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BearWorks

BearWorks

MSU Graduate Theses

Fall 2018

Diamond Intracrystalline Stable Isotope Chemical Image Analysis

Diamond Intracrystalline Stable Isotope Chemical Image Analysis

by Time-of-Flight Secondary Ionization Mass Spectrometery

by Time-of-Flight Secondary Ionization Mass Spectrometery

Tyler J. Sundell

Missouri State University, Sundell127@live.missouristate.edu

As with any intellectual project, the content and views expressed in this thesis may be considered objectionable by some readers. However, this student-scholar’s work has been judged to have academic value by the student’s thesis committee members trained in the discipline. The content and views expressed in this thesis are those of the student-scholar and are not endorsed by Missouri State University, its Graduate College, or its employees.

Follow this and additional works at: https://bearworks.missouristate.edu/theses

Part of the Geochemistry Commons, Geology Commons, Materials Chemistry Commons, and the Mineral Physics Commons

Recommended Citation Recommended Citation

Sundell, Tyler J., "Diamond Intracrystalline Stable Isotope Chemical Image Analysis by Time-of-Flight Secondary Ionization Mass Spectrometery" (2018). MSU Graduate Theses. 3332.

https://bearworks.missouristate.edu/theses/3332

This article or document was made available through BearWorks, the institutional repository of Missouri State University. The work contained in it may be protected by copyright and require permission of the copyright holder

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DIAMOND INTRACRYSTALLINE STABLE ISOTOPE CHEMICAL IMAGE ANALYSIS BY TIME-OF-FLIGHT SECONDARY IONIZATION

MASS SPECTROMETERY

A Master's Thesis Presentedto The Graduate College of Missouri StateUniversity

TEMPLATE

In Partial Fulfillment

Of the Requirements forthe Degree Master of Science, Geospatial Sciences

By

Tyler James Sundell December 2018

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DIAMOND INTRACRYSTALLINE STABLE ISOTOPE CHEMICAL IMAGE ANALYSIS BY TIME-OF-FLIGHT SECONDARY IONIZATION MASS

SPECTROMETERY

Geography, Geology, and Planning

Missouri State University, December 2018 Master of Science

Tyler James Sundell

ABSTRACT

The chemical resistance of diamond allowsin-situ study ofthe diamond source regions. For a majority of gem quality diamonds,this source regionis the sublithospheric mantle keel of a cratonic nuclei. Through analysis of stableisotopes, radiogenic isotopes andtrace elements, diamond geochemical analyses can define chemical fluxesinthe mantle keel. However, such studies require multiple methodologies for each chemical suite, high spatial resolution and analytical precision. Here, I evaluate Time-of-Flight Secondary Ionization Mass Spectrometer (ToF-SIMS) as an alternative method for diamond geochemical analyses. ToF-SIMS analysis can performin cation and anion modeto measurethe entire periodictable of an analyte. Establishing a quantitative ToF-SIMS methodology would dramaticallyincreasethe accessibility of diamond geochemical analyses. I determinethe feasibility of developing such a ToF-SIMSdiamond geochemical methodology. For ToF-SIMSto be considered a plausible method, ToF-SIMS needs to replicate establishedinstrumentation spatial resolution and analytical precision.I determine that ToF-SIMSis ableto replicate established spatial resolutions with single pixelsin our element maps being < 2 µm2.Through measurements of13C and 12C, I showthat ToF-SIMSis unableto replicatethe analytical precision necessary for δ13C values. I conclude by establishing a

frameworkforfuture ToF-SIMS studiestothe end of obtainingthe necessary analytical precision for quantifyingisotopic variation andimproved massresolution.

KEYWORDS:method development, chemicalimage analysis, ToF-SIMS, diamond geochemistry, stable isotope geochemistry

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DIAMOND INTRACRYSTALLINE STABLE ISOTOPE CHEMICAL IMAGE ANALYSIS BY TIME-OF-FLIGHT SECONDARY IONIZATION MASS

SPECTROMETERY

By

Tyler James Sundell

A Master's Thesis

Submittedtothe Graduate College Of Missouri StateUniversity In Partial Fulfillment ofthe Requirements Forthe Degree of Master of Science, Geospatial Science

December 2018

Approved:

Gary Michelfelder, Ph.D., Thesis Committee Chair

Kevin Mickus, Ph.D., Committee Member

Melida Gutierrez, Ph.D., Committee Member

Julie Masterson, Ph.D., Dean ofthe Graduate College

Intheinterest of academic freedom andthe principle of free speech, approval ofthisthesis indicatesthe formatis acceptable and meetsthe academic criteria forthe discipline as

determined bythe facultythat constitutethethesis committee. The content and views expressed inthisthesis arethose ofthe student-scholar and are not endorsed by Missouri State University, its Graduate College, orits employees.

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TABLE OF CONTENTS

Introduction Page 1

Background Page 4

Mineral Properties Page 5

Residence and Exhumation Page 7

Stable Isotopes Page 9

Time of Flight Secondary Ionization Mass Spectrometry Page 15

Methods Page 18

Internal Standard Page 18

Sample Preparation Page 18

ToF-SIMS Analysis Page 20

Signal Intensity Conversion Page 22

Results Page 23

Carbonatite Stable Isotopes Page 24 ToF-SIMS Signal Intensity Conversion Page 24 Diamond ToF-SIMS Analysis Page 24 AR and SL-2 Additional ToF-SIMS Analysis Page 61

Discussion Page 76

Signal Intensity Conversions Page 76 ToF-SIMS Considerations Page 77 Chemical Characterization Page 81

Conclusions Page 92

Signal Intensity Conversion Page 93 Chemical Characterization Page 94

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LIST OF TABLES

Table 1.Isotope Ratio Mass Spectrometry of Magnet Cove carbonatite Page 25 Table 2. Signal Intensity conversion ofthree Magnet Cove carbonatite

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LIST OF FIGURES

Page 12 Figure 1. Frequency plots of diamonds fromthe Guaniamo mine in

Venezuela and Argyle minein Australia.

Figure 2. Frequency plots of diamonds from New South Wales,

Australia and Premier minein South Africa. Page 13 Figure 3. Diamond AR window-112C analysis. Page 29 Figure 4. Diamond AR window-212C analysis. Page 30 Figure 5. Diamond AR window-312C analysis. Page 31 Figure 6. Diamond AR window-712C analysis. Page 32 Figure 7. Diamond SL-1 window-1 and window-212C,16O and1H

element maps. Page 35

Figure 8. Diamond SL-1 window-012C analysis. Page 36 Figure 9. Diamond SL-1 window-112C analysis. Page 37 Figure 10. Diamond SL-1 window-212C analysis. Page 38 Figure 11. Diamond SL-1 window-312C analysis. Page 39 Figure 12. Diamond SL-1 window-412C analysis. Page 40 Figure 13. Diamond SL-1 window-512C analysis. Page 41 Figure 14. Diamond SL-1 window-612C analysis. Page 42 Figure 15. Diamond SL-2 window-2612C analysis. Page 46 Figure 16. Diamond SL-2 window-2812C analysis. Page 47 Figure 17. Diamond SL-2 window-2912C analysis. Page 48 Figure 18. Diamond SL-2 window-3012C analysis. Page 49 Figure 19. Diamond SL-2 window-3112C analysis. Page 50

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Figure 20. Diamond SL-2 window-3212C analysis. Page 51 Figure 21. Diamond SL-2 window-3312C analysis. Page 52 Figure 22. Diamond SL-4 window-012C analysis. Page 55 Figure 23. Diamond SL-4 window-112C analysis. Page 56 Figure 24. Diamond SL-4 window-212C analysis. Page 57 Figure 25. Diamond SL-4 window-312C analysis. Page 58 Figure 26. Diamond SL-4 window-412C analysis. Page 59 Figure 27. Diamond SL-4 window-512C analysis. Page 60 Figure 28. Diamond SL-512C element maps for each analysis window. Page 62 Figure 29. Diamond AR13C/12C element maps for each analysis

window. Page 65

Figure 30. Diamond AR window-113C/12C analysis. Page 66 Figure 31. Diamond AR window-213C/12C analysis. Page 67 Figure 32. Diamond AR window-313C/12C analysis. Page 68 Figure 33. Diamond AR window-713C/12C analysis. Page 69 Figure 34. Diamond SL-2 window-2812C,13C/12C and16O element

maps. Page 72

Figure 35. Diamond SL-2 window-2812C analysis. Page 73 Figure 36. Diamond SL-2 window-2813C/12C analysis. Page 74 Figure 37. Diamond SL-2 window-2816O analysis. Page 75 Figure 38. Morse potential energy plot showingthe effect ofincreasing

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Figure 39. Diamond SL-2 window-2816O chemical characterizations. Page 84 Figure 40. Diamond SL-2 window-2813C/12C chemical

characterizations. Page 86

Figure 41. Diamond SL-2 window-28 modelinginfluences on chemical

characterization accuracy. Page 87 Figure 42. Diamond AR window-213C/12C chemical characterization. Page 90 Figure 43. Diamond AR window-2 chemical characterization of

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INTRODUCTION

Diamond chemical andthermodynamic stability makethem anideal mineralto studythe interior ofthe Earthincludingthe sublithospheric mantle and deep mantle (<660 km; Stachel et al., 2000a; 2000b). Through diamondinclusion and chemical variability,insightintothe

pressure,temperature,timescale and chemical (P-T-t-X) changesinthe sublithospheric mantle and mantle are elucidated (Kaminsky et al., 2009; Palot et al., 2009; Sobolev et al., 2009). Over the past 30 years geochemical study of diamonds andtheirinclusions have constrainedthe temporal evolution ofthelithosphere and mantle, butthese studies face a series of difficulties (Shirey et al., 2002). Diamond host rocks such as kimberlites andlamproites usually contain mixed populations of diamond each with a unique P-T-t-X history. These historiesinclude diamondstravelingthrough hundreds of kilometers ofthe mantle, exposerto extreme chemical environments of kimberlite magmas and metastability for millionsto billions of years at surface conditions or residence withinthe mantle. Of all gem quality diamonds, an estimated 80% go throughthesetrials and crystallize withinthe Diamond Stability Field (DSF) from 120to 260 km depth (Harte, 2010). Withinthe DSF, carbon super saturation occursin metasomatic fluids

causing crystallization of diamondininterstitial grain boundaries from highlyincompatible mantle fluids (Shirey and Shigley, 2013; Logvinova et al., 2015). Thus, models and constraints of diamond genesis and evolution require focused studies of different populationsto characterize and constrain growth conditions (Thomassot et al., 2007).

Analysis of stable and radiogenicisotope ratios andtrace element concentrations within diamonds, fluidinclusions, mineralinclusions, or elementalimpurities allow forthe constraint of diamond evolution and classification of diamond populations. Diamond growth zones record

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equilibrium conditions of mantle fluids as growth, residence, and exhumation occur. Analysis of multiple growth zones within diamond allowsthe measurement of fluxes within mantle fluids. Whereas mantle xenoliths are exposedto open system mantle melting and subjectto

metasomatism andlocal-scale re-equilibration during mantle residence as well as alteration during and after kimberlite eruption (Viljoen et al., 2004). Diamond growth zones andinclusions remain closed with negligible diffusion betweenthe host magma and diamond. Therefore, characterization of fluid sources andthe history of diamonds can be determined by studies of growth zones,inclusions and multiple generations ofinclusions. Such studies provideinsight intothe evolutionary history of cratons (Cartigny et al., 2009; Richardson et al., 2009; Stachel et al., 2009; Timmerman et al., 2017).

Radiogenicisotope studies are primarily restrictedtoinclusions and are relatively straight forward. Stableisotope studies of host diamond growth zones, however, require high analytical precision and minimal destruction ofthe sample. The precision and accuracy required for high spatial resolution studies have only recently been established. Priorisotopic studies of diamonds required destruction ofthe samplethrough whole combustion of fractured diamond shards within a pure oxygen environment (Deines et al., 1984; McCandless et al., 1991). Current

methodologiesinclude Secondary Ionization Mass Spectrometry (SIMS) for carbonisotope values andlaser ablationinductively coupled plasma mass spectrometry (LA-ICP-MS) or Electron Microprobe fortrace element concentrations (Kaminsky et al., 2015; Van Rythoven, 2012). Each ofthese methods have spot sizes and ablation pits onthe order oftens of

micrometersin diameter with minimal sample destruction. Withthe development ofthese methodologies, questions such as what role does subduction playinthe carbon cycle or how carbon reservoirs have changedinthe mantlethroughtime are possibleto address.

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The primaryissue withtheinstruments mentioned above arethe cost andlimited access to facilities. This study exploresthe viability of Time-of-Flight SIMS (ToF-SIMS) for measuring intracrystalline stableisotope variability of carbon within diamonds. ToF-SIMSis a useful method forin situ microanalysis of solids because ofthe spatial resolution,low detectionlimits and rapid analysistime comparedto dissolution methods. During analysis a wide range of elements can be measuredincluding carbon, oxygen, nitrogen and various cations while not requiring elaborate sample preparation or destroyingthe sample.

Calculation of stableisotope values from ToF-SIMS analysis comparesthe diamond compositiontothe composition of carbon based multi-elementinternal standards of carbonatite hosted calcite crystals. Such an approachis needed for analytical modeling ofthe ToF-SIMS data. The outcome ofthis projectisto establish a frame work for a potential methodology for diamond analysisthatis relatively cost effective while maintainingthe spatial precision of more costly methods. As well as characterizingthe stableisotope values of carbon and oxygeninthe Magnetite Cove carbonatite.

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BACKGROUND

Diamond crystallization can occur at any depth on Earth. Meteoriteimpacts have been knownto produce micro-diamondsin carbon-rich strata (Popigai Crater, Russia - Koeberl et al., 1997), ultrahigh pressure metamorphic belts have produced sub-gemto gem quality diamonds withinthe shallowto midlithosphere (Kokchetav Massif, Kazakhstan - Schertl and Sobolev, 2013) and diamond crystallization from fluids cantheoretically occur from 120 kmtothe core (Bundy, 1989). Thelatter population of diamondsis volumetricallythelargest andthe primary source of active mines. Independent ofthe crystallization environment, once diamonds form, they areindefinitely metastable at surface conditions or withinthe DSF.

Diamonds are subdivided based on mineralinclusions ratherthan chemistry or host deposit. Peridotitic (P-type) and eclogitic (E-type) diamonds refertothetype of mineral

inclusions found withinthe stones (Harte, 2010).Ultra-deep (U-type) diamondtypes have been identified with mineralinclusions such as perovskite or carbides (Stachel et al., 2005). Sulfide inclusions have been observedin all diamondtypes. P-type show silica undersaturatedinclusions such as olivine, ringwoodite, and wadsleyite. While E-type stones are silica enriched with

inclusions such as clinopyroxene and majorite garnet. Diamondtypeis associated withthe contiguous mantle rockthat diamonds crystallize within. P-type reflect diamonds growingin relationto depleted mantle residue withinthe diamond stability field (DSF) and E-type diamonds growin relationto subducted oceanic crust (Walter et al., 2011). P and E-type diamonds can mutually occur withinthe DSF.

The bonding structure of diamond restricts substitution of most elements onthe periodic table. Nitrogen and boron arethe only elementsthat significantly substitute carboninthe

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diamondlattice. Ofthese elements, nitrogenisthe only elementthat has been studiedin detail. However,in rare blue diamonds, boron concentrations have been measuredinthe hundreds of parts per million (ppm) (Eaton-Magaña et al., 2018). Nitrogen contentin ppmisthe only criteria usedto subdivide diamonds by chemistryinto Type I and Type II diamonds. Type I diamonds have N ≥ 20 ppm and Type II diamonds have N < 20 ppm (Shirey et al., 2013). Type I diamonds arethe most common for mantle derived diamonds (Cartigny et al., 2009). Diamonds crystallized within kimberlite melts orin metamorphic environments commonly have high N concentrations ≥ 200 ppm (Cartigny, 2005). Concentration of nitrogeninthe diamondlatticeis dependent on temperature, residencetime and fluids crystallizing diamonds (Cartigny, 2005; Koga et al., 2003). Nitrogenimpurities can diffuseinthelattice and form aggregation states aA, aB, and b. In type IaA diamonds,the nitrogenis presentin concentrations ≥ 20 ppm as paired atoms. Type IaB diamonds,the nitrogenis presentin concentrations ≥ 20 ppm as clusters of four atoms. Type Ib diamonds,the nitrogenis presentin concentrations ≥ 20 ppm as single atoms. Nitrogen

aggregation stateis measured via Fourier-transform Infrared Spectroscopy (FTIR) and presented as a percent between states. For example, atype I diamond may have an aggregation state as 30% aA. Where 30% of nitrogen presentinthe structure are pairs of nitrogen. Aggregation state has been suggested as a measure of residencetime withinthe mantle (Shirey et al., 2013). With Type Ib diamonds being under mantle conditions for shorter periods oftime and IaB beingthe longest. However,the diffusivity of nitrogen withinthe structure changes as higher order aggregations occur andthe kinetics of aggregation slow considerably at higher states (Koga et al., 2003).

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This study focuses on diamonds crystallized or which resided within a sublithospheric mantle keel. Pressure andtemperature conditions ofthese environments often surpassthe minimum crystallization conditions for diamond at 2 GPa and 1100 K (Bundy, 1989).

Crystallization within hightemperature and pressure regimes and subsequent relaxation during residence and exhumation demonstratethe unique robust nature ofthe diamond minerallattice.

The mineralis a face-centered cubic crystal withtetrahedral bonds. The most common crystal habit of diamondis octahedral or cubic. Resorption duringtransit within kimberlitic magma may form more complicated habits such as rhombicosidodecahedron or a rounding of octahedral habit. Within kimberlitic magma crystallization of diamondinthe form of fibrous coatings may also occur. Natural diamondsthat have experienced chemical etching during kimberlitic eruption showtrigons.

Carbon bondsintetrahedral coordination with four adjacent carbon atoms withtheideal bond angle for C-C-C of 109.5°. These carbon atoms have hybridized sp3orbitals with covalent bondlengths of 1.5 Å (Hazen et al., 2013). The combination of carbon atomsin diamond having theideal bond angle and hybridized electron exchangeinthe covalent bond allows forthe extreme refractory capacity and stability/metastabilityin multiple chemical environments (Cherniak, 2010). Diffusivity of carbon withinthe diamond structureis also amongthelowest diffusivities modeled (Koga et al., 2003). Koga et al. (2003) modeled heterogeneity of carbon isotopes between growth zones as preserved at 1.6 µm wavelength after one billion years of 1300 to 1400 K. These properties explain why diamondisthe premiertarget forin situ mantle

chemistry studies. The resistance characteristic of diamond meansthatthe chemical bondsinthe lattice orimpurities andinclusions remainin a closed system during residence, exhumation, and surface exposure. While migration of atoms withinthese structuresis minimal.

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Residence and Exhumation

While most diamondiferous minestarget gems originating withinthe mantle, diamonds must betransportedtothe base ofthelithosphere and residethere for millions of years before exhumation (Helmstaedt, 1993; Stern et al., 2016). Atthe base ofthelithosphere, diamonds reside withinthe mantle keel attachedtothe crust. Withinthe mantle keel, diamond residenceis firmly withinthe DSF and diamonds exist at anindefinite state of equilibrium (Hazen et al., 2013). However, withinthelattice, nitrogen migrationinto different aggregation states and diffusivity between growth zones occurs. As such, diamonds crystalized at greater depth will havelower resolution of growth zones and maytransition fromtype Ito II duetotheincreasein diffusivity of carbon and nitrogenloss astime undertemperatureincreases (Koga et al., 2003). Once diamondis exhumed fromthe DSF,thelattice existsindefinitely as a metastable phase (Hazen et al., 2013).

In order for diamondsto beliberated,low percent melts derived fromthe extreme pressures atthe base of cratonic nuceli mantle keels needto occur (Tainton and McKenzie, 1994). These melts occur withinthe DSF or deeper and arethe medium for diamond exhumation (Stern et al., 2016). The relationship between cratonic nuclei and diamondiferous kimberlitesis known as Clifford’s Rule (Clifford, 1966). Residence of diamonds outside of a cratonic mantle keel arelikelyto slowly graphitize and/or resorb before exhumation (Shirey and Shigley, 2013). Rocks derived fromthese kimberlitic eruptions arethe only known source of diamonds fromthe mantle (Shirey and Shigley, 2013; Stern et al., 2016). Kimberlite volcanismincludesthree rocks: kimberlite,lamproite, andlamprophyre. Each rocktypeis ultramafic and ultrapotassic with high abundances of volatile phases such as carbon dioxide and water. The distinction between each

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typeisthat kimberlitestendto be concentratedin potassium and have abundant phenocrysts of olivine. Whilelamproite has phenocrysts of diopside and phlogopite withless olivine.

Lamprophyreis absent of olivine. The primary source region of eachisthoughtto be within or belowthe DSF (Sparks, 2013). These distinctions are often difficult dueto 59.7% of global kimberlitic volcanism occurring duringthe Mesozoic (Stern et al., 2016) andthe rapid

erosion/alteration of ultramafic material. Duringtransport of magmatothe surface, alteration due to changesintemperature and resorption of entrained materials also changesthe chemistry ofthe magma making original chemistry difficultto elucidate (Sparks, 2013). Changes occurring

withinthe ascending magma duetothese changes may also cause serpentinization (Hausel, 1998).

Withinthe primary source region of kimberlitic magmas, diamond entrainment occurs. Kimberlitic melts destabilize silicate grains and entrain diamondiferous xenoliths or xenocrysts (Shirey and Shigley, 2013). This disequilibrium of silicate phasesis a factor ofthe extreme partitioning ofincompatible and volatile elementsto kimberlite magmas (Sparks, 2013). However, Russell et al. (2012) proposedthat during ascent of kimberlite magmas, xenoliths or xenocrysts of mantle material will resorb and cause carbon dioxideto disseminate fromtheorized original carbonatite composition. Thisincreasein gaseous phases effect onthe rate of ascension or propagationis highly debated (Lensky et al., 2006; Wilson and Head, 2007; Menand and Tait, 2001). Lensky et al. (2006) and Wilson and Head (2007) hypothesizedtheincrease of volatile components have a proportional effect on ascension rate and predicts the rate of ascent beingin thetens of meters per second. This rate of ascent for kimberlite magmasisthoughtto bethe distinguishing factor for diamondiferous kimberlites and diamond free basalts (Shirey and

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Shigley, 2013). Withoutthe rapid ascent, diamond resorption or graphitization occurs before eruption.

Stable Isotopes

Withinthe DSF, carbon supersaturation conditions are an enigma. Duringthe Haden magma ocean, researchers have hypothesizedthat volatile elements such as H, C, N, and S are removed fromthe mantletothe early atmosphere (Yuan et al., 2016). What volatile elements remainedinthe mantle after a siliceous crust had formed areisotopically homogenized

(Dasgupta, 2013). The homogenized mantleis standardly referredto by many asthe primordial reservoir.

Of all stable carbon on Earth, 97.93%is Carbon-12 and 1.1%is Carbon-13. Isotopes of lighter elements are easily fractionated at surfacetemperature and pressure regimes. This phenomenonis a result ofthe changein behavior of a molecule with variantisotopic

composition dueto quantum mechanical operations. Bonds of molecules have been generalized in multiple models such as harmonic and anharmonic oscillation. Within a molecule, atoms boundtogether will constantly vibrate further and closer apart. This vibrational frequencyis a function of mass, bondlength andthe vibrational state ofthe molecule. Generally, bonds with isotopicallylighter compositions will have higher vibrational frequencies and areless stable. While bonds withisotopically heavier compositions havelower vibrational frequencies and are more stable. Changesto preferredisotopic compositionin bonds are duetotheinverse

relationship betweenisotopic weight and vibrational frequency. However,ifthe moleculeis not inthe ground vibrational state,thenisotopicinfluence on bond stabilityis mitigated. Changesto

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the vibrational state of a molecule are a function of ambient conditions such astemperature and pressure. Chacko et al. (2001) derivesthe physical chemistry summarized here.

Diamonds are made of C, N, B andimpurities. Carbon, nitrogen and boronisotopes are expressed using delta notation. Fortheseisotopic systems,13C/12C,15N/14N, and11B/10B ratios are measured. For carbon,the ratio of13C/12Cis weighted relativeto Vienne Pee Dee Belemnite (VPDB) and expressed as a per mille (‰) value.

Equation 1 δ13C = ( 13C 12CSample 13C 12CStandard-1)*1000

In equation 1,13C/12C sampleisthe analyte and13C/12C standardistheinternationally accepted ratio of VPDB from Verkouteren and Klinedinst (2004). Delta notation for nitrogen and boron are similarto equation 1 though with different standard mediums. Nitrogenin diamondanalyses are measured for concentration and usedto classify diamondtypes (Deines et al., 1993). Part per million concentrations of boron have been observedin somelocalities, but no systematic studies have been conducted on boronisotopes or concentrationsin diamond (Cartigny, 2005).

Diffusivity of carbon and nitrogeninthe diamond structure are similar at mantle

conditions. Accordingto experimental modeling by Koga et al. (2003), heterogeneity of carbon isotopes between growth zones are preserved at a 1.6 µm resolution after one billion years of 1300to 1400 K. Nitrogen diffusivity changes after one million years at similar conditions dueto changesinthe aggregation state. With relatively faster diffusivity of nitrogenin Type Ib states comparedto Type IaA. Nitrogenisotopic heterogeneity after one billion years of 1300to 1400 K are preserved at 0.1 µm resolution for aggregation states IaA and IaB.

Carbon Isotopes. Diamonds worldwide show δ13C valuesintwo major populations, δ13C values of -5 ± 1‰ and < -10 ‰ VPDB (Cartigny, 2005; Shirey et al., 2013; Shirey and Shigley,

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2013). The first δ13C valueis equaltothe average mantle. Whilethe deviation fromthe average mantle showninthe second valueis an active research question. This variation may be explained by mixing surficial carbon reservoir(s) withthe primordial reservoir,isotopic fractionation of carbon at mantle conditions, or heterogeneities withinthe primordial reservoir. The different carbon source model argues forthe mixing of carbon from materialsinthe subducted oceanic slab withthe primordial mantle (Kirkley et al., 1991). Stableisotopes readily fractionate atlower temperature and pressure;increasingthese conditions causes a changeinthe bonding habit of isotopes. Thischange may be marked by crystallization of diamond from different carbon phases such as methane or carbon dioxide. Deines (1980) modeledtheisotopic fractionation of

crystallizing diamond from methane and carbon dioxide. These models showedthat

crystallization from carbon dioxide resultsin diamond showingthe primordial value. While starting with methane resultsin carbon values heavier, more negative,thanthe primordial value. Heterogeneity ofthe primordial reservoir arguesthat mantle convection did not fully mixthe mantle oncethe crust had formed. Evidence of primordial heterogeneity of carbon valuesis attributedtothe variancesin carbon valuesin meteorites.

The different carbon source model has been growingin popularity (Logvinova et al., 2015; Shirey and Richardson, 2011; Walter et al., 2011). Using data compiled by Cartigny et al. (2009) regions such as Guaniamo and Argyle have E-type diamond populations with average values of -16 and -11 ‰ and P-type average values of-6.3 and -6.8 ‰ VPDB,respectively (Figure 1). However, E-type diamonds fromNew South Wales have an average value of 0.4 ‰ and P-type have an average value of-3.5 ‰ VPDB (Figure 2). Some minesin Africa plot within error ofthe primordial reservoir (Figure 2). Mass balancing of fractionated carbon sources from the surface withthe primordial mantle could explain each regions deviation fromthe primordial

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Figure 1. Frequency plots of diamonds fromthe Guaniamo minein Venezuela and Argyle mine in Australia. Data forthe Venezuela plotis from Kaminsky et al. (2000) and Galimovet al. (1999). Data forthe Australia plotis from Jaques et al. (1986). Cartginy et al. (2009) compiled data fromthese sources.

0 5 10 15 -35 -30 -25 -20 -15 -10 -5 1 Fr eq ue nc y δ13C Guaniamo, Venezuela

E-type P-type 0 10 20 30 -35 -30 -25 -20 -15 -10 -5 1 Fr eq ue nc y δ13C Argyle, Australia

E-type P-type

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Figure 2. Frequency plots of diamonds from New South Wales, Australia and Premier minein South Africa. Data forthe Australia plotis from Davies et al. (1999, 2002, and 2003). Data for the South Africa plotis from Deines et al. (1989). Cartginy et al. (2009) compiled data from these sources. 0 4 8 12 -35 -30 -25 -20 -15 -10 -5 1 Fr eq ue nc y δ13C New South Wales, Australia

E-type P-type 0 10 20 30 40 50 -35 -30 -25 -20 -15 -10 -5 1 Fr eq ue nc y δ13C Premier, South Africa

E-type P-type

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mantle. For example,in New South Wales,the primordial mantle may have mixed with carbon derived from carbonate rocks. Carbonate rocks have an average δ13C value of 1 ± 2 VPDB. Whilethe more negative carbon values of Guaniamo and Argyle could be from mixing of organic material withthe primordial source. Organic material such as benthic or algae have an average δ13C value of -20 ± 2 VPDB. However, fractionation of carbonisotopes during diamond crystallization at mantle conditions have achieved similar δ13C values by precipitating diamond from gases such as methane forisotopicallylighter values or carbon dioxide for replicatingthe primordial reservoir (Deines, 1980). Duetotheinconclusive nature of analyzing carbonisotopesin diamond, nitrogen studies needto be doneintandem.

Nitrogen Contents. Nitrogenin diamondsisthe most common exchange. For sublithospheric mantle diamonds, nitrogen concentration are on average > 300 ppm. While diamonds formedin metamorphic environments range from hundredstothousands of ppm (Deines et al., 1993). Diamonds fromthetransition zone or shallow mantle are morelikely Type II with worldwide average values < 50 ppm (Cartigny, 2005). The aggregation state of nitrogen inthe diamond structureis a measure oftime underlithospheric or sublithospheric conditions (Shirey et al., 2013). Intype Ib diamonds, single nitrogen atoms have similar diffusivities as carbon. However, at mantle conditionsthe energy fortype IaA, N-N bonds, aggregations are easily achieved. Once IaA aggregation occurs,the diffusivity of nitrogen dropstothe pointthat heterogeneity of nitrogen concentrations across growth zones are preserved for atleast 1 billon years at mantle conditions (Koga et al., 2003). Thus, Type Ib diamonds spendlesstimethan Type IaA and Type IaB spendthe mosttime atlithospheric or sublithospheric conditions. Stark contrastsin nitrogen concentrations have been observedin natural diamonds by FTIR

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(Mendelsohn and Milledge, 1995). With dramatic changes beinginterpreted as different generations of fluids.

Nitrogen Isotopes.Nitrogenisotopes show similar variation as carbonin diamond. The average value of δ15Nin diamondis -5 ± 3‰ relativeto air. Withthe global dataset of diamonds ranging from -25to +15 ‰ relativeto air. The mixing model hypothesis for explaining

isotopicallylighter carbon valuesis appliedto nitrogen values. Metasediment nitrogenisotope values are allisotopically heavy or positive relativeto air. Nitrogenisotope values, duetothe fractionation of15N during subduction, contradictthe mixing model (Haendel et al., 1986). The mixing of carbon sources predictsthat E-type diamonds are genetically relatedto organic material volatizing fromthe subducted slab. This relationship would predictthat fractionation of 15Ntothe subducted slab should cause E-type diamondsto haveisotopically heavier δ15N values. However, E-type diamonds show 70% of analyses haveisotopicallylighter nitrogen values

(Cartigny, 2005).

Time of Flight Secondary Ionization Mass Spectrometry

Secondary Ionization Mass Spectrometry (SIMS) methods are relatively new (Siljeström, 2011). These analysis methodologies are necessary for spatially restricted orlow analyte

questions. These questionsinclude diffusion between/along growth zones or between phases, geochronology ofindividual growth zones, and geochemical analyses ofinclusions. Inthe case of diamond geochemistry, SIMS methodologies are well suited for measuring stableisotope geochemistry between growth zones or geochemical/geochron analysis ofinclusions.

SIMS analyses all operatein a similar fashion. Theion source bombards a polished sample surface of analyte with anion beam(s) generating secondaryions (Stern, 2009). This

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beamtransfers kinetic energy and changes electron stateinthe bonded analytelattice. Kinetic exchanges sputter minor pits relativeto other mass spectrometry methods. For example,laser ablation mass spectrometry generate 50 µm wide and ≥ 20 µm depth pits. While SIMS methods can be as minimal as 10 µm wide and ≤ 5 µm depth pits. Sputtered material can beionized dueto the excitation/exchange of electrons. Theionization efficiencyin SIMS analysisislow. With ≈ 1 % of sputtered material beingionized (Stephan, 2001).

While a minimum portion of analyteisionized,the process by whichtheseions are

generated occurs afterthe materialisliberated fromthe analyte surface (Stephan, 2001). Leading uptothis moment,the matrix effectisthe dominant phenomena for which atoms are sputtered fromthelattice (Stephan, 2001). The matrix effectis most generically summarized asthe

probabilisticinfluence on whichionic sites areliberated duringion gunimpacts (Stephan, 2001). As a primaryion hitsthe crystallattice, someionic sitesinthe structure are morelikelyto be removed while other sites are more robust. The matrix effectis assumedto be normalizedinthe use of a standard mineralthatis crystallographically as similar as possibletothe unknown mineral (Stephan, 2001). Such a standardintheory would havethe same matrix effect fractionations as an unknown mineral andthus what effectif any can be normalized.

Time of Flight Secondary Ionization Mass Spectrometry (ToF-SIMS) instruments operate by firingtwoion beamsin pulses. Theseion beams fire every nanosecond andimpactthe

analysis material. The cesium sputter beamis fired andimplants Cs+ions. This processimproves ionization efficiencies of secondaryions during analysis beam operation. The analysis beamis a Bi+ion beam. This beam generates secondaryions bytransferring kinetic energyintothe sample and overcomingthe dissociation energy of bonds. Stephan (2001) has describedthe quantum mechanical operationstaking place during secondaryion generation. Secondaryions fromthe

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surface are acceleratedthrough a potential fieldtowardlenses, correction filter(s) and finally a detector. These filters account forthe different velocity ofionized phases dueto different masses.

Equation 2 v = √2qVm

Equation 2 showstheinverse relationship between velocity (v) and mass (m) accounting for charge (q) andthe potential field (V). To correct for velocity differences,the acceleratedion phases are directedinto a magnetic dampening field withinduced currents antiparalleltothe primary vector of secondaryions. Ions of different masses penetratethe dampening field asthey are accelerated alongthe new V. This filter generates a gap betweenions dueto different mass. This gap closes as both phasestraveltothe detector. The detector on some ToF-SIMS works by measuringthetime fromions hittingthe first focusingleans and secondaryion detection.

However, other ToF-SIMSinstruments allow secondaryionsto driftin a neutraltube after being acceleratedthrough a potential fieldtowardthe detector.

Equation 3t = L√m 2qV

In equation 3, Lislengththation speciestravel. Detectors are only ableto detections every 10s of microseconds duetothe flighttime ofions. Sincethe detectors operatein a kinetic sense, ToF-SIMSis ableto resolve all mass spectra during analysis. Comparedto other SIMS methods where mass spectrometer detect specific mass-to-charge ratios.

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METHODS

This studyincludes chemicalimaging andisotope ratio mass spectrometry. A T ime-of-Flight Secondary Ionization Mass Spectrometer (ToF-SIMS) chemicallyimaged six diamonds from Canada and United States andthree calcite crystals fromthe Magnet Cove carbonatite in Arkansas, USA. ToF-SIMS element mapstransect from core-to-rim of crystals. Element maps of 12C,13C/12C, and16O were usedto characterizeintracrystalline variation. Eleven drill bores of calcite were analyzed for δ13C and δ18O values. Reported stableisotope values of calcite are used as aninternal standard for ToF-SIMS analysis. This standard was usedto calculate stableisotope values for carbonin diamond andtestifthe ToF-SIMS could replicate carbonatiteisotope values.

Internal Standard

Calcite crystals of Magnet Cove carbonatite from Malvern, AR were used as aninternal standard. Eleven drill bores from regions of pure calcite were cleaned and milledinto a powder using a micro drill at Missouri State University. Each sample yielded 1to 10 mg of powdered calcite and were brushedinto vials. Powders were analyzed for δ13C and δ18O values atthe Stable Isotope Laboratory at University of Missouri - Columbia by Isotope Ratio Mass Spectrometry (IRMS). δ13C and δ18O values reported as per mille (‰) relativeto VPDB.

Analysis methodology follows Bassett et al. (2007). Average δ13C and δ18O values with error ofthe eleven bores was -5.51 ± 0.16 and -22.6 ± 1.5 VPDB,respectively.

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Diamonds forthis study originate from a kimberliteinthe Slave Province ofthe

Northwest Territories of Canada and alamproitein Murfreesboro, Arkansas. The diamonds from the Slave Province originated fromthe Diavik pipe. This pipeintrudedthe Slave Province craton between 45to 140 Ma from a depleted mantle keel (MacKenzie and Canil, 1998). The diamond from Murfreesboro, Arkansasis fromthe Prairie Creeklamproite. Thislamproiteintruded a shallow marine environment alongthe southern margin of Laurentia 100 Ma from an enriched section of sublithospheric mantle (Howard and Hanson, 2008).

This study used five diamonds fromthe Northwest Territories of Canada and one

diamond from Murfreesboro, Arkansas. These diamonds will be referredto as SL-1, SL-2, SL-3, SL-4, and SL-5 forthe Northwest Territory diamonds and AR forthe Arkansas diamond.

Diamonds fromthe Diavik pipe were selected because of recent stableisotope characterization by Van Rythoven (2012) and diamond accessibility. Van Rythoven (2012) analyzed core and rim of diamonds with intracrystalline δ13C variability outside analytical error. Van Rythoven (2012) carbonisotope values of diamonds fromthe Diavik mine were usedto determinethe accuracy of ToF-SIMS analyzing diamonds fromthe same mine. The diamond from Arkansas was selected becausethere are no known high spatial resolutionisotope data sets. The Magnet Cove

carbonatite selected for ourinternal standard represents rock relatedtothe diamondiferous host ofthe Arkansas diamond.

Using a standard stereo microscope with camera, morphologic descriptions of each diamond were made for cuttinginstructions. Each diamond was marked with aline parallelto theirlongest dimension. Diamonds were senttothe Gemological Institute of America Research Labin New York City forlaser cutting and polishing. Diamonds were cut alongthis markedline andintersectedthe center of each diamondto exposethe greatestinternal surface area. Polished

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diamond halves and Magnet Cove carbonatite crystals were pressed withthe polished side up intoindium metal with an Arbor Press. Indium pucks with mounted crystals arein one-inch diameter hollow aluminum pucks standing a quarter of one-inch. Thethree pucks contain a combination of diamonds and carbonatite calcite crystals. Puck one containsthe diamonds SL-3 and SL-4 with one standard. Pucktwo containsthe diamonds SL-2, AR, and SL-5 with one standard. Puckthree contains SL-1 withtwo standards. Puck four only contains four standards.

ToF-SIMS Analysis

Oneinch round pucks withthe mounted diamond and carbonatite wheretransportedto Oak Ridge National Labs Center for Nanophase Material Science. The Atomic Force

Microscope ToF-SIMS was operated with a Bi+primary analysis beam at a voltage of 30 keV and a Cs+sputter beam at a voltage of 2 keV. The primary beam operational voltage was

progressivelyincreasedto 30 keVtoimprove counts received. This Bi+primary beamis usedto generate secondaryions for analysis. Whilethe Cs+sputter beamis usedto cleanthe surface and implantintothe crystal for greaterionization potential during primary beam operation. The analysis window for Canadian diamonds and carbonatite samples was 500 x 500 µm with 700 x 700 µm sputter window. The Arkansas diamond hadthree analysis windows of 500 x 500 µm and 700 x 700 µm sputter window and one analysis window of 300 x 300 µm with 500 x 500 µm sputter window. The Arkansas diamond had different analysis dimensionsto maximize how much ofthe diamonds polished surface was analyzed. Theinstrument operatedin random raster mode, dividingthe analysis windowinto 256 x 256 pixels with each pixel being 1.95 x 1.95 µm for 500 x 500 µm and 1.12 x 1.12 µm for 300 x 300 µm analysis windows. All Canadian diamond analyses weretransects of core-to-rim. The Arkansas diamond had analysis windows

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coveringthe polished surface. Each analysis windowis separated by 400 µmto ensurethatthe edges ofthe sputter window are notinthe analysis window. Ridges formed atthe edge ofthe sputter window can causetopologic errorsifincludedinthe analysis window.

During analysis, each pixelin a windowis randomly analyzedtwice withthe detector capable of measuring anion species every picosecond. The averagetime of anion analysis for Canadian diamonds was 31 minutes for each window. The average analysistime for Arkansas diamond windows was 41 minutes. The anion species presented here are12C,13C, and16O. However,the ToF-SIMS gave spectral data for allionized anions. Cation analyses were done on diamonds AR and SL-2. These cation analysesincluded Al, Ca, Fe, Mg and Si. Anion analyses are reported as unmodified spectral counts. Element maps and ASCII files were created for each anion and cation species per analysis window. ASCII files are organized as a matrixto retain spatialinformation ofthe analyzed pixels withtheintensity of each pixel beingthat species count. Data sets were reduced and processedin Microsoft Excel.

ASCII files of analysis windows were usedto createimagetransects and spectral

intensity conversion models. Two ofthe five Canadian diamonds, SL-3 and SL-5, hadtopologic errorsthat made ToF-SIMS analysistrivial. Errors observedinthese diamondsincluded

extensive crackingthroughout SL-5 and parallel surficiallineationsin SL-3 potentially fromthe polishing or mounting procedure. For all other diamonds,three vertical and horizontaltransects were created forthe12C speciesin each analysis window. Transects are atthe quarter, half, and three quarterslengthinthe vertical and horizontal dimension. Rawtransects are reducedto 4th order polynomial best fitlinesto determine variationin12C counts. This reduction was done due tothe amount of points pertransect making evaluation computationally expensive.

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The Arkansas diamond and one Canadian diamond were selected for further anionimage analysis. The Arkansas diamond was usedtotestthe viability ofimaging12C or13C/12C

variabilityin a gem quality homogenous diamond. The Canadian diamond selected, SL-2, has a subhedral mineralinclusion showingintracrystalline zonation of12C. Additionalimage analysis includedtransects of13C/12C for AR alongthe same12Ctransects and12C,13C/12C and16O along tighterintervals forthe SL-2 mineralinclusion.

Signal Intensity Conversion

ToF-SIMS analysis potentially generates Signal Intensities (SI) of all atoms presentinthe analyte. However,theinstrument can not directly convert SIto stableisotope values.Theisotope values for carbonatite were used as a conversion standard of SIin carbonatite.

Equation 4 [EE⁄ ]Samp0 le =[SI (E)

SI(E0) ⁄ ]Sample [SI(E)SI(E

0)

⁄ ]Standard*[EE⁄ ]S0 tandard

Stephan (2001) uses equation 4 as a general conversion of SItoisotope ratios. Using equation 4 and equation 1 givesisotopic values. These values show error percentages of 500to 32000% for carbonatite and have calculated values -2000to +100 ‰ VPDB for carbon valuesin diamond.

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RESULTS

To determine whether Time of Flight Secondary Ionization Mass Spectrometry (ToF-SIMS)is a viable stableisotope methodology for diamond, Itestedif our data could replicate published data from otherinstrument methods andif chemical spatial characterizationis

possible. This replication processincludedthe establishment of aninternal standard by Isotope Ratio Mass Spectrometry (IRMS) and chemicalimagetransects of ToF-SIMS analysis windows. Theinternal standard was established with 11 drill bores of optically pure calcite fromthe Magnet Cove carbonatite. These calcite powders were analyzed by IRMS for δ13C and δ16O values. Calcite crystals were also mounted with diamond and analyzed by ToF-SIMS. Chemical image evaluation of four diamondsincluded 24 analysis windows and 15012C, 3013C/12C, and six16Otransects. Thesetransectsincluded 44,493 data points, 0.94% ofthetotal anion dataset for elemental carbon and oxygenisotopesin diamond.

IRMS analysis characterizedthe Magnet Cove carbonatite carbon and oxygen stable isotope values. Inequation 4 (Stephan, 2001), Eis an element, E0is anisotope ofthe same element and SIis signalintensity. IRMS values were used asthe standardisotopic ratio [EE⁄ ] 0to convert SItoisotopic values. Results ofthese conversions andthe associated errors are

presented.

All chemicalimagetransects of12C are described for four diamonds. Additionalimage analysis for ARincludes six13C/12Ctransects for four windows. Additionalimage analysis for SL-2includes sixtransects of12C,13C/12C and16O for an inclusionin window-28. Surficial errors are presented for SL-3 and SL-5.

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Carbonatite Stable Isotopes

Carbon and oxygen stable isotope values for Magnet Cove carbonatite presented arethe first known stableisotope values. IRMS analysis of calcite millings from carbonatite yielded isotopic values for δ13C and δ18O with ranges of -5.36to -5.70 and -20.90to -23.11‰ VPDB, respectively (Table 1). Average values for δ13C and δ18O with error are -5.51 ± 0.16 and -22.6 ± 1.5‰ VPDB. The average value of δ13C was convertedto anisotopic ratio for usein ToF-SIMS SIto value conversions. Standard VPDB13C/12C ratio used was 0.0112372 (Verkouteren and Klinedinst, 2004). Solving equation 1 for13C/12C sample,the range for13C/12Cin calcite bores was 0.011174467to 0.011176969 with a 2σ error of 1.8e-6(Table 1).

ToF-SIMS Signal IntensityConversion

Four crystals of calcite were mountedinindium with diamonds for ToF-SIMS analysis. Each calcite was analyzed for12C and13C for a minimum oftwo analysis windows. Using a calcite crystal withthree analysis windows asthe [SI(E)SI(E

0)

⁄ ] standard. SIto δ13C conversion ofthe remainingthree calcite crystals and diamonds AR and SL-1 were calculated using

equation 4 andthe average13C/12C ratio from Table 1. Table 2 contains all ofthese calculated 13C/12C ratios and δ13C values. Calculated values arethen comparedto known δ13C values of calcite from IRMS,the Diavik mine (Van Rythoven, 2012) for SL-1 and Murfreesboro, AR (McCandless et al., 1991) for AR.

Diamond ToF-SIMS Analysis

Morphology of diamondsinthis study range fromindustrial quality bortsto resorbed octahedral sub-gemto gem quality stones. ARis a sub-gemto gem quality homogenous yellow

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Table 1. Isotope Ratio Mass Spectrometry carbon and oxygen stable isotope data of Magnetite Cove carbonatite millings. δ13C and δ18O values are per mille relative to Vienna Pee Dee Belemnite. δ13C 13C/12C Ratio δ18O msu-1.0 -5.46 0.011175793 -22.95 msu-1.1 -5.47 0.011175694 -22.96 msu-2 -5.46 0.011175803 -22.95 msu-3 -5.47 0.011175704 -21.34 msu-4 -5.58 0.011174467 -23.06 msu-5 -5.54 0.011174964 -23.11 msu-6 -5.36 0.011176969 -20.90 msu-7 -5.50 0.011175422 -22.87 msu-8 -5.54 0.01117499 -22.82 msu-9 -5.49 0.011175555 -23.01 msu-10 -5.70 0.011173197 -22.63 average -5.51 0.011175324 -22.60 2σ error 0.160973512 1.80889e-06 1.500

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Table 2. Signal Intensity (SI) conversion of Magnet Cove carbonatite crystals and diamonds SL-1 and AR. SI conversion follows equation 4 (Stephan, 2001). Calcite-2 was used as the SI(standard) value. Percent errors of calcite crystals are relativeto Isotope Ratio Mass Spectrometry values from eleven drill bores ofthe same calcite material (Table 1). Percent errors for SL-1 are relativeto an average δ13C value from sixdiamonds fromtheDiavik mine (Van Rythoven, 2012). Percent errors for AR are relativeto an average δ13C value fromtwentydiamonds from Murfreesboro, AR(McCandless etal., 1991).

Crystal Window counts 12C counts SI(sam) SI(sam)13C /SI(std) 13C/12C δ13C δ13C average 2σ error % error STD (Calcite-2) 1 8714 50 0.005738 2 7422 51 0.006871 3 5548 60 0.010815 Calcite-1 0 1 179459 475 0373974 8162 0.002647 0.021825 2.3389904 .7952083 00.0037883 -.0312374 1779662.876 558.4702942 2442.693064 11930 .817 32402 Calcite-3 2 3 5960 7494 75 34 00.012584 1.004537 0.5810644 .6116622 00.0064936 -.0180108 602422.7877.135 2292.047 6283.383795 11040 7561 4 1622 98 0.060419 7.7380981 0.0864757 6695.489 121615 Calcite-4 1 2 1975 8047 61 37 00.018734 2.00758 0.9708560 .3993501 00.0268135 1386.0108496 -34.4899.138 675.8242 1420.628236 25257 526 SL-1 0 3147277 12803 0.004068 0.5209977 0.0058223 -481.871 -148.311788 285.5850204 14958 1 3460034 26765 0.007735 0.9907085 0.0110715 -14.7467 361 2 2676031 18192 0.006798 0.8706594 0.0097299 -134.135 4092 3 2496842 17094 0.006846 0.8768224 0.0097988 -128.006 3900 4 2704496 20253 0.007489 0.9590959 0.0107182 -46.1853 1343 5 2226361 15145 0.006803 0.8712297 0.0097363 -133.568 4074 6 2413029 17057 0.007069 0.9053137 0.0101172 -99.6713 3015 AR 1 1296875 9455 0.007291 0.9337324 0.0104348 -71.4091 -28.5986803 122.8289078 1256 2 1096498 7887 0.007193 0.9212192 0.0102949 -83.8534 1492 3 1348896 10257 0.007604 0.9738698 0.0108833 -31.4927 498 7 836523 7043 0.008419 1.0782980 0.0120503 72.36048 1474 26

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to orange octahedral stone 2.5 mm wide. SL-1, SL-2, SL-3, SL-4 and SL-5 are equant borts rangingin size from 3.8to 5.1 mm with cloudyto clear clarity and some visible mineral

inclusions. SL-1 has an octahedral habit and SL-3 has a cubic habit all other diamonds fromthe Slave province are clusters.

Six diamonds were chemicallyimaged by ToF-SIMS at Oak Ridge National Labs. Diamonds SL-1 and SL-2 have seven anion analysis windows. SL-4 has six anion analysis windows. AR has four anion analysis windows. AR has cation for all analysis windows. SL-2 has cation for all windows except window-28. SL-4 has cation for window 4. Diamonds SL-3 and SL-5 have surficial errorsthat made ToF-SIMS analysestrivial. Presentation of ToF-SIMS chemicalimagetransects willinclude Row (R) and Column (C)transectsthat will be referredto as R or C andthe pixel for whichthattransects was ran.

AR. ToF-SIMS analysis of diamond ARincluded four windows: window-1, window-2 and window-7 with analysis windows 500 µm2and window-3 with an analysis window of 250 µm2. Anion and cation analyses were raninthe same analysis region for each window. Anion analysis on averagetook 41 minutes and cation analyses on averagetook 6.3 minutes. Total anions received ranged from 5.3to 8.2 million counts per window. Total cations received ranged from 11.8to 49.2 million counts per window.

Window-1. 12C within window-1 shows gradational and systematic SIincrease with a linear feature void of12C (Figure 3a and 3b). This gradationalincreaseis shown by a 30% averageincreasein12C SI fromleftto right within all analysistransects (Figure 3a). This gradationalincreaseis apparentin R128 withthe 4thdegree polynomial (Figure 3c) and moving average (Figure 3d) polylines. Thelinear featureis measured by R192 (Figure 3a) withthe moving average showing a 65% decreasein SI over 41 µm.

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Window-2.12C within window-2 shows gradational and systematic SI response and a homogenous diamond crystal. This gradationalincrease roughlyfollows fromthe NW to SE withinthe analysis window (Figure 4b). Thistrendis shown by variable changein R and C transects. R64 shows a7 to 10%increasein SIfromleftto right. R128 shows a 4to 8%increase in SIfromleftto right. R192 shows a 10to 12%increasein SIformleftto right (Figure4a). C64 showslittleto no average change fromtopto bottom. C128 show minor change <2%in SIfrom topto bottom. C192 shows a 5to 7%increaseinSI fromtopto bottom (Figure 4a).

Window-3.12C within window-3 shows homogenous12C SIresponse (Figure 5b). Minor systematic increase from leftto rightis showninthe12C element map (Figure5b), howeverthis trendis difficultto seeinthe movingaverage polylines for transects (Figure 5a). In Figure 5c, the 4thorder polynomialbest fit line of R128 shows a 20% increase fromleftto right. While the moving average plot of R128 (Figure 5d) showsthe SIresponseto have sporadicincreases and decreases backto an average base levelthatincreases fromleftto right by 10to 15%.

Window-7.12C within window-7 shows a concave down shape withlower SIinthe corners and higher SI towardsthe center of12C element map (Figure 6b). Moving average polylines of C64 and C192 (Figure 6a) show this trend withtheir maximum occurringwithinthe center of eachtransect and minimum valuestowardthe start and end of eachtransect.

SL-1. ToF-SIMS analysis of diamond SL-1included seven anion windows: window-0, window-1, window-2, window-3, window-4, window-5 and window-6 with analysis windows of 500 µm2. Anion analysis of each window on averagetook 30.7 minutes. Total anionsreceived ranged from 9.2to 14 million counts per window. Analysis window-1includes an example figure for other anion phases usedtoidentifylinear features andinclusion minerals (Figure 7).

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[Grab your reader’s attention with agreat quote fromthe

document or use this spaceto emphasize a key point. To place thistext box anywhere onthe Pixels

Figure 3. Diamond AR window-112C analysis. A shows 4thorder moving average polylines of row and columntransects. B showsthe 12C element map withtransects. C showsthe raw data of R128transect with a 4thorder polynomial best fitline and R2value. D shows the moving average polyline of data from row 128.Y-axisis Signal Intensity (SI).

R² = 0.0591 0 10 20 30 0 50 100 150 200 250 R128 0 10 20 30 0 50 100 150 200 250 Total12Ctransects C64 C128 C192 R64 R128 R192 0 10 20 30 0 50 100 150 200 250 R128 A B C C C D Pixel SI Pixel SI Pixel SI 29

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Figure 4. Diamond AR window-212C analysis. A shows 4thorder moving average polylines of row and columntransects. B showsthe 12C element map withtransects. C showsthe raw data of row 128transect with a 4thorder polynomial best fitline and R2value. D showsthe moving average polyline of data from row 128. Y-axisis Signal Intensity (SI).

0 10 20 30 0 50 100 150 200 250 Total12Ctransects C64 C128 C192 R64 R128 R192 R² = 0.0186 0 10 20 30 0 50 100 150 200 250 R128 0 10 20 30 0 50 100 150 200 250 R128 A B C D Pixel SI Pixel SI Pixel SI 30

Figure

Figure  42 .  D iamond  AR  w indow -2 13 C / 12 C  4 th order  mov ing  average  po ly l ine transec ts

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