Development and testing of an in situ method of ion beam analysis for measuring high-Z erosion inside a tokamak using an AIMS diagnostic

Development and testing of an in situ method of ion beam

analysis for measuring high-Z erosion inside a tokamak using

an AIMS diagnostic

by

Leigh A. Kesler

B.S. Nuclear, Plasma, and Radiological Engineering, University of Illinois at

Urbana-Champaign (2012)

Submitted to the Department of Nuclear Science and Engineering

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy in Nuclear Science and Engineering

at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

February 2019

© Massachusetts Institute of Technology 2019. All rights reserved.

Author . . . .

Department of Nuclear Science and Engineering

October 22, 2018

Certified by. . . .

Dennis G. Whyte

Hitachi America Professor of Engineering; Head, Nuclear Science and Engineering;

Director, Plasma Science and Fusion Center

Thesis Supervisor

Certified by. . . .

Zachary S. Hartwig

John C. Hardwick Assistant Professor of Nuclear Science and Engineering

Thesis Supervisor

Certified by. . . .

Michael P. Short

Class ’42 Career Development Associate Professor of Nuclear Science and

Engineering

Thesis Reader

Accepted by . . . .

Ju Li

Battelle Energy Alliance Professor of Nuclear Science and Engineering and

Engineering Professor of Materials Science and Engineering

Chair, Department Committee on Graduate Students

Development and testing of an in situ method of ion beam analysis for

measuring high-Z erosion inside a tokamak using an AIMS diagnostic

by

Leigh A. Kesler

Submitted to the Department of Nuclear Science and Engineering on October 22, 2018, in partial fulfillment of the

requirements for the degree of

Doctor of Philosophy in Nuclear Science and Engineering

Abstract

While many ex situ measurements exist to measure plasma-facing component (PFC) surfaces of materials extracted from tokamaks, developing a deeper understanding of the dynamics of erosion, redeposition, and fuel retention in these surfaces will require in situ measurements. A first-of-a-kind technique, Accelerator-Based In-Situ Materials Surveillance (AIMS), was developed for this purpose and first demonstrated on Alcator C-Mod to study divertor surfaces with shot-by-shot resolution [1]. However, the original AIMS methods are not applicable to studying the erosion of bulk, high-Z PFCs like molybdenum and tungsten. Thus, a new method of ion beam analysis (IBA) has been developed to expand the capabilities of AIMS to directly measure this high-Z bulk erosion.

This new method, called DEA (Depth markers for Evaluating high-Z materials with AIMS), combines the traditional IBA technique of particle-induced gamma emission (PIGE) with implanted depth markers. The implanted markers enable the study of bulk material by providing a reference to the surface that can be monitored for erosion and redeposition. Implanting the marker eliminates the need for specially-manufactured "marker tiles" formed by deposited layers that can delaminate and otherwise fail under operational conditions. Two variations of this method were developed: ex situ DEA (eDEA) and in situ DEA (iDEA). Both use PIGE spectroscopy with implanted markers, but they take advantage of different features in gamma production cross sections to analyze data. eDEA, which has shown promising results in ex situ analysis of materials exposed in a tokamak, can also be used to validate the use of depth markers. iDEA provides AIMS with the ability to measure in situ high-Z bulk erosion.

As part of this thesis, the following ex situ experiments have been carried out to assess the viability of these techniques. eDEA samples with implanted depth markers have been studied after plasma exposure on the Material and Plasma Evaluation System (MAPES) in the Experimental Advanced Superconducting Tokamak (EAST). Stability of the marker to temperature excursions was studied by exposing samples to temperatures from 200 to 1000◦_{C for times from 1 to 24 hours. iDEA} samples were implanted at different depths to determine the sensitivity of the technique to depth. Two simulations were developed to allow interpretation of the experimental data and to test the sensitivity, with initial studies showing a match between predicted and experimental results. eDEA measured erosion of 42.0 ± 23.5 nm on one sample exposed in EAST, and iDEA depth markers were located with ∼40 nm of accuracy. These results show that DEA, as a part of an AIMS experiment, has the appropriate resolution to monitor surfaces inside a tokamak for time-resolved bulk erosion. Thesis Supervisor: Dennis G. Whyte

Title: Hitachi America Professor of Engineering; Head, Nuclear Science and Engineering; Director, Plasma Science and Fusion Center

Thesis Supervisor: Zachary S. Hartwig

Title: John C. Hardwick Assistant Professor of Nuclear Science and Engineering Thesis Reader: Michael P. Short

Why think? Exhaustively experiment, then think.

— Claude Bernard, French physiologist, The Great Influenza,

2004

Hurry it up. We’re burning daylight.

— Wil Andersen, as portrayed by John Wayne, The Cowboys,

1972

Acknowledgments

Each person and group I recognize here has made this work possible, either through technical expertise and advice or through personal guidance and support. Through blood, sweat, and many tears, this thesis was completed, and the people here were the ones there to lift me up when I needed it most.

Each of my committee members has contributed to my thesis and my development as a scientist and as a person in a unique way. Dennis, thank you for teaching me how to think on my feet, to adapt. Your weekly board sessions helped me not just to pass quals but to find ways to answer questions when I don’t know they answer. Zach, you’ve gone from a grad student to a postdoc to a professor while I’ve been here (which say something I don’t like about how long I’ve been a student...). Through all of it, you’ve demanded both technical and personal excellence. I appreciate your willingness to make stupid Dune jokes while still being a serious scientist. Mike, you’ve saved my butt multiple times in this process, not just on my committee, but when I was struggling to retake quals and you spent the time and energy to make sure I was up to speed in your classes. Thanks for being my committee member... I mean thesis reader! Graham, you taught me so much about being an experimentalist and about accelerators. You were an excellent mentor, especially in my early days as a young clueless grad student. And finally, Anne, my chair: you kept me on course in the beginning, when it would have been so easy to quit and walk away. You reminded me that I was smart and strong and capable and fighting my way through.

The team in the Vault has been a family to me. Brandon, my science twin, I don’t know what this would have been like without you, but I am glad I don’t have to imagine it. Lab was never a place I felt out of place or alone when we were working together. I could rely on you, whether it was to figure out a technical problem or to force me to go to the doctor when I broke my foot, and you will forever hold a special place in my heart. Kevin and Harold, you both intimidated me when I first came to MIT, with your quiet deftness with accelerator operation, but you have both slowly passed your experience and knowledge down to me over the years. I’m glad to have you both in my life. Steve, you have been a perfect fit in the lab (and a great temporary Brandon replacement). You are kind and hard-working, and I’m excited to see the kind of work you achieve in the coming years. Don’t let the accelerators get you down.

Caroline and Erica, my fellow CFS, you guys have only been around for a short while, but what a time. When I most needed laughter and kindness, you ladies were there, bringing smiles and support to my life. I can’t wait for the next year of science with you two!

To the CMOD team: What an amazingly talented group of people. I can’t begin to think of the times that you all have saved the accelerator lab a lot of time and tears by offering time, equipment, and expertise to fix whatever machining, vacuum, or electronic issues we had at the time. Your dedication to students is what makes this place special.

Anastasia Alexandridis, you are a jewel. You are one of the most passionate, kind, and conscientious people with whom I have ever had the chance to work. From scheduling committee meetings to getting a fan in my office, you have done everything I could have asked and more to help me write

this thesis. I could not have done it without you. I hope you know what a blessing you are to this organization, and how little any of us deserve your devotion.

Heather and Brandy, you two have done so much to keep me (and the rest of the cats) herded and on track. Thank you for responding to frantic emails or in person requests, and to preemptive emails that kept me from becoming truly frantic. Thanks, above all, for caring.

Marina and the NSE Communication Lab (consisting at various times of Brandon, Mareena, Steve, Mike, Cody, and Patrick) have been pivotal in my development as a communicator. I have long held a passion for writing, but Marina and the rest of the Comm Lab have supported and guided me as I have learned to communicate my efforts with both the scientific community and the broader world. This thesis, the words, organization, and visualization, is a product of those lessons.

My girls back home, Sam, Beth, Kate, Ally, I don’t know how you put up with me the past few years. I’ve been absent, physically and mentally, so many times, but you kept me involved and included in your lives. I love you all.

Margie, Katherine, Sally, you ladies have been my partners in crime and climb. You each left me, one by one, for bigger and better things, but the times we’ve shared in Boston and beyond have taught me the importance of strong female friends. I love belaying you, hiking with you, and laughing with you. I can’t wait for our future adventures

My MITOC friends, there are so many of you that have spent your weekends (and some weekdays, but don’t tell my boss!) with me, exploring the wilds of New Hampshire, New England, and beyond. Without the Outing Club, I don’t know how I would have maintained my body or my mind during this time of my life. Climbing and hiking gave me chances to get outside and be free of the rigors of science, to focus my mind on challenges outside of the lab. Kyle, Kelly, Nathaniel, Avi, Neil, Alex... the list goes on, but you all were there for me at various times and I couldn’t have done it without you.

Dr. Chris, my thesis buddy, we did it! I forgive you for beating me to your defense by a week. I can’t wait to be real people and actually have fun together again, instead of just being sad together. Adventure is out there!

Cody, I can’t express how much you have done for me throughout this process. From MATLAB help to making me laugh when things seem like they are falling apart, you have been my rock. I took my turn; now it’s yours. Go get ’em, sweetie.

To Cody’s family, thank you for welcoming with open arms into your homes and hearts. With my own so far away, it has been hard to go through holidays and other times of family gatherings alone. You all have made me feel welcome, whether at Thanksgiving or just when I want to sit in front of a fire with a dog.

Jackson and Lane, you boys were spots of sunshine in the darkness of grad school. From Jackson’s arrival during one of my lowest points to Lane’s adventures in potty training, you both have made my life a little brighter. I can’t wait to watch you grow up, and I hope you don’t mind calling me Aunt Leigh Ann, PhD from now on out.

Jacob and Ryann, you’ve been supporting and understanding beyond my wildest dreams. Big brother, you ask so many questions and spend time researching and understanding the things I care about. It has meant so much to have you express so much interest in my life. Ryann, I don’t think you expected such a crazy family when you picked my brother, but you have become such an integral part of us. Your efforts to make sure my nephews know me have meant so much to me, along with you welcoming me so openly into your home.

Mama and Daddy, I don’t know how to begin to thank you. From Mom reading to me for hours as a child to Dad arguing with me about politics, you two continue to shape who I am as a person to this day. I could never have gotten into MIT, much less finish a PhD, without your love and support. I

will never deserve the kind of love and devotion that you have given me, and I thank God every day that I have parents like you. Thank you. This is for you.

Contents

Acknowledgments 7

Introduction 17

1 Surface diagnostics in fusion research 21

1.1 Review of existing tokamak surface diagnostics and their limitations . . . 22

1.1.1 Ex situ erosion/redeposition diagnostics . . . 23

1.1.2 In situ erosion/redeposition diagnostics . . . 24

1.1.3 Insertable probes . . . 25

1.2 Review of AIMS: Accelerator-based In situ Materials Surveillance . . . 26

2 Ion Beam Analysis: IBA and applications to surface studies in fusion devices 29 2.1 Rutherford Backscattering Spectroscopy: RBS . . . 29

2.2 Elastic Recoil Detection: ERD . . . 31

2.3 Nuclear Reaction Analysis: NRA . . . 32

2.4 Particle-Induced Gamma Emission: PIGE . . . 33

3 Depth markers for Evaluating high-Z materials with AIMS: DEA 35 3.1 Implanted depth markers using PIGE . . . 35

3.2 Ex situ analysis with resonances: eDEA . . . 38

3.3 In situ analysis with yield ratios: iDEA . . . 43

4 Experimental facilities and equipment 47 4.1 Ion accelerators and facilities . . . 47

4.1.1 AIMS RFQ - Radio Frequency Quadrupole . . . 47

4.1.2 DANTE - Deuterium Accelerator for Neutron-producing Tandem Experiment 48 4.1.3 CLASS - Cambridge Laboratory for Accelerator Surface Science . . . 50

4.2 Gamma detectors and data collection . . . 52

4.2.1 Gamma detectors . . . 52

4.2.2 Signal processing equipment and software . . . 54

4.3 EAST: Experimental Advanced Superconducting Tokamak . . . 55

4.4 Experimental setup . . . 56

4.4.1 CLASS setup for implantation . . . 56

4.4.2 eDEA experiments with CLASS and EAST . . . 57

4.4.3 iDEA verification on DANTE . . . 60

5 Simulation and data analysis 63 5.1 DEA simulations . . . 63

5.1.1 eDEA simulation . . . 66

5.1.2 iDEA simulation . . . 68

5.2 Analysis of data . . . 71

5.2.1 eDEA . . . 71

5.2.2 eDEA and CLASS energy drift . . . 74

6 Gamma-producing reactions for DEA 81

6.1 Resonant reactions for eDEA . . . 81

6.1.1 p-F reaction for eDEA . . . 81

6.1.2 Deuterons and7_{Li . . . . 84}

6.2 Deuterium reactions for iDEA . . . 85

6.2.1 d-B reaction for iDEA . . . 86

6.2.2 Deuterons and6_{Li . . . . 87}

6.2.3 Deuterons and13_{C . . . . 90}

7 eDEA results and discussion of significant results 93 7.1 Simulated erosion and redeposition . . . 93

7.2 Temperature stability experiments with fluorine in TZM . . . 97

7.3 First EAST Campaign - MAPES probe . . . 99

8 iDEA Results and discussion of significant results 103 8.1 Initial results . . . 103

8.2 Calibrating DANTE . . . 105

8.3 Results with improved statistics . . . 106

9 Conclusions and Future Work 113 9.1 Major accomplishments of this thesis . . . 113

9.2 Recommendations for Future Work . . . 114

A Cross section and yield reference tables 117 B Code and scripts 123 B.1 MATLAB code . . . 123

B.1.1 eDEA simulation . . . 123

B.1.2 eDEA simulation analysis . . . 126

B.1.3 eDEA data analysis . . . 129

B.1.4 iDEA simulation . . . 132

B.2 ROOT code . . . 136

B.2.1 eDEA gamma spectra analysis . . . 136

C Experimental settings 149 C.1 Accelerator settings . . . 149

C.1.1 DANTE settings . . . 149

C.1.2 CLASS settings . . . 150

D Additional eDEA data 153

List of Figures

0-1 A tokamak is shown with annotated magnetic fields. . . 17

0-2 An illustration of the complex interactions that can take place between the plasma and the surface. . . 19

1-1 A schematic of the MAPES diagnostic on EAST. . . 26

1-2 The AIMS accelerator as modeled with a CAD program, showing the various compo-nents of the diagnostic. . . 28

2-1 Sketches of RBS implementation using an aluminum layer on a carbon substrate. . . 30

2-2 Sketches of the surface when there is hydrogen/deuterium present using an ERD diagnostic. . . 32

2-3 Comparison of NRA and PIGE techniques. . . 33

2-4 The sharp resonances in the proton energy-yield spectrum of the 19_F(p,αγ)16_O reac-tion allow PIGE analysis to take place. Replotted from Dababneh et al. [52]. . . 34

3-1 Images of sputter-coated tungsten layers, reproduced from Maier et al. [56] and Matthews et al. [57]. . . 35

3-2 Replotted data from Maier et al. [56] showing the impurities present in a tungsten layer that was deposited on carbon via a type of PVD called magnetron sputtering. . 36

3-3 SRIM [51] is used to simulate the depth profile of the implanted species. . . 37

3-4 Sketches of implementation of DEA technique before and after erosion. . . 37

3-5 This energy-calibrated gamma spectrum, taken with a NaI gamma detector, shows the gamma peaks produced by 1400 keV protons incident on a 50 nm LiF target. . . 40

3-6 Replotted data from Dababneh et al. [52] in the proton energy range of interest (800-1600 keV). . . 41

3-7 Surface and implanted fluorine gamma yield curves. . . 41

3-8 Sketch of the ex situ DEA method. . . 42

3-9 The gamma production cross sections for deuteron bombardment of boron [49]. . . . 43

3-10 Sketch of the in situ DEA method. . . 45

4-1 A CAD rendering of the RFQ in place on one of Alcator C-Mod’s diagnostic ports. . 48

4-2 Pictures of DANTE and CLASS. . . 49

4-3 Schematic of the DANTE accelerator. . . 50

4-4 A schematic of the DANTE system . . . 51

4-5 The CLASS system, showing both ion sources and beamlines. . . 53

4-6 The gamma yield of an AmBe source on three different detectors. . . 54

4-7 Pictures from the MAPES experiment. . . 55

4-8 Picture of the implantation setup on the CLASS accelerator. . . 56

4-9 eDEA experimental setup. . . 57

4-10 The apparatus used to mount the eDEA samples on the MAPES probe. . . 58

4-11 iDEA experimental setup. . . 60

5-1 A comparison between a monolayer as opposed to an implanted distribution. . . 63

5-2 The SRIM simulations of implanted layers, using 200,000-500,000 ions per simula-tion [51]. . . 64

5-3 A SRIM simulation of 2000 keV 11_{B implanted in W is divided into slices as an} illustration of the simulation technique [51]. . . 65 5-4 This flowchart depicts the eDEA simulation process. . . 66 5-5 Both the energy loss and stopping power are approximately linear for the proton

energy range of interest (∼1300-1500 keV). . . 67 5-6 The yield curve result of the simulation for the 4800 keV implant in TZM with no

erosion and redeposition is shown. . . 68 5-7 This flowchart depicts the iDEA simulation process. . . 69 5-8 These plots show the parameters calculated by the simulation in order to calculate the

ratio between the gamma yields shown in Figure5-9for a 2000 keV fluorine implant with a 1133 keV deuteron beam. . . 70 5-9 Erosion and redeposition are plotted as a function of the yield ratio between the

0.953 MeV and 1.674 MeV gammas. . . 71 5-10 An example gamma spectrum and current vs. time plot from the ROOT analysis

script for eDEA. . . 71 5-11 Data acquired from the 400 keV implanted control sample plotted alongside simulated

data. . . 72 5-12 An example yield curve from the experimental data is plotted alongside the three fits. 73 5-13 These histograms show the results of the Monte Carlo error analysis of fitting an

example dataset. . . 74 5-14 The data from the 500 keV and 4500 keV control samples plotted with the simulated

data from the same implantation. . . 75 5-15 Plots of the centroids of the control experiments and the simulations, divided by

implantation energy/depth. . . 75 5-16 The 1371 keV resonance was measured using the same 50 nm LiF target on several

different days. . . 76 5-17 A gamma spectrum from a 1133 keV deuteron beam on sample W07 (implanted with

11_{B at 2000 keV). . . . 78} 5-18 The resolution fit was calculated by finding the resolution of the 0.511 MeV

annihila-tion peak, the 0.871 MeV oxygen peak, and the 3.089 MeV carbon peak on the same spectrum. . . 78 5-19 Two spectra analyzed with ROOT to fit and integrate the 1.674 MeV peak. . . 79 5-20 A gamma spectrum from a 1133 keV deuteron beam on sample W07 (implanted with

11_{B at 2000 keV). . . . 79} 6-1 The yield data presented in Dababneh et al. [52] and extracted via

WebPlotDigi-tizer [80], with annotated resonances based on the values in the Handbook of Modern Ion Bean Materials Analysis [15]. . . 82 6-2 Comparison of the yield curve from the integrated area from 4.5-9 MeV to the curve

from Dababneh et al. [52]. . . 83 6-3 Comparison of the yield curve from the integrated area from 4.5-9 MeV to the curve

from the 6.1 MeV peak. . . 84 6-4 The 6.1 MeV yield curve is acquired from gamma spectra at proton energies from 810

to 1650 keV. . . 85 6-5 The green gamma spectrum shows the high-energy gammas produced by the7_Li(d,γ)9_Be

reaction, with a deuteron energy of 300 keV. . . 85 6-6 The11_B(d,pγ)12_{B reaction produces two gammas of different energies. . . . 87} 6-7 Gamma spectrum collected with an HPGe gamma detector from a deuteron beam

incident on a LiF target, composed of natural Li (5%6_{Li). . . . 87} 6-8 A deconvolved NaI spectrum from deuterons incident on a natural lithium target. . . 88 6-9 Gamma spectra from a 1200 keV incident deuteron beam on6_{Li depth marker on the}

(a) HPGe and (b) LaBr detectors. . . 89 6-10 The diffusion coefficients and mean times to diffuse for6_{Li in Mo and W. . . . 90} 6-11 Gamma spectrum from a13_{C target. . . . 91}

7-1 The simulated values for the change in surface thickness (both erosion and redeposi-tion of TZM) as a funcredeposi-tion of the change in energy of each of the resonance centroids. 93 7-2 The widths predicted by the simulation show a trend of decreasing resonance width

with increased material on the surface, which breaks down when the erosion reaches

the bulk of the implanted layer. . . 94

7-3 These figures show the original implantation profiles of the (a) 400 keV and (b) 4800 keV19_{F, simulated by SRIM. . . . 95}

7-4 The simulated values for the change in surface thickness due to deposition of LiOH as a function of the change in energy of each of the resonance centroids. . . 95

7-5 A comparison of the different lithium compounds that might form on the surface of a fusion device with a lithium coating. . . 96

7-6 The resonance centroids of the samples measured for temperature stability. . . 97

7-7 The resonance widths of the samples measured for temperature stability. . . 98

7-8 The samples that were exposed in EAST, photographed after exposure. . . 99

7-9 The results of the Gaussian fits for the 1370 keV resonances plotted for each day data was acquired. . . 100

7-10 The results from the same day measurements are plotted after being converted from resonance centroid to a change in surface thickness (erosion/redeposition). . . 101

7-11 The image from the FIB/SEM shows the uneven coatings on the KW03 sample, which was exposed to both lithium and plasma in EAST. . . 102

8-1 The implanted boron concentration of a 2000 keV 11_{B ion beam into tungsten} cor-rected for beam current and density, giving an atomic concentration relative to the bulk, calculated via SRIM. . . 103

8-2 The results from the first iDEA experience show that it is possible to obtain a ratio that correctly represents the removal (or lack thereof) of material from the sample surface. . . 104

8-3 Sample W02 after exposure to the deuteron beam shows melted nylon screws and a carbon-coating on the sample surface. . . 105

8-4 DANTE was used to acquire this gamma yield curve, showing the lower energy reso-nances from the proton reaction with fluorine. . . 105

8-5 The resolution of the 1.674 MeV peak from all the spectra taken from the iDEA samples.107 8-6 The spectrum in (a), acquired from sample W07, shows more prominent peaks than the spectrum in Figure8-2. . . 108

8-7 The calculated results for implant energy, plotted as points with error bars for easier viewing on a single plot, compared to the known implantation values. . . 109

8-8 The erosion curves for different probing deuteron beam energies. . . 110

List of Tables

1.1 The key aspects of a surface diagnostic in a fusion device are listed and compared to

the existing suite of diagnostics. . . 27

3.1 Possible isotopes for DEA. . . 39

4.1 Resolution and efficiency of three DEA detectors. . . 52

4.2 A tabulated list of the samples that were exposed in EAST via the MAPES probe. . 59

4.3 A tabulated list of the samples that were used for temperature tests. . . 60

4.4 A tabulated list of the samples used for iDEA measurements. . . 61

5.1 Possible peaks in the spectra presented here. . . 77

6.1 The energies of the resonances in the19_F(p,αγ)16_{O cross section. . . . 83}

6.2 A comparison of the literature values of the most prominent resonances for the p-F reaction to the centroids from the yield measured in the eDEA experimental setup. . 84

6.3 The reactions in literature [48,49,83] for deuterium and potential DEA isotopes. . . . 86

6.4 Wu [85] and McCracken and Love [84] recorded diffusion coefficient values of6_{Li at a} range of temperatures and the resultant Doand Q values. . . 88

6.5 The peaks from deuteron-induced reactions with 12_{C and} 13_{C are tabulated along} with their single and double escape peaks. . . 91

7.1 HT02 and HT03 were implanted at 400 keV and HT04 and HT05 were implanted at 4800 keV in order to ascertain if marker depth affected temperature stability. . . 98

7.2 The results from the six samples that were measured coincidentally with the controls. 102 8.1 The results of fitting the peaks from the yield curve in Figure8-4(b) show a constant offset in energy from the expected DANTE value and the known resonance centroid. 106 8.2 The results shown in Figure8-7are tabulated for easier comparison. Though the cal-culations from simulation generally match the known implant energies/lack of erosion, there is still some disparity. . . 109

A.1 This data was acquired in the experimental setup described in Section4.4.2, using a 50 nm LiF target instead of an implanted sample. . . 117

A.2 The data from Dababneh et al. [52] was extracted via WebPlotDigitizer [80] for com-parison to the data taken for this work . . . 119

C.1 Settings for the DANTE accelerator. . . 149

C.2 Settings for the CLASS accelerator. . . 150

D.1 These samples were all implanted with fluorine as a part of eDEA experiments. . . . 154

D.2 The centroids and widths of the Gaussian fits to the resonances of all the samples measured as controls. . . 155

D.3 The centroids and widths of the Gaussian fits to the resonances of all the samples measured for eDEA. . . 156

Introduction

Fusion was first demonstrated in a laboratory in 1932 by Oliphant et al. [2], when a Cockcroft-Walton ion accelerator was used to accelerate deuterium nuclei into a solid target. The deuterons fused unexpectedly, producing protons, neutrons, tritium nuclei, and3_{He nuclei. After many years,} the scientific community began trying to transition fusion from a laboratory experiment to a reliable source of clean energy, a task that has thwarted them for half a century. To achieve this goal, the energy from fusion has to exceed the energy used to generate the reaction. From the beginning, it was clear that overcoming the Coulomb barrier between nuclei would require large amounts of energy, and that establishing a self-sustaining reaction similar to that in fission would be required; instead of neutrons generating the next reaction as in fission, the energy produced by the fusion reaction would keep the fuel hot enough to continue fusing. In order for this self-heating to occur, the hot fuel would have to be confined at a sufficient density for a sufficient time.

Figure 0-1: A tokamak is shown with annotated magnetic fields. The poloidal direction (the flat green arrow for magnetic field) goes up and around the body of the torus, while the toroidal direction (the round blue arrow for magnetic field) is around the length of the torus. By generating both a toroidal and a poloidal field, the net magnetic field wraps around the torus, preventing fuel loss via common transport mechanisms [3].

Many designs have been tested using different methods of heating and confinement, but the leading design for a reactor currently is the tokamak. A tokamak (a Russian portmanteau that means “toroidal chamber with magnetic coils”) is a donut-shaped device that has both toroidal and poloidal magnetic fields (see Figure 0-1), which confine the fusion plasma. This plasma, a neutral fluid that consists of energetic ionized nuclei and their free electrons, is difficult to contain because of the complex magneto-hydrodynamics and turbulent transport that govern its physical behavior. In addition, confinement failures result in the hot (up to 15 keV or 150 million K) plasma hitting the

walls of the vacuum chamber, which can result in chemical/nuclear reactions, erosion, vaporization, melting, and more. Thus, materials in this environment must be carefully studied for their resistance to plasma modifications, in addition to studying their vulnerability to the high radiation fields in a fusion device. The material requirements for plasma-facing components include, but are not limited to, high melting temperatures, high thermal conductivity, low fuel (deuterium/tritium) retention rates, and low plasma erosion rates.

Materials that can withstand the high heat density of a fusion reactor are rare; the surface tem-perature of the first wall could reach 1000 K or greater [4]. Additionally, materials that have and can maintain high thermal conductivity will be necessary for removing the heat from the walls of the device and transferring it to a fluid for conversion to electricity. Changes to the material that could affect the thermal conduction include radiation damage, sputtering, fuel implantation, and redeposition. Fuel retention is also problematic as a nuclear hazard, since implanted tritium fuel would render material as a low-level nuclear waste, and as a mechanism for fuel loss. Another major concern when choosing a plasma-facing material is preventing impurities from entering the plasma via erosion. Radiative energy losses in the plasma trend with the atomic number as Z2_{; thus, there} is a strong preference for low-Z plasma-facing materials.

Because of these restrictions, few materials can be used in fusion devices. Tungsten and carbon are the top contenders, since both have high melting temperatures1_{. However, carbon, which as a}

low-Z material limits radiative losses in the plasma, has been shown to absorb fusion fuels at an unacceptably high rate, and could erode at a rapid rate due to its low mass. Thus, the commonly accepted conclusion is that any future fusion device will have a tungsten divertor (an engineered structure outside of the main portion of the tokamak where fuel, waste, and energy are removed from the system) in order to maximize melting temperature and minimize sputtering rates and fuel retention [4].

Limiting sputtering of tungsten is a major concern, but the picture of plasma-material interactions is more complicated than simply the sputtering rate. Figure0-2shows the many physical, chemical, and nuclear processes that can affect surfaces under plasma exposure. All of these factors can change the erosion rates of the material, in addition to affecting other material properties, leading to the eventual conclusion is that systems for monitoring surfaces in a fusion reactor need to be able to show more details than simple sputtering. A few of the major concerns for the first wall include net erosion, redeposition/co-deposition, and fuel retention.

The AIMS technique was developed at MIT’s Alcator C-Mod experiment in order to monitor these phenomena. AIMS (Accelerator-based In situ Materials Surveillance) is an experimental method that uses ion beam analysis (IBA) to study surfaces that are inside a fusion reactor. Initial AIMS results were promising for measuring low-Z components of the surface, i.e. the thin boron layer on the surface and the implanted deuterium fuel [1]. However, using any surface diagnostic to measure erosion and redeposition of the bulk material, e.g. tungsten or molybdenum, has significant challenges. For AIMS specifically, there is the difficulty of initiating nuclear reactions with a high-Z surface; the Coulomb barrier precludes low-to-medium energy (∼1 MeV) ions from reacting with high-Z nuclei [7]. Even if it were possible to initiate these nuclear reactions via a higher energy ion beam, it would not be effective without a marker, from which the initial surface can be referenced. Thus, the true limitation of measuring a bulk material is the lack of a reference to the surface. Maximum erosion rates for high-Z refractory metals in most current experimental fusion devices are ∼1 nm/s [4,8], while wall tiles are usually several centimeters thick. Measuring the change in the surface without being able to reference a marker to the surface of some sort becomes very difficult, relying on exact measurements of weight or indirect methods. While this may be possible outside of a tokamak, it is impossible with an in situ system.

Depth markers for Evaluating materials with AIMS, or DEA, attempts to resolve this situation by artificially creating a reference to the surface without significantly changing the plasma interactions

Figure 0-2: An illustration of the complex interactions that can take place between the plasma and the surface. Sputtering, or the physical knocking out of atoms from the surface, is the most commonly referenced reaction. Additionally, chemical sputtering can occur when the plasma species is chemically reactive with the surface. Sputtered atoms can be redeposited or reenter the plasma; fuel ions can also reflect back into the plasma at a much lower energy. Redeposited surface atoms can also be co-deposited with fuel, or fuel can be implanted into the surface. Both high energy ions and neutrons can cause displacements in the surface (radiation damage), which can then conglomerate into larger features. Of particular importance to this work are the reactions that change the surface composition, such as erosion and redeposition of surface materials that are intermixed with each other and fuel atoms [6].

with the surface or the surface’s material properties. These depth markers are implanted in the surface via an ion accelerator, then probed with a different ion beam. The probing beam induces nuclear reactions, whose products can be monitored to determine the location of the surface with respect to the marker.

The techniques described in this thesis specifically use IBA to monitor depth markers. However, the major conclusion from this work is broader than the particular interrogation diagnostic used. This series of experiment and simulations show a generalized viability of depth markers to study bulk erosion with ex situ IBA methods (i.e. extracting and measuring plasma facing components after a partial or full plasma campaign at an off-site IBA facility) and in situ IBA (i.e. an AIMS diagnostic on a magnetic confinement fusion device). These markers have been shown to be stable to temperature excursions and capable of surviving the conditions inside an experimental tokamak. As such, they are not limited to use with AIMS or even IBA. Any technique that probes the surface can use these markers as a reference point, opening up the possibilities for adapting existing diagnostics for the measurement of bulk materials. For example, the laser-induced breakdown spectroscopy (LIBS) method discussed in Chapter 1 could utilize depth markers to monitor the plasma for the characteristic emission spectrum of the marker element.

In this thesis, the development and validation of the DEA techniques will be outlined. Chapter 1 describes the strengths and weaknesses of extant diagnostics for measuring surface changes in fusion devices, including the AIMS diagnostic. Chapter 2goes on to describe the various ion beam anal-ysis techniques, including more details on how they currently are used in fusion devices, and their limitations. The DEA technique is described in detail in Chapter3, including how it overcomes the shortcomings of other diagnostics. Chapters 4 and5 describe the experimental and computational procedures that were used in the initial development for DEA. The potential reactions that could be used for DEA, including the limitations on such reactions, are described in Chapter 6. The results of these experiments and simulations, and the comparison of the two, are in Chapters 7 and 8. Chapter9contains discussion of these results, and the implications for further use and study of the DEA technique.

Chapter 1

Surface diagnostics in fusion research

The demands on the plasma-facing components (PFCs) of future fusion devices will be great. While the currently existing short pulse devices have shown limited damage to surfaces, future devices will use long-pulse or continuous operation in order to produce electricity. For example, pulses on the Alcator C-Mod tokamak were ∼1 second long [4], while the EAST tokamak recently achieved a record-setting 1 minute-long, high-confinement discharge [9]. EAST aspires to discharges of up to 1000 sec [9], and future devices like ARC would likely operate in steady state for a year or longer [10], increasing the exposure time from 1-100 sec to 10 million sec (five orders of magnitude more exposure to plasmas, heat, and radiation). Beyond the extended exposure times, the heat load of the divertor is predicted to be 10-20 MW/m2 _{during normal operation (compared to 1-2 MW/m}2 _{on most} current devices) [11], with temperatures reaching 1000 K or greater [10] (compared to <800 K in EAST discharges [9]. Comparatively, in a fission nuclear reactor, the maximum temperature is ∼600 K in the fuel cladding [12]. As discussed in the Introduction, the high operational temperature limits material choices. To make the matter more complicated, fusion reactors also must deal with plasma-surface interactions (PSIs). There will be a high flux (∼7×1023_/m2_{s) of energetic particles} from the plasma at the divertor surfaces [13]. These particles can interact with the surface via many mechanisms including surface sputtering, redeposition, co-deposition with plasma particles, implantation of plasma particles, chemical reactions, radiation damage, and nuclear reactions. This complex picture, shown in Figure0-2, has made predicting changes in the surface difficult.

Understanding how the first wall of a fusion device is affected by the plasma is important for several reasons, including tracking the loss of fuel in the surface and tracking changes to the chemical structure of the first wall. The condition of the first wall has significant effects on both plasma and tokamak performance, and has to be exactly designed to allow optimal plasma conditions [10,11]. As the surface changes due to inevitable erosion and redeposition, it must not significantly degrade plasma performance, or it will become the limiting factor to plant operation.

Erosion is a major concern since it can cause both introduction of impurities to the plasma and loss of the protective first wall. The first wall protects the coolant channels and the vacuum vessel, which maintains the pressure and purity of the vacuum system. Any breach would cause immediate shutdown and necessitate extensive repairs in an operating reactor. The first wall must be limited to 1 sputtered atom from the surface per 106 _{incident ions. Additionally, overall erosion for the} lifetime of the first wall must be less that 1 mm to protect coolant channels in the surface [4]. These limitations place a cap on both the instantaneous and overall erosion rates in a tokamak. Beyond erosion, redeposition of the eroded material is also problematic. As seen in Kuang et al. [14], the tolerances for materials in a fusion device are a carefully calculated balance between decreasing the thickness of the wall to allow more efficient heat transfer to the coolant channels and to reduce surface temperatures and increasing the material to protect the coolant channels. Erosion affects the latter, but the former can be negatively affected by redeposited layers that likely have worse thermal

properties and potentially are co-deposited mixture of fuel, wall materials, and contaminants. In order to design devices that within these bounds, accurate understanding of the erosion and redeposition of PFCs, both over the lifetime of the device and during operation, must be obtained. Unfortunately, one of the most underemphasized and unknown areas of magnetic fusion research is the study of the plasma interactions with the PFCs of the first wall and divertor. Many diagnostics, with limited capabilities, have been developed to overcome this gap in knowledge [11].

1.1

Review of existing tokamak surface diagnostics and their

limitations

Observing surfaces in a fusion device is a challenging task. Tokamak surfaces are inside a high vacuum system (∼10−7 _{Torr), undergo cyclic exposure to radiation (neutron, gamma, and charged} particle) beyond levels seen even in fission power plants [12], can be heated up to 1000 K [10] over short time periods (leading to rapid heating and mechanical issues), and interact with chemically reactive materials like boron, lithium, beryllium, etc. While these conditions degrade the surface, they also will affect any diagnostic introduced to the system. Attempting to overcome this challenge by physically accessing material requires either an elaborate sample insertion and removal mecha-nism, or breaking vacuum of the device to access and remove samples for analysis, which is only possible between run campaigns. While detailed ex situ analysis can then be done on the surfaces, these campaign-integrated results show only general trends and not how specific conditions affect the surface. Accessing surfaces in situ presents its own set of challenges. Any equipment inside the containment will be exposed to high fluxes of radiation (both neutron and gamma), high tempera-tures, high magnetic fields, and will require remote handling and calibration. There is limited space available for diagnostics, as port and wall space must be used for operations equipment and for other diagnostics. Line-of-sight access to surfaces is difficult to achieve, which limits the number of surfaces that can be observed by the same equipment. Further, studying the high-Z, bulk material is nontrivial, as determining nm-µm changes on a cm-scale object is difficult without some reference to the surface against which to measure.

Because of these restrictions, the existing suite of surface diagnostics that measure high-Z erosion/re-deposition in magnetic fusion is a creative array of techniques. These diagnostics can be divided generally into the categories of in situ or ex situ, with a few being “insertable probes,” which fall somewhere in between. Ex situ diagnostics include any technique that can be applied to surfaces outside of the tokamak environment. As stated earlier, materials can only be removed from large magnetic fusion devices in between run campaigns (around once a year), so ex situ results are typ-ically campaign-integrated, over thousands of different plasma discharges and conditions. Ex situ techniques can also be applied to materials from insertable probes, which can be removed at shorter intervals. In situ diagnostics operate within the fusion device, and have a variety of time resolutions. A very few function during operation, offering time-resolved changes in surface morphology during plasma operation. Others can operate between every shot, while still more require more time to operate and will take data during breaks in tokamak operation.

In order to show the gaps in the extant material diagnostics, the strengths and weaknesses of each of these techniques will be discussed here. Not all possible diagnostics are listed, of course, particu-larly ex situ methods as the possibilities are nearly endless; those included here have shown useful results when applied to fusion materials. In addition, the focus here is on erosion and redeposition diagnostics; there are some diagnostics for other phenomena, particularly fuel retention, that have been omitted since they are not the primary concern of the current study.

1.1.1

Ex situ erosion/redeposition diagnostics

Ex situ measurements are advantageous as they remove the access and environmental limitations of operating diagnostics in the tokamak. However, they are typically limited to campaign-integrated measurements as vacuum breaks to remove samples are rare.

Ion beam analysis (IBA) consists of a wide variety of techniques for studying surfaces with ion beams. The details of several of these techniques will be discussed in Chapter2. Here the basic concepts and results that have been achieved in fusion materials will be discussed. Three major techniques have been used to study fusion materials: Rutherford backscattering spectroscopy (RBS), elastic recoil detection (ERD), and nuclear reaction analysis (NRA). All have limitations and best-use cases, which often means they are combined with other techniques, including other methods of IBA. RBS measures the energy of backscattered ions from an energetic ion beam to determine the mass and depth of atoms in the surface (See Section 2.1 for more details). Because a monoisotopic sample would produce a continuous counts per energy spectrum, RBS can only be used to measure non-bulk materials in the surface. Additionally, kinematics restrict it to measuring atoms heavier than the probing ion (usually protons or alpha particles), and atoms with a larger mass relative to the substrate will be easier to distinguish [15]. As such, RBS is typically used to measure thin films (<1 micron). These films are either prepared in advance (by coating a low-Z substrates with tungsten, etc.) or created by placing low-Z substrates near high-Z surfaces inside a tokamak so that eroded particles may be redeposited there [16–20]. This technique has been used on samples from many machines to quantify erosion and redeposition, including but not limited to EAST [18], DIII-D [17,19], JET [16], and C-Mod [20]. RBS requires an medium-energy ion beam; fusion samples have been measured with beams from ∼1–7 MeV. The surface thicknesses that can be measured are set by the energy of the beam, and are usually ∼0.1 µm [16,21,22]. The error values in a well-calibrated RBS system can be less than 1 nm, or about 1% of the thickness of the layer [21]. While much has been learned from this methodology, multiple problems exist with thin coatings. Sputter deposited coatings are not guaranteed to have the same thermomechanical properties or grain structure as bulk materials [23], making results from such experiments non-conclusive on the behavior of bulk materials. Another consequence can be delamination of layers, in which the deposited layer separates from the substrate, which can both damage the sample and cause other issues inside the tokamak as dust or pieces from the delaminated structure contaminate the system [24].

While RBS can measure heavy isotopes, ERD is best suited to measuring light isotopes that are present in lower concentrations in the surface. In ERD, the forward scattered ions that are “knocked out” of the surface by the energetic ion beam are measured, instead of the backscatters (See Sec-tion 2.2) [15]. Because of kinematics, ERD can only measure isotopes that are lighter than the probing beam (which can be almost any species, but16_{O ions are often used). As such, it works as a} complementary technique to RBS, and the two measurements can even be made simultaneously [22]. ERD is typically performed with higher beam energies (>10 MeV) [16], and has higher error in the estimates of impurity concentration (∼15%) [22]. Another downside to ERD is that it cannot make measurements of high mass (thus, high-Z) components, and therefore isn’t useful for making bulk erosion methods. It has been used to study low-Z impurities in the surface, including hydrogen, deuterium, helium, carbon, nitrogen, and more [16]; in particular, the unique effects of helium on materials have been studied [22,25].

NRA comprises many different techniques, depending on the chosen definition, including particle-induced gamma emission (PIGE) and particle-particle NRA. PIGE was not utilized significantly for fusion surface studies until the AIMS technique was developed, and will be discussed later. Particle-particle NRA is most commonly used in fusion (referred to simply as “NRA” here). It measures charged particles produced by nuclear reactions of the ion beam with the nuclei in the surface (see Section2.3) [15]. Because inducing nuclear reactions is restricted by the ability of the beam ions to overcome the Coulomb barrier, it generally cannot be used to directly induce reactions with high-Z materials with medium energy (∼ 1 MeV) ion beams. However, low-Z materials can be depth profiled

to depths of up to a few µm by observing the energy loss of the charge particles produced by such nuclear reactions. A common use of NRA is to measure deuterium retention [21] via the D(3_He,p)α reaction. The redistribution of low-Z surface coatings like carbon, beryllium, and boron can also be measured [19,21,26]. Concentration accuracy is highly variable with NRA, from 1-30%, due to the wide range of cross-sections [19], and resolution in depth is ±0.5 µm over a range of 0-3 µm [27]. Another ex situ technique that has proved useful is profilometry (specifically contact profilometry), which uses a precisely calibrated stylus to measure a surface profile. By applying a mask or other method of control, erosion or redeposition of any species can be measured based on the difference in profile height between the control and exposed regions [28]. This technique can be used to measure erosion [11,28,29], surface roughness [30], or to validate layers used for RBS [18]. It can also be used in conjunction with other methods to validate results, which can be especially useful for in situ techniques. The spatial resolution of this technique is typically on the order of nm in depth, and µm in lateral surface location.

1.1.2

In situ erosion/redeposition diagnostics

While ex situ diagnostics allow much more flexibility in measurement technique, they offer little to no time resolution. Tiles/samples can only be removed during vacuum breaks, which only occur between run campaigns. Data from ex situ diagnostics tells only of general trends, not of direct correlation between plasma conditions and surface effects. Thus, many of the diagnostics used to study surfaces in fusion devices operate in situ. In fact, many of them have been adapted for in situ use from ex situ diagnostics in order to gain time resolution.

Optical spectroscopy allows for observation of the gross erosion of material on the surface within line of sight of the diagnostic [4,28,29]. This method looks for ionization states of materials sputtered from the first wall, which gives it both atomic and molecular discretion. While a good measure of gross erosion, it cannot track redeposition, and is limited by optical access to surfaces. Within that access, however, it can have high (<1 cm) spatial precision [11,28,31].

Colorimetry is a technique that uses mapping of the colors of films on surfaces to monitor co-deposition films [11,32]. The simplicity of this system is desirable, as it only needs an appropriately high-resolution camera to image the films as they form. However, it has been primarily applied to transparent C-H films that form in machines with carbon-based first walls [32–34]. Recent advances have expanded the technique to give qualitative results in more complicated mixed, opaque films containing tungsten and other metals [34]; however, colorimetry is still limited in application. Another common method of measuring redeposition is with quartz crystal microbalances (QMBs) [11].1

QMBs measure the change in the resonance frequency of the crystal as mass is added to the surface. Since they are usually positioned along magnetic field lines in the tokamak but recessed from plasma exposure, they don’t see erosion itself, but are in the path of material that is removed from surfaces in the divertor or first wall. In this way, they are capable of detecting re/co-deposition, agnostic of species. In addition to areal mass dependence, the frequency is dependent on temperature and stress forces. The extreme sensitivity of these devices to temperature make them very vulnerable to temperature spikes inside a tokamak, though this can be remedied to some extent by water-cooling and temperature calibration. Additionally, the lack of species discrimination highly limits what can be determined about the type or thickness of material [35].

Recent developments in laser-induced breakdown spectroscopy (LIBS) have moved it from an ex situ diagnostic to an in situ one. LIBS operates by using a high-power laser to ablate the surface of the first wall and create a small plasma. Spectroscopy like described above can then be done on this plasma (generated between shots) to determine the elemental and molecular structure of the 1_{Quartz (crystal) microbalances are most commonly referred to as QMBs in fusion literature, though they typically}

are called QCMs elsewhere. Presumably this is to avoid confusion with the quasi-coherent mode seen in H-mode plasmas.

wall. LIBS is micro-destructive technique, as it destroys a small area of the surface to create the plasma (∼10 mm2_{), but this ablation also allows for depth profiling as multiple pulses can burrow} deeper into the surface to get a qualitative depth profile. When combined with post-exposure calibration measurements, as ex situ LIBS is, this can become a quantitative measure (reaching depths of ∼500 µm), but currently the in situ technique cannot correlate individual laser pulses with depth [36]. LIBS can operate in between shots in high magnetic field, making it well-positioned to get time-resolved measurements of surface composition. However, LIBS is limited to a single probing location and cannot measure bulk erosion [36–39]. LIBS also destroys a portion of the surface in order to achieve a depth profile, rendering measurements which are inherently unreproducible. Limitations that affect spectroscopy also affect LIBS, including the need for knowledge of plasma conditions near the surface. A final concern for LIBS is its reliance on optical windows for signal transmission. While these can be recessed from the plasma to avoid ion damage, neutron damage from fusion plasmas would be a concern for future devices.

A sister-diagnostic to LIBS is laser-induced ablation spectroscopy (LIAS), which observes the sur-face during plasma operation. Similarly to LIBS, a laser pulse ablates some of the sursur-face. This ablated material is then ionized by the plasma, and the ionization states can be measured via optical spectroscopy. Since this technique is not limited to particles sputtered by the plasma like optical spectroscopy is, the changes in the composition of the surfaces can be monitored more exactly. However, it can be limited by changes in the plasma parameters near the surface, and it shares many of the limitations of LIBS. The technique is still in development, which may allow some of the challenges to be resolved in coming experiments [39].

1.1.3

Insertable probes

The use of ex situ or in situ diagnostics have distinct advantages with respect to time resolution and accuracy. Insertable, or retractable, probes allow scientists to, in some ways, achieve both of these goals. These probes have been implemented in several tokamaks, including DiMES (Divertor Mate-rial Evaluation System) at the DIII-D tokamak in the US [21] and MAPES (Material and Plasma Evaluation System) on the EAST (Experimental Advanced Superconducting Tokamak) device in China [18].

These probes vary in application and position within the experimental device where they are located; however, the general principle remains the same. The probe allows samples of various shapes and sizes to be inserted into the device in order to expose materials to the operating conditions of a tokamak. Then, after one or more plasma exposures, the samples can be removed and analyzed ex situ. Researchers have the opportunity to run many different experiments with different materials and designs during a single campaign. This will be seen in Chapters 4 and 7, where the MAPES probe was utilized to perform validation experiments. Figure 1-1 shows how the MAPES probe is inserted into the EAST tokamak. Additionally, this can allow the samples to be analyzed with in situ diagnostic during exposure, along with ex situ evaluation, giving a useful on-line validation of the in situ techniques [11,18,21,40,41].

The disadvantages of using probes such as these are access issues, time resolution, and realistic surface conditions. Because these probes require port access to insert and remove samples, the number of locations within a single device is limited by the availability of diagnostic space. Typically, they are present in one or two locations in a device, covering a very small fraction of the device’s surface area. In addition, while the time resolution of these probes is a vast improvement over campaign-integrated measurements, it is still a far cry from the shot-to-shot resolution of most in situ diagnostics. Finally, the surfaces of samples freshly introduced mid-campaign or even midday don’t have the same conditions as those that have been in the device for extended periods. Surface conditioning, such as boronization, can often be excluded and the pristine surfaces don’t well-represent the complex, redeposited structures that are present in other areas of the device [11].

MAPES probe Upper and lower divertor EAST tokamak Inner wall

Figure 1-1: A schematic of the MAPES diagnostic on EAST. An internal picture of the tokamak on the right [42] shows the general layout of the device, while an enlarged CAD drawing [8] shows the relationship of MAPES to the rest of the device. Note that the white dashed line on the right does not show the exact location of the probe but the location relative to the major device components (e.g. divertor, center stack).

1.2

Review of AIMS: Accelerator-based In situ Materials

Surveil-lance

In Table1.1, it is clear that several diagnostics, while not quite meeting all the qualifications for the ideal surface diagnostic, are quite close. Indeed, NRA could meet all these goals except measuring bulk erosion and function during plasma operation. This is exactly what motivated the development of the Accelerator-based In situ Materials Surveillance, or AIMS, diagnostic. As described in Hartwig et al. [1], this technique utilizes PIGE and a new form of NRA called particle-induced neutron emission in situ by attaching a compact particle accelerator to the Alcator C-Mod tokamak. This approach has several advantages. By using an ion beam, the incident beam can be steered by the toroidal and poloidal magnetic fields of the tokamak in order to reach multiple locations between shots. Because only neutral particles (i.e. neutrons and gammas) were studied, the magnetic steering of the beam did not affect the products. Finally, because of the penetration of the these neutral particles, the detectors do not have to be in close proximity or even within line-of-sight to the surface being studied, allowing them to be protected from plasma exposure.

Figure1-2shows the set-up of the AIMS diagnostic used on Alcator C-Mod. A compact accelerator was used to create a 900 keV deuteron beam in order to probe the PFCs of the tokamak in situ, instigating nuclear reactions which can be studied via spectroscopy. The deuterium content in the first wall was measured using the neutron spectra produced by the D(d,n)3_{He reaction [43], and} erosion/redeposition of the protective boron coating was measured using gamma rays produced by the11_B(p,γ)12_{B reaction [26]. There are still two weaknesses to AIMS, however. Realistically, IBA} is never going to be used during tokamak plasma operation. The high magnetic field required for plasma confinement would create an extremely small orbital path for the beam particles, precluding access to the surface. Thus the technique is limited to a time resolution of pulse-to-pulse, which is much shorter than ex-situ analysis, but is comparable to insertable probes. Measuring bulk erosion, not using thin films or other engineered surfaces, is a challenge even for traditional IBA.

Of the other techniques discussed here, only one, profilometry, can make bulk erosion/redeposition measurements (though it cannot make elemental or isotopic distinctions). Profilometry measure-ments rely on masking one part of the sample, which acts as a “control,” or reference location. In this way, profilometry has done what the other techniques have not been able to: create a reference point from which to measure changes of the bulk material. Marker layers have been used for this purpose, particularly using RBS in concert with insertable probes [18,21]; the problems with these

Table 1.1: The key aspects of a surface diagnostic in a fusion device are listed and compared to the existing suite of diagnostics.

AIMS RBS NRA

Profilometry Optical spectroscop

y Colorimetry QCM/ QMB LIBS LIAS Insertable prob es In situ _{x x x x x x ∼}

Shot-to-shot time resolution _{x x x x x x}

Can acquire during plasma

oper-ation x x

Not restricted to global informa-tion, provides high spatial

reso-lution x x x x x x x x x

Can use actual wall tiles, not

spe-cially manufactured samples x x x x x x ∼

Measures bulk erosion, not

sur-face layers x

Doesn’t require thin films of

ma-terial x x x x x x x

Doesn’t require line of sight to

the surface(s) being studied x x x x x x

Access to large areas of the first wall/divertor, not restricted to

single measurement location x x x x

Makes direct measurement of the surface, as opposed to measuring

movement of material x x x x x x x x

Elemental resolution _{x x x x x x x x}

Figure 1-2: On the left is a CAD drawing of an AIMS accelerator in position on the Alcator C-Mod tokamak. The right figure shows the various engineering aspects of the diagnostic. The orange toroidal field coils (left) produce a magnetic field which steers the ion beam to reach surfaces on the inner divertor. The radio-frequency quadrupole (RFQ) accelerator (right) creates a beam that is separated from the tokamak by a gate valve when not in operation. Optics and instrumentation allow the beam to be focused prior to use on the first wall. The 900 keV deuteron beam is then incident on the surfaces where it causes neutron- and gamma-producing reactions. The products of these reactions can be detected by the detectors in the re-entrant tube, allowing for neutron or gamma spectroscopy to be used to determine the make-up of the surface [26].

layers will be discussed in more detail in Chapter3, but they do not represent bulk material in the most ideal way.

In order to develop the AIMS diagnostic to measure bulk erosion, there must be a marker to the surface which is detectable by ion beam analysis. This marker must allow the surface to maintain its original properties and allow the use of real tokamak components. In order to develop this bulk erosion technique, a more in depth understanding of IBA must be ascertained, including previous attempts to solve this problem.

Chapter 2

Ion Beam Analysis: IBA and

applications to surface studies in

fusion devices

Ion beam analysis (IBA) describes a broad range of techniques that use energetic ions from particle accelerators to probe surfaces, some of which were introduced in Chapter1. Some of these techniques, including Rutherford Backscattering Spectroscopy (RBS) and Elastic Recoil Detection (ERD), use nuclear kinematics in order to calculate the concentrations of atoms of different masses. These methods require precise knowledge and control of the angle of incidence of the ion beam and the detector, making them ill-suited to use in a tokamak environment. Other techniques, such as Nuclear Reaction Analysis (NRA) and Particle-Induced Gamma Emission (PIGE), use nuclear reactions to determine information about surfaces. Each of these techniques will be discussed to provide a background of knowledge on the state of the art of ion beam analysis, and to give a better understanding of the building blocks of the AIMS system.

2.1

Rutherford Backscattering Spectroscopy: RBS

Named after “the father of nuclear physics,” RBS uses the kinematic principles of charged particle interaction with matter discovered in Rutherford’s gold foil experiment [44]. As the bombarding ion beam (usually light ions like protons or helium nuclei) traverses the surface, it loses energy due to Coulomb interactions with the charged particles in the surface, i.e. the electrons and the nuclei. Primarily, the beam loses energy to electronic stopping power, (dE

dx)e, with very little change in direction. The rare energy loss to nuclei, (dE

dx)n, also can result in large-angle scatters, including full π backscatters. For most of the beam path, the energy loss is dominated by the small losses to electrons, but at low energies, the probability of nuclear stopping drastically increases. These backscatters can reveal surface composition through kinematics. These calculations are based on the formulas E0= kRBSEo (2.1a) kRBS=  _M b Mb+ Ms 2 cos θ ± Ms Mb − sin 2_θ 1/2!2 (2.1b)

where Mb is the mass of the ions in the beam [amu], Msis the mass of the surface nuclei [amu], and θis the scattering angle [sr] (see Figure2-1a). Eois the energy of the incident beam particles [keV], E0 _{is the energy of the backscattered particles [keV], and kRBS is the ratio of the two [15]. kRBS} is independent of energy and determines the maximum energy at which the beam particles can backscatter from the atoms. This creates the "leading edge" in the particle energy spectrum for each surface isotope. This equation is determined by solving the conservation of momentum and conservation of energy equations [15].

By using a charged particle detector at a specific scattering angle, θ, the only unknown in Equa-tion2.2 is Ms, leaving a relatively straightforward calculation to determine the mass of the atoms in the surface. However, the calculation becomes more complicated beneath the surface monolayer of atoms, as both the incident beam and the backscattered ions lose energy in the material. As such, the stopping power of the material on the incident and reflected beam must be taken into account. This significantly complicates the calculation, so data is usually compared to a computa-tional simulation, such as SIMNRA (SIMulation of NRA), a program which takes inputs of surface composition, roughness, and detector parameters and outputs a predicted spectrum of counts vs. energy from a charged particle detector. By adjusting unknown factors (surface composition/thick-ness/roughness), the predicted spectrum can be made to match the experimental one, giving results for the unknowns [45].

(a) (b)

Figure 2-1: Sketches of RBS implementation using an aluminum layer on a carbon substrate. Carbon is black in the sketch, while aluminum is gray. (a) In this exaggerated sketch showing the atoms of carbon (M=12) and aluminum (M=27) in the surface, the scattering angle, θ is labeled, showing how ions backscattered from multiple locations at the same angle would be incident on the charged particle detector. (b) The number of counts vs. backscattered energy in each portion of the spectrum is dependent on the density of that isotope, but also on the cross section for scattering. Aluminum has a higher backscattering cross section than carbon, so even though the materials have similar density, aluminum shows a higher number of counts.

Figure2-1bshows a rough sketch of what an RBS spectrum might look like from a carbon substrate coated with aluminum. The leading edge of the aluminum spectrum indicates kEo, or the maximum recoil energy possible. As the beam loses energy in the surface, the energy of the aluminum recoils decreases. Thus, the leading edge of the spectrum indicates the species of the surface, while the width in energy space corresponds with a depth in physical space. The carbon spectrum represents the substrate. It is important to note that the front edge of the aluminum spectrum will not move as material is removed; rather, the width will decrease. The carbon edge will move as aluminum is removed from the surface, but once the aluminum is gone, the carbon spectrum will remain identical even if carbon is removed/added to the surface.

RBS can only measure species that are heavier than the probing beam; additionally, the relative masses of the species being measured are important. For example, in Figure 2-1, a thin layer of aluminum (A=27) on top of bulk carbon (A=12) is shown. Because carbon is closer in mass to the