite-(Ce) (Ce, La, Y)NbO4 5.45-5.48 Paramagnetic dipyramidal Fergusonite-(Nd) (Nd, Ce)(Nb, Ti)O4 n/a

Table 2-3 Three types of fergusonite [1, 18]

Zircon

Zircon occurs in the earth crust ubiquitously as the major mineral in igneous, metamorphic and sedimentary rocks [19, 20]. Its chemical formula is defined as ZrSiO4, and a common empirical formula ((Zr1–y, REEy)(SiO4)1–x(OH)4x–y) is defined to show its substitution range and variation

[21]. Besides REE, it is also the major host for U, Th, and Hf [9, 22, 23]. As the REE carrier, zircon prefers to carry HREE because the radius of Zr4+ ion is more similar with the smaller-radii of HREE than the larger-radii of LREE [24]. As the most common HREE minerals in Nechalacho deposit, numerous detailed studies have been conducted about zircon. Sheard et al. and Hoshino et al. both studied the zircon grains in different drill holes, and developed different classifications according to their chemical and structural properties [4, 8, 25]. The relations between zircon and

8 fergusonite were stated that the interstices between zircon grains were filled with fergusonite, and the proportion of fergusonite increases with the progress of hydrothermal alteration [8].

2.1.2 Metallurgical processing of Nechalacho deposit ores

For the exploitation of REM, firstly the ores are ground to liberate the desired REM, then followed by a series of separation stages to remove impurities and enrich REM content to a satisfying level [26]. The fraction selected by the separation stages contains high grade of REE, and will be used as the feed for froth flotation to produce a RE concentrate. The beneficiation of

REM mainly includes gravity separation, magnetic separation, electrostatic separation and froth flotation. Here, a brief review of gravity and magnetic separation is presented, and a more detailed summary of the literatures on REM beneficiation techniques should consult Jorden’s review [1].

Liberation

Mineral liberation aims to separate the valuable minerals from gangue minerals by crushing, grinding and classification [27]. Figure 2-1 shows the typical comminution process illustrated in

Rao’s work, where the locked black phases are liberated by crushing the large ore into small particles [28]. It is an important stage for concentrating and separating the target minerals with a satisfying degree of purity. The liberation degree of a certain mineral, which depends on the comminution size, is an area percentage of this mineral occurring as free particles to the total of

9 this mineral occurring in any forms [5]. For designing and optimising mineral processing, the liberation size was introduced based on the minerals textural relationships within an ore. The size does not refer to liberate a pure mineral piece, but separate the value minerals from gangue with an acceptable commercial efficiency [29].

Figure 2-1 Typical comminution products [28]

Gravity separation

Gravity separation which has been continually developed over the last 25 years, is one of the oldest techniques for separating minerals according to the specific gravities. REM usually have larger gravities compared with their associated silicate gangue [30, 31]. The separation operations should be chosen according to the specific mineralogy of different deposit. In Bayan Obo deposit in China, bastnäsite and monazite have been concentrated by gravity separation between a rougher and cleaner flotation [1, 32].By this series of steps, the iron bearing minerals and silicate gangues are removed. In Turkish deposits, lab-scale multi-gravity separations have been utilized for concentrating REM[10]. Several types of gravity separation techniques were discussed in

Falconer’s work [31]. Among the various techniques, centrifugal gravity separators were regarded

10 as the most successful types for fine particles. The high specific gravity minerals are trapped by the centrifugal forces and the light specific gravity minerals are carried away by the flowing fluid as the tailings. As the main fine particle centrifugal separators, Falcon and Knelson gravity separators were used to concentrate fine REM [33, 34]. The main advantages and disadvantages of batch and continuous Falcon Concentrator, and batch Knelson Concentrator are listed in Table

2-4.

Batch Batch Continuous Unit Knelson Falcon Falcon Cost Moderate High Very High Automatic discharge Available Available Essential Ability to recover dense Good but Very good below Not known minerals (ρ=5-7) overloads easily 75 μm Excellent down to about Very good up to Probably like the Ability to recover gold 15 μm on non-flakes 37 μm batch unit

Table 2-4 Comparison of Falcon and Knelson Concentrator [35, 36]

In 1980s, Steve McAlister developed Falcon Concentrator, which consisted of a sluice and a centrifuge [37]. The centrifuge keeps continuously (continuous Falcon Concentrator) or periodically (batch Falcon Concentrator) operating at a high speed of rotation, enabling separating fine particles with different specific gravities [31]. The separation force can be adjusted by the rotation speed: the higher rotation speed the stronger separation force. There is a maximum for the feed rate and density, beyond which the separation would be hindered. The heavier particles remain in the bottom of slurry stream, and the light particles tend to move to the top of slurry. Then the lower portion are collected as the concentrate and the upper portion are discharged as tails, as

11 shown in Figure 2-2. Falcon Concentrator has the advantages of high capacity for fine particles

(particle size down to 15-20 μm) and low operator attention [31].

Figure 2-2 Schematic drawing of Falcon Concentrator [31]

The first Knelson Concentrator was manufactured by Byron Knelson in 1980 [38]. After years’ development, it has become one of the most common centrifugal separators [33]. It consists of a “V” shaped concentrate bowl and a pressurized water jacket around the bowl. The concentrate bowl can rotate at high speed resulting in the separation of particles with different gravities. The feed minerals are fed as a slurry to the centre of the bowl. In the inner bowl, there is a series of grooves along the perimeter. During the operation, the lighter particles tend to move upward and the heavier particles are trapped in the grooves. The pressurized water is periodically injected into

12 the bowl, carrying away and concentrating the heavier mineral particles. Knelson Concentrator is operated as a batch process, but water keeps overflowing, discharging the lighter particles continuously. The schematic drawing of Knelson Concentrator is illustrated in Figure 2-3. Knelson

Concentrator has been successfully applied to concentrate the high specific gravity minerals which have low concentrations [39]. However, the efficiency of the concentration reduces with the decrease of feed particle size [36].

Figure 2-3 Schematic drawing of Knelson Concentrator [38]

Magnetic separation

The conventional magnetic separation was designed to remove the strong ferromagnetic minerals. With years of development, varieties of magnetic separators were invited, and the high gradient magnetic separation devices were developed for the weak magnetic minerals in small particle size [40, 41]. There are three different magnetic behaviours exhibited by minerals: ferromagnetic, paramagnetic and diamagnetic. Ferromagnetic and paramagnetic mineral particles will be attracted along the applied magnetic field lines, whereas diamagnetic mineral particles will be repelled. Normally, most iron bearing minerals are ferromagnetic, and the minerals containing nickel, cobalt, and platinum prefer to be paramagnetic [18]. Even though some of REE are applied in manufacturing strong magnets, most REM are only moderately paramagnetic [18]. Magnetic separation stage is necessary for REM beneficiation, because during the froth flotation, the selective collectors can recover not only REM, but also the ferromagnetic minerals [42, 43]. So before froth flotation stage, the strongly ferromagnetic iron bearing minerals should be removed.

2.2 Characterization method for REM

The demand for REE keeps continuously rising because of their significance in the electronic and electrical fields, urging to seek the techniques to collect the detailed characteristics of REM. Qualitative description of mineralogy and quantitative measurement of composition and texture are both important for the optimisation of mineral processing. A variety of instruments have been developed to provide reliable mineralogical data, and widely applied in the industry.

This work emphasizes on the analytical systems developed with SEM, and compares the advantages and disadvantages of different techniques.

2.2.1 SEM

The first “scanning microscope” was built in 1935, providing the resolution of around 100

μm because of no demagnifying lenses. In 1938, Manfred von Ardenne invented the first true scanning electron microscope, which was oriented to observe the sample surface. The first SEM used to examine thick specimens was developed by Zworykin in 1942. He pointed that secondary electrons could be collected to characterize topographic contrast. The interrelationship of lens aberrations, gun brightness, beam current and spot size was described and discussed. The main contribution of Zworykin’s group was to improve the resolution to 50 nm [44-46]. During the

1960s and 1970s, SEM was developed into a commercially usable instrument with a serious of improvement in detectors and image quality [47].

Figure 2-4 shows the typical SEM column and electron gun, lenses, deflection system, and electron detector. A dry, conductive specimen is required by the high vacuum in the column. The condenser lenses and the objective lens are used to demagnify the electron beam and focus the beam onto the specimen. The apertures limit the convergence angle of beam and make the electrons concentrate to reduce the beam diameter. Many studies show that the smallest electron beam diameter and the highest current density produce the best results.

Figure 2-4 Schematic drawing of the typical SEM column [44]

There are different kinds of electron source, such as tungsten, lanthanum hexaboride (LaB6) and field emission electron guns. The comparison of the different electron guns are listed in Table

2-5. Tungsten and LaB6 electron guns are both thermionic emission, hearted by the filament hearting current. LaB6 electron gun has about 5-10 times more brightness and a longer lifetime than tungsten [48]. Even though the thermionic emission guns are inexpensive and don't require special vacuum, they provide low brightness, short lifetime and large energy spread. By contrast, field emission electron guns have more advantages in these respects. Among the various field emitter, cold field emitter (CFE) is used in SEM. This electron source provides small source size

16 of 3-5 nm, small energy spread, and long lifetime of years, but the atomically clean of emitter surface and a high vacuum level of 10-8 – 10-9 Pa are required [44].

Source Brightness* Lifetime Source size Energy spread Beam current (A/cm2 sr) (h) ∆퐄 (퐞퐕) stability (%/h) Tungsten 105 40-100 30 -100 μm 1-3 1 6 LaB6 10 200-1000 5 – 50 μm 1-2 1 Field emission Cold 108 >1000 < 5 nm 0.3 5 Thermal 108 >1000 < 5 nm 1 5 Schottky 108 >1000 15- 30 nm 0.3- 1.0 ~ 1 * Brightness is defined as the beam current per area per solid angle.

Table 2-5 Comparison of electron sources at 20 kV [44]

When the electron beam interacts with the specimen, different signals are generated (as shown in Figure 2-5) [49]. The direction of each signal indicated is just the relative manner where the signal is detected, not the real direction. The secondary electron (SE) is the electron with an energy less than 50 eV generated by the excited atoms. Because of the low energy, this signal gives more information about the specimen surface and has strong relation with topographic contrast.

Backscattered electron (BSE) is the incident electron which travels back to the specimen surface.

It usually has a high energy and strongly depends on the average atomic number of the material.

The function between BSE signal intensity and specimen average atomic number can be used to identify minerals. Characteristic X-ray is the basic signal for X-ray microanalysis, which is a more reliable method to identify elements relative to BSE signal. Each element has a unique atomic structure producing X-ray lines with certain energy, which allows the element identified by its spectrum.

Figure 2-5 Signals generated when a high-energy beam of electrons interacts with a thin specimen [49]

The beam current 푖퐵 flows into the specimen, then flows out as the secondary electrons

푖푆퐸 and the backscattered electrons 푖퐵푆퐸. There is a current of the remaining electrons 푖푆퐶 flowing from the specimen to ground (the specimen stage is regarded as ground) to avoid the accumulation of negative charge. The balance of the current through the specimen is given by equation (2-1) and

(2-2) [44]

∑ 푖푖푛 = ∑ 푖표푢푡 (2-1)

푖퐵 = 푖퐵푆퐸 + 푖푆퐸 + 푖푆퐶 (2-2)

But if the specimen is nonconducting, the path for 푖푆퐶 from the specimen to ground is broken, resulting in the accumulation of electrons in the specimen. So the specimen will have a negative electrical charge relative to ground, causing the reflection of electron beam before reaching the specimen surface. This phenomena is referred as “charging”, which highly degrades the SEM images.

There are several methods preventing charging phenomena, such as conductive coating and low-vacuum in the chamber. Vacuum evaporation coating method is widely used to coat samples with thin carbon layers, which is regarded as one of the best coatings. However, if carbon is the analyzed target element, the coating will cause confusion due to introduce other carbon source. In this case, low-vacuum method prefer to be used by a VP-SEM. The positive ions generated by the interaction between electron beam and gas in the chamber (water vapor, nitrogen, or air), neutralizing the negative charge of the specimen surface [50]. This method allows characterization of almost any specimen, since the high-vacuum is no longer required. But the contrast and resolution will be degraded because of the signals generated from both the specimen and the gas molecules [50].

SEM is one of the most widely-used characterization techniques for chemical and physical properties of minerals. The simple sample preparation and high image quality make it become the basic component for qualitative and quantitative analysis system. In terms of minerals, powders dispensed in different binders and polished or unpolished bulk samples are all available for SEM characterization. Although minerals usually do not have a good conductivity, the charging effect can be avoided easily.

2.2.2 EDS and WDS

SEM is one of the most common surface characterization and component analysis method in the field of mining when combined with X-ray microanalysis techniques. Energy dispersive

19 spectrometer (EDS) and wavelength dispersive spectrometry (WDS) are widely used as the X-ray microanalytical techniques for chemical composition of minerals. SEM/EDS or SEM/WDS systems were used to analyze mineral grains with diameters longer than 0.5-1um, but now the two systems have been developed to a lower detection limits [51]. The block or powder samples, no matter polished or not can be utilised, contributing the main advantage of the simple sample preparation of the two systems.

Operating characteristic WDS (crystal diffraction) EDS (silicon, energy- dispersive) Detectable elements Variable, <30% ∼100% for 2–16 keV Detects Z ≥ 4 Detects Z ≥ 10 (Be window); Detects Z ≥ 4 (windowless or thin window) Resolution Crystal-dependent (5 eV) Energy-dependent (130 eV at 5.9 keV) Maximum count rate 50,000 on an X-ray line Resolution-dependent, <4000 (counts/s) over full spectrum for best resolution Minimum useful probe ∼200 ∼5 size (nm) Data acquisition time Tens of minutes Minutes Spectral artifacts Rare Major ones include escape peaks, pulse pileup, electron- beam scattering, peak overlap, and window absorption effects

Table 2-6 Comparison between EDS and WDS techniques [44]

WDS can provide higher peak to background ratio and better spectral resolution, but the analysis process is time-consuming and the interferences effect is unavoidable [52]. For a long time, WDS is the preferred technique for detecting trace elements, even though it is more expensive, time-consuming, and difficult to use relative to EDS [53]. EDS is able to acquire spectra

20 covering large energy ranges at once, but it is only reliable and valid for the elements with high concentration. The energy resolution limits its application for analyzing trace elements [54].

However, recent research indicated that EDS with a silicon drift detector (SDD) can get the similar satisfying results with shorter analysis time and less cost [53]. And more improvements applied to

EDS provide a similar accuracy and limit of detection with WDS in qualitative and quantitative analysis [52, 55, 56]. Table 2-6 summaries the comparison between EDS and WDS techniques

[44].

Silicon drift detector (SDD) refers to an advanced detector for EDS with high count rate and excellent energy resolution. It converts the energy of each X-ray into a voltage signal, minimising electronic noise to detect low X-ray energies. The solid angle Ω is used to describe the size of detector and determine the input count rate, as shown in Figure 2-6(a). The definition of solid detector is “ the ratio of the area of the face of the detector to the square of the radial distance to the beam impact point”, described as the equation (2.3) [44]

퐴 훺 = (2-3) 푟2

(the unit of solid angle is the steradian (sr); A: the area of the face of the detector; r: the radial distance from the face of the detector to the beam impact point). The larger solid angle of EDS relative to WDS can greatly improve detection efficiency, which lowers the required dose compared with WDS [57]. According to the equation, either increasing the detector collecting area or decreasing the distance between the detector and sample can enlarge the solid angle and increase the count rate. Figure 2-6(b) illustrates the relationship between solid angle and detector collecting

21 area visually. When the detectors are posed at the same position, the larger collecting area contributes to larger solid angle, which allows an acquisition of larger amounts of data at low accelerating voltage in a short time. Figure 2-6(c) illustrated the relationship between the accuracy, productivity and active area of the detector [58, 59]. In the figure, beam current (Y axis) is used to stand for the accuracy of measurement, and count rate (X axis) stands for productivity of the detector. Enlarging the detector active area can improve both the accuracy and productivity.

(a) (b)

(c)

Figure 2-6 Characteristics of conventional EDS detectors (a) the definition of the solid angle; (b) the relationship between solid angle and active area of the detector; (c) the relationship between the accuracy, productivity and active area of the detector [44, 58, 59]

(a) (b)

(c)

Figure 2-7 Characteristics of annular EDS detector (a) Annular silicon drift detector [61]; (b) Schematic drawing of the annular detector position [44]; (c) Variation of solid angle with detector distance for the conventional SDD and the annular SDD [60]

Different from the conventional SDD posed on the side of the chamber, the annular SDD is inserted below the polepiece of the objective lens, as illustrated in Figure 2-7(a) and (b). Such geometry minimized the distance between the detector and the specimen. A low detector-specimen distance contribute to a high solid angle and high count rate. Figure 2-7(c) compares the variation of solid angle between the conventional SDD and the annular SDD [60]. The collecting area for

23 the annular detector utilised is 60 mm2. For the annular detector, the maximum solid angle of 1.35 sr was achieved at the detector-specimen of 1.5 mm. however, for the conventional detector, the largest solid angle for the detector with 150 mm2 collecting area was only 0.9 sr at its minimum detector-specimen distance of 40 mm. Additionally, the geometry of annular detector allows shorter working distance, which gives a smaller beam diameter [44].

It is difficult to analyze REE-bearing minerals because these elements have similar atomic numbers and their X-ray lines overlap. Even though Malz et al. insisted that the better way is to choose lines with negligible interferences, many approaches to resolve the problems of overlaps in X-ray spectra has been developed [62]. Ziebold and Ogilvie fit empirical calibration curves for electron microanalysis and presented the fitting function [63]. Bence and Albee expended such calibration method to the multicomponent systems, and determined the empirical correction factors for REE oxides [64]. Albee and Ray calculated the correction parameters for 36 elements exciting in silicates, oxides, carbonates, phosphates and sulfates theoretically [65]. Amli et al. determined the empirical correction factors in the rare earth oxide system. The factors were used to analyze several REE bearing minerals, which provided better results than use the theoretical factors [66]. Roeder et al. used the relative intensities of Lα1,Lβ1, Lβ2, Lβ3and Lγ1 lines of ten

REE phosphates to calculate peak-overlap corrections for the Lα1 and Lβ1 lines of REE [67].

Donovan and Snyder proposed a method to identify the hierarchy of all interference with shorter time and less sensitivity [68]. Fialin and Outrequin reported a new method to improve the minimum detection limit to better than 0.1 wt. % [69]. With SEM/EDS-SDD system, the limits of detection as low as 0.0005 mass fraction for most elements can be achieved even though there was the strong interferences [57]. With the developments of instruments and the correction of spectra,

SEM/EDS system can obtain the results with short processing time, low cost, high accuracy and low detect limitation, contributing EDS to be the principle technique for characterizing REM.

2.2.3 EBSD

Electron Backscattered Diffraction (EBSD) is a powerful technique to analyze crystallographic orientation and texture materials by detecting the backscattered electrons. This technique is achieved by combining SEM system with an EBSD detector. Furthermore, EBSD system can be integrated with EDS or WDS system, giving rise to a more powerful characterization method for analysis microstructure and other properties of the specimen, collecting the chemical information and crystallographic information at the same time [70, 71]. It is useful to identify the different phases containing the similar chemical composition. For example, austenite and ferrite have similar composition, which cannot be distinguished only by EDS or WDS. However, EBSD successfully told them apart by indexing their different crystallographic structure [54]. EBSD is also a powerful technique to provide the information about mineral magnetic properties, which have influence on flotation stages during the REM beneficiation [42, 43, 72]. It can identify Fe- oxides and Fe-Ti oxides and explain their microstructural and orientation relationships [73]. It also can provide the physical and crystallographic information about the magnetic phases, aiding to understanding the phase transformation [74].

2.2.4 Quantitative X-ray analysis in mineral processing

Quantitative X-ray microanalysis is based on the relationships between the characteristic

X-ray intensity of an element and its concentration. X-ray microanalysis based on SEM can be used to determine chemical composition of minerals in the micrometer scale. Quantitative mineralogy is essential for guiding ore processing design and optimization. The conventional quantitative X-ray microanalysis requires standard samples and have the advantages of analytical accuracy at the ±1-2% level [44]. However for the minerals, it is difficult to find an appropriate standard, and the acquisition of standard spectra for all the potential minerals is a long and tedious work. So the standardless X-ray microanalysis is widely applied in the industry.

Standard and standardless quantification

In the initial development stage of quantification with X-ray intensity, some fundamental parameters were not accurately known. Castaing first proposed to use the ratio of characteristic X- ray intensities of the same element in a specimen and a standard measured under the exactly same conditions in order to cancel out the most affecting factors [75]. If the fluorescence and absorption effects were very small, the ratio of measured intensities of element i in a specimen and a standard is roughly proportional to the ratio of its weight fraction, the equation was expressed as:

퐶푖 퐼푖 0 = 0 = 푘푖 (2-4) 퐶푖 퐼푖

0 Where Ci and Ci are the weight fraction of the element i in the specimen and the standard, and Ii

0 and Ii are the measured X-ray intensities of the same peak of i. During the quantitative analysis, the measured intensities from specimen and standard should be corrected because of the matrix effect, then the equation (2-4) was rewritten as:

퐶푖 퐼푖 0 = [푍퐴퐹]푖 0 = [푍퐴퐹]푖푘푖 (2-5) 퐶푖 퐼푖

Where ZAF is the correction factors, which were divided into the atomic number effect Zi, the absorption effect Ai and the fluorescence effect Fi. The atomic number and absorption effect are determined as:

∞ −휒푖휌푧 ∫0 휙푖(휌푧)푒 푑(휌푧) 훾푖 [푍퐴] = ∞ = (2-6) 푖 0 −휒푖휌푧 훾0 ∫0 휙푖 (휌푧)푒 푑(휌푧) 푖 0 where ϕi(ρz) and ϕi (ρz) are the functions describing the depth distribution of X-ray generation without absorption in specimen and the standard. χi is the absorption parameter, equal to μ/ρ cscψ

(ψ is the detector take-off angle). However, this calculation requires the same measurement conditions of the specimen and the standard, including specimen surface, electron beam energy, working distance, especially beam current, which is hardly achieved by a field emission SEM because of its beam current fluctuations. So a method which is independent of the beam current is required by a field emission SEM.

Cliff and Lorimer method was first developed for thin films using EDS in analytical electron microscopes [76]. For a binary system containing element A and B, the compositions of the constituent elements, CA and CB (CA + CB = 1), have relationship with the measured characteristic X-ray intensities above background (IA and IB), as shown:

퐶퐴 퐼퐴 = 퐾퐴퐵 (2-7) 퐶퐵 퐼퐵

where KAB is the Cliff-Lorimer factor, which is independent of the composition. The effect of beam current can be ignored because intensities of the two elements are measured in the same spectrum, so the currents for the two lines are the same. For a very thin specimen, the absorption of photons and the energy loss of electrons are negligible. When the effects of absorption and fluorescence are introduced, the Cliff-Lorimer method can be applied to a bulk sample with a SEM. The Cliff-

Lorimer factor can be calculated theoretically or determined experimentally with a standard sample with known composition [77-79].

However, when IB is close to zero, the ratio of IA/IB increases rapidly. So another method, f-ratio method is proposed in order to avoid this problem [80]. For a binary system containing elements A and B, f-ratio was used instead of IA/IB,

퐼퐴 푓퐴 = (2-8) 퐼퐴+퐼퐵

퐼퐵 푓퐵 = (2-9) 퐼퐴+퐼퐵

Equation 2-8 can be linked to the Cliff and Lorimer expression as:

1 1 (2-10) 푓퐴 = 퐼퐵 = 퐹퐵훾퐵퐶퐵 1+ 1+퐾퐴퐵 퐼퐴 퐹퐴훾퐴퐶퐴 where KAB is defined in equation 2-7, γi is defined in equation 2-6 and Fi describes the fluorescence effect. If there is no absorption and no fluorescence,

1 1 (2-11) 푓퐴 = 퐶퐵 = 1−퐶퐴 1+푘퐴퐵 1+푘퐴퐵 퐶퐴 퐶퐴

As shown in the equation 2-11, the f-ratio is proportional to the concentration of the elements and the Cliff-Lorimer factor. So the relationship between the f-ratio and the concentration of element can be used for quantitative analysis. As the same, the effects of absorption and fluorescence are required to be introduced when applied to bulk samples.

There are still uncertainties on the KAB factors, such as the detector efficiency and the accuracy of the fundamental parameters of X-ray generation, such as the ionization cross-section.

So a calibration factor Λ was introduced to remove these uncertainties [80]. For a binary system,

푡ℎ 퐼퐴 푓퐴 = 푡ℎ 푡ℎ (2-12) 퐼퐴 +훬퐴퐵퐼퐵

푡ℎ 퐼퐵 푓퐵 = 푡ℎ 푡ℎ (2-13) 퐼퐵 +훬퐵퐴퐼퐴

(훬퐴퐵)(훬퐵퐴) = 1 (2-14)

푒푥푝 푒푥푝 In order to calculate the calibration factor, the experimental ratios 푓퐴 and 푓퐵 are calculated

푒푥푝 푒푥푝 푡ℎ 푡ℎ with the measured X-ray characteristic intensities 퐼퐴 and 퐼퐵 ; the theoretical ratios 푓퐴 and 푓퐵

푡ℎ 푡ℎ are calculated with the simulated X-ray characteristic intensities 퐼퐴 and 퐼퐵 by a model of X-ray generation. Then the calibration factor Λ퐴퐵 is determined as:

푒푥푝 푡ℎ 푒푥푝 퐼퐴 퐼퐴 푓퐴 = 푒푥푝 푒푥푝 = 푡ℎ 푡ℎ (2-15) 퐼퐴 +퐼퐵 퐼퐴 +훬퐴퐵퐼퐵

푡ℎ 푡ℎ 퐼퐴 푓퐴 = 푡ℎ 푡ℎ (2-16) 퐼퐴 +퐼퐵

푡ℎ 푒푥푝 푡ℎ 푒푥푝 퐼퐴 퐼퐵 푓퐴 1−푓퐴 훬퐴퐵 = 푡ℎ 푒푥푝 = ( 푡ℎ) ( 푒푥푝 ) (2-17) 퐼퐵 퐼퐴 1−푓퐴 푓퐴

Then the calibration factor Λ퐴퐵 is calculated and can be applied to the binary system. This method can also be applied in the multielement system, but the calibration factor for each pair of two elements is required. This method has been utilised to calculate the composition of a gold-copper standard alloy with accuracies below 5%, which is greatly lower than the routine standardless quantitative analysis [80].

Quantitative analysis with standard specimen provides high accuracy, but acquiring and processing the spectra of the standards is a time-consuming work, and it is difficult to find all the

“standard” minerals. So the standardless quantitative analysis methods are widely used for mass analysis, providing the standardless mass (weight) fraction (wt %) of each constituted element.

The standardless quantification methods are attempted to eliminate the need for the spectrum of standard specimen by calculation of standard intensities [81, 82]. Quantitative microscopy with

SEM-based automatic measurement method and data processing techniques could provide broad ranges of applications in mining industry. When the element’s mass fraction is above 0.01, EDS-

SDD standardless analysis can perform as well as, or even better than WDS [83]. Among the recent quantitative EDS microanalysis results acquired, approximately 98% were obtained with the

30 standardless analysis [57]. With SEM/EDS system, both individual rock section and particulate samples can be measured. It also can perform specific mineral search and trace mineral search [84].

The accuracy of standardless X-ray microanalytical of SEM/EDS-SDD system in the condition of severe peak interference is listed in table 2-7 [57]. However, the accuracy is relatively low compared to the traditional standard quantitative analysis, so the convenience and accuracy are compromised [85].

Accuracy Mass faction Within ±5% >0.1 Within ±10% 0.01~0.1 Within ±25% 0.001~0.01

Table 2-7 Standardless X-ray microanalytical accuracy of SEM/EDS-SDD system [57]

Automated quantitative mineralogy

There were three significant developments of the application of electron beam system on automated quantitative mineralogy. Firstly, Petruk identified minerals based on grey level shown on BSE images, then EDS analysis was utilized on the area with the same BSE response automatically [86]. The limitation of this method is the adjacent phases with same BSE grey level cannot be separated. Secondly, Jones utilised WDS to collect X-ray signals at 4 wavelengths at the same time, allowing understanding the sample’s mineralogy in terms of 4 elements [87]. This method could separate bornite and chalcopyrite according to different copper contents. Thirdly,

QEM*SEM, which could identify the common minerals in about 25 ms with a SEM/EDS system, was developed by CSIRO [84, 88, 89].

Recently, there is an increasing demand for quick and accurate analysis with SEM/EDS system because of this convenience and wide application. QEMSCAN, which consists of a SEM, a BSE detector, at least one EDS detector and a software for acquiring and analyzing data, was developed based on the existed automated mineral analysis systems which began with QEM*SEM, then became a general-purpose instrument for element analysis and chemical characterization of minerals [5-7, 90-96]. It is a fully-automated microanalysis system, which is able to acquire a variety of quantitative information including distribution, composition and textures of minerals

[84]. It identifies and quantifies elements by decomposing one spectrum acquired into the spectra of each element, which then are compared with the criteria to identify the analyzed area as a particular mineral. However, it still has the limitations:

(1) A specific number of X-ray counts is required by the mineral database. It limits application on arbitrary spectra with different X-ray counts [97].

(2) In order to make sure the S/N ratio (signal-to-noise ratio) is high enough to discriminate the certain elements, sufficient X-ray counts and long acquisition time is required. But the low concentration still can cause the misidentification of minerals [97].

(3) The system picks the first match it finds, no matter whether there is a better match in the mineral database[97].

(4) This technique cannot distinguish the surface phase and contaminants, so it cannot replace the surface analytical techniques [92].

(5) This technique strongly depends on the database, so it require the database is complete enough in order to avoid misinterpretations [92].

By the automated quantification, the information of phase abundance, mineral texture and particle liberation can be obtained and applied in the mineral processing operations [98]. However, the automated techniques are excessively dependent on the instruments and the database, so they cannot deal with unexpected situations and satisfy different requests flexibly. The careful quantification with standard is still the method with the highest accuracy and reliability.

2.3 Summary

The common metallurgical processing of REM in Nechalacho deposit and the analytical techniques based on SEM were reviewed in this work. Even though there is a large number of

REM exploited in this deposit, beneficent and extraction are still tough tasks. The characterization methods keep improving, allowing qualitative and quantitative analysis of minerals at the micro scale, assisting in evaluating the potential REE reserve and to optimize the extraction process.

With the developments of instruments and the correction of spectra, SEM/EDS system can obtain the results with short acquisition time, low cost, high accuracy, and high resolution. For quantification, the standard method provides high accuracy, but it is time-consuming. Based on the standardless quantification, automated quantitative mineralogy is widely applied in the mineral processing. However, by comparison, the careful quantification with standard is still the method with the highest accuracy and reliability.

3 Experimental Method

3.1 Sample preparation

3.1.1 Nechalacho ore sample

A bulk sample from the Nechalacho deposit in the Northwest Territories was mechanically fractured and polished. At first, the sample was polished with silicon carbide papers from 100 to

1200 grit unit, then further polished by using diamond suspensions of 1µm grain size and followed by applying colloidal silicon suspension of 50 nm grain size. As the mineral had poor conductivity, the sample was coated with a thin (nominal 10-20 nm-thick) amorphous carbon layer utilizing the

Edwards vacuum carbon coater E306 in order to avoid surface charging.

3.1.2 Nechalacho powder samples

The ores from Nechalacho deposit were ground by rod mill and ball mill to produce powders with 80 wt. % passing size being 40 μm, then were prepared for scanning electron microscopy imaging and X-ray microanalysis. The powders were sprinkled at the bottom of a cylindrical holder, then dispersed in LR white resin and mixed thoroughly. The holder containing the dispersed powders was cured in a vacuum oven for 48 hours with the temperature kept at 60℃.

After finishing curing, the sample was polished with silicon carbide papers from 100 to 1200 grit

34 unit, then polished using diamond suspensions of 1 µm grain size and followed by using alumina suspension of 50 nm grain size. The samples prepared for analysis with the cold field emission

SEMs (Hitachi SU8000 and SU8230) was coated with a thin (nominal 10-20 nm-thick) amorphous carbon layer by an Edwards vacuum carbon coater E306, in order to avoid surface charging. There was no carbon layer coating on the samples prepared for analysis with the tungsten emission SEM

(SU3500).

3.2 Sample characterization

3.2.1 SEM

Hitachi SU8000

The Hitachi SU8000 (Figure 3-1) cold field emission SEM has a resolution of 0.5 nm at 30 keV and 2 nm at 0.2 keV. It has one secondary electron (SE) lower detector, one SE upper detector and one backscattered electron (BSE) top with various modes of energy filtration, a five quadrant

BSE detector and an 80 mm2 X-Max SDD EDS detector (Oxford Instrument) allowing acquiring

X-ray spectra with a maximum count rate of 500 kcps. The maximum probe current is 30 nA and this allows to perform quantitative X-ray microanalysis at low accelerating voltage. INCAEnergy software (version 4.15) from Oxford Instruments was used for X-ray microanalysis. During the

EDS map acquisition, the Oxford INCAEnergy drift correction was used. The distance from a

35 detail position at the end of the acquisition relative to the same detail position at the beginning of the acquisition over the full length of the image was used to measure the percentage of image drift.

Figure 3-1 Hitachi SU8000

Hitachi SU8230

The Hitachi SU8230 (Figure 3-2) cold field emission SEM has a resolution of 0.4 nm at 30 keV and 3 nm at 0.05 keV. The maximum probe current is 60 nA and drops only by 10% after 10 hours, providing a higher signal to noise ratio (S/N ratio) and higher X-ray count rate. It has one

SE lower detector, one SE upper detector and one BSE top with energy filtration of BSE electrons, and a five quadrant BSE detector. A conventional SDD with a 60 mm2 collecting area and a new

FlatQuad SDD EDS annular detector (Bruker Instrument) with a 60 mm2 collecting area are

36 equipped on the SEM. The new annular detector is located below the pole piece, giving a maximum

X-ray count rate of 1,500 kcps. Bruker ESPRIT software (version 1.9) was used for X-ray microanalysis.

Figure 3-2 Hitachi SU8230

Hitachi SU3500

The Hitachi SU3500 (Figure 3-3) variable pressure scanning electron microscope (VP-

SEM) is a tungsten emission SEM, and has a resolution of 3 nm at 30 keV and 7 nm at 3 keV in the vacuum mode. It is equipped with a 5 quadrant BSE detector and an 80 mm2 X-Max SDD EDS detector (Oxford Instrument). The high probe current of 1 μA allows the EDS mapping at high

37 speed. The low vacuum mode allows characterizing non-conductive materials without coatings.

Oxford AZtec software (2.2 SP2) was used for X-ray microanalysis. AutoLock was applied during the acquisition of EDS map to achieve automatic drift correction.

Figure 3-3 Hitachi SU3500

For the spectra analysis, DTSA-II software was used to assist in identifying peaks in the spectra [99]. The interaction volume at each spot was simulated with CASINO Monte Carlo software [100].

3.2.2 f-ratio method

For a binary system, f-ratio is defined as the equation below [101]:

퐼퐴 푓퐴 = (3-1) 퐼퐴+퐼퐵

In the system with N elements:

퐼푖 퐼푖 푓푖 = 푁 = (3-2) ∑푗 퐼푗 퐼푇

푁 퐼푇 = ∑푗 퐼푗 (3-3)

The statistical error 휎푓푖 is [102]:

퐼 ∗퐼 1 1 푇−푖 푖 (3-4) 휎푓푖 = 2 √ + 퐼푇 퐼푖 퐼푇−푖

푁 퐼푇−푖 = ∑푗≠푖 퐼푗 = 퐼푇 − 퐼푖 (3-5)

For EDS point analysis, the f-ratio of each selected point was calculated with the equations above.

For EDS map map acquired with SU8000, the INCAEnergy software (version 4.15) was used to obtain the standardless mass fraction map of each constituted element. Then f-ratio method was utilised to convert the standardless weight fraction map into an f-ratio map.

3.2.3 Phase map

The elemental EDS standardless mass fraction maps were converted into phase maps, which were used to identify each phase and show the distribution of REE bearing phases. Two methods were applied to obtain the phase maps. For maps acquired with SU8230, the Bruker

ESPRIT software was used to obtain the net intensity (without background intensity) and standardless quantification was used to get mass concentration element map. Then the phases were defined and the compositional range were set by Python script for each phase manually, then the script was run with the quantitative results to convert the element maps into phase maps. For the maps acquired with SU3500, AZtec software can produce phase map by AutoPhaseMap function.

After standardless quantification, this software chose the areas with similar composition and merged them into one phase automatically, so the distribution, constituent elements and composition of each phase can be obtained simultaneously. After obtaining phase maps, ImageJ software was used to measure the area fraction of each phase [103].

4 Characterization of Nechalacho Ores

Although there are large amount of rare earth elements existing in the earth crust, the number of mineable rare earth mineral (REM) deposits is limited. The mineralogical and chemical complex makes it a challenge to recover REE, which are concentrated in variable minerals with different chemical and physical properties. Nechalacho deposit located in Canada’s Northwest

Territories, which is regarded as one of the largest resources of HREE, is an ideal setting to investigate and exploit REM. Characterization of REE is helpful to identify the REM and to define the associations of adjacent phases in this deposit. Two cold field emission SEM will be used:

Hitachi SU8000 and Hitachi SU8230. BSE image, EDS point analysis, and EDS map at different experimental conditions will be acquired.

4.1 Identification of REM

Observations were performed with a cold field emission SEM (Hitachi SU8000), which has the advantages of small source size, small energy spread, high reliability, and high reproducibility [44]. A SDD detector with an 80 mm² collecting area is equipped on the SEM as the EDS detector, allowing acquiring X-ray spectrum with 500 kcps. The backscattered electron

(BSE) microscope images (Figure 4-1(a) and (b)) were obtained for a polished (flat) Nechalacho deposit ore sample at 20 kV. They illustrate the complexity of the ore: the phases with different brightness and the association between different phases are observed. Different brightness

41 represent the compositional variations across the sample surface. The phase with higher average atomic number present brighter in BSE image. The object of this project is to explore the optimal extraction process for REE, which usually have higher atomic number relative to other common elements existing in minerals. So the brighter phases should be the target phases, and a small area containing the bright phases was selected (marked black frame in Figure 4-1(a)) for further analysis at higher magnification, as shown in Figure 4-1(b).

At higher magnification, the different phases are observed in details: there are some bulk phases adjacent together, and small inclusions surrounded among others. Three main different phases are observed in Figure 4-1(b) according to the brightness. However, BSE images can just distinguish phases with different average atomic number, more accurate identification requires characteristic X-ray lines. X-ray spectra of the different phases were obtained with SEM-EDS system for three locations shown in Figure 4-1(b). All spectra were acquired for 300 s, and the dead time was less than 15% in order to reduce the sum peaks. Because of large collecting area

(80 mm2) and high count rate, such acquisition time is long enough to get a high peak to identify each phase. As the main peaks occur below energy of 8 keV, the spectra are presented within the range of 0 to 8 keV, in order to show the peaks of all elements clearly (Figure 4-1(c)-(f)). The carbon peak was not labeled in all the spectra. Analysis of carbon is always complicated, because the coating and contamination both contributes to the total amount of this element in the spectrum, but the analyzer cannot distinguish their real sources. Major peaks were labeled with the element symbol and X-ray line family; the other elements with smaller peaks in the spectra were not labeled, but still included in the analysis.

Figure 4-1 Qualitative X-ray microanalysis of Nechalacho ore with an accelerating voltage of 20 kV (a) low magnification BSE image; (b) high magnification BSE image of the chosen area in (a); (c)(e)(f) EDS spectra of the three locations shown in (b); (d) EDS spectrum of standard bastnäsite spectrum.

The EDS spectra 1, 2, and 3 (Figure 4-1(c) (e) (f)) were acquired at the location 1, 2, and

3 respectively and they are helpful to identify each selected phase. Figure 4-1 (d) was acquired in a sample which has been identified as bastnäsite. It was used as the standard sample in this work.

By comparing the spectrum 1 (acquired at location 1) with the standard bastnäsite spectrum, the bulk bright phase was identified as bastnäsite, which is a common LREE carrier previously found in the Nechalacho deposit. Bastnäsite has the chemical formula as REE(CO3)F with REE mainly including La, Ce, Nd, Sm, and Gd [1, 3]. Figure 4-1(e) illustrates the spectrum of the black phase, which was identified as a carbonate mineral of the dolomite group. Among the dolomite group,

2+ ankerite (Ca(Mg,Fe Mn)(CO3)2), dolomite (CaMg(CO3)2), and kutnahorite

(Ca(Mn,Mg,Fe)(CO3)2) were suggested to exist in this deposit. The grey phase (Figure 4-1(e)) was identified as zircon (ZrSiO4), a silicate mineral. Plenty studies pointed that zircon was a main carrier for REE, but spectrum 3 does not illustrate that. There are other small peaks not labeled in the spectra, such as Si, Ca, and Y in spectrum 1; Si and V in spectrum 2; Al, Si, As, and Hf in spectrum 3.

The selected phases were analyzed according to their spectra. Because most minerals do not have a certain formula, some phase can just be identified as one of a mineral group. The association among different minerals should be considered to make an accurate identification. The presence of kutnahorite near zircon is expected because kutnahorite is an alteration to zircon by the hydrothermal fluids. Also, carbonatization (dolomite group) is associated with REE fluorocarbonate crystallization (bastnäsite) [4]. As the LREE carrier, bastnäsite is always associated with silicates, magnetite, and carbonates [7]. By the BSE image and EDS point analysis, bastnäsite,

44 silicates, such as zircon, and carbonates, such as dolomite group, have been identified. And the association between the identified minerals has been discussed.

4.2 Standardless f-ratio method

Contrast observed in a BSE image is helpful to find REM because the brightness is strongly dependent on the average atomic number, and the accurate identification can be achieved by EDS analysis. For a phase with similar composition, EDS analysis can reveals the subtle compositional differences hidden by the low contrast of BSE image. F-ratio method can further calculate the sample homogeneity, and recover the contrast in EDS qualitative and standardless quantitative map.

4.2.1 Sample homogeneity

As illustrated in the Figure 4-1(c), the large bright phase was a major source of REE. Figure

4-2 illustrates the spectrum obtained at five locations selected in the bright phase. The BSE images and EDS spectra were obtained by the Hitachi SU8000 SEM equipped with the SDD detector. The characterization was performed at 20 kV for 300 s. Major elements were labeled in the spectra in

Figure 4-2(b). Also, Si, Ca, and Y were detected. In order to obtain the compositional changes and compare the difference over the whole phase, five locations were chosen randomly scattered across

45 the bright phase. The overlap of the five spectra indicates that the different locations have similar composition. In order to compute the homogeneity of the bight phases, f-ratios for La L lines and

Ce L lines at each spectrum were calculated [80], and 푓푖 ± 3휎푓푖 of the two rare earth elements were diagramed in Figure 4-3. In the calculation, intensities of all detected elements were included.

(a) (b)

Figure 4-2 Qualitative X-ray microanalysis of the bright phase with an accelerating voltage of 20 kV (a) BSE image; (b) EDS spectra of the five locations shown in (a).

Theoretically, if the values of 푓푖 ± 3휎푓푖 of the same element at random locations have overlaps, the phases can be confirmed to be homogeneous. The f-ratios of La L lines and Ce L lines were calculated with the intensities measured in a standard bastnäsite sample, spectrum of

which has been shown in Figure 4-1(d). The scatter diagraph of 푓푖 ± 3휎푓푖 of the two REE are

displayed in Figure 4-3(a) and (b), and the overlaps of 푓푖 ± 3휎푓푖 values are marked by the blue

frames. On the contrary, there are no overlaps of 푓푖 ± 3휎푓푖 values of La L lines and Ce L lines detected in this bright phase, as shown in Figure 4-3(c) and (d). Thus, this bright phase is confirmed not to be homogeneous. Even though there is no contrast observed in BSE image, or the EDS

46 spectra at different location are almost identical, a careful calculation is still necessary for judging homogeneity.

(a) (b)

Figure 4-3 Calculation of sample homogeneity. (a)-(b) Scatter diagraph of 푓푖 ± 3휎푓푖 of La L lines and Ce L lines detected in a standard bastnäsite sample; (c)-(d) Scatter diagraph of 푓푖 ± 3휎푓푖 of La L lines and Ce L lines detected at the five locations shown in Figure 4-2.

To characterize the continuous compositional changes on the surface, only point EDS analysis is not enough. EDS elemental maps were acquired to illustrate the changes. Normally,

EDS qualitative map and standardless quantitative map can be obtained easily. In this work, the f- ratio intensity map was also computed for calculating the sample homogeneity and demonstrating higher contrast of the similar composition. Firstly, the maps of the standard bastnäsite sample were

47 acquired by the Hitachi SU8000 SEM-EDS system. Figure 4-4 displays the EDS standardless quantitative maps and f-ratio intensity maps of La L lines and Ce L lines obtained at 20 kV for 3 hours. There is no contrast observed in the four maps, demonstrating a good example of a real homogeneous sample. However, there is still subtle changes of La and Ce concentration (shown in the scatter diagraph in Figure 4-3(a) and (b)), which are observed as the noise in the f-ratio maps.

Secondly, the maps of a phase similar to Figure 4-2 were acquired with the same system at the same condition, as shown in Figure 4-5.

Figure 4-4 Standardless Quantitative X-ray microanalysis of standard bastnäsite sample with an accelerating voltage of 20 kV.

Figure 4-5 displays the EDS qualitative maps, standardless quantitative maps and standardless f-ratio intensity maps of La L lines, Ce L lines and Nd L lines detected in a bastnäsite

48 phase in the Nechalacho ore. As shown in the BSE image, the contrast is relatively low across the bright bastnäsite phase because of the compositional similarity, and the black phase included was identified as quartz. The area marked with the red frame in the BSE image indicates where EDS mapsat 20 kV for 3 hours were acquired. In the BSE image, cracks are observed due to a part of second electrons were collected by the BSE detector. Compared with the topographic contrast, little compositional contrast was observed. Different from the BSE image, EDS qualitative maps of the three REE show contrast associated with the compositional changes. The light and dark contrast can be observed, and the black areas are corresponding to the cracks. Then the maps were standardless quantified, and the compositional changes are observed as the different colours in the quantitative EDS maps. For La, the weight fraction at some places is as high as over 30%, but that at some other places could be only about 10%. The high compositional range can also be observed in the quantitative maps of Ce and Nd. According to the intensities of each peak detected in this area, the f-ratio maps were converted. f-ratio at each pixel was calculated with each constituted element’s characteristic X-ray intensities, which has the direct relationship with its concentration.

Thus, the f-ratio map is also a quantitative map. The same compositional change tendency is observed in the f-ratio maps, which also provide higher contrast relative to the EDS quantitative maps. Even though the values of f-ratio labeled in the maps do not have any physical meanings, the high contrast allows the f-ratio map to be a better way to illustrate the compositional changes when the composition is pretty similar across a phase. Compared with maps of standard bastnäsite sample shown in Figure 4-4, f-ratio map is confirmed to be an effective method to judge the sample homogeneity.

BSE image

Qualitative EDS map Standardless quantitative EDS map Standardless f-ratio intensity map

Figure 4-5 Standardless Quantitative X-ray microanalysis of the Nechalacho ore with an accelerating voltage of 20 kV.

4.2.2 Recovery of contrast

Except the calculation of sample homogeneity, f-ratio map is also helpful to recover the contrast in EDS standardless quantitative map, as shown in Figure 4-6. Bright-grey patterns are observed in the BSE image in Figure 4-6(a), indicating the different composition on the surface.

EDS point analysis showed that the constituted elements are identical across the surface, but the weight fractions are different, representing the compositional contrast in the BSE image. In order to show compositional changes on the surface, EDS map was acquired in this area with Hitachi

SU8000 SEM-EDS system for 3 hours. Due to the small features of the patterns, a lower accelerating voltage, 15 kV, was used. Among the constituted elements, the concentration changes of Si and Y play more important roles than other elements. Thus, the EDS qualitative maps, standardless quantitative maps and standardless f-ratio maps of Si K lines and Y L lines are displayed in Figure 4-6. The contrast shown in the qualitative map of Si is on the contrary with the contrast in the BSE image and the qualitative map of Y, indicating that the bright phase shown in the BSE image mainly results from the higher concentration of Y and lower concentration of Si.

However, the contrast is hardly observed after quantification. The two elements both have low weight fraction in the sample, contributing the low compositional contrast in the standardless quantitative maps. However, another kind of quantitative map, f-ratio map improves the contrast greatly, presenting the changes of Si and Y concentration clearly, which are consistent with the changes of brightness shown in the qualitative map.

(a)BSE image

(b) Qualitative EDS map of Si (c) Qualitative EDS map of Y

(d) Standardless quantitative EDS map of Si (e) Standardless quantitative EDS map of Y

(f) Standarless f ratio map of Si (g) Standarless f ratio map of Y

Figure 4-6 Standardless Quantitative X-ray microanalysis of the Nechalacho ore with an accelerating voltage of 15 kV.

Figure 4-5 and Figure 4-6 introduced two different situations. First one is that there is almost no contrast shown in BSE image, but EDS qualitative and standardless quantitative maps present small compositional changes on the surface (Figure 4-5). In this situation, f-ratio map can provide a higher contrast even though the composition is very similar across the surface. The second one is that the atomic number contrast is obviously presented in BSE image and EDS qualitative map, but the standardless quantitative map provides little useful information because of the low concentration of the key element (Figure 4-6). In this situation, f-ratio map is a more effective method to present the compositional changes quantitatively, demonstrating high contrast consistent with the BSE image and the qualitative EDS map.

In this work, EDS qualitative map, standardless quantitative map and standardless f-ratio map were acquired to show the continuous compositional changes across the surface. The standardless f-ratio method is proved to be a more effective way to judge the sample homogeneity, and can provide higher compositional contrast in a quantitative element map.

4.3 Characterization with high spatial resolution

Around location 3 shown in Figure 4-1(b), several bright inclusions were observed in the grey phase, which was identified as zircon at the low magnification. Figure 4-7(a) shows this area at a larger magnification, and EDS point analysis was done to identify the bright phases and to determine if the presence of REE explains the contrast. Six locations were selected to analyze the composition of the chosen area. Figure 4-7(b) shows the spectra acquired at locations 1, 2, and 3

53 in the bright inclusions, which were identified as fergusonite-Y (REENbO4). Monte Carlo simulation was used to simulate the interaction volume of the incident beam in fergusonite phase at 20 kV. The interaction volume was simulated to be around 3 µm (Figure 4-8(a)), which is smaller than the bright inclusions. So the spectra displayed in Figure 4-7(b) is confirmed to be acquired within the bright phases. Different from the previous REE-bearing mineral, it contains large amount of HREE, such as Y, Dy, Ho, and Er. The grey phase was identified as zircon according to spectra 4 and 5, which are consistent with spectrum 3 in Figure 4-1. At high magnification, black spots at nanoscale were observed distributed in the grey phases. The interaction volume of the incident beam in zircon phase at 20 kV was simulated to be 5.6 µm

(Figure 4-8(b)). This simulation was assumed in a bulk sample, but this one was a geological sample, which was constituted by several minerals. In such condition, the porosity was high and the interaction volume was increased. Since the interaction volume was bigger than the black spots in the zircon phase, it was difficult to analysis those spots. There are also other elements observed in the spectra of zircon phase, such as Ca, Mn, and Fe, which are the main elements in the dark phase (location 6) as shown in Figure 4-7(d). The dark phase was identified as a mineral of dolomite group, similar with Figure 4-1(e).

Zircon and fergusonite were always regarded as the main carriers for HREE [4, 6, 7]. And it has been revealed that a mixture of zircon and fergusonite particulates can carry large amounts of REE [104]. However, in most previous studies, the limited resolution could not distinguish the two phases, which are always mixed together. This work indicates that zircon is not a carrier for

REE, but it could be a signal for finding fergusonite, which is the real carrier for HREE [105].

Fergusonite phases usually have very small size, so it is a challenge to identify and concentrate them, but zircon phases have larger size and are easier to detect relative to fergusonite.

(a) (b)

Figure 4-7 Qualitative X-ray microanalysis with an accelerating voltage of 20 kV (a) BSE image; (b)-(d) EDS spectra of the six locations shown in (a).

(a) (b)

Figure 4-8 Interaction volume of incident beam simulated by Monte Carlo simulation at 20 kV in fergusonite (a) and zircon (b).

(a) (b) (c)

Figure 4-9 Qualitative X-ray SDD-EDS maps of Nechalacho ore sample at 20 kV. (a) BSE image; (b) EDS qualitative map of Y L lines; (c) EDS qualitative map of Zr L lines.

Figure 4-9 displays two quantitative EDS maps of Y L lines and Zr L lines detected in the

Nechalacho ore. The BSE image shows bright-dark patterns on the bright phase surface, and the

EDS maps further confirm the compositional changes across the phase. The qualitative maps were acquired with the Hitachi SU8000 SEM-EDS system at 20 kV for 7 hours, and only the maps of

Y and Zr are displayed in Figure 4-9. Even though Y and Zr show different distribution regularity, there are overlapped areas between the two maps. According to the previous study, Y mainly comes from fergusonite and Zr mainly comes from zircon [104]. In order to analyze the two phases, the area where the two elements both concentrate marked in the red frame in Figure 4-9(a) was

56 chosen for further analysis at higher magnification and at different accelerating voltage, which is observed in Figure 4-10. 20 kV 10 kV 5 kV

Figure 4-10 Qualitative X-ray EDS maps of zircon and fergusonite at 20, 10 and 5 kV obtained with an annular detector.

The EDS point analysis and maps shown in the previous figures were all acquired with a conventional SDD with a collecting area of the 80 mm2 equipped on the Hitachi SU8000. However, decreasing accelerating voltage decreases the X-ray emission, so an annular detector equipped on another cold field emission SEM (Hitachi SU8230) was used in order to get higher count rate. The acquired maps are displayed in Figure 4-10. The annular detector is designed to be inserted below objective lens, which providing the highest count rate with the minimal beam current. Even though

57 the annular detector only has a 60 mm2 collecting area, higher solid angle provides a count rate of as high as 1,500 kcps, which allows a shorter acquisition time and less sample drift. With high count rate, X-ray microanalysis at low accelerating voltage and low beam current becomes possible, which lower the minimum distinguishable feature size. Figure 4-11 illustrates the interaction volumes of the incident beam in fergusonite and zircon at 5 kV and 10 kV. The interaction volumes in zircon and fergusonite are both as small as around 0.4 µm at 5 kV. It has been confirmed that the higher spatial resolution can be realized by the annular detector with the low accelerating voltage.

(a) (b)

Figure 4-11 Interaction volume of incident beam simulated by Monte Carlo simulation. (a) fergusonite at 5 kV; (b) fergusonite at 10 kV; (c) zircon at 5 kV; (d) zircon at 10 kV.

The maps at 20 kV and 10 kV were acquired for only 20 minutes, and the maps at 5 kV were acquired for 30 minutes (shown in Figure 4-10). With the decrease of the accelerating voltage, the improved spatial resolution make the edges of each phase much clearer. In the maps acquired by the conventional detector at 20 kV displayed in Figure 4-9, Y and Zr cannot be differentiated in the chosen area. With the annular detector, 20 kV is still too high for characterizing so small features. But its high count rate allows the characterization at low accelerating voltage without long acquisition time. At 10 kV, the patterns begin to be visible in the maps of Y and Zr. At 5 kV, the patterns are much clearer, and the two phases are definitely separated, presenting complementary relationship with each other. The maps at 5 kV show Zr-bearing phases and Y- bearing phases mix together at micron-scale. By EDS point analysis, the whole selected grey phase was identified as zircon, containing Y and other HREE. That is the reason that zircon is regarded as a carrier for HREE. But via the careful EDS map analysis with the high spatial resolution at high magnification, Zr-bearing phases and Y-bearing phases were differentiated. The maps layered both Y and Zr indicate the complementary relation between the two phases clearly and directly.

EDS point analysis at low magnification sometimes may have misidentification. The X- ray microanalysis with higher accuracy can be obtained by the combination of EDS point analysis and map at high magnification. The high spatial resolution is required to identify small phases and to distinguish the relationships between phases mixed in a small area. The annular detector allows the analysis with high spatial resolution at low accelerating voltage, differentiating Zr-bearing phases and Y-bearing phases in micron-scale successfully.

4.4 Summary

Two cold field emission SEM were used: one is the Hitachi SU8000 equipped with a conventional SDD with a collecting area of the 80 mm2, and the other one is the Hitachi SU82300 with an annular SDD with a collecting area of the 60 mm2. By the BES image and EDS point analysis, the common minerals in Nechalacho deposit were identified. For demonstrating the continuous compositional change across the surface, EDS qualitative maps, standardless quantitative maps and standardless f-ratio maps were acquired. The standardless f-ratio method is a more effective way to judge the sample homogeneity, and can provide higher compositional contrast in a quantitative element map. A high spatial resolution was obtained by the annular detector to identify small phases and to distinguish the relationships between the adjacent phases in a small area. Zr-bearing phases and Y-bearing phases in micron-scale were differentiated successfully.

5 Characterization of REM at Different Physical

Separation Stages

The ores from Nechalacho deposit are ground to powders for the beneficiation of REM.

The object of this part is to find the optimal separation flowsheet by characterizing the products from each gravity and magnetic separation stage with high resolution SEM. A cold field emission

SEM (Hitachi SU8230) and a tungsten filament SEM (Hitachi SU3500) were utilized, and two methods of processing phase map will be presented.

5.1 Gravity separation and magnetic separation

The ores from Nechalacho deposit were ground by rod mill and ball mill to powders, then followed by the beneficiation process of REM. The beneficiation of REM have several steps, including gravity separation, magnetic separation and flotation. Among the different flotation methods, froth flotation is the most common. However, during the froth flotation, the selective collectors can recover not only REM, but also some ferromagnetic ones, like Fe-oxide minerals

[42, 43]. So before froth flotation, these Fe-oxide minerals should be removed physically by magnetic separation. A flowsheet of the main steps is illustrated in Figure 5-1.

Figure 5-1 Flowsheet of main gravity and magnetic separation steps applied to the Nechalacho ore.

P80 of 40 μm means that passing size of 80 wt. % feed is 40 μm; KC Mag is labeled for the products selected by the low intensity magnetic separation stage from Knelson concentrate; FC Mag for the products selected by the low intensity magnetic separation stage from Falcon concentrate; KC RE Mag for the products selected by the medium intensity magnetic separation stage from Knelson concentrate; FC RE Mag for the products selected by the medium intensity magnetic separation

62 stage from Falcon concentrate; KC Non-Mag for the non-magnetic fraction from Knelson concentrate; FC Non-Mag for the non-magnetic fraction from Falcon concentrate.

At first, the ores from Nechalacho deposit were ground to the powders with the size of 80 wt. % passing 40 μm as the feed for gravity separation. Usually, REM have relatively higher gravities than other gangue, so they can be differentiated by gravity separation. Knelson and

Falcon centrifugal gravity concentrator have been widely used, especially for the very fine particles. In this work, a combination of a lab-scale Knelson Concentrator and a lab-scale Falcon

Concentrator were applied. The ground particles were fed to Knelson Concentrator firstly, then

Falcon Concentrator was fed with the accumulated tailings from Knelson Concentrator (KT in

Figure 5-1). The concentrates from Knelson Concentrator (KC in Figure 5-1) and Falcon

Concentrator (FC in Figure 5-1) were fed to magnetic separator respectively. Magnetic separation can be used to remove ferromagnetic minerals and to concentrate paramagnetic REM [30, 106,

107]. There are two steps in magnetic separation: the first step is to remove strongly ferromagnetic

Fe-oxide minerals by a low intensity separator, as labeled Fe Magnet in Figure 5-1; the second step is to remove the remaining Fe-oxide minerals and to concentrate strongly paramagnetic REM by a medium intensity separator, as labeled RE Magnet in Figure 5-1.

One sample was taken from each fraction of magnetic separation, labeled as KC Mag for the products selected by the low intensity magnetic separation stage from Knelson concentrate; FC

Mag for the products selected by the low intensity magnetic separation stage from Falcon concentrate; KC RE Mag for the products selected by the medium intensity magnetic separation stage from Knelson concentrate; FC RE Mag for the products selected by the medium intensity magnetic separation stage from Falcon concentrate; KC Non-Mag for the non-magnetic fraction

63 from Knelson concentrate; FC Non-Mag for the non-magnetic fraction from Falcon concentrate.

The samples were prepared for the characterization with SEM-EDS system to evaluate the efficiency of gravity and magnetic separation and to decide the optimal separation process for

REM beneficiation.

5.2 Characterization with a FE-SEM

Six samples labeled as KC Mag, KC RE Mag, KC Non-Mag, FC Mag, FC RE Mag, and

FC Non-Mag were characterized with a cold field emission SEM (Hitachi SU8230). A conventional SDD and an annular SDD are equipped on this SEM as the EDS detectors, both with

60 mm2 collecting area. At first, EDS qualitative maps are acquired with the conventional SDD at the magnification being 250 at the accelerating voltage of 15 kV for 1 hour. The qualitative maps were standardless quantified by Bruker ESPRIT software, then converted into phase maps by

Python scripts.

5.2.1 Characterization of Fe bearing minerals

BSE image Standardless quantitative map of Fe Phase map of Fe-oxide phases

KC MAG

KC RE MAG

KC Non-MAG

Figure 5-2 Standardless Quantitative X-ray microanalysis for iron bearing phases of the magnetic separation products from Knelson concentrate from Nechalacho deposit with an accelerating voltage of 15 kV. The left column displays BSE images; the middle column displays EDS standardless element maps; the right column displays phase maps.

BSE image Standardless quantitative map of Fe Phase map of Fe-oxide phases

FC MAG

FC RE MAG

FE Non-MAG

Figure 5-3 Standardless Quantitative X-ray microanalysis for iron bearing phases of the magnetic separation products from Falcon concentrate from Nechalacho deposit with an accelerating voltage of 15 kV. The left column displays BSE images; the middle column displays EDS standardless element maps; the right column displays phase maps.

Since the flotation collectors are selective for not only the REM, but also the ferromagnetic minerals, it is important to remove the Fe-oxide minerals before froth flotation. Figure 5-2 and

Figure 5-3 display the standardless quantitative maps of iron and the phase maps of the phases containing different Fe concentration. The BSE images have been displayed in the left column.

The same magnification was chosen to compare the particle size for each fraction. As shown in the two figures, Knelson concentrate has an obviously larger particle size than Falcon concentrate.

As the feed for Falcon Concentrator is Knelson tailings, the BSE images indicate that the coarser particles are preferentially concentrated by Knelson Concentrator, which acts both a gravity concentrator and a size separator. By comparing the magnetic separation products from one gravity separator, no variation regularities for particle size are observed, indicating that the magnetic separators seem to have no bias for particle size.

Figure 5-2 and Figure 5-3 middle columns display EDS standardless quantitative element maps of Fe, demonstrating the distribution and the compositional range of iron by different colors.

For Knelson concentrate, most iron-bearing phases in KC Mag fraction appears to have the range of Fe content from 15% to 30%; but in KC RE Mag fraction, Fe content in most phases is below

15%; in KC Non-Mag fraction, the iron-bearing phases become much less compared with another two fractions from Knelson concentrate. The results are consistent with the expectation of experiment. KC Mag fraction is the removed fraction from the low intensity magnetic separation stage which is aiming to remove Fe-oxide minerals, so it is supposed to have a high concentration of Fe. KC Non-Mag fraction is the products produced after the two magnetic separation, so the map of this sample shows that most ferromagnetic Fe-oxide minerals were removed by the two magnetic separation stages effectively. For Falcon concentrate, the highest content of Fe appears

67 to be the FC RE Mag fraction, which is different from that of Knelson concentrate. According to the Fe maps of the three samples from Falcon concentrate (Knelson tailing), the medium intensity magnetic separation stage seems to be more effective to remove Fe-oxide minerals than the low intensity. However, there are still remaining Fe-bearing minerals in KC Non-Mag fraction and FC

Non-Mag fraction, so more procedures are needed to remove them after magnetic separation and before froth flotation.

Because of the high resolution of FE-SEM, the features of Fe bearing phases are observed clearly, even with relative low concentration or at pretty small size. The element map shows the distribution of iron, but it is not enough to understand the Fe bearing minerals. In order to show the distribution of Fe-oxide phases more visually, the phase maps were converted and displayed in the right column. The phase maps were converted from the standardless quantitative element maps displayed in the middle column by defining the phases and setting the compositional range manually. The adjustable composition range is very important in analyzing minerals because the chemical composition of minerals is not constant. According to the standardless quantitative maps, there are mainly two classes of Fe concentration, below 15% and above 15%. So the ranges of Fe concentration for phase analysis were set as 5% to 15% (yellow phases), and 15% to 40% (blue phases), as shown in the right column.

Compared with EDS elements map, phase map is a more direct and visual method to observe a phase with a certain compositional range. The range of composition for each phase is adjustable, so it is much easier to analyze the distribution of certain phases. For the Knelson concentrate, iron bearing phases with Fe concentration from 15% to 40% were effective removed

68 by the low intensity magnetic separator, and the iron bearing phases with the two concentration ranges were decreased effectively by the two magnetic separation stages. However, for Falcon concentrate, the low intensity magnetic separator seems no effect on the Fe-oxide phases with Fe concentration from 15% to 40%, because there is almost no blue phases presented the phase map of FC Mag fraction.

Additionally, Figure 5-4 illustrates the area fractions of Fe-oxide phases in the different products. The area fraction over all phases in the samples was calculated by ImageJ software according to the phase map displayed in Figure 5-2 and Figure 5-3 right columns. It is helpful to understand the variation of iron-oxide phases through the separation stages quantitatively. As illustrated in the histogram, the KC Mag fraction has significantly more Fe-oxide phases than other fractions, having area fraction of over 80% of Fe-oxide phases. Compared with KC RE Mag fraction, KC Non-Mag fraction has obviously less phases with Fe concentration from 5%-15%, but almost the same area fraction of phases with Fe concentration from 15%-40%. For the Falcon concentrate, FC RE Mag fraction has the highest area fraction of Fe-oxide phases, and FC Non-

Mag fraction almost has no phases with Fe concentration from 15% to 40%, indicating that the medium intensity magnetic separator selected the ferromagnetic phases effectively and removed almost all the phases with high Fe concentration.

Figure 5-4 Area fraction of Fe-oxide phases in the products at different separation stages calculated according to the phase maps displayed in Figure 5-2 and Figure 5-3.

The standardless quantitative maps and phase maps give the similar results, but phase map is more direct and visual. The calculation of area fraction according to the phase map is helpful to understand the phase map quantitatively. By characterizing with the two kinds of maps, the efficiency of removing ferromagnetic Fe-oxide minerals were discussed. For Knelson concentrate, the low intensity magnetic separator removed the Fe bearing phases with high efficiency, and the medium magnetic separator more inclined to remove the phases with lower Fe concentration. But for Falcon concentrate, the low intensity magnetic separator seems not to work on removing Fe bearing phases, which can be effectively removed by the medium intensity magnetic separator. As the main difference between Knelson and Falcon concentrate (Knelson tailings) is the particle size, so the influence of particle size to the efficiency of magnetic separation should be considered during choosing of magnetic separator intensity.

5.2.2 Characterization of RE bearing minerals

In order to find the optimal physical separation flowsheet to produce high grade REE concentrate, characterization REM with the six samples has been processed. In the previous section, the efficiency of removing Fe bearing minerals by the two magnetic separation stages was discussed. In this section, the results of exploring the efficiency of concentrating RE bearing phases will be presented.

The phase map of main phases and RE phases in the six samples are displayed in Figure 5-

5 and Figure 5-6. In the main phases maps displayed in the left columns, light rare earth mineral

(LREM), fergusonite, Fe-oxide mineral, quartz and zircon, were labeled for ease of tracking the target phases through the separation stages. Other minerals without RE were labeled ‘other’ in the maps. The main LREM in Nechalacho deposit are monazite-(Ce) ((REE, Th)PO4), allanite-(Ce)

2+, 3+ (Ca(REE,Ca)Al2(Fe Fe )(SiO4)(SiO2O7)O(OH)), bastnäsite-(Ce) (REE(CO3)F) and

Synchisite/Parisite-(Ce) (Ca(REE)2(CO3)3F2) [6]. Because of the similar constituted elements, all the phases mainly containing LREE were labeled as LREM. The main heavy rare earth mineral

(HREM) are fergusonite (Y(HREE)(Nb, Ta)O4) and zircon (ZrSiO4), which have totally different formula, so the two phases were both labeled. In the middle columns, the phases with and without

REE are distinguished by different color (blue phases are non-RE phases, yellow phases are RE phases); in the right column, only LREM (red phase) and fergusonite (green phase) are shown.

KC MAG

KC RE MAG

KC NON-MAG

Figure 5-5 Phase map of main phases (the left column) and RE phases (the middle column displays the RE phases (yellow) and non-RE phases (blue); the right column displays different RE phases) of the magnetic separation products from Knelson concentrate.

FC MAG

FC RE MAG

FC NON-MAG

Figure 5-6 Phase map of main phases (the left column) and RE phases (the middle column displays the RE phases (yellow) and non-RE phases (blue); the right column displays different RE phases) of the magnetic separation products from Falcon concentrate.

The phase changes through the gravity and magnetic separation stages can be observed by comparing the phase maps of main phases in different fractions (the left columns in Figure 5-5 and

Figure 5-6). For Knelson concentrate, the low intensity magnetic separator concentrated a majority of iron bearing minerals. A large amount of yellow phases present in the map of KC Mag fraction,

73 which agrees with the results discussed in Figure 5-2, Figure 5-3 and Figure 5-4. The medium intensity magnetic separator concentrated part of zircon, since there are more pink phases observed in the map of KC RE Mag fraction than KC Mag fraction. The proportion of REM and zircon increases after the two magnetic separation stages. For Falcon concentrate, the magnetic separation stages seem no selection for certain minerals. Quartz and other minerals without REE are the major components in the three factions. Most of zircon and REM remained in Knelson concentrate, and were efficiently concentrated by the two magnetic separation stages, contributing the KC Non Mag fraction to be the best fraction for recovering REM and zircon.

Phase map allows not only identifying phases in a sample, but also studying the certain phases. The distribution of phases with and without REE can be obtained easily, as shown in the middle columns in Figure 5-5 and Figure 5-6. The proportion of RE phases in each fraction and changes of RE phases through the two magnetic separation stages were observed visually.

Additionally, the area fractions of RE phases in different products through the separation stages are illustrated in Figure 5-7. According to the phase maps and the histogram, the conclusion can be drawn that the KC Non Mag fraction has the most RE phases compared with other fractions.

However, for the Falcon concentrate, a large portion of RE phases were concentrated by the medium intensity magnetic separator, resulting in that the FC RE Mag fraction has more RE phases compared with the other two fractions. Since the compositional ranges of each phase were set manually and some particles are the mixtures of various phases, overlaps always occur in phase map. In order to analyze REM, the phase maps of only LREM and fergusonite are displayed in the right columns in Figure 5-5 and Figure 5-6, and the area fractions of the two phases through the separation stages are illustrated in Figure 5-8. The phase maps and histogram illustrate that there

74 are always more HREE bearing phases than the LREE phases in each fraction, but the two kinds of phases are observed the same tendency by different processes.

Figure 5-7 Area fraction of RE phases in the products at different separation stages calculated according to the phase map displayed in the middle column in Figure 5-4.

Figure 5-8 Area fraction of LREM and fergusonite in the products at different separation stages calculated according to the phase map displayed in the right column in Figure 5-4.

The most rare earth minerals are considered to be moderately paramagnetic, so theoretically, the intensity of the first magnetic separator should be set to only attract the strong ferromagnetic minerals but no moderately paramagnetic ones [18]. But actually, parts of REM were unavoidably selected by the two magnetic separator. In Nechalacho deposit, the main LRE carrier is bastnäsite, which has been determined to be less strongly paramagnetic than fergusonite

[18, 33]. For Knelson concentrate, LREM only associated with iron bearing minerals were selected by the low intensity magnetic separator as shown in the maps of main phases in Figure 5-5. More

REM were selected by the medium intensity magnetic separator. For Falcon concentrate, the small particle size limits the efficiency of magnetic separator [108], resulting in no great differences of

RE phases in the three fractions.

As shown in Figure 5-5 and Figure 5-6, there are little green phases (fergusonite) observed in the left columns. However, in the right columns, when only the LREM and fergusonite phases are displayed, the green phases become visible. The compositional range of this phase was not changed, indicating that fergusonite overlapped with other phases. By comparing the left and the right column, zircon was found to be the phase with which fergusonite overlapped. Zircon and fergusonite are both regarded as the main HREE carrier in Nechalacho deposit and the target REM in rare earth recovery [4, 6, 7]. Thus, the phase maps of zircon and fergusonite were computed and displayed in Figure 5-9 and Figure 5-10, demonstrating the overlaps of the two phases. The left columns display phase map of the two phases; the other two columns display the two phases separately. As discussed in the previous session, the high resolution allows maps distinguishing the two phases in a small size and indicating that fergusonite is the real mineral carrying REE. The phase map (Figure 5-9 and Figure 5-10) further proves that zircon can be regarded as the target

76 mineral for RE recovery because it mixes with fergusonite, and the mixture usually cannot be physical separated. So the mixtures of zircon and fergusonite particulates can be regarded as the main target minerals for HREE recovery.

KC MAG

KC RE MAG

KC NON-MAG

Figure 5-9 Phase map of fergusonite and zircon of the magnetic separation products from Knelson concentrate. The left column displays the two phases together, and the other two columns display the two phases separately.

FC MAG

FC RE MAG

FC NON-MAG

Figure 5-10 Phase map of fergusonite and zircon of the magnetic separation products from Falcon concentrate. The left column displays the two phases together, and the other two columns display the two phases separately.

Figure 5-11 illustrates the area fraction of fergusonite and zircon in the products at different separation stages. Compare zircon in different fractions, especially in Knelson concentrate, the medium intensity magnetic separators select more zircon than the low intensity one. In previous

78 studies, zircon was indicated to be slightly ferromagnetic and paramagnetic [33]. When zircon associates with REE, the mineral appears paramagnetic; when zircon associates with locked particles of ferromagnetic component, the mineral appears ferromagnetic. In this deposit, most zircon is associated with fergusonite, so it is more inclined to be paramagnetic and the low intensity magnetic separator appears little selection for it. That is the reason why the proportion of zircon phase is lower in KC Mag fraction compared with KC RE Mag fraction and KC Non-Mag fraction.

For Falcon concentrate, the majority of zircon phases are selected by the two magnetic separators, especially the medium intensity one. In each fraction, zircon phases are greatly more than fergusonite phases.

Figure 5-11 Area fraction of fergusonite and zircon in the products at different separation stages calculated according to the phase map in Figure 5-9 and Figure 5-10.

Through the analysis by phase map, the main phases in each fraction were identified and the phase changes through the physical separation stages were discussed. For Knelson concentrate,

KC Non-Mag fraction was proved to be the best fraction for recovering REM and zircon. However

79 for Falcon concentrate, FC RE Mag fraction has more REM and zircon compared with the other two fractions. The overlaps of zircon phases and fergusonite phases were shown, indicating that the mixtures of the two phases can be regarded as the main target minerals for HREE recovery.

5.2.3 Characterization of liberation size

The phase map can also be used to analyze the associations between different phases. For example, in the phase map of the main phases in KC Mag fraction shown in Figure 5-5, some

LREE bearing phases were not liberated, surrounded by iron bearing phases. So when the Fe-oxide minerals were rejected by the low intensity magnetic separator, the included LREM were also rejected. Combine BSE image and phase map, the liberated, mid-liberated and unliberated target minerals are easy to be identified, assisting to choose the proper liberation size. For Knelson concentrate, the larger size of the particles results in that some phases were not separated thoroughly. Some phases were included in or mixed with others phases over a smaller size.

However, the particle size will limit the efficiency of the magnetic separator, as Falcon concentrate has a relative smaller particle size, but the results of magnetic separation are not satisfying.

(a)

(b)

(c)

Figure 5-12 SEM images and phase maps of three kinds of particles with different liberated classes (a) unliberted REM; (b) mid-liberted REM; (c) liberated REM.

Figure 5-12 displays the BES images and phase maps of the liberated, mid-liberated and unliberated target phases to characterize the liberation degree of REM. The sample was from the

KC Non-Mag fraction, which has been proved to be the best fraction for recovering REM. To understand the relationships between REM with other minerals, and different liberation degree, a high magnification was needed. The annular detector was used, allowing for a higher count rate, a shorter acquisition time, less sample drift, a lower accelerating voltage and a smaller beam current.

The maps shown in Figure 5-12 was acquired at 10 kV for 1 hour with a relative low beam current.

The low accelerating voltage contributes to a high spatial resolution, and the low beam current results in a small probe size. As shown in Figure 5-12, the small features in micro-scale were observed clearly.

Figure 5-12(a) shows a particle with locked REM. The RE phases mixed with other phases in a scale smaller than the particle size. Figure 5-12(b) shows a particle with middling liberated

REM. Part of the RE phase has been liberated, but it also associates with other phases. Figure 5-

12(c) shows a liberated RE particle, but K-Feldspar, Fe-oxide and zircon are observed inside. In the three phase maps, all the phases were identified and the red phases refer to the target LREE bearing phases. The high resolution allows characterization of small phases in a few microns, and the relationships between them were illustrated. The identification of the phases round REM is helpful to select REM during separation.

The phase map can indicate the liberation degree directly, which relates to the process selection during the beneficiation of REM. The proper crushed or ground size should be chosen to produce the separate particles of target minerals, and to remove other unwanted minerals, such as

82 gangue and ferromagnetic minerals. Usually, the decrease of particle size corresponds to a higher liberation degree, as more small phases can be separated. However, the efficiency of a concentrator or a separator is reduced with the decrease of particle size. So the liberation degree and the efficiency of concentration is compromised. SEM image and phase map are helpful to decide the particle ground size by characterizing liberation degree of REM. The high resolution of an annular detector allows the features in micro-scale to be observed.

5.3 Characterization with a VP-SEM

In the previous section, all the samples were coated with thin carbon layers to eliminate charging effect, because of the requirement of high vacuum condition by the field emission SEM.

However, a large amount of minerals contain carbon, but the analyzer cannot distinguish whether this element comes from the coatings or the minerals. Hence, a tungsten filament VP-SEM (Hitachi

SU3500) was used to prevent charging effect, and the samples without carbon coatings were analyzed. All the images and maps were acquired at 15 kV with the chamber pressure of 60 Pa, as displayed in Figure 5-13. The EDS maps were acquired for 3 hours and analyzed by AZtec software, which chose the phases with similar composition and merged them into one phase automatically according to the standardless quantification results. The phase maps of the target phases layered with the corresponding BSE images are displayed in Figure 5-13.

Knelson Concentrate Falcon Concentrate

KC MAG FC MAG

KC RE MAG FC RE MAG

KC NON-MAG FC NON-MAG

Figure 5-13 Phase maps layered with the BSE images of the products at different separation stages from Nechalacho deposit with a VP-SEM (left column displays the products from Knelson concentrate; right column displays the products from Falcon concentrate). The yellow phases refer to iron bearing phases; blue phases refer to zircon; red phases refer to LREM; purple phases refer to fergusonite.

As shown in the maps: yellow phases refer to iron bearing phases; blue phases refer to zircon; red phases refer to LREM; purple phases refer to fergusonite. The sensitivity of merging phases can be changed by adjusting the boundary tolerance and the grouping level parameters, but the range of composition for each phase is still unknown and cannot be adjusted. Even though different experiment conditions and analytical approaches were applied, the results were similar.

For Knelson concentrate, the low intensity magnetic separator collect the iron bearing phases effectively, and KC Non-Mag fraction appears to be the the best fraction for recovering REM and zircon. For Falcon concentrate, there are no obvious rules for phases changes due to the limitation of particle size.

5.4 Comparison of the two phase map methods

Here two methods of processing phase map are discussed. The first method was developed

(using Python script) to specify the compositional range of each phase manually. At first, the quantitative element maps were acquired, then the phases were defined and the compositional range were set for each phase, then the script was run with the quantitative results to convert the element maps into phase maps. The second method used the Oxford AZtec software. After standardless quantification, this software chose the areas with similar composition and merged them into one phase automatically, so the distribution, constituent elements and composition of each phase can be obtained simultaneously.

(a)

(b) (c)

Figure 5-14 Comparison of the two phase map methods. (a) BSE image of the KC Non-MAG fraction acquired with a VP-SEM; (b) phase map produced by AZtec software; (c) phase map of main phases calculated by Python scripts. The yellow phases refer to iron bearing phases; blue phases refer to zircon; red phases refer to LREM; purple phases refer to fergusonite.

Figure 5-14 displays the BSE image and phase maps of KC Non-MAG fraction computed by the two methods. The two methods both can be used to identify the main phases, but the results are different. The phases identified as zircon by AZtec (blue phases in Figure 5-14(b)) were identified as the mixture of zircon and fergusonite, displayed as the overlaps of the purple phases and blue phases in Figure 5-14(c). As shown in Figure 5-9 and Figure 5-10, the overlaps of

86 different phases can be further distinguished by Python scripts. By comparison, the method with

AZtec software is much easier and more automatic, but the range of composition for each phase is unadjustable. This limitation may cause misidentifications and omissions of target phases. For example in Figure 5-13, there are some phases with very high brightness in BSE images and identified as REM by point EDS analysis, but not observed in the phase maps. Additionally, the method of Python scripts allows analyzing any target phases by adjusting the compositional range.

Thus, the method of Python scripts is more accurate, more reliable, and more applicable to different requirements.

5.5 Summary

A cold field emission SEM (Hitachi SU8230) and a tungsten filament SEM (Hitachi SU

3500) were utilized. Via the X-ray microanalysis by phase map, the main phases in each fraction at different physical separation stages were identified. The phase changes through the separations and the efficiency of the two magnetic separators were discussed. For Knelson concentrate, the low intensity magnetic separator removed the Fe bearing phases with high efficiency, but the medium intensity magnetic separator worked better on the Falcon concentrate. KC Non-Mag fraction was proved to be the best fraction for recovering REM and zircon. The main difference between Knelson and Falcon concentrate is the particle size, which associates with the efficiency of separators and the liberation degree. The liberated, mid-liberated and unliberated target phases in small scale were characterized by the annular detector, assisting to choose the proper liberation

87 size. Decrease of particle size corresponds to a higher liberation degree, but also results in a limitation of the ability of separators. Two methods of processing phase map were presented and compared. The similar results were provided, but Python scripts are more accurate, reliable, and applicable for satisfying different requirements compared with the method of AZtec software.

6 Conclusion

The aim of this work was to find the optimal extraction processes for rare earth bearing minerals in Nechalacho deposit by high resolution SEM/EDS system. The main minerals were identified and the associations of adjacent phases were discussed. To find the optimal separation flowsheet, the products from each gravity and magnetic separation were characterized. Three scanning electron microscopes were used, playing different roles in REM characterization. EDS analysis, phase map and f-ratio intensity map were applied to acquire compositional information.

The following observations were made:

1. EDS point analysis can be used to identify minerals, but might have misidentifications at

low magnification. The X-ray microanalysis with higher accuracy can be obtained by the

careful combination of EDS point analysis and map at high magnification.

2. EDS qualitative map, standardless quantitative map and standardless f-ratio intensity map

can illustrate the continuous compositional changes across the surface. The standardless f-

ratio method is proved to be a more effective way to judge the sample homogeneity and

can provide higher compositional contrast in a quantitative element map.

3. A high spatial resolution can be achieved by the annular detector to identify small phases

and to distinguish the relationships between the adjacent phases in a small area. Zr-bearing

phases and Y-bearing phases in micron-scale were differentiated successfully.

4. The phase changes through gravity and magnetic separation stages were characterized by

phase map, which was converted from EDS standardless quantitative map. For Knelson

concentrate (KC), the low intensity magnetic separator removed the Fe bearing phases with

high efficiency, but the medium intensity magnetic separator worked better on the Falcon

concentrate. KC Non-Mag fraction was proved to be the best fraction for recovering REM

and zircon.

5. Phase map acquired by the annular detector can identify the liberated, mid-liberated and

unliberated target phases in small scale, assisting to choose the proper liberation size.

Decrease of particle size corresponds to a higher liberation degree, but also results in a

limitation of the ability of separators. So the liberation degree and the efficiency of

concentration should be compromised.

6. Python scripts and Aztec software were used to obtain phase map respectively. Even

though Aztec software is much easier because the phases with similar composition were

merged automatically, Python scripts are more accurate, reliable, and applicable for

satisfying different requirements.

7. The cold field emission SEM can provide high resolution, but the high-vacuum required

the good conductivity of samples. The variation pressure mode on a tungsten emission

SEM can simplify the mineral sample preparation procedures.

In conclusion, the high resolution, high count rate SEM/EDS system can characterize the associations between the adjacent phases in micron-scale and optimize the REM separation processes.

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