Spectroelectrochemistry of Nanomaterials by Anjli Kumar

ABSTRACT

As the development and application of novel nanomaterials is continually expanding, so

is the need for versatile characterization methods that can probe nanoscale processes as

they occur. Such characterization techniques would permit non-destructive and parallel spectroscopy and imaging of nanomaterials. The objective of this work is to address the need for such techniques. First, a new spectroelectrochemical characterization technique,

Micro-Extinction Spectroscopy (MExS), is presented, including instrumental design as well as data acquisition and analysis algorithms. Two nanomaterial systems are then studied with MExS: two-dimensional transmission metal dichalcogenides (TMDs) and plasmonic nanoparticles. For TMDs, in situ and hyperspectral reflectance studies coupled with cyclic voltammetry and chronocoulometry enable the time-resolved observation of the process of electroablation, an oxidative technique recently developed for the synthesis of monolayer TMDs. For plasmonic nanoparticles, dark-field microscopy is used in conjunction with chronocoulometry to observe, in a hyperspectral manner, single-particle electrodeposition, a technique developed for nanoparticle surface modification. Together, these two techniques position spectroelectrochemistry as a rich tool for optical nanomaterials, capable of both synthesis and concurrent characterization.

I would rather have questions that can’t be answered than answers which can’t be questioned. - Richard Feynman

Acknowledgments

I dedicate this thesis to my parents. Dad introduced me to the world of science and reason at a very young age. Mom always pushed me just one step further.

And of course, there’s Khushi, who came into my life at just the right time. She supported me in ways that she may never understand.

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Contents

Acknowledgments ...... iii Contents ...... iv List of Figures ...... vii List of Tables ...... xvi List of Equations ...... xvii Nomenclature ...... 18 Chapter 1 ...... 19 Introduction ...... 19 Chapter 2 ...... 23 Background ...... 23 2.1. Electrochemistry ...... 23 2.1.1. Fundamentals ...... 23 2.1.2. Electroanalytical Techniques ...... 26 2.2. Transition Metal Dichalcogenides (TMDs) ...... 28 2.2.1. Background of 2D Materials ...... 28 2.2.2. Chemical properties of TMDs ...... 28 2.2.3. Opto-Electronic Properties of TMDs ...... 30 2.3. Plasmonic Nanoparticles ...... 33 2.3.1. Fundamentals of Plasmonics ...... 33 2.3.2. Electrochemistry of Plasmonic Systems ...... 36 2.3.3. Plasmonic Spectroelectrochemistry ...... 37 2.3.4. Discrete Dipole Approximation (DDA) ...... 39 Chapter 3 ...... 41 Micro-Extinction Spectroscopy (MExS): A Versatile Optical Characterization Technique...... 41 3.1. Abstract ...... 41 3.2. Introduction ...... 42 3.3. Technique Description ...... 44 3.3.1. Optical Setup ...... 44

3.3.2. Hyperspectral Data Acquisition ...... 47 3.3.3. Data Post-Processing ...... 50 3.4. Case Studies ...... 52 3.4.1. Case Study: Reflectance Characterization of 2D Materials on Non-Transparent Substrates ...... 52 3.5. Conclusion ...... 54 Chapter 4 ...... 55 In situ Optical Tracking of Electroablation in Two-Dimensional Transition Metal Dichalcogenides ...... 55 4.1. Abstract ...... 55 4.2. Introduction ...... 56 4.3. Experimental ...... 59 4.3.1. Sample preparation ...... 59 4.3.2. In situ spectroelectrochemical cell ...... 59 4.3.3. Reflectance measurements ...... 60 4.4. Results and Discussion ...... 61 4.4.1. Optical detection of the electroablation process ...... 61 4.4.2. Determination of the onset of electroablation ...... 63

4.4.3. Electroablation of MoS2 ...... 66

4.4.4. Electroablation of WS2 ...... 69

4.4.5. Electroablation of MoSe2 ...... 73

4.4.6. Electroablation of WSe2 ...... 76 4.5. Conclusion ...... 79 Chapter 5 ...... 80 Electrodeposition on Plasmonic Nanoparticles Using In Situ Spectroelectrochemistry ...... 80 5.1. Introduction ...... 80 5.2. Experimental ...... 82 5.2.1. Au nanoparticle Synthesis ...... 82 5.2.2. Spectroelectrochemical Cell ...... 82 5.2.3. Dark-field Optical Measurements ...... 84 5.2.4. Particle Transfer to TEM grid ...... 84 5.2.5. Statistical Analysis ...... 84 vi

5.3. Results and Discussion ...... 87 5.3.1. Electrodeposition of Cu ...... 87 5.3.2. Concurrent Electrodeposition of Cu and Photodeposition of Ag ...... 96 5.3.3. Change in Plasmon Resonance Fits ...... 101 5.4. Conclusion ...... 102 Chapter 6 ...... 104 Discrete Dipole Approximation (DDA): Numerical Studies of Plasmonic Nanoparticles ...... 104 6.1. Aqueous Nanogaps in Core-Shell Plasmonic Nanoparticles ...... 104 6.2. Shape and Size dependence of AuDh on refractive index sensitivity ...... 107 Chapter 7 ...... 111 Conclusions and Outlook ...... 111 References ...... 112 Appendix A: Spectroelectrochemical Cell Design and Assembly...... 132 Appendix B: Single-Particle, Hyperspectral, and Time-Lapse Data Analysis Code ...... 134

List of Figures

Figure 2.1 Circuit diagram of a 3-electrode cell. The potentiostat and the function generator are used for controlled-potential experiments. (Figure adapted from 5). 24

Figure 2.2 Electroanalytical techniques: cyclic voltammetry (CV) and chronocoulometry (CC)...... 26

Figure 2.3 General structure of layered transition metal dichalcogenides. Each sheet contains a central layer of the transition metal (M) that is sandwiched by two layers of the chalcogen (X). Figure from 28...... 29

Figure 2.4 d-Orbital filling and electronic character of various TMDs. a, Qualitative schematic illustration showing progressive filling of d orbitals that are located within the bandgap of bonding (σ) and anti-bonding states (σ*) in group 4, 5, 6, 7 and 10 TMDs. Figure and caption taken directly from 41...... 31

Figure 2.5 Band structure for MoS2. Bulk, 4 layers, and 2 layers have indirect band gaps. Monolayer has a direct band gap. Figure adapted from 50...... 32

Figure 2.6 Localized surface plasmon resonance (LSPR). The electric field induces an oscillation of free electrons in nanoparticles...... 33

Figure 2.7 Factors that affect LSPR energy. References are as follows: (a)76 (b)72 (c)74 and (d)80...... 35

Figure 2.8 Plasmonic electric field enhancement at nanoparticle tips of Pt-decorated Au nanoprisms. (a-b) HAADF-STEM images of Au nanoprisms before and after Pt decoration. (c) STEM-EELS corner plasmon mode depicting electric-field enhancement at NP tips (Figure and caption adapted from 84). Scalebar = 50 nm. . 37

Figure 2.9 Spectroelectrochemical setups and corresponding results from various experiments. Figures are adapted from: (a)6,7 (b)19 and (c)20,21...... 38

Figure 3.1 MExS configurations. (a) Transmission: Light passes through the sample, transmitted light is collected by the objective. This configuration provides information about the sample’s optical absorption properties. (b) Reflectance: A half-mirror is used to direct light throgh the objective to the sample. The light that the sample reflects back is collected by the objective. This configuration enables the study of materials on non-transparent substrates. (c) Dark-field: The dark-field condensor blockes direct signal from the light source resulting in the collection of only light scattered by the sample. This configuration allows the study of small vii

viii

scattering structures such as plasmonic NPs. (d) Detector system: Light that is collected by the objective goes through the spectrometer and the intensity of dispersed light is recorded by the EMCCD...... 45

Figure 3.2. Microscope configuration – photograph: (a) EMCCD, (b) spectrometer, (c) halogen lamp, (d) dark-field condenser, (e) piezo stage, (f) imaging camera, and (g) LED lamp...... 46

Figure 3.3 User process diagram describing MExS preparation (optical, hyperspectral) and spectral imaging (imaging and processing of data)...... 47

Figure 3.4 Data acquisition. (a) Block diagram depicting the hyperspectral code design corresponding to the LabVIEW user interface (shown in Figure 3.5). When the user selects the “start” button in the user interface, the code enters the “Initiate Software” mode, launching the software for both the piezoelectric stage (PI – Physik Instrumente stage software) and the spectrometer (LF – Lightfield, Princeton Instruments software for the detector system). Once the parameters have been set for the experiment in the user interface and the user advances the code into the “Acquire Data” section, the code goes through N cycles of data acquisistion and stage movement. (b) Representation of an as-acquired stack of 2D matrices...... 49

Figure 3.5 LabVIEW user interface for MExS spatial-scanning hyperspectral data acquisistiom. The corresponding LabVIEW block diagram is shown in Figure 3.4a...... 50

Figure 3.6 Post-processing of acquired data. (a) As-acquired stack of 2D matrices. Integration along the Nλ-axis creates an integrated spectrum image (SI) as shown in (c). A point-spectrum at a particular x,y-location can be extracted for the region of interest (ROI). (b) Representation of a raw datacube. (c) Integrated spectrum image generated by summation along the Nλ-axis. This serves as a map in data post- processing. (d) Representative point spectra extracted as shown in (a) for absorbance, reflectance, and dark-field scattering...... 51

Figure 3.7 Reflectance MExS Characterization of WS2. Spectrum images (a-b) and corresponding optical images (c-d) are both used to location regions of interest. (e) Reflectance spectra for the region of interest indicated by arrows in (a) and (b) for a mutlilayer of WS2 (646 nm exciton peak) and for a monolayer of WS2 (616 nm exciton peak), respectively...... 53

through the spectrometer and spectral data recorded on the EMCCD. (b) Spectroelectrochemical cell. The sample is placed face down at the top of the electrochemical cell. (c) Schematic of 2D transition metal dichalcogenides. Purple atoms represent the transition metal, yellow atoms represent the chalcogen. (d-f) Optical images before and after electroablation for MoS2, WS2, MoSe2, WSe2. The initial images (d-g) show that the flakes have high reflectance in contrast to the TiN substrate. The images taken after electroablation for MoS2, WS2, MoSe2 (h-j) show the flakes remain but have much lower reflectance intensity. This is attributed to modification of the flake from multilayer to monolayer. In the case of WSe2 (k) no flake remains post electroablation. Scalebars: (h-j) 10 μm, (k) 5 μm...... 62

Figure 4.2 Cyclic voltammograms for all 4 materials studied. As the potential is swept positive, a peak appears near the electroablation potential. No peak is present on the reverse sweep...... 63

Figure 4.3 Electroablation onset potential. (a) Optical image series for MoS2. Each applied potential step was held for 60 seconds. Images acquired between each step. Increments of 50 mV (increments of 100 mV after 1.0 V). The initial onset of electroablation for MoS2 is qualitatively observed to be 750 mV. Full ablation occurs at 1.0 V. Scalebar: 10 μm. (b-e) Reflectance spectra for MoS2, WS2, MoSe2, WSe2, respectively. The arrow in (b) shows the optical trend of decreasing reflectance that occurs as a result of applied potential steps. The spectral data supports the qualitative observation of the electroablation onset potentials (corresponding optical image series’ for (c-e) are in Figure S1). The onset potential for WS2 was both qualitatively and quantitatively determined to be 600 mV. For both MoSe2 and WSe2 the onset potential was determined to be 1.0 V. (c-e) Inset figure widths: 11 μm, 19 μm, 10 μm, respectively...... 64

Figure 4.4 Optical image series for (a) WS2, (b) MoSe2, and (c) WSe2. Qualitative observation shows the respective onset potentials to be 0.6 V for WS2, and 1.0 V for both MoSe2 and WSe2. Scalebars: 5 μm...... 65

Figure 4.5 MoS2 reflectance line scan during electroablation. (a) Reflectance spectra of the electroablation process for the position shown in the inset. Exciton A region is indicated by an arrow. As the electroablation process occurs, exciton A blue-shifts from ~690 to ~650 nm. The reflectance intensity of exciton A post-electroablation is 2/3 of its initial intensity. (b) Line scan for the region indicated by red arrow in the inset (post electroablation). The peak wavelength for exciton A is 650-660 nm in regions of low reflectance intensity and ~680 nm in regions of high reflectance intensity. (c-e) Optical images (before, c, and after, d) and (e) spectral data for the cyclic voltammetry experiment. The peak wavelengths for exciton A (left axis) at x

locations indicated in (c) as a function of potential sweep (right axis). The onset of electroablation occurs just above 1 V. Scalebars: 5 μm...... 66

Figure 4.6 MoS2 photoluminescence (PL). (a) Optical image of MoS2 flake. (b) The PL signal at the brighter region in the optical image (red) has a lower PL intensity. The PL signal at the darker region (blue) has a higher PL intensity. Scalebar: 10 μm...... 67

Figure 4.7 MoS2 energy map, exciton A. (a-e) MoS2 flake pre-EA. Applied potentials for (b-e) are 0.7 V, 0.8 V, 0.9 V, and 1.0 V, respectively. Pre-EA, peak wavelength is >700 nm. Inset: optical images of flake at corresponding potential. (f) MoS2 flake at the EA potential (1.1 V). The peak wavelength is uniformly ~660 nm post-EA. Inset: optical image of the flake at 1.1 V. Reflectance intensity first decreases for outer regions of flake. Scalebar: 5 μm...... 68

Figure 4.8 WS2 reflectance line scan during electroablation. (a) Reflectance spectra after electroablation for positions shown in (c). Exciton A indicated by arrow. At the center of the flake, where it is still very thick post-ablation, the reflectance intensity is very high. At the outer regions of the flake, the intensity of the reflectance spectrum decreases and the exciton A peak blue shifts. (b) Line scan for region shown in inset (after electroablation). The peak wavelength is 620-630 nm at the edges, where the flake thickness has been reduced; it remains at ~640 nm for the central portion of the flake, which remains thick after electroablation. (c-d) Optical images of WS2 flake before and after electroablation. Line scan (circles and arrow) indicate data shown in (c-d). (e) Exciton A peak wavelength for positions shown in (c). For regions at the edge of the flake, the peak wavelength decreased from ~645 nm to ~615 nm at the electroablation potential (0.6 V). For the unablated region at the center of the flake, the exciton A peak wavelength remains high (~640 nm). Scalebar: 5 µm...... 70

Figure 4.9 WS2 energy map for exciton A. (a-b) WS2 flake prior to electroablation. Applied potential = 0.4 V and 0.5 V, respectively. Peak wavelength is 635-650 nm prior to electroablation. Inset: optical image of flake at 0.4 V. (c) WS2 flake at electroablation potential (0.6 V). Peak wavelength for the outer portion of the flake is ~625 nm; for the inner portion of the flake is ~635 nm. Inset: optical image of the flake at 0.6 V. Reflectance intensity decreases for outer region of the flake. (d-f) WS2 flake at potentials above the electroablation potential (0.7 V, 0.8 V, 0.9 V). The peak wavelength remains consistent as applied potential is increased. Inset: Corresponding optical image also shows that the flake remains the same as the applied potential is increased above the electroablation potential (0.6 V, 0.8 V). Scalebar: 10 µm...... 71 xi

Figure 4.10 MoSe2 reflectance line scan during electroablation. (a) Full reflectance spectra for positions ranging from center of flake to edge of flake. No exciton B peak is visible for the two reflectance peaks with greatest intensity. Inset scalebar: 10 µm. (b) Expanded view of exciton B region. The exciton B peak appears at a higher wavelength (approx. 640 nm, red curve) towards the center of the flake. At the edge, the exciton B peak is at a lower wavelength (approx. 600 nm, blue curve). (c) Pixel- by-pixel peak wavelength for exciton B from center to edge of flake. The peak potential starts at approx. 630 nm and blue shifts radially outward to approx. 600 nm. (d) Exciton B peak wavelength at various positions (1-9) at 3 potential steps (1.0, 1.1, 1.2 V). The exciton B peak for the center of the flake starts at the higher end of the wavelength range, while the exciton B peak at the edge of the flake starts at lower wavelengths. Once the voltage is taken beyond the electroablation potential, the peak wavelength for all regions drops below 600 nm...... 73

Figure 4.11 MoSe2 energy maps for exciton B. (a-b) MoSe2 flake prior to electroablation. At low voltages, the exciton peak is not visible. (c) Once the electroablation potential has been reached (1.0V) the exciton B peak becomes visible (see Figure 8) at the edges. The exciton B energy gradient observed at 1.0 V indicates an edge-in electroablation mechanism. The higher wavelengths closer to the center of the flake (635 nm, red) are indicative of multilayer MoSe2, while the lower wavelengths at the edges of the flake (610 nm, green and 595 nm, blue) suggest the presence of few- and mono-layer MoSe2, respectively. (d-f) As the potential is increased beyond the electroablation potential (1.1-1.3 V), exciton B is uniformly at a lower wavelength (590-600 nm, blue) indicating that the monolayer has been achieved and is stable. Scalebars: 20 µm (white) and 10 µm (black)...... 74

Figure 4.12 WSe2 reflectance line scan during electroablation. (a) Reflectance spectra for WSe2 showing Exciton A' peak that is tracked. (b) Optical image of WSe2 flake before electroablation. (c) Line scan for region shown in (d). The peak wavelength for exciton Aʹ ranges from 540-550 nm prior to electroablation. The bottom two curves show that at the electroablation potential (1.0 V) the exciton Aʹ peak is no longer present at the edge of the flake. The remnant of the flake (top five curves) shows that the peak wavelength decreases to 530-540 nm at the edges and remains unchanged in the center of the remnant. (d) Optical image of WSe2 flake after electroablation...... 77

flake edges are ablated first. The exciton Aʹ peak is no longer present at the edges. Inset: the corresponding optical image also shows the flake is ablated at the edges first. (e-f) WSe2 flake above the electroablation potential. The flake has been ablated entirely, with the exception of a few remnants. Inset: the optical image shows that the flake is no longer present at potentials greater than the electroablation potential. Scalebar: 10 µm...... 78

Figure 5.1 Single-particle electrodeposition schematic. A voltage is applied to immobolized bare gold nanoparticles (left). Metal ions in solution deposit onto the nanoparticle surface (right)...... 81

Figure 5.2 Spectroelectrochemical Micro-Extinction Spectroscopy (SE-MExS) dark- field mode schematic. (a) Hyperspectral dark-field optical setup. Illumination from a halogen lamp passes through a dark-field condensor prior to sample interaction. Only light scattered by the sample is collected in the objectctive. All direct light is blocked. (b) Spectroelectrochemical cell. The nanoparticles on an indium tin oxide coverslip act as the working electrode...... 83

Figure 5.3 Graph of t-distribution for 20 degrees of freedom ( = ). A t statistic = 2.3, for example, has a p-value of 0.0162, which is statistically significant for a 𝒗𝒗 𝟐𝟐𝟐𝟐 significance level of 0.05...... 85

Figure 5.4 Single-particle scattering spectra before and after electrodeposition. Prior to electrodeposition, the resonance wavelength of the single particle is 603 nm. After electrodeposition, the resonance wavelength red-shifts to 615 nm. Inset: single- particle SEM image post electrodeposition. Scalebar: 50 nm...... 88

Figure 5.5 Scanning transmission electron microscopy (STEM) energy dispersive X- ray spectroscopy (EDS) elemental analysis of a Au nanoparticle post Cu deposition after 72 mC of charge transfer. (a) STEM image of the nanoparticle. (b-c) EDS integrated intensity maps, Au (Lα, 9.7 keV) and Cu (Kα, 8.04 keV) respectively, of the corresponding particle. Scalebar: 25 nm...... 89

Figure 5.6 Monitoring electrodeposition of Cu on Au by tracking LSPR during charge transfer. The LSPR wavelength remains at 650-651 nm from 0-180 seconds; no charge transfer occurs during this period. Charge transfer is initiated at 180 seconds. The LSPR wavelength shifts from 651 nm to 660 nm during a charge transfer of -2.7 mC. Once charge transfer is complete, the LSPR wavelength remains at 659-660 nm...... 90

Figure 5.7 Optical image (left), hyperspectral integrated spectrum image (right) maps of immobilized NPs on ITO coverslips. Single particles correlated between xiii optical and hyperspectral integrated spectrum images during data analysis. Integrated spectrum images, explained in detail section 0, are constructed by integrating the intensity across all wavelengths at each pixel. Scalebar: 20 µm...... 91

Figure 5.8 Statistical analysis of change in LSPR wavelength at different charge transfers. Charge transfer of 6 mC resulted in an average LSPR shift of 12 ± 1.5 nm, N = 125 particles. Charge transfer of 12 mC caused an average LSPR shift of 40. ± 7 nm, N = 151 particles. The p-value obtained from the t-test comparison of these two datasets is 1.5 x 10-37 (indicating greater than 99% statistical significance)...... 92

Figure 5.9 SEM images of AuDh immobilized on an ITO-coated coverslip. Images reveal two points of interest: (1) particles are of varying sizes and corner roundness, (2) particles immobilize onto the surface at different positions relative to the potential drift field and expore the tips of the particle to the drift differently (Figure 5.10). Scalebars: 100 nm...... 93

Figure 5.10 Comparison of bare ITO and drop of AuNPs onto ITO. Both ITO coverslips were cleaned with basic piranha (1 NH4OH : 1 H2O2 : 5 H2O) to smoothen the as-purchased ITO-coated (sputtered) coverslip. The image on the left shows scattering locations on the substrate due to remaining roughness. Scalebar: 10 µm...... 94

Figure 5.11 Potential step required for charge transfer and LSPR shift. Application of -100 mV potential bias resulted in a total charge transfer of 0.05 mC and an average LSPR shift of 0.6 ± 1.0 nm. Application of -200 mV potential bias resulted in a total charge transfer of 0.70 mC and an LSPR shift of 1.0 ± 0.7 nm. Application of a -300 mV potential bias resulted in a total charge tranfer of 5.0 mC and an LSPR shift of 7 ± 1.4 nm. Values for charge transfer and LSPR shift are cumulative; i.e., the effect of each step is incorported with each subsequent step. N = 10 particles. .. 95

Figure 5.12 Monitoring electrodeposition of Cu and Ag by tracking change in LSPR during charge transfer. The LSPR wavelength remains at 666-668 nm from 0-600 seconds; no significant charge transfer occurs during this period. Charge transfer is initiated at 600 seconds. The LSPR wavelength first red-shifts from 668 nm to 680 nm; then the LSPR wavelength blue shifts from 680 to 676 nm...... 97

Figure 5.13 Single-particle scattering spectra tracked in situ during electrodeposition. (a) Initiation of charge transfer causes simultaneous red-shift and decrease in scattering efficiency of the plasmon resonance, consistent with Cu deposition. (b) Once Ag+ ions have photooxidized into solution, the plasmon resonance reverses: blue-shift and increased scattering efficiency...... 99 xiv

Figure 5.14 Scanning transmission electron microscopy (STEM)-energy dispersive X-ray spectroscopy (EDS) elemental spectral analysis of a single particle post electrodeposition. The spectrum shows the presence of Au (2.1 and 9.7 keV), Cu (8.04 keV) and Ag (2.98 keV). Also present are elements from the substrate, O (0.5 keV) and Si (1.7 keV). A detector escape peak is also recorded. Inset: Correlated TEM image. Scalebar: 40 nm...... 99

Figure 5.15 Statistical analysis of change in LSPR wavelength at different charge transfers (N = 23 particles). As charge transfer increases from 0 to 10.4 mC, the LSPR shift increases upto 12 ± 2 nm. After 120 seconds post charge transfer, the plasmon resonance experiences a blue-shift to 7 ± 3 nm. Corresponding p-values are listed in Table 5.4...... 100

Figure 5.16 Plasmon resonance shift during charge transfer. (a) 0-10 mC: At low charge transfer, the corresponding plasmon resonance shift shows a logarithmic fit. The plasmon resonance shift approaches 12 nm after 10 mC of charge has been transferred. (b) 0-80 mC: At high charge transfer, the shift shows a linear trend. After 80 mC charge transfer, there is an LSPR shift of 100 nm...... 102

Figure 6.1 Nanorods with aqueous nanogap. (a-b) HAADF-STEM images4 of nanorods with Au core, Au/Ag shell, and aqueous nanogap containing 4- mercaptopyridine. Elemental analysis and detailed synthesis information can be found in the original publication.4 (c) Nanorod modeled after particles in (a-b). The shape generated for DDA numerical simulations consists of more than 46,000 dipoles...... 105

Figure 6.2 Various shape files used for DDA simulations to predict plasmonic response to light-particle interaction. (a) Pure Au rod, (b) Au core, Au/Ag alloy shell, (c) Au core, Au/Ag alloy shell, partial nanogaps, and (d) Au core, Au/Ag alloy shell, full nanogaps...... 106

Figure 6.3 Normalized DDA simulated extinction spectra. Peak wavelengths are as follows: (a) 696 nm, (b) 629 nm, (c) 599 nm, (d) 661 nm...... 107

Figure 6.4 Decahedron shapes with sharp (left) and rounded (right) tips...... 108

Figure 6.5 Scattering spectra for 80 nm side-length Au Dh. Sharp tips (left) and rounded tips (right)...... 109

Figure 6.6 Scattering spectra for 120 nm side-length Au Dh. Sharp tips (left) and rounded tips (right)...... 109 xv

Figure 6.7 Refractive index sensitivity. Top two lines show the 120 nm Dh and the bottom two lines show the 80 nm Dh. The corresponding shape and RIS are listed to the right of the graph...... 110 xvi

List of Tables

Table 3.1 Acquired dataset dimensions for various objective magnifications...... 47

Table 3.2 Example of optical and hyperspectral parameters to be set by the user prior to data aqcuisition. *Objective magnification: 40x and 100x respectively. ** Only Ny or Nt is relevant in a given experiment...... 48

Table 3.3 Examples of experiment-specific computations utilized in data post- processing...... 48

Table 5.1. Statistical analysis equations...... 86

Table 5.2 Electrochemical half reactions. Reduction potentials are versus the standard hydrogen electrode (SHE)...... 87

Table 5.3 Results of Welch’s t-test. The p-value (0-1) is an indicator of probability that the difference in LSPR shift for two sample sets is due to chance. The p-values between the first two potential steps is 0.29; i.e., high probability that the LSPR shift is due to chance, statistically insignificant shift. The p-value between the second two potential steps is 2.9 x 10-10, i.e., there’s a 99% confidence interval that the difference in these two shifts is statistically significant...... 95

Table 5.4 Results of Welch’s t-test. The p-value (0-1) is an indicator of probability that the difference in LSPR shift for two sample sets is due to chance. All of the p- values in this experiment are less than 0.01, indicating a 99% confidence interval that each LSPR shifts in Figure 5.15 are significantly different from the previous...... 101 xvii

List of Equations

Equation 1 = ...... 25

Equation 2 ∆𝑮𝑮= −° 𝒏𝒏𝒏𝒏𝒏𝒏 ...... 25

Equation 3 𝑬𝑬 𝑬𝑬 − ...... 𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹𝑹 ...... 25

Equation 4 ∅𝑴𝑴=− ∅𝑺𝑺 ...... 27

Equation 5 𝒊𝒊𝒊𝒊 = 𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝟏𝟏𝟏𝟏𝑪𝑪𝑪𝑪 ∗ 𝝅𝝅𝟏𝟏𝟏𝟏𝒕𝒕𝟏𝟏𝟏𝟏 ...... 27

Equation 6 𝑸𝑸 =𝟐𝟐𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝟏𝟏𝟏𝟏𝑪𝑪𝑪𝑪 ∗ 𝒕𝒕𝟏𝟏𝟏𝟏 (𝝅𝝅𝟏𝟏𝟏𝟏) ( + ) + ...... 34

Equation 7 𝑬𝑬𝑬𝑬 = 𝟐𝟐𝟐𝟐𝝅𝝅𝟐𝟐[𝑵𝑵𝑵𝑵𝟑𝟑𝜺𝜺𝜺𝜺𝟑𝟑𝟑𝟑𝝀𝝀𝐥𝐥𝐥𝐥] ...... 𝟏𝟏𝟏𝟏 𝜺𝜺𝜺𝜺 𝜺𝜺𝜺𝜺 𝝌𝝌𝝌𝝌𝝌𝝌 𝟐𝟐...... 𝜺𝜺𝜺𝜺𝟐𝟐 35

Equation 9 ∆𝝀𝝀 , 𝒎𝒎, ∆𝒏𝒏 𝟏𝟏 −, 𝒆𝒆,−=𝟐𝟐𝟐𝟐𝟐𝟐,𝟐𝟐 , , , ...... 36

Equation 10. Welch’s∅𝒙𝒙′ t𝒚𝒚 statistic′ 𝒛𝒛′ − ∅ ...... 𝒙𝒙 𝒚𝒚 𝒛𝒛 𝒙𝒙 𝒚𝒚 𝒛𝒛𝒛𝒛′ 𝒚𝒚...... ′ 𝒛𝒛′ − 𝝃𝝃 ∙ 𝒅𝒅𝒅𝒅 ...... 86

Equation 11. Welch’s degrees of freedom (ν) ...... 86

Equation 12. Probability density function of the t-distribution ...... 86

Equation 13. p-value...... 86

Equation 14. Matlab function for Welch’s t-test...... 86

Nomenclature

CE Counter Electrode

DDA Discrete Dipole Approximation

DF Dark-field

Dh Decahedra

EA Electroablation

EDS Energy Dispersive X-ray Spectroscopy

EMCCD Electron-Multiplied Charge Couple Device

LSPR Localized Surface Plasmon Resonance

MExS Micro-Extinction Spectroscopy

NP Nanoparticle

QRE Quasi-reference Electrode

RE Reference Electrode

RI(S) Refractive Index (Sensitivity)

ROI Region of Interest

SEM Scanning Electron Microscope

SE-MExS Spectroelectrochemical MExS

SI Spectrum Image

STEM Scanning Transmission Electron Microscopy

TEM Transmission Electron Microscopy

TMD Transition Metal Dichalcogenides

WE Working Electrode 19

Chapter 1

Introduction

The engineering of novel nanomaterials is based on the sound scientific knowledge that is acquired in fundamental research. To design, understand, and characterize novel materials, we need versatile methods that can elucidate otherwise hidden material properties, detect and analyze intricate processes as they occur, and develop novel materials with unique properties. This thesis presents a new spectroelectrochemical characterization approach to probe fundamental understanding of nanomaterial optical and chemical properties by observing electrochemical processes as they occur in real time. Using this characterization approach, the specific scientific problems that this thesis aims to explore are (1) the synthesis of 2D transition metal dichalcogenides (TMDs) via electroablation, and (2) the surface modification of plasmonic nanoparticles (NP) through electrodeposition. 20

The spectroelectrochemical characterization approach that this work presents responds to

the declared need1 for novel techniques that enable non-destructive and concurrent spectroscopy and imaging of nanomaterials (Chapter 3). By using this hyperspectral and time-resolved characterization method, we expand understanding of the electroablation

process for the formation of 2D TMDs (Chapter 4) and observe the surface modification

process on a statistically significant number of plasmonic nanoparticles (Chapter 5).

The first content chapter of this thesis, Chapter 2, contains background information laying the foundation for work presented in subsequent chapters. It begins with explanations about the fundamentals of electrochemistry and the types of electroanalytical techniques utilized in the work presented in this thesis. Then, there is a discussion about 2D transition metal dichalcogenides (TMDs), which is one of the two classes of nanomaterials studied in this thesis. Following is a discussion of plasmonic nanoparticles (NPs), the second class of nanomaterials studied in this thesis. Finally, there is a brief exploration regarding the importance of the work presented this thesis to the field of nanomaterials and nanomaterial characterization.

Chapter 3 introduces the innovative and versatile Micro-Extinction Spectroscopy (MExS) technique for optical characterization of nanomaterials.2 The optical setup and design are

described in detail. Discussion of the hyperspectral data acquisition method and data

post-processing analysis algorithm follows. The chapter is concluded with a case study

that demonstrates the utility of the MExS technique.

Chapter 4 shows the application of the MExS technique in spectroelectrochemical

imaging (SE-MExS). Transition metal dichalcogenides (TMDs) are studied in highly 21

corrosive (oxidative) environments.3 In situ spectral analysis and optical imaging reveal the electroablation (i.e., layer-by-layer removal driven by an electrical bias) mechanisms for four TMDs of interest: MoS2, WS2, MoSe2, WSe2. The electroablation onset potential

of each material is determined. Location-dependent electroablation is studied within

flakes of each material. The substrate-induced stability of the monolayer is explored for

each material.

Chapter 5 explores the utility of spectroelectrochemical MExS (SE-MExS) for single-

particle electrodeposition on plasmonic Au NPs. The plasmon resonance frequency is

tracked to monitor the electrodeposition process of Cu and Ag onto the surface of Au

NPs. We use time-lapse acquisition to observe the electrodeposition in situ and spatial

hyperspectral acquisition for statistical studies on the order of 100s of particles.

Chapter 6 presents numerical calculations for plasmonic nanoparticles of various

compositions and geometries using Discrete Dipole Approximation (DDA) software. The

first project discussed in this chapter explores the impact of internal pockets of water

inside plasmonic nanoparticles. These results are used to support experimental data

acquired by colleagues in the field.4 The second project discussed here explores the

changes in refractive index sensitivity (RIS) of the plasmon resonance energy of Au Dh

as a result of differences in particle size and shape (e.g., corner rounding).

Finally, Chapter 7 discusses the conclusions and contributions of this work.

Recommendations are made for future research relative to the work presented in this thesis. 22

Details regarding the hyperspectral acquisition code (introduced in Chapter 3 – MExS), the spectroelectrochemical cell design (Chapters 4 and 5), and the single-particle, hyperspectral, and time-lapse data analysis code (Chapters 4 and 5), that were designed and written by the author of this thesis are presented in Appendices A, B, and C, respectively. 23

Chapter 2

Background

2.1. Electrochemistry

2.1.1. Fundamentals

Research in electrochemical systems can be conducted using a two- or three-electrode setup. Two-electrode setups are typically used for research that studies an entire electrochemical system (e.g., batteries). Three-electrode setups, on the other hand, are more often used in fundamental studies that probe the understanding of a particular half- reaction or electrode. The electrochemical work presented in this thesis was done using three-electrode systems.

A three-electrode electrochemical cell consists of a working electrode (WE), a reference electrode (RE), and a counter or auxiliary electrode (CE) (Figure 2.1). 24

Figure 2.1 Circuit diagram of a 3-electrode cell. The potentiostat and the function generator are used for controlled-potential experiments. (Figure adapted from 5).

The half-reaction or electrode being studied is set at the WE. The potential of the WE can

be modulated against the unchanging potential of the RE. No appreciable current is

allowed to flow through the RE. In small-scale electrochemical cells, a quasi-reference

electrode (QRE) is used in place of the RE due to size limitations.6–10 While REs have

long-term use and are periodically calibrated to ensure they remain a constant reference,

QREs, which are typically just a metallic wire (e.g., Pt, Ag), can be replaced frequently

because their potential will drift with use. The CE closes the circuit for charge transfer

with the WE. If the oxidation half-reaction occurs at the WE, it is considered to be the

cathode; conversely, if the reduction half-reaction occurs at the WE, it is the anode. The

CE is where the opposite half-reaction occurs. 25

Electrochemical reactions occur in response to electromotive forces (E) that act on

chemical species. Electromotive forces, or reduction potentials, are related to Gibbs free

energy (∆G) in the following manner:

Equation 1 =

∆𝑮𝑮 −𝒏𝒏𝒏𝒏𝒏𝒏 Here, n is the stoichiometric number of electrons transferred and F is Faraday’s constant

(96,485 C/mol). Because the electromotive force is typically reported under standard conditions (1 atm, 298 K, and activity = 1), the Nernst equation (Equation 2) is used to relate the standard reduction potential (E°) with experimental conditions in order to calculate the non-standard reduction potential (E):

Equation 2 = ° 𝑹𝑹𝑹𝑹 𝑬𝑬 𝑬𝑬 − 𝒏𝒏𝒏𝒏 𝒍𝒍𝒍𝒍𝒍𝒍 Here, R is the universal gas constant (8.314 J K-1 mol-1), T is the absolute temperature,

and Q is the reaction quotient as it relates to the equilibrium constant. Note that n and F

are the same as in Equation 1.

The electrostatic potential (φ), defined as the work done by an electric field in carrying a

unit of positive charge from infinity to a given point, of a material also affects

electrochemical processes. The interfacial potential difference,

Equation 3 𝑴𝑴 𝑺𝑺 ∅ − ∅ 26

between the metal (φM) and the solution (φS) is a function of the physical size of the

interface.5 This size dependence indicates that bulk and nanoscale electrochemical

systems must be inherently different.11–15

2.1.2. Electroanalytical Techniques

A potentiostat can be used to control the voltage or potential of a three-electrode system

(Figure 2.1). In controlled-potential experiments such as cyclic voltammetry (CV) or

chronocoulometry (CC), the current or the charge is measured as the potential of the WE

is varied over time (Figure 2.2).

In CV experiments, the potential is swept forward and reverse at a linear sweep rate, ν,

while the resulting faradaic current is measured. CV experiments are useful in identifying

electrochemical processes in complicated systems (e.g., electroablation, Chapter 4).

Figure 2.2 Electroanalytical techniques: cyclic voltammetry (CV) and chronocoulometry (CC). 27

In CC experiments, the potential is stepped positive or negative of the reduction potential

for the electroactive species; the resulting current is integrated, and the total charge

transfer is recorded. The Cottrell Equation (Equation 4) describes the current generated as

a function of time in potential step experiments.

( ) 𝟏𝟏 Equation 4 = �𝟐𝟐 ∗ 𝒏𝒏𝒏𝒏𝒏𝒏𝑫𝑫𝑶𝑶 𝑪𝑪𝑶𝑶 𝟏𝟏� 𝟏𝟏� 𝒊𝒊 𝒕𝒕 𝝅𝝅 𝟐𝟐𝒕𝒕 𝟐𝟐 Here, A is the electrode area, is the diffusion coefficient of the oxidized species, is ∗ 𝑂𝑂 𝑂𝑂 the concentration of the oxidized𝐷𝐷 species and t is time. Note that n and F are defined𝐶𝐶 in

Equation 1. Integration of the Cottrell Equation (Equation 5) over the duration of the

potential step provides the cumulative charge transfer, Q.

Equation 5 = 𝟏𝟏� 𝟏𝟏 𝟐𝟐 ∗ �𝟐𝟐 𝟐𝟐𝒏𝒏𝒏𝒏𝒏𝒏𝑫𝑫𝑶𝑶 𝑪𝑪𝑶𝑶𝒕𝒕 𝟏𝟏� 𝑸𝑸 𝝅𝝅 𝟐𝟐 Work presented in Chapter 5, regarding the electrodeposition of Cu and Ag onto Au,

correlates the measured cumulative charge transfer (as described by Equation 5) with

changes in the optical signature of the plasmonic nanoparticles (theory of plasmonics

introduced in Section 2.3).

Spectroelectrochemistry is a technique in which optical properties of electrochemical

processes are studied. For most spectroelectrochemical experiments, an in situ

electrochemical cell is utilized that permits the concurrent use of optical spectroscopy.6–

8,16–22 Further background information on spectroelectrochemistry in plasmonic systems is provided in Section 2.3.3. The in situ spectroelectrochemical systems used in this thesis 28

are shown in Figure 4.1 and Figure 5.2; detailed information regarding the

spectroelectrochemical cell, designed in-house, can be found in Appendix A.

2.2. Transition Metal Dichalcogenides (TMDs)

2.2.1. Background of 2D Materials

The origin of the modern field of two-dimensional (2D) materials is credited to graphene.

The original postulation regarding 2D graphite (i.e., graphene) dates back to 1947,23 but it was not until 2004 that single-atom thick layers were successfully extracted from bulk graphite.24 The discovery of graphene was an exciting one because it demonstrated what

had been previously theorized – that unique physical phenomena, e.g., the quantum Hall

effect, result from the confinement of charge and heat into a 2D space.1 Graphene is an example of a van der Waals solid, a material in which covalent bonds (bond strength 100-

1000 kJ/mol) connect atoms in a two-dimensional plane, while the third dimension is held together with considerably weaker van der Waals forces (bond strength < 4 kJ/mol).1

2.2.2. Chemical properties of TMDs

Like graphene, transition metal dichalcogenides (TMDs) are materials that are van der

Waals solids. TMDs are layered materials that have the general formula MX2, where M = transition metal and X = chalcogenide (Figure 2.3). Two-dimensional sheets of TMDs consist of three layers – one layer of the transition metal that is sandwiched between two layers of chalcogenide atoms. Because TMDs are van der Waals solids, mechanical exfoliation can be exploited to isolate single- and few-layer TMDs.1,25–27 29

In fact, mechanical exfoliation has been used since the 1960s to separate single- and few- layer van der Waals solids.25–27 It is an extremely popular method because of its robust nature; mechanical exfoliation gently separates individual layers while causing minimal damage to the resulting flakes.1 Relatively large flakes – on the order of 10 μm – can be extracted by mechanical exfoliation.29–31 In a seemingly simplistic, but common,

“Scotch-tape” method, commercial tape is used to lift exposed layers of a bulk sample of a van der Waals solid; the resulting flakes can be used for further study of 2D

TMDs.28,30,32–40 The work presented in Chapter 4 of this thesis on electroablation utilized 30

2D flakes of four TMD materials, MoS2, WS2, MoSe2, and WSe2, that were prepared

using the mechanical exfoliation method.

2.2.3. Opto-Electronic Properties of TMDs

In TMDs the inter-layer spacing is approximately 7 Å, compared to 4 Å intra-planar

metal-metal bond length.41 This metal-metal bond length is 15-20% longer than the

metal-metal bond length in pure metals, indicating that there is reduced d-orbital overlap in TMDs.41 The exposed surface of the TMD is a sublayer of chalcogen atoms which are

terminated with lone pairs of electrons, as opposed to dangling bonds. The absence of dangling bonds makes TMDs generally stable and non-reactive to the ambient medium.41

The electronic structure of TMDs varies by composition, which is attributed to the

increased d-orbital filling of the transition metal for groups 4-10.41 The composition of

the chalcogen atom does not influence the electronic structure as much as the transition

metal does; however, there is a decrease in band gap with increased atomic number. For

example, the band gap decreases down the chalcogen family for molybdenum-based

42 systems; the band gap of MoS2, 1.3 eV, is greater than that of MoTe2, 1.0 eV.

TMD properties also differ due to the phase structure; TMDs form three main phases –

1T, 2H, 3R, where the number represents the number of layers in a unit cell, and the letter

represents the packing structure (tetrahedral, hexagonal, and rhombohedral). While

compositionally identical, 2H-MoS2 and 3R-MoS2 are distinct; the former is the typical

42 structure of natural MoS2, the latter is the structure of synthetic MoS2. In general, the

phase can be altered by induced chemical effects. For example, Li intercalation causes a 31

phase change in MoS2 and TaS2; for MoS2 the phase change is from 2H to 1T, and for

43–47 TaS2 the phase is 1T to 2H. In some cases, alkali metal intercalation can cause a

periodic distortion in the crystal lattice that often remains even after the removal of the

intercalant.45,48,49 This effect is credited to the change in free energy that occurs because

of energetic splitting of degenerate orbitals that are partially filled (Figure 2.4).48

While bulk TMDs are indirect bandgap semiconductors, monolayers are direct bandgap

semiconductors (Figure 2.5).50 In bulk TMDs, the valence band maximum and the

conduction band minimum are not aligned (Γ point and Γ-K middle point, respectively).

Conversely, in monolayer TMDs, the valence band maximum and the conduction band

minimum are aligned at the K-point. This change from indirect to direct is due to quantum confinement effects. 32

Figure 2.5 Band structure for MoS2. Bulk, 4 layers, and 2 layers have indirect band gaps. Monolayer has a direct band gap. Figure adapted from 50.

Layer thickness can be tracked optically due to these changes in the electronic structure.

Commonly, the energy of excitons, i.e., bound electron-hole pairs, is tracked and can

provide information regarding the layer thickness of the material. For example,

reflectance measurements show a redshift of exciton A (630-650 nm) when comparing a

51 1 layer WS2 sample to a 3-layer sample. Transmission spectroscopy can also be used to

track exciton movement and layer thickness.2

Recently, electroablation (EA) has been introduced as a technique for 2D TMD

synthesis.52–55 Li intercalation has been the best known method for synthesizing quality

2D TMDs;30 however the need for high temperature and extended duration (~100 °C, 3 33

days) required for Li intercalation generates a need for a simpler method of synthesis. EA can be done at ambient temperature and on the scale of minutes. In EA, TMD flakes on a conductive substrate are subject to oxidative potentials in a Li electrolyte solution.3,52–55

This electrochemical -assisted Li-intercalation is a new technique for simpler monolayer

TMD synthesis. Further information regarding EA for monolayer TMD synthesis can be found in Chapter 4.

2.3. Plasmonic Nanoparticles

2.3.1. Fundamentals of Plasmonics

Figure 2.6 Localized surface plasmon resonance (LSPR). The electric field induces an oscillation of free electrons in nanoparticles. 34

Localized surface plasmon resonances (LSPRs) are collective oscillations of conduction

electrons in particles that are smaller than the wavelength of light (Figure 2.6). Plasmonic

nanoparticles (NPs) hold promise for applications in many technologies from sensing,56–

62 to solar capture,63–65 to catalysis.66–70 Mie theory (Equation 6) is a solution to

Maxwell’s equations that describes the interaction of spheroid NPs with the electric field

of light:

Equation 6 ( ) = 𝟑𝟑� 𝟐𝟐 ( 𝟑𝟑 ) 𝟐𝟐 ( ) 𝟐𝟐𝟐𝟐𝝅𝝅 𝑵𝑵𝑵𝑵 𝜺𝜺𝒎𝒎 𝜺𝜺𝒊𝒊 𝟐𝟐 𝟐𝟐 𝑬𝑬 𝝀𝝀 𝝀𝝀𝐥𝐥𝐥𝐥 𝟏𝟏𝟏𝟏 � 𝜺𝜺𝒓𝒓+𝝌𝝌𝝌𝝌𝒎𝒎 +𝜺𝜺𝒊𝒊 �

Here, E(λ) is the wavelength dependent extinction spectrum, N is the number of particles,

a is particle diameter, εm is the dielectric constant of the surrounding material, λ is the

wavelength of incident light, and εi and εr are the imaginary and real parts of the dielectric

function for the metallic NP. The imaginary component, εi, describes the dissipation

process of the electron oscillation as the free electron gas interacts with the material.

Fundamentally, a NP sustains a plasmon if εi is a small negative value; i.e., the oscillating

free electron gas experiences minimal dissipation. Because the dielectric function varies

with the frequency of the incident light,71 this oscillation occurs more strongly at certain

wavelengths.

In addition to exhibiting a material dependence, the plasmon frequency is also influenced

by shape, size, and local environment of the NP (Figure 2.7).56,72–82 Larger particles show

a lower frequency plasmon resonance than do smaller particles. Similarly, materials

surrounded by higher refractive index (RI) substances have a lower energy plasmon

resonance than those surrounded by lower RI substances. 35

Figure 2.7 Factors that affect LSPR energy. References are as follows: (a)74 (b)72 (c)75 and (d)81.

The sensitivity of a plasmonic NP to the RI of the surrounding medium is quantified by the following equation:

Equation 7 = [ ] −𝟐𝟐𝟐𝟐 �𝒍𝒍𝒅𝒅 ∆𝝀𝝀 𝒎𝒎∆𝒏𝒏 𝟏𝟏 − 𝒆𝒆 Here, ∆λ is the shift in the peak plasmon wavelength, m is the refractive index sensitivity

(RIS), ∆n is the change in RI introduced to the NP, d is the depth of thickness of the

adsorbed layer, and ld is the plasmon field decay length. Due to plasmonic field 36

localization effects,58,83 NPs have a higher RIS and subsequently experience a higher shift in the peak plasmon wavelength when the local environment is changed at the tips of the

NP. In a study by Beeram et al.,84 antibodies were deposited onto triangular AuNPs; in the cases where the antibody bound closer to the tips of the NP, the plasmon resonance frequency shifted more due to binding than those in which binding occurred at the facets

of the NP. This heightened sensitivity to changes in the local environment is a result of

the plasmonic electric field enhancement that occurs at the tips of the NPs.83

2.3.2. Electrochemistry of Plasmonic Systems

The plasmonic electric field also impacts the electrostatic potential (introduced in 2.1.1)

within an electrochemical system. Between two points exposed to an electric field, the

electrostatic potential can be defined as

, , ( , , ) ( , , ) = Equation 8 , , 𝒙𝒙′ 𝒚𝒚′ 𝒛𝒛′

∅ 𝒙𝒙′ 𝒚𝒚′ 𝒛𝒛′ − ∅ 𝒙𝒙 𝒚𝒚 𝒛𝒛 ∫𝒙𝒙 𝒚𝒚 𝒛𝒛 −𝝃𝝃 ∙ 𝒅𝒅𝒅𝒅 Here, ξ is the electric field strength vector and dl is the tangent to the path.5 Because the

plasmonic oscillation of electrons results in near-field enhancements of the electric field

in the vicinity of the NP, with greatest enhancement at the NP tips (Figure 2.8),83,85 the

tips will have greater electrostatic potential and enhanced electrochemical activity.

The presence of undercoordinated sites at NP tips further point towards increased

electrochemical activity. From a chemical perspective, metallic atoms that are

undercoordinated, i.e., are in direct contact with fewer atoms than predicted by the crystal

structure in bulk material, are more reactive.86–90 37

2.3.3. Plasmonic Spectroelectrochemistry

Various optical techniques have been used in spectroelectrochemical experiments (Figure

2.9). In a study conducted by Chirea et al., Ag was electrodeposited onto Au nanostars

causing a shift in the LSPR wavelength of greater than 100 nm.8 In that study, dark-field

(DF) optical microscopy was used to observe the changes in plasmonic properties.

Byers et al. and Hoener et al. have also published studies recently that utilize DF

microscopy to observe electrochemistry-induced plasmonic tuning (Figure 2.9a).6,7,9,10 In

a study by Hill et al., DF spectroelectrochemistry was used to monitor NP synthesis

(Figure 2.9b).19 Spectroelectrochemistry can be conducted by measuring UV-Vis

spectroscopy as shown by Fernandez-Blanco et al., and Gonzalez-Dieguez et al. in

studies where UV-Vis was used to observe chemical detection as well as to observe NP

synthesis (Figure 2.9c).20,21 The optical methods used for spectroelectrochemical

experiments in this thesis are reflectance (Chapter 4) and dark-field (Chapter 5). 38

Figure 2.9 Spectroelectrochemical setups and corresponding results from various experiments. Figures are adapted from: (a)6,7 (b)19 and (c)20,21. 39

2.3.4. Discrete Dipole Approximation (DDA)

Discrete dipole approximation (DDA) is an open source91 numerical method commonly used to understand the interaction of light with matter. It was used in this thesis to help guide experiments dealing with the synthesis and characterization of plasmonic particles and interpret results from such experiments. DDA was chosen because it is simple to code and it is easily applicable for multicomponent systems.

Figure 2.10 In DDA, objects (i.e., nanoparticles) are modeled as a set of individual dipoles.92

In DDA, an object is approximated as a set of discrete dipoles; simulations that employ

more dipoles provide more accurate information, but are computationally more

expensive. Unlike other numerical methods used in similar applications, DDA relies on

no physical approximations. There are a number of different types of DDA codes 40

available. In this thesis, Discrete Dipole Scattering (DDSCAT) is employed. It is a

Fortran-based code that requires the user to input information regarding the particle size, shape, material, and environment (the latter two rely on the dielectric data of the NP and the medium). 41

Chapter 3

Micro-Extinction Spectroscopy (MExS): A Versatile Optical Characterization Technique

Reprinted here with permission from Kumar, A., Villarreal, E., Zhang, X. &

Ringe, E. Micro-Extinction Spectroscopy (MExS): A versatile optical

characterization technique. Adv. Struct. Chem. Imaging (2018).2 Note that some

sections of this chapter were taken verbatim from the publication listed, while

others have been modified to focus on this author’s contributions.

3.1. Abstract

Micro-Extinction Spectroscopy (MExS), a flexible, optical, and spatial-scanning hyperspectral technique, has been developed and is described with examples. Software and hardware capabilities are described in detail, including transmission, reflectance, and scattering measurements. Each capability is demonstrated through a case study of 42

nanomaterial characterization, i.e. transmission of transition metal dichalcogenides

revealing transition energy and efficiency, reflectance of transition metal dichalcogenides

grown on non-transparent substrates identifying the presence of monolayer following

electrochemical ablation, and scattering to study single plasmonic nanoparticles and

obtain values for the refractive index sensitivity and sensing figure of merit of over a

hundred single particles with various shapes and sizes. With the growing integration of

nanotechnology in many areas, MExS can be a powerful tool to both characterize and test

nanomaterials.

3.2. Introduction

The optical properties of nanoscale materials are exquisitely sensitive to their local

structure, composition, and shape. Gold nanoparticles (NPs), for instance, can absorb

green or red light, depending on their size.72 Two dimensional (2D) materials such as

transition metal dichalcogenides (TMDs) sustain excitons that can be maneuvered in

energy; their corresponding band gaps vary as a function of thickness and composition.93

While this sensitivity provides an exciting opportunity to tune light-matter interactions, it leads to large NP-to-NP and area-to-area variations and often results in heterogeneous

broadening of the ensemble response. In these cases, reliable structure-property

relationships can be obtained by performing spatially resolved optical spectroscopy,

typically followed by structural characterization with electron or scanning probe

microscopy.94 43

Traditionally, spectroscopy relies on exciting a circular area of a sample with a focused

probe, such as a laser or electron beam. The signal is then dispersed in energy and

acquired on a camera that records intensity along the energy axis NE. The probe can be

scanned over the sample, along both x and y with a dwell time t, to yield a spectrum image of dimensions Nx x Ny x NE in txy units of time. This raster-scan approach to

acquiring a hyperspectral datacube becomes prohibitively time-consuming when

considering large areas.

To overcome this limitation, several parallel acquisition schemes have been developed

for optical spectroscopy, which have been reviewed elsewhere.95,96 One typical approach

is the use of a tunable filter and a pixel array camera, enabling the acquisition of a range

(albeit narrow) of wavelengths at a time; an Nx x Ny x NE datacube can then be acquired

by scanning the filter’s wavelength.97 This comes at the cost of spectral resolution given

the intrinsic wavelength spread (few nm) of the filter and the number of frames acquired.

The acquisition time indeed scales as tdE, where dE is the energy step, and is ultimately

indicative of the energy resolution. Another approach is the construction of a datacube by

physically scanning the position of a spectrometer, acquiring one spectral and one spatial

dimension at a time (“push-broom”) enabling the acquisition of spectral information

across the field of view. This allows for varied energy resolution, limited only by the

spectrometer’s dispersion, and saves time by reducing the acquisition time to ty.6

Here, we describe Micro-Extinction Spectroscopy (MExS), a novel optical characterization technique that can capture information regarding a variety of light-matter interactions in nanomaterials. In MExS, a LabVIEW interface orchestrates a piezoelectric 44

sample stage, a spectrometer, and an electron-multiplied charge coupled device

(EMCCD) camera to acquire one lateral and one spectral dimension at a time, and then scans the other lateral dimension by moving the sample stage. Because it scans a line of pixels instead of a point, this approach greatly reduces the acquisition time, which scales as ty (line scan) rather than txy (point scan). This approach also allows for excellent spectral quality, and is therefore advantageous compared to creating datacubes via stacking energy- filtered images, which compromises spectral resolution. Finally, the size of the datacube is not limited by the field of view, but rather by the travel range of the (piezoelectric) stage.

Many types of samples can be explored using MExS due to the various optical modes that

MExS can support, including transmission, reflectance, and dark-field scattering. We have implemented these three optical capabilities, and describe a case study for each (Section

3.4), demonstrating the applicability range of MExS.

3.3. Technique Description

3.3.1. Optical Setup

The MExS setup (Figure 3.1 and Figure 3.2) consists of an inverted microscope system

(Nikon Eclipse Ti), a piezoelectric stage (Physik Instrumente P-545.3C7), a spectrometer

(Princeton Instruments IsoPlane SCT 320 equipped with a 50 grooves/mm grating), a

1024x1024 pixel array EMCCD camera (Princeton Instruments ProEM-HS: 1024BX3), an imaging camera (QImaging QIClick), a 12V 100W halogen lamp (Nikon D-LH/LC) for transmission and dark-field measurements, and an LED lamp (Lumen Dynamics X- 45

Cite 120LED) for reflectance measurements. For scattering measurements, a dry dark-

field condenser (Nikon, N.A. 0.95-0.80) is inserted in the optical path before the sample.

The microscope is equipped with two objectives (Nikon CFI Plan Fluor 100XS Oil and

Nikon CFI S Plan Fluor ELWD 40X) and an 80:20 beam splitter that sends 80% of the light to the spectrometer and 20% of the light to the imaging camera for simultaneous optical imaging and spectral measurements.

Figure 3.2. Microscope configuration – photograph: (a) EMCCD, (b) spectrometer, (c) halogen lamp, (d) dark-field condenser, (e) piezo stage, (f) imaging camera, and (g) LED lamp.

The dimensions of the acquired data set depend on the spectrometer slit’s height and the stage’s range of motion (Table 3.1). The x dimension is defined by the slit height and scales with magnification (325 μm for the 40x, 140 μm for the 100x objective). The range of motion of the piezoelectric stage (here, 200 μm) defines the y dimension. The spatial 47

resolution is diffraction-limited and the spectral resolution is defined by the choice of dispersion grating.

Acquired dataset dimensions x dimension y dimension (stage Objective magnification (slit-length range-of-motion limited) limited) 100x 140 µm 200 µm 40x 325 µm 200 µm

Table 3.1 Acquired dataset dimensions for various objective magnifications.

3.3.2. Hyperspectral Data Acquisition

Figure 3.3 User process diagram describing MExS preparation (optical, hyperspectral) and spectral imaging (imaging and processing of data).

Figure 3.3 shows the five steps involved when performing MExS. Prior to spectral imaging, the optical setup is constructed and the sample is mounted onto the stage of the microscope. An image of the region of interest (ROI) is acquired using the imaging 48

camera, creating a map used in data acquisition and analysis. The optical and

hyperspectral parameters are then set; example parameters are shown in Table 3.2. Next,

hyperspectral imaging is initiated and data is exported in comma-separated value (CSV)

files. Finally, experiment-specific post-processing calculations (Table 3.3) are performed

on the data.

Optical parameters Hyperspectral parameters Parameter Example Parameter Example Exposure time 0.5 seconds Image width passed through slit *3.85 μm Exposures/frame 4 *1.54 μm Grating grooves/mm 50 Step size 0.8 μm

Center wavelength 600 nm Number of steps (Ny)** 250 Number of time-lapse acquisitions 100 (Nt)** Acquisition frequency 10 seconds

Variable of Technique Computation Example application interest

2D Materials Transmission Absorption log Electronic Properties 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 − � 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 �

Differential | | 2D Materials Reflectance Reflectance Substrate Interactions 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅−𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅

Plasmonic NPs Dark-field Scattering Single-particle Studies 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆−𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆−𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆

Table 3.3 Examples of experiment-specific computations utilized in data post- processing. 49

The operation of this hyperspectral spatial-scanning technique relies on communication between two components: the sample stage and the detector system (Figure 3.1). This communication is established and manipulated through a LabVIEW control (LabVIEW block diagram in Figure 3.4a, LabVIEW user interface in Figure 3.5). At the onset of the

experiment, the LabVIEW code enters the “Initiate Software” mode.

Once both software programs have been initiated and a line of communication has been

established, the user enters the spatial-scanning hyperspectral parameters (step size, number of steps Ny) into the LabVIEW user interface. The user then advances the

LabVIEW program into the “Acquire Data” mode; the detector and stage are manipulated

in series to record data and move the sample Ny times. At each step the detecto0r system 50

records a 2D matrix with a spatial dimension (Nx) and a spectral dimension (Nλ), the

length of which are defined by the EMCCD chip size, here Nx = 1024, Nλ = 1024. An Ny

stack of these 2D matrices creates an x-y-λ datacube (Figure 3.4b).

Figure 3.5 LabVIEW user interface for MExS spatial-scanning hyperspectral data acquisistiom. The corresponding LabVIEW block diagram is shown in Figure 3.4a.

MExS can also be adapted for time-resolved studies, where time substitutes the y-axis i.e., the stage remains immobile and the detector records data for one row location (y) over time. In this setup, the user assigns the time-lapse parameters (number of time-lapse

acquisitions, Nt, and acquisition frequency) and a series of 2D matrices (Nx, Nλ) are

recorded, leading to an x-λ-t - datacube (Nx, Nλ, Nt).

3.3.3. Data Post-Processing

In the post-processing step (Figure 3.6), the previously exported 2D matrices are

imported into the Matlab-based analysis software (code structure in Figure S3, code 51

details available upon request). The datacube contains up to 1024 x 1024 x Ny elements for spatial-scanning experiments and 1024 x 1024 x Nt elements for time-lapse experiments. The datacube is first integrated along the λ dimension to create an integrated spectrum, resulting in a picture-like 2D x-y matrix (Figure 3.6c). The pixel locations for each ROI are then determined using the integrated spectrum image. These pixel values are then used to extract the λ element values (point spectra) within the datacube corresponding to the selected ROI. An ROI usually covers several pixels, such that several point spectra in a square array around the center of the ROI are typically added together. Experiment-specific computations (Table 3.3) are then conducted on the extracted data. The processed data is displayed (Figure 3.6d), and written to CSV and

Matlab workspace files.

3.4. Case Studies

Three case studies were conducted to exemplify the scope of applicability of MExS.2 The

case study conducted using reflectance spectroscopy is provided here in detail. Two other

case studies (utilizing transmission mode and dark-field mode) are reported in the

corresponding publication.2

3.4.1. Case Study: Reflectance Characterization of 2D Materials on Non-

Transparent Substrates

MExS in reflectance mode provides additional characterization functionality, opening the door to the study of non-transparent materials inaccessible with transmittance. Typical growth substrates for TMDs and other 2D materials (e.g., Cu, sapphire) are opaque, as are many electrode materials (TiN, C) and surfaces of interest for anti-wear/anti- corrosion coatings (steel and other metal alloys). In this case study, the interaction between WS2 and a TiN substrate was observed as an example of TMD behavior in

corrosive environments, demonstrating the applicability of MExS to non-transparent

substrates.

The samples used in this case study were prepared by using a micromechanical

53 exfoliation technique that is described elsewhere. The stacked layers of WS2 are held together via van der Waals interactions, whereas the base layer is held to the TiN substrate via strong covalent bonds.53,98–100 Various conditions were applied to these

WS2/TiN samples in order to simulate corrosive environments. Because the bandgap of

the bulk state and monolayer state of WS2 vary significantly (1.3 eV vs. 2.1 eV, 53

respectively), the exciton energy can be used to track the layer thickness, providing

insight into the effects of corrosive environments.28

The integrated spectrum images (Figure 3.7a-b) are referenced against the corresponding

optical images (Figure 3.7c-d) to locate the region of interest (identified here by an

arrow).

The reflectance signal contains a feature related to an exciton at 646 nm for the

multilayer thin film (Figure 3.7e). This exciton blue shifts to 616 nm as the film reaches

monolayer thickness as a result of corrosion. The use of reflectance-mode MExS offers

an opportunity to study large TMD flakes and monitor corresponding substrate

interactions efficiently with the flexibility afforded by the use of non-transparent

substrates.

3.5. Conclusion

MExS is a hyperspectral tool suitable for nanomaterials characterization. The automation

of the system’s hardware and software relies on LabVIEW, and a Matlab-based analysis

interface was developed for the data post-processing. Three different case studies using different optical modes were conducted. By using reflectance mode, the interaction between WS2 flakes and a TiN substrate was studied. MExS is not only effective in the

described examples, but can also be used in chemical/biological in-situ experiments, thin film and coating analysis, or in-situ catalytic mechanism studies.

Chapter 4

In situ Optical Tracking of Electroablation in Two-Dimensional Transition Metal Dichalcogenides

Reprinted here with permission from Kumar, A., Sebastian, A., Das, S., and

Ringe, E. In situ Optical Tracking of Electroablation in 2D Transition Metal

Dichalcogenides. Submitted (2018).3 It is to be noted that the text has been taken

verbatim from the publication.

4.1. Abstract

Two-dimensional (2D) transition metal dichalcogenides (TMDs) are a unique class of 2D materials that gain unique optoelectronic properties when exfoliated into mono- and few- layer sheets, while maintaining their bulk characteristics. Recently, electroablation (EA) has become of interest as a promising synthesis method for single-layer sheets of TMDs. 56

There is a declared need for techniques that enable parallel use of spectroscopy and

imaging in order to non-destructively characterize 2D materials. Here, we introduce

spectroelectrochemical Micro-Extinction Spectroscopy (SE-MExS) as a nondestructive

and high-throughput technique to study electrochemical thinning of TMDs as they occur.

We unravel optoelectronic of TMDs by observing changes in optical properties in situ

during EA. We find that the EA process for MoS2, WS2, MoSe2, and WSe2 occurs edge-

first, generating a high density of edge sites. Our results show that stable monolayers of

MoS2, WS2, and MoSe2 can be synthesized by the EA process, while conversely, no

WSe2 remains post-ablation. EA is a promising technique for the synthesis of 2D materials from bulk precursors.

4.2. Introduction

Two-dimensional (2D) materials have attracted increasing interest since the report of the

fascinating properties of graphene in 2004.24 In graphene and other atomically thin

materials, quantum hall confinement occurs because charge and heat are limited to a 2D

space, leading to unique physical and electronic properties including charge density

waves and high-temperature superconductivity.1,101,102 Similar to graphene, mechanical

exfoliation has been used to extract atomically thin sheets from a variety of van der

Waals solids,1,25–27 for instance hexagonal boron nitride (h-BN),103 transmission metal

104,105 dichalcogenides (TMDs, e.g., MoS2, WS2), and other metal chalcogenides (e.g.,

106,107 Bi2Te3 and FeSe). Such exfoliation is enabled by the bonding structure of van der 57

Waals solids: covalent bonds strongly connect atoms within layers, while stacks of layers

are held together via weaker van der Waals forces.1

Atomically TMDs have diverse chemical properties, and, unlike graphene,31,41 do not

require functionalization in order to be chemically active, allowing the facile use of their

confinement-related electronic properties. Furthermore, bulk TMDs have a spectrum of

useful properties that remain present when exfoliated into thin sheets; for example they

can range from insulators, to semiconductors and semi-metals, to pure metals.41 TMDs

also exhibit various low-temperature phenomena such as superconductivity, charge

density waves, and Mott transitions.108–110 This unique combination of chemical and

physical properties of TMDs results in broad application versatility, fueling the need for

large-scale, cost-effective synthesis methods.

In a recent set of studies, Das and co-workers reported EA as a new technique for the

52–55 synthesis of atomically-thin sheets of TMDs. MoS2, WS2, and MoSe2 were observed

to undergo a self-limiting electrochemical process where a high positive potential ablates

topical layers, leaving behind a stable monolayer. Ex situ atomic force microscopy

(AFM), Raman spectroscopy, photoluminescence (PL), and scanning transmission

electron microscopy (STEM) studies, in conjunction with optical imaging, revealed the

basics of the EA process. However, there remains significant gaps in our understanding

of this exciting, scalable EA process, which are difficult to address with such ex situ

characterization. The use of in situ techniques has the potential to elucidate the actual

process of EA and monolayer formation. 58

Numerous characterization techniques are available to explore properties of single- and

few-layer TMDs. Fluorescence quenching microscopy yields layer thickness111–113 but

requires surface modification which limits the concurrent use of other techniques to study

pure TMD nanosheets. Atomic force microscopy also provides layer thickness114–116 and

scanning tunneling microscopy, electronic and topographic properties,117–121 however

they both rely on physical contact between the sample and a cantilever tip that can lead to

contamination. Raman spectroscopy informs on composition, thickness, local

temperature122–124 but uses a laser probe that can ultimately destroy the sample.125 Lastly,

transmission electron microscopy unravels composition, single-layer size, interlayer stacking, and crystal structure37,114,116,122,126–128 yet uses a potentially damaging electron probe. Because such sample damage and contamination are non-reversible processes they

prevent subsequent analysis of the sample, Butler et al. declared the need for novel high-

throughput characterization techniques that nondestructively detect properties of 2D

materials; specifically stating that the development of new techniques that use

spectroscopy and imaging in parallel will prove useful in exploring the unique properties

of atomically-thin materials.1

In a recent study, we introduced an optical spatial-scanning hyperspectral approach:

Micro-Extinction Spectroscopy (MExS).2 MExS is a versatile optical characterization technique in which spectroscopy is done in tandem with optical imaging of “large” regions (100s of microns). The use of white light illumination enables measurements with no discernible beam damage or probe contamination. 59

Here, we introduce spectroelectrochemical-MExS (SE-MExS) as a nondestructive, high throughput, in situ characterization technique. We use this technique to probe, in situ, the

optical properties of TMDs as they undergo EA. This enables us to elucidate the process

of EA for MoS2, WS2, MoSe2, and WSe2, where tracking changes in energy of excitonic

transitions provides information on changes in sample thickness. Further, potential- dependent excitonic energy maps help us explore spatial dependence of the EA process, and we find that ablation occurs “edge-in” for all four materials studied.

4.3. Experimental

4.3.1. Sample preparation

Sample preparation has been described in detail elsewhere.52 Briefly, Si wafers with a

sputtered TiN coating were used as the sample substrates. TMDs (5-20 μm wide flakes)

were mechanically exfoliated from multilayer crystals and deposited onto the TiN/Si

substrate.

4.3.2. In situ spectroelectrochemical cell

The electrochemical cell encasing (Figure 4.1b) was machined in acrylic by ProtoLabs specifically for SE-MExS. The outer dimensions of the electrochemical cell were determined by the sample mounting area of the optical stage (6.5 cm x 9.5 cm). The

TMD/TiN system served as the working electrode (WE). The counter electrode (CE) was a Pt mesh (Alfa Aesar, 100 mesh woven, 0.0762 mm diameter wire, 99.9% purity). The wire connectors for both the WE and the CE to the potentiostat was Pt wire (Goodfellow, 60

0.125 mm diameter, 99.95% purity). A Pt quasi-reference electrode (QRE) was employed as the reference electrode. Silicone isolators (Grace Bio-Labs, 13 mm diameter opening,

500 μm depth) were used as spacers in the cell to create a leak-proof seal to contain the electrolyte. Approximately 200-250 μL of 1 M LiCl, pH = 2-3 electrolyte solution was used.

A plain glass coverslip was used as the bottom face of the cell. A silicone spacer

(modified to expose the CE and the QRE to the electrolyte solution) was placed on the coverslip securing it in place. Then the CE and QRE, along with their respective wire connectors, were placed over the spacer such that they would be exposed to electrolyte.

An unmodified spacer was placed on top of the CE and QRE, followed by electrolyte

(200-250 µm). The sample was placed face down, ensuring contact between the WE and the Pt wire connector, and secured shut with screws. For in situ studies, the cell was secured to the stage. The potentiostat leads of a Gamry Reference 600 were attached to their corresponding electrodes.

4.3.3. Reflectance measurements

Reflectance spectra were acquired and analyzed using reflectance-mode SE-MExS

(Figure 1, full optics details previously reported2). Briefly, reflectance-mode SE-MExS experiments were done on a microscope system (Nikon Eclipse Ti) equipped with a piezoelectric stage (Physik Instrumente P-545.3C7), a spectrometer (Princeton

Instruments IsoPlane SCT 320, 50 grooves/mm grating), a 1024x1024 pixel array

EMCCD camera (Princeton Instruments ProEM-HS: 1024BX3), an imaging camera

(QImaging QIClick), a 40x objective (CFI S Plan Fluor ELWD 40X), and an LED lamp 61

(Lumen Dynamics X-Cite 120 LED). All reflectance data reported is differential

reflectance, ΔR/R0 = (R-R0)/R0, where R and R0 are the reflectance of the sample and the

substrate, respectively.

4.4. Results and Discussion

4.4.1. Optical detection of the electroablation process

We first determined that SE-MExS, a white-light optical scanning-spatial hyperspectral

technique,2 is an appropriate technique for detecting EA processes. In SE-MExS, a three-

electrode electrochemical cell is placed in the MExS optical light path such that the

working electrode is the sample, here a TMD on a TiN/Si substrate (Figure 4.1).

This setup is then biased to positive potentials, allowing in situ observation of the

oxidative behavior of TMDs. The optical response, tracked in reflectance geometry

during oxidation, shows a marked change for all the materials observed, i.e. MoS2,

MoSe2, WS2, and WSe2 (Figure 4.1c-j). Prior to the potential step, the samples are bright, indicative of the strong reflectance of multilayer samples.129 After the potential step, a significant reduction of the reflectance signal is observed. The outline of the sample is still visible after oxidation for MoS2, WS2, and MoSe2. This indicates that the positive

potential reduces the sample’s thickness without removing it entirely. In contrast, WSe2

shows less resistance to the EA process; a large positive potential resulted in its entire

removal (Figure 4.1j). 62

Figure 4.1 Spectroelectrochemical Micro-Extinction Spectroscopy (SE-MExS) reflectance mode. (a) Reflectance-mode optical setup. Illumination source is an LED lamp. After interaction with the sample, the light is reflected back and passed through the spectrometer and spectral data recorded on the EMCCD. (b) Spectroelectrochemical cell. The sample is placed face down at the top of the electrochemical cell. (c) Schematic of 2D transition metal dichalcogenides. Purple atoms represent the transition metal, yellow atoms represent the chalcogen. (d-f) Optical images before and after electroablation for MoS2, WS2, MoSe2, WSe2. The initial images (d-g) show that the flakes have high reflectance in contrast to the TiN substrate. The images taken after electroablation for MoS2, WS2, MoSe2 (h-j) show the flakes remain but have much lower reflectance intensity. This is attributed to modification of the flake from multilayer to monolayer. In the case of WSe2 (k) no flake remains post electroablation. Scalebars: (h-j) 10 μm, (k) 5 μm.

This variation in the extent of EA for different TMDs can be attributed to differences in

binding energies (interlayer, basal plane/substrate). The interlayer binding energy (e.g.,

MoS2 -0.16 eV), due to weak van der Waals forces, is lower than that of the covalently-

53 bound basal plane/substrate interface (e.g., MoS2 -1.25 eV). For all systems, a

sufficiently high positive potential breaks the weak interlayer bonds, i.e. EA occurs. 63

These results confirm the suitability of SE-MExS as an in situ characterization tool useful for characterizing the EA process and suggest that MoS2, WS2, and MoSe2 monolayers can withstand highly corrosive environments.

4.4.2. Determination of the onset of electroablation

An advantageous feature of in situ SE-MExS is the ability to acquire spectroscopic data during EA. Thereby, SE-MExS can characterize processes in real time and reveal information beyond that obtained with before and after snapshots (Figure 4.1c-j). Cyclic voltammetry (CV) experiments were first conducted on each material in order to determine the voltage range where EA occurs (Figure 4.2).

Figure 4.2 Cyclic voltammograms for all 4 materials studied. As the potential is swept positive, a peak appears near the electroablation potential. No peak is present on the reverse sweep. 64

For CV experiments, the potential was swept from 0 to 2 V at 10 mV s-1. The EA occurs

only on the forward sweep (0  2 V) and no peak is visible in the reverse sweep (2  0

V). For in situ chronocoulometry (CC) experiments, small potential steps were applied

(50 or 100 mV) and the reflectance spectral signature of the material was acquired

concurrently; optical images were acquired between applied potential steps (Figure 4.3

and Figure 4.4).

The optical image series for MoS2 shows initial signs of EA at 0.75 V (Figure 4.3a). The

general onset of the EA process occurs at 1.0 V (pH 2); at this potential, there is a

decrease in reflectance intensity across the entire sample area. Corresponding spectral

data offers confirmation of this observation (Figure 4.3b). For multilayer MoS2 the peak

for exciton A occurs between 670-685 nm. As layers of material are removed, the energy of exciton A blue-shifts.130

Similar analysis of WS2, MoSe2, and WSe2 shows EA onset potentials of 0.6 V for WS2

and 1.0 V for both MoSe2 and WSe2 (optical image series reported in Figure 4.4, spectral

data, Figure 4.3c-e). The exact EA potentials differ from those previously reported;52,55

this is attributed to differences in the electrochemical cell geometry (i.e., electrode 66

surface area, inter-electrode distance). Our results are consistent with previous reports in

that the EA onset for MoS2, WS2, and MoSe2 occurs when there are initial signs of oxidation in the corresponding CV curve, while in WSe2 no EA is seen until the oxidation

peak potential (Figure 4.4).52,55 The following sections report on the behavior of each

material during EA.

4.4.3. Electroablation of MoS2

The energy of exciton A (650-690 nm)130 provides a way to optically track the EA process in MoS2 (Figure 4.5a), where exciton A has longer wavelengths for thicker

samples.28,129–131

regions of low reflectance intensity and ~680 nm in regions of high reflectance intensity. (c-e) Optical images (before, c, and after, d) and (e) spectral data for the cyclic voltammetry experiment. The peak wavelengths for exciton A (left axis) at locations indicated in (c) as a function of potential sweep (right axis). The onset of electroablation occurs just above 1 V. Scalebars: 5 μm.

Here, exciton A has a shorter wavelength for thinner regions of the flake (650-660 nm)

than for thicker regions (680 nm), as shown in Figure 4.5b. Enhanced PL signal for

regions with lower reflectance intensity confirms that brighter regions of the sample are

thicker (Figure 4.6).131

An in situ cyclic voltammetry experiment enabled the detection of the exact onset of EA

(Figure 4.5c-e). A full cycle (0  2  0 V) was carried with a scan rate of 10 mV s-1;

exciton A was tracked by the concurrent collection of reflectance. At low potentials (0 to

1.1 V) the peak wavelength of exciton A remained unchanged (675-685 nm). The onset

of EA occurred just above 1.0 V; at this point, exciton A’s energy blue shifted by more 68

than 40 nm. Correlating the change in wavelength with position on the sample shows that

EA occurs in an “edge-in” manner, confirming results from previous ex situ

experiments.55 It is, however, interesting to note that the variance in wavelength post- ablation suggests that the sample is not of uniform thickness.

As the potential sweep continues up to 2 V and then back to 0 V, exciton A at each location remains constant, indicating sample stability post-EA. Previous studies have attributed this finding to the relative binding energies; the binding energy between the basal MoS2 layer and the TiN substrate shows a stronger interaction (-1.25 eV, covalent

53 bond) than that between single sheets of MoS2 (-0.16 eV, van der Waals forces).

optical image of the flake at 1.1 V. Reflectance intensity first decreases for outer regions of flake. Scalebar: 5 μm.

Energy maps of step-wise EA of MoS2 are shown in Figure 4.7. Prior to the onset of EA,

exciton A is uniformly 685-695 nm across the entire sample (Figure 4.7a-d). The onset

potential for EA is 1.0 V; a change in the exciton energy is detected at the center of the

flake (Figure 4.7e). Comparing this energy map to the optical map insets in Figure 4.7d

and Figure 4.7f, it is clear that there are actually two separate flakes meeting at this

central location. The drop in the peak wavelength of exciton A to below 680 nm where

these two flakes both have edges further suggests “edge-in” EA.55 Numerically, we can understand this difference in reactivity by considering surface energies; the surface

132 energy at the edge is 100x the surface energy at the terrace for MoS2. Above the EA

potential (1.1 V) exciton A is uniformly 655-665 nm indicative of a monolayer (Figure

4.7f).28,129–131

4.4.4. Electroablation of WS2

133 Exciton A appears between 620-650 nm for WS2 (Figure 4.8a). A line scan of a WS2

sample shows position dependence of the EA process. 70

On the edge of the sample, the energy of this exciton shifts from 640 to 610 as the potential bias increases, while only a slight blue shift occurs in the center of the sample

(Figure 4.8b). After EA, exciton A is 20 nm lower at the edge than at the center (Figure

4.8b,e). The spatial variance suggests that the layers at the center of the WS2 sample are

strongly bound in comparison to the layers at the edges; WS2 seems to electroablate

“edge-in”.55 71

Potential-dependent energy maps demonstrate the spatial variance of the change in

exciton A in further detail, revealing information about the “edge-in” mechanism (Figure

4.9).

At low potential biases (0.4 V, 0.5 V), exciton A is between 640-650 nm (Figure 4.9a-b);

small energy fluctuations are attributed to the effects of varying sample thickness.41 72

At the EA potential (0.6 V), exciton A blue-shifts to ~620 nm due to quantum confinement effects created by a decrease in layer thickness (Figure 4.9c).41,133

Reflectance studies for WS2 on quartz showed a similar blue shift in the 630-650 nm

133 range. Variance in the exact exciton wavelength between our results (WS2/TiN) and

WS2/quartz are attributed to substrate effects: the local dielectric environment influences

exciton energy.134 Substrate-sample contrast changes further support that this excitonic

shift is correlated to changes in the sample thickness (Figure 4.9 insets).129 While exciton

A is uniformly shifted to 620 nm in the outer regions of the sample, in the central region it remains close to its initial energy with a slight blue-shift from 655 nm to 645 nm.

Reflectance contrast in the optical image inset confirms that the blue-shift of exciton A is

due to the changing thickness of the sample. This pattern of spatial dependence is similar to that of atmospheric moisture-induced oxidation, and is attributed to the decreased stability of the non-hexagonal bonds existing at grain boundaries.135,136

Increasing the potential past the EA onset (0.7 V, 0.8 V, 0.9 V) causes exciton A to blue-

shift in the central, multilayer regions (645-650 nm to 635 nm); it does not modify the

overall shape of the flake or the energy of exciton A at the edge of the flake (Figure 4.9d-

f). This remarkable stability of monolayer WS2 was previously suggested by ex situ

measurements conducted by Schulman et al.55 Interestingly, even at higher applied

potentials (0.7 V, 0.8 V, 0.9 V), exciton A remains at 635 nm in the center of the flake.

This observation is due to passivation of the TiN substrate; oxidation of TiN forms non-

conductive TiO2. All TMD flakes that have large lateral and thickness dimensions exhibit

this passivation. In such cases, the central regions of the flake show resistance to

EA.52,53,55 73

4.4.5. Electroablation of MoSe2

MoSe2 is a visible bandgap semiconductor displaying a thickness-dependent exciton B in

mono- to few-layer materials at 595-630 nm (blue-shifted for thinner samples137) and no

visible exciton at multilayer thicknesses (Figure 4.10a, magnified in Figure 4.10b).

The appearance and shift of exciton B enables us to optically track the EA of MoSe2

(Figure 4.10 and Figure 4.11). Before EA, exciton B is invisible across the thick sample.

At the potential of EA onset (1.0 V), the outmost parts of the sample are sufficiently thin as to feature a measurable peak (600-630 nm), however thicker regions remain at the

center with no measurable excitonic transitions (Figure 4.10 and Figure 4.11c). Going

radially inwards from the edge of the sample (Figure 4.10c), exciton B energy shifts from

just below 600 to 630 nm, then disappears, indicating an increase in thickness toward the

sample’s center. Once the monolayer is achieved at the edges, the ablation continues

inward as higher potentials are applied (Figure 4.10d). At the EA potential (1.0 V), 75

exciton B’s energy varies by 50 nm from the center to the edge of the flake; this

difference nearly vanishes at 1.1 V and 1.2 V as the flake becomes of increasingly

uniform thickness.

Figure 4.11 shows the spatial distribution of the peak wavelength for exciton B in MoSe2,

which support our findings from the line scan analysis presented in Figure 4.10. Briefly,

at low potential biases (0.9 V, 0.95 V) the peak for exciton B is not present (Figure 4.11a- b). At the EA potential (1.0 V), high-energy exciton B (595 nm) appears at the sample’s edge (Figure 4.11c). An intermediate few-layer region is formed between the edge and the center, with variance in exciton B (595-630 nm). No exciton B peak is present in the center of the flake suggesting that the center remains multilayer. Above the EA potential

(1.1 V), the exciton B peak blue-shifts to ~595 nm for the entire sample, indicating that the layer thickness has gone from few-layer to uniformly monolayer (Figure 4.11d).

These observations reveal an edge-first EA process followed by the creation of a uniform

MoSe2 monolayer, a result consistent with previous ex situ studies conducted by Huang

52 et al. of MoSe2 EA. This “edge-in” mechanism is attributed to increased surface energy

for edge sites (compared to terrace regions) which is likely caused by the heightened

reactivity of dangling bonds at the edge of the flake.132,138 Our experiments demonstrate

that MoSe2 shows step-wise removal of the superficial layer at the EA potential, which

generates more edge sites than present in the multilayer precursor (Figure 4.10, inset).

Interestingly, when the potential is further raised (1.2 V, 1.3 V), exciton B remains

unchanged (595 nm); the stability of exciton B indicates the monolayer is stable at high

voltages (Figure 4.11e-f). Previous EA studies on MoSe2 postulate that this unique 76

stability is due to differences in rates of corrosion for the bulk and the monolayer (rB =

52 bulk, rM = monolayer). For the MoSe2 system described here, the rB >> rM can be

attributed to the presence of OH- ions at the electrode surface (due to the position potential drift field). At the pH studied, the [OH-] concentration was high enough that the

OH- ion drift was no longer diffusion limited and the magnitude of OH- ions present at the electrode surface could intercalate the multilayers, but not so high that there was interference with the monolayer-substrate interaction.52

4.4.6. Electroablation of WSe2

In WSe2, we track exciton Aʹ (Figure 4.12a, 515-565 nm) because its energy depends

more strongly on the number of layers than that of either exciton A or B.139

Optical images show that, unlike for MoS2, WS2, and MoSe2, EA of WSe2 results in

complete removal of the sample with no remnants of a monolayer (Figure 4.12b, d and

52 Figure 4.4c). This finding is in agreement with previous reports of the EA of WSe2. In

order to understand the spatial dependence of EA, line scan data of a small remaining

fragment is analyzed in Figure 4.12c-d. Prior to EA, the wavelength of exciton Aʹ at the

edge regions of the sample is slightly lower (7 nm) than at the central regions. This

observation indicates that the initial sample has non-uniform thickness. As increasing

potential biases are applied, the wavelength of exciton Aʹ blue-shifts at the edges, first

decreases by 10 nm and then disappears, indicating the entire removal of the sample at

that location. Exciton Aʹ in the intermediate regions also blue-shifts from 540 to 530 nm,

suggesting local ablation from multi- to few-layers. No exciton energy change, i.e. little 77

to no ablation, is observed in the central regions. These results confirm an “edge-in,”

52 complete EA mechanism for WSe2 with no remnant of a monolayer.

Energy maps for WSe2 are shown in Figure 4.13. Prior to the onset of EA (0.8 V, 0.9 V), exciton Aʹ (Figure 4.13a-c) has a constant energy. At the EA potential (1.0 V) exciton Aʹ is no longer detectable at the edges of the flake (Figure 4.13d). At potentials higher than the EA potential (1.1 V, 1.2 V) the exciton Aʹ peak is undetectable for nearly the entire region, suggesting that the flake is entirely ablated (Figure 4.13e-f). Optical images confirms that the disappearance of exciton Aʹ corresponds to complete removal of WSe2 78

(Figure 4.13insets and Figure 4.4c). Close examination of the energy map at the EA

potential (Figure 4.13d) reveals that the peak wavelength for the bottommost edge has shifted from 545 nm to 535 nm, while the peak wavelength at the center of the flake remains at 545 nm. This further supports the theory of “edge-in” EA for WSe2.

Figure 4.13 WSe2 energy map for exciton Aʹ. (a-c) WSe2 flake prior to electroablation. This is the same flake shown in Figure 9. Applied potential = 0.8 V and 0.9 V (b and c, respectively). The exciton Aʹ peak is present prior to electroablation and is uniform throughout the flake (540-550 nm). Inset: optical image of flake at 0.8 V. (d) WSe2 flake at the electroablation potential (1.0 V). The flake edges are ablated first. The exciton Aʹ peak is no longer present at the edges. Inset: the corresponding optical image also shows the flake is ablated at the edges first. (e-f) WSe2 flake above the electroablation potential. The flake has been ablated entirely, with the exception of a few remnants. Inset: the optical image shows that the flake is no longer present at potentials greater than the electroablation potential. Scalebar: 10 µm. 79

4.5. Conclusion

We demonstrated the use of spectroelectrochemical Micro-Extinction Spectroscopy (SE-

MExS) for in situ observation of the EA of MoS2, WS2, MoSe2, and WSe2. By using

reflectance-mode SE-MExS, we tracked excitonic transitions that are dependent on layer thickness for two-dimensional TMDs.130,133,137,139 The ability to track and monitor the EA

process in real-time reveals a venue for the synthesis of 2D TMDs that have a high

density of edge sites, a structural feature that is of particular interest for catalytic

applications due to the heightened reactivity of edge sites.138,140–142 The in situ optical- tracking approach presented in this work introduces a new tool for the synthesis and study of two-dimensional TMDs. We have unraveled the dynamics of the EA process, a promising technique to manufacture thin 2D materials from bulk. We directly visualize the manipulation of the optoelectronic properties during this synthesis process. We show that EA occurs edge-first for MoS2, WS2, MoSe2, and WSe2. Further we show that EA

can be used to generate stable monolayers of MoS2, WS2, and MoSe2, while no

monolayer remains for WSe2 during the EA process.

Chapter 5

Electrodeposition on Plasmonic Nanoparticles Using In Situ Spectroelectrochemistry

5.1. Introduction

Plasmonic nanoparticles (NP) have a wide breadth of potential application, e.g.,

catalysis,66–70 biosensing,57–62 and photovoltaics.63–65,143,144 A localized surface plasmon

resonance (LSPR) is the coherent oscillation of conduction electrons (detailed

explanation of the fundamental properties of plasmonics can be found in Chapter 2).

Noble metal NPs, such as Au and Ag, are commonly studied because they have strong

plasmons and are relatively easy to work with.67,145 There has been an effort to move

beyond Au and Ag due, in part, to the high material cost of noble metals.146 Recently, non-noble metal plasmonic nanocrystals have been synthesized from Al147 and, most recently, from Mg.148 Both Mg and Al maintain plasmonic properties while reducing the 81

material cost significantly relative to Au and Ag. Manipulation of the surface chemistry

of plasmonic NPs has been used to enhance their utility. For example, reactor-antenna

nanocrystals have been made by decorating Al NPs with small particles of other metals,

such as Pd and Pt, which are non-plasmonic, but are able to harvest plasmonic energy for

applications such as catalysis.149,150 Surface modification of NPs also adds versatility in

functionalization for bioapplications, where biocompatibility is a key concern.151,152

This chapter presents a new technique for electrochemical surface modification of

plasmonic NPs, which is the first step towards developing a controllable nanoscale

surface synthesis tool. In a proof-of-concept system, Au NPs are coated with Cu, a low- cost plasmonic material (Figure 5.1).

Figure 5.1 Single-particle electrodeposition schematic. A voltage is applied to immobolized bare gold nanoparticles (left). Metal ions in solution deposit onto the nanoparticle surface (right).

The newly-developed Micro-Extinction Spectroscopy (MExS) characterization method,

introduced in Chapter 3, is utilized in conjunction with an electrochemical cell for

spectroelectrochemical-MExS (SE-MExS) measurements, introduced in Chapter 4. Dark-

field (DF) optics are used to measure changes in plasmonic scattering while 82

electrodeposition is occurring in situ. Hyperspectral measurements enable statistical studies for on the order of 100s of particles. Welch’s t-test is employed to determine statistical significance of changes in scattering energy.

The goal of the work presented here is to probe fundamental scientific questions about the heterogeneity of both metal deposition and plasmonic sensing.

5.2. Experimental

5.2.1. Au nanoparticle Synthesis

Au NPs were made using a previously published synthesis by for decahedral NPs.153 A gold salt solution was made by dissolving 10 mg of HAuCl4 in 1 mL of DEG. A separate solution was made by dissolving 3.5 g of PVP (NP capping agent) in 12.5 mL of DEG.

The PVP solution was refluxed for 5 minutes before the gold salt solution was added to it. A color change occurred, indicating NP growth. After 10 minutes of NP growth, the reaction was removed from heat and cooled to room temperature (~20 minutes). The synthesis mixture was cleaned (i.e., excess reactants removed) by diluting with 12.5 mL of ethanol and centrifuged at 6000 RPM for 30 minutes. The cleaning procedure was repeated 4 times.

5.2.2. Spectroelectrochemical Cell

The electrochemical cell encasing (Figure 5.2b) is the same as was used in Chapter 4.

The electrochemical experiments for the work presented in this chapter used an ITO- coated glass coverslip (SPI supplies, 8-12 Ω resistance, #1.5 thickness, depth 0.16-0.19 83

mm) containing drop-casted Au NPs as the working electrode (WE). Indium tin oxide

(ITO)-coated glass coverslips were selected due to its electrical conductivity and optical

transparency. Prior to Au NP drop cast, the ITO-coated coverslips were cleaned

immersion into a basic piranha solution (1:1:5 mixture of NH4OH, H2O2, and water) at 70

°C for 1 minute, followed by deionized (DI) water rinse.

A Pt mesh (Alfa Aesar, 100 mesh woven, 0.0762 mm diameter wire, 99.9% purity) was used as the counter electrode (CE). For experiments regarding Cu deposition (Section

5.3.1) the quasi-reference electrode (QRE) was Pt wire (Goodfellow, 0.125 mm diameter, 84

99.95% purity); experiments on Cu/Ag deposition (Section 5.3.2) used a Ag wire QRE

(Sigma-Aldrich, 0.25 mm diameter, 99.9% purity). Experiments were conducted in

approximately 250 µl of electrolyte solution containing 5 mM CuSO4 (Sigma-Aldrich,

98% purity) and 100 mM NaCl. Silicone isolators (Grace Bio-Labs, 13 mm diameter

opening, 500 μm depth) served as spacers in the cell to ensure a leak-proof seal.

Electrochemical experiments were conducted on a Gamry Reference 600 potentiostat.

5.2.3. Dark-field Optical Measurements

Plasmonic scattering data was collected using the dark-field mode of the MExS optical setup (Figure 5.2, complete details in Chapter 3). An inverted optical microscope was used with a Princeton Instruments IsoPlane SCT320 spectrometer and a Princeton

Instruments exelon enabled ProEM HS 1024 x 1024 camera. Dark-field conditions were established using a 0.95-0.80 numerical aperture dark-field condenser and a 0.5-1.3

numerical aperture 100x oil-immersion objective.

5.2.4. Particle Transfer to TEM grid

Post electrodeposition, particles were transferred from the ITO-coated glass coverslip to a

TEM grid using an adapted sodium citrate method.7 The ITO surface was sonicated for

90 minutes in 100 µL of a 2 mM sodium citrate solution.

5.2.5. Statistical Analysis

Welch’s t-test, also referred to as the unequal variances t-test, was chosen for all

statistical analysis done in this thesis. This t-test, used to compare two sample sets 85

( , ) that have differing sample sizes ( , ) and differing variances ( , ),

1 2 1 2 1 2 generates𝑋𝑋 𝑋𝑋 a p-value that determines whether𝑁𝑁 the𝑁𝑁 two sample sets have statistically𝑠𝑠 𝑠𝑠 significant mean values ( , ) (Figure 5.3).

𝑋𝑋�1 𝑋𝑋�2

Figure 5.3 Graph of t-distribution for 20 degrees of freedom ( = ). A t statistic = 2.3, for example, has a p-value of 0.0162, which is statistically significant for a significance level of 0.05. 𝒗𝒗 𝟐𝟐𝟐𝟐

When conducting Welch’s t-test, Equation 9 and Equation 10 (Table 5.1) are used to

calculate t and v. Note that the degrees of freedom, v, can only be approximated in the

Welch’s t-test since the sample size for the two datasets are not equal. The value of v is

used to determine the probability density function of the t-distribution (Equation 11,

Figure 5.3). An example of a t-distribution for = 20 is shown in Figure 5.3. The p-

𝑣𝑣 86

value is the integration from t to of the area under the probability density function of the t-distribution (Equation 12, Figure∞ 5.3 shaded area).

Table 5.1. Statistical analysis equations.

= Equation 9. Welch’s t statistic 𝑋𝑋�1 − 𝑋𝑋�2 𝑡𝑡 2 2 𝑠𝑠1 𝑠𝑠2 � 1 − 2 𝑁𝑁 𝑁𝑁 + 2 2 2 Equation 10. Welch’s degrees of 𝑠𝑠1 𝑠𝑠2 � 1 + 2� freedom (ν) 𝑁𝑁4 𝑁𝑁 4 𝑣𝑣 ≈ 1 2 𝑠𝑠2 𝑠𝑠2 1 1 2 2 𝑁𝑁+𝑣𝑣1 𝑁𝑁 𝑣𝑣 2 𝑣𝑣+1 ( ) = 1 + − Equation 11. Probability density 𝑣𝑣 2 2 Γ � � function of the t-distribution 2 𝑡𝑡 𝑓𝑓 𝑡𝑡 𝑣𝑣 � � √(𝑣𝑣𝑣𝑣)Γ=� (� 1)!𝑣𝑣

Γ 𝑛𝑛= 𝑛𝑛(−) ∞ Equation 12. p-value 𝑝𝑝 �𝑡𝑡 𝑓𝑓 𝑡𝑡 𝑑𝑑𝑑𝑑 [ , ] = 2( , )

Equation 13. Matlab function for 𝐻𝐻 𝑝𝑝 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑋𝑋1 𝑋𝑋2 Welch’s t-test

Various software can be used to conduct the Welch’s t-test. In this thesis the statistical analysis was done using Matlab (Equation 13), where H is the hypothesis test result (H =

1 indicates rejection of the null hypothesis, H = 0 indicates failure to reject null hypothesis) and p is the p-value, which is the probability [0-1] that differences between sample sets (X1, X2) is due to chance. 87

5.3. Results and Discussion

5.3.1. Electrodeposition of Cu

The electrodeposition of Cu onto Au nanoparticles is tracked on single particles by the change in the surface plasmon resonance wavelength (Figure 5.4). This shift occurs because of the simultaneous change in particle size and composition due to the reduction

of the electroactive species, Cu2+ (Table 5.2, Reaction 5.1).

Reaction Description

5.1 ( ) + ( ) ° = . Reduction of copper from 𝟐𝟐+ − electrolyte; electrodeposition onto 𝑪𝑪𝑪𝑪 𝒂𝒂𝒂𝒂 𝟐𝟐𝒆𝒆 → 𝑪𝑪𝑪𝑪 𝒔𝒔 𝑬𝑬 𝟎𝟎 𝟑𝟑𝟑𝟑𝟑𝟑 𝑽𝑽 nanoparticle surface 5.2 ( ) ( ) + Photooxidation of the quasi- + − reference electrode (QRE) 𝐴𝐴𝐴𝐴 𝑠𝑠 → 𝐴𝐴𝐴𝐴 𝑎𝑎𝑎𝑎 𝑒𝑒 5.3 ( ) + ( ) ° = . Reduction of silver in electrolyte + − solution on nanoparticle surface 𝑨𝑨𝑨𝑨 𝒂𝒂𝒂𝒂 𝒆𝒆 → 𝑨𝑨𝑨𝑨 𝒔𝒔 𝑬𝑬 𝟎𝟎 𝟕𝟕𝟕𝟕𝟕𝟕𝟕𝟕 𝑽𝑽

Table 5.2 Electrochemical half reactions. Reduction potentials are versus the standard hydrogen electrode (SHE).

As layers of Cu are deposited onto the surface of the particle, two changes happen to the structure of the particle that affect the plasmon resonance energy. The first is that the particle becomes larger after electrodeposition due to the addition of layers of metal on

the surface of the bare particle; a larger particle has a smaller plasmon resonance

energy.72,75 The second is that the cumulative dielectric function of the particle changes;

Cu has a lower energy plasmon compared to Au.154 88

Scanning transmission electron microscopy (STEM) energy dispersive X-ray

spectroscopy (EDS) was used for elemental analysis of the Au nanoparticle post

electrodeposition (Figure 5.5). The elemental maps show the presence of Au as expected

(Figure 5.5b) and confirm the presence of Cu (Figure 5.5c). The Au signal is an

integration of the Au Lα X-ray line at 8.04 keV. The EDS maps shown here does not

show elemental data for the right side of the particle. This is due to the particular TEM grid used for this measurement; in order to eliminate the possibility of an added Cu signal

from the Cu grid, the particles were deposited onto silicon nitride grids. The particle

shown here was directly adjacent to the edge of the grid; because silicon nitride grids

have thick edges, X-ray from one side of the particle were unable to reach the detector.

An interesting finding is that there is no site-specific preferential deposition of Cu on any

particular region of the particle. 89

In time-resolved studies (described in section 3.3.2), the LSPR wavelength is tracked in

situ for individual nanoparticles during electrodeposition (Figure 5.6). First, the stability

of LSPR is monitored without applying any potential bias; from 0 to 180 seconds the

wavelength remains unchanged within the 650-651 nm range. At 180 seconds, a potential

bias is applied, and charge transfer is initiated (Figure 5.6, right axis). During charge

transfer, the LSPR wavelength increases from 651 nm to 660 nm, indicating a change in

the electronic properties of particle. The total charge transfer is -2.7 mC; a negative

charge transfer in a 3-electrode cell is indicative of a reduction at the working electrode.

All reactions in this chapter are reduction reactions. For the remainder of this chapter, the

charge transfer absolute value is reported.

At 240 seconds, the potential bias is terminated, and the charge transfer stops. Post

charge transfer, from 240 to 400 seconds, the LSPR wavelength remains constant within

a 1 nm range (659-660 nm). The post charge transfer result indicates that the 90

electrochemical process that occurs during charge transfer is non-reversible; irreversibility eliminates the possible explanation that the change in plasmon resonance results from change in electron density (e.g., charging6,10).

In hyperspectral imaging experiments, dark-field scattering spectra were acquired for

“large” (on the order of 200 x 200 µm) regions of interest (Figure 5.7). In such experiments, on the order of 100 particles were characterized via dark-field spectroscopy to determine the change in the LSPR wavelength before and after electrodeposition

(Figure 5.8). Statistical analysis shows that for a total charge transfer of 6 mC, the average change in LSPR was 12 ± 1.5 nm, for N = 125 particles. Comparatively, the 91

average change in LSPR was 40. ± 7 nm, for N = 151 particles, during a total charge transfer of 12 mC.

Figure 5.7 Optical image (left), hyperspectral integrated spectrum image (right) maps of immobilized NPs on ITO coverslips. Single particles correlated between optical and hyperspectral integrated spectrum images during data analysis. Integrated spectrum images, explained in detail section 0, are constructed by integrating the intensity across all wavelengths at each pixel. Scalebar: 20 µm.

Welch’s t-test, which is used when comparing two sample sets with unequal sample size

an unequal sample variance, was performed to determine the statistical significance of the

difference in LSPR shift between these two experiments. The p-value from the t-test for

the comparison of these two datasets is 1.5 x 10-37. (Note: the p-value, 0-1, is the probability that the difference between the two sample sets is due to chance.) Because the p-value between these two datasets is less than 0.01, the difference in LSPR shift for these two experiments has greater than 99% statistical significance.

The variance in LSPR shift for the 12 mC experiment, 7 nm, is more than four times greater than the variance for the 6 mC experiment, 1.5 nm. This can be attributed to differences in the amount of electrodeposition that occurs on each nanoparticle.

Differences in electrodeposition per particle is caused by differences in the position of the immobilized nanoparticles on the substrate surface and roughness of the ITO surface, in addition to heterogeneity in particle size and shape (Figure 5.9, Figure 5.10). Plasmonic electric field enhancement is greatest for particles with sharper tips.83 Sharper tips and

rough sections of the ITO substrate have greater electrostatic potential which causes a

greater potential drift field, thereby enabling heightened electrochemical activity.5 In these experiments, the heightened electrochemical activity in certain regions likely results in more electrodeposition at those sites, encouraging additional research in the tip- 93

specificity of electrodeposition. The differences in electrodeposition per particle becomes more prominent as more charge transfer occurs.

Cu electrodeposition occurred by applying a series of potential steps (Figure 5.11).

Potential steps of -100 and -200 mV resulted in minimal charge transfer, 0.05 mC and

0.70 mC, respectively, and negligible change in the LSPR wavelength, 0.6 ± 1 nm and

1.0 ± 0.7 nm, respectively. The p-value between these two potential steps, 0.29, indicates

a high probability that the LSPR shift is, in fact, due to chance (Table 5.3). The p-value is

greater than 0.05 and supports the null hypothesis that there is no significant difference

between these two datasets. A significant change does occur at the following potential

step, -300 mV. The total charge transfer, 5.0 mC, causes a total LSPR shift of 7 ± 1.4 nm.

The p-value between these two potential steps, 2.9 x 10-10, is less than 0.01, indicating a

99% confidence interval of statistical significance. 95

Sample Set A Sample Set B Samples A to B p-value Potential Bias Potential Bias Average (mV) (mV) LSPR Shift Difference (nm) -100 -200 0.4 0.29 -200 -300 6.3 2.9 x 10-10

5.3.2. Concurrent Electrodeposition of Cu and Photodeposition of Ag

Numerous experiments were also conducted using a Ag quasi-reference electrode (QRE),

which is a commonly used reference electrode in small-scale electrochemical

systems.6,8,15 Interestingly, it was found that the geometry of the electrochemical cell,

designed in-house (details in Appendix A), combined with the electrochemical

parameters enabled the photooxidation of Ag from the QRE (Table 5.2, Reaction 5.2).

The presence of electrolyte on the surface of the Ag QRE reduces the work function of the free electrons in the Ag QRE; this reduction in work potential is due to the higher optical density of the electrolyte, relative to air.155 When the metal has a lower work

function, the amount of energy required to remove an electron decreases. Consequently,

the Ag QRE is more easily oxidized; i.e., electrons are ejected with less energy. Further,

it has been shown in photoemission studies that application of cathodic potentials to the

Ag metal in electrolyte solution results in a greater quantum yield of ejected electrons

than photoemission studies conducted with no potential bias.155 This yield of electrons

simultaneously results in the production of Ag+ at the electrode-electrolyte interface

(Table 5.2, Reaction 5.2). Because the geometry of the spectroelectrochemical cell, Ag

wire was directly exposed the illumination used in each dark-field scattering experiment.

Consequently, Ag was photooxidized and Ag+ entered the electrolyte solution as a new

electroactive species.

The changes in plasmon resonance wavelength in these experiments (Figure 5.12) differ

from the plasmon resonance wavelength shift observed in previous experiments (Figure 97

5.6), indicating a change induced by the presence of Ag+. A discussion of the reason for

this difference follows.

Stability of the system was measured without charge transfer; from 0-600 seconds, the

plasmon wavelength remained between 666-668 nm. Subsequently, the charge transfer

was initiated at 600 seconds; the plasmon resonance red-shifted by 12 nm after a total 10

mC charge transfer over the course of 300 seconds. This initial result is similar to what

was seen in Cu2+ experiments (Figure 5.6). However, as the reaction continued, the

plasmon resonance reversed direction and blue-shifted by 4 nm.

These two steps can be explained by examining competing reduction reactions,

and (Table 5.2, Reactions 5.1 and 5.3, respectively), and by 2+ + 𝑟𝑟𝑟𝑟𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 knowing𝐶𝐶𝐶𝐶 the relative𝐴𝐴𝐴𝐴 LSPR energy: increase in the order of Cu < Au < Ag.8,154,156 In the 98

presence of just Cu, only the reaction is occurring (Section 5.3.1, Figure 5.6); 2+ 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑐𝑐𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 electrodeposition of Cu onto𝐶𝐶𝐶𝐶 AuNPs results in a red-shift of the resonance

wavlength.154,156 However, in the presence of both Cu and Ag here, the two reduction

reactions, and , are competing. Initially, Cu2+ reduction 2+ + 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 dominates because𝐶𝐶𝐶𝐶 [ ] 𝐴𝐴𝐴𝐴> 0 while [ ] = 0. In the single-particle tracking 2+ + 𝑡𝑡=0 𝑡𝑡=0 experiment (Figure 5.𝐶𝐶𝐶𝐶12), a red-shift occurs 𝐴𝐴𝐴𝐴from t = 600 seconds (potential bias initiated) to t = 925 seconds; this red-shift is attributed to the dominating reaction. 2+ 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 Concurrently, during this time the reaction (Table 𝐶𝐶𝐶𝐶5.2, Reaction 5.2) is

𝑝𝑝ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 occurring introducing Ag+ into the𝐴𝐴𝐴𝐴 electrolyte solution. At t = 925 seconds, [Ag+] >> 0

and the reaction dominates; the movement of the LSPR switches directions + 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 and begins𝐴𝐴𝐴𝐴 to blue-shift.

The change in scattering efficiency further confirms the electrodeposition processes

occurring in each phase of the spectroelectrochemical reaction (Figure 5.13). Initial

charge transfer induces a decrease in scattering efficiency (Figure 5.13a). The decrease in

scattering intensity is expected with the presence of Cu on the nanoparticle surface.156

The scattering efficiency and resonance energy begin to increase once enough Ag has

photooxidized (Reaction 5.2) due to the competing reduction reactions at the surface of

the nanoparticle, as described previously (Figure 5.13b). The increase in scattering

intensity is indicative of the presence of Ag on the surface the nanoparticle.8

STEM-EDS spectral analysis confirms the presence of both Ag and Cu on the Au

nanoparticle (Figure 5.14). The Ag Lα peak appears at 2.98 keV and the Cu Kα peak appears at 8.04 keV. 99

Time-resolved hyperspectral imaging experiments (described in section 3.3.2) enabled optical tracking of multiple particles to observe the electrodeposition of Cu and Ag on single particles. In an experiment that tracked the plasmon resonance of N = 23 particles, the plasmon energy red-shifted up to an average of 12 ± 2 nm during a total charge transfer of 10.4 mC (Figure 5.15). In contrast, measurements taken 120 seconds post charge transfer show a blue-shift in resonance wavelength to 7 ± 3 nm. The Welch’s t-test was conducted to determine the statistical significance of these LSPR shifts (Table 5.4).

The p-values between each shift and the preceding shift in Figure 5.15 is less than 0.05, and therefore each step is statistically different.

101

Sample Set A Sample Set B Samples A to B p-value Charge Transfer Charge Transfer Average (mC) (mC) LSPR Shift Difference (nm) < 1 1.4 + 2.4 2.7 x 10-9 1.4 3.3 + 2.9 1.6 x 10-10 3.3 5.2 + 1.7 1.4 x 10-4 5.2 10.4 + 2.5 2.9 x 10-5 10.4 120 seconds post - 5.0 6.7 x 10-7 charge transfer

The statistical analysis shown here (Table 5.4) for Cu/Ag deposition can be compared to the statistical analysis in the previous section for a pure Cu system (Figure 5.8).

Interestingly, similar magnitude of charge transfer, 12 mC for pure Cu and 10 mC for

Cu/Ag, resulted in significantly different average plasmon resonance shifts: 40. ± 7 nm

and 12 ± 2 nm, respectively (p-value = 2.0 x 10-9). This difference in average LSPR shift is explained by the competing reduction reactions as described previously in this section.

5.3.3. Change in Plasmon Resonance Fits

At low charge transfer (0-10 mC), the plasmon resonance shift follows a logarithmic trend (Figure 5.16a), while at higher charge transfer (up to 80 mC) the shift follows a linear trend (Figure 5.16b). 102

5.4. Conclusion

The work presented in this chapter demonstrates the use of spectroelectrochemical

Micro-Extinction Spectroscopy (SE-MExS) for in situ observation of the

electrodeposition of Cu and Ag onto Au NPs. MExS dark-field mode enables the tracking

of the localized surface plasmon resonance (LSPR) energy as a function of charge

transfer. Time-resolved measurements show a red-shift in the LSPR energy occurring

with electrodeposition of Cu. In cases where Ag is also present in the

spectroelectrochemical cell, competing reduction reactions of Cu2+ and Ag+ occur.

Deposition of Cu dominates the spectral response initially until a threshold amount of

Ag+ has been generated; then Ag deposition dominates the spectral response causing a

blue-shift in the LSPR energy. The ability to monitor electrodeposition as it is occurring 103

using SE-MExS is a step towards achieving controllable surface modification of plasmonic nanocrystals.

104

Chapter 6

Discrete Dipole Approximation (DDA): Numerical Studies of Plasmonic Nanoparticles

The numerical calculations presented in this chapter were conducted to

complement experiments conducted by collaborators (section 6.14 and section

6.22). Some of the data shown comes directly from the corresponding

publications.2,4 For distinction, work conducted by the author of this thesis does

not explicitly reference the publications in the text of this section.

6.1. Aqueous Nanogaps in Core-Shell Plasmonic Nanoparticles

Extinction spectra resulting from simulating light-nanoparticle interaction were calculated using discrete dipole approximation, DDA (for more details regarding DDA, 105

see section 2.3.4). The particles of interest were nanorods composed of an Au core and an

Ag/Au alloy shell which were separated with an aqueous gap (Figure 6.1).

Experimental data4 showed that the volume of the aqueous gap could be controlled by

varying concentrations of the alloying solution (AgNO3, 4-mercaptopyridine).

To generate corresponding simulations, four different shape files were made: plain rod,

core-shell rod, core-shell rod with partial nanogaps, and core-shell rod with full nanogaps

(Figure 6.2). TEM images, presented in the publication,4 were measured using ImageJ, an

image-processing program that relies on Java. The bare Au particle was simulated as a

rod (34,399 dipoles) with an aspect ratio of 2.9 (66.5 nm x 23.1 nm). The other three

particles (Au core with alloy shell, Au core with alloy shell and a partial nanogap, and Au

core with alloy shell and a complete nanogap) were simulated as rods (46,229 dipoles)

with aspect ratios of 2.4 (81.3 nm x 33.8 nm). 106

The dielectric data used for the core was pure Au,71 and for the alloy shell was a ratio of

20% Au to 80% Ag157 to match experiment elemental data.4 The ambient medium was simulated as water (RI = 1.33) in order to compare to experimental spectra that were acquired in aqueous solution. The nanogaps were simulated as combinations of water (RI

= 1.33) and 4-mercaptopyridine (approximated as benzene, RI = 1.45), modeling the nanogap composition after the solution used in the nanoparticle synthesis.4

Simulated extinction spectra (Figure 6.3) matched experimental extinction data.4 A blue-

shift is observed when the particle goes from a core-shell structure (Figure 6.3b) to a

particle with a partial gap (Figure 6.3c). Further, a blue-shift is also predicted when the 107

particle goes from a pure Au nanorod (Figure 6.3a) to nanorods with full or partial nanogaps (Figure 6.3c and Figure 6.3d).

Figure 6.3 Normalized DDA simulated extinction spectra. Peak wavelengths are as follows: (a) 696 nm, (b) 629 nm, (c) 599 nm, (d) 661 nm.

6.2. Shape and Size dependence of AuDh on refractive index

sensitivity

DDA was again used to probe the light-nanoparticle interaction. While in Section 6.1, the extinction spectra were of interest in order to compare to UV-Vis experimental data, here

the scattering spectra are relevant for comparison to dark-field scattering experimental

data. The nanoparticles of interest here are Au decahedra (Dh). A comparison of the

refractive index sensitivity (RIS) is done for AuDh that have varying tip geometry: sharp

tips and rounded tips (Figure 6.4). 108

Figure 6.4 Decahedron shapes with sharp (left) and rounded (right) tips.

The sharp tip shape was simulated as 18,207 dipoles and the rounded tip shape was simulated as 17,931 dipoles. The dielectric data used for the Dh throughout these simulations was for pure Au.71 The refractive index of the ambient medium was varied from 1 to 1.6 in order to observe the change in plasmon resonance frequency that occurred, and to thereby calculate the RIS for each shape type.

Simulated scattering spectra are shown in Figure 6.5 and Figure 6.6, for 80 nm and 120 nm side length, respectively. As expected,75 increasing the refractive index from 1 to 1.6, the plasmon resonance wavelength red-shifts for each case.

109

Figure 6.5 Scattering spectra for 80 nm side-length Au Dh. Sharp tips (left) and rounded tips (right).

Figure 6.6 Scattering spectra for 120 nm side-length Au Dh. Sharp tips (left) and rounded tips (right).

The refractive index sensitivity can be determined from this data by observing the slope of the graph shown in Figure 6.7. While the shape does affect the RIS of the NP, the

effect of shape is minimal compared to the effect of size. When comparing the 80 nm and

120 nm sharp tips, there is an approximately 60 nm/RIU change in the RIS. However, 110

when comparing the sharp and the rounded tips for the 80 nm Dh, the change in RIS is only approximately 10 nm/RIU.

Figure 6.7 Refractive index sensitivity. Top two lines show the 120 nm Dh and the bottom two lines show the 80 nm Dh. The corresponding shape and RIS are listed to the right of the graph.

The results presented in this section conflict with experimental data presented elsewhere,2 and further studies must be completed prior to making any concrete determinations regarding this comparison.

111

Chapter 7

Conclusions and Outlook

The work presented in this thesis demonstrates the versatility of a novel characterization

technique, spectroelectrochemical Micro-Extinction Spectroscopy (SE-MExS), in probing nanoscale processes in real time. In Chapter 3 the instrumental design, data acquisition, and data analysis programs of MExS are described in detail. In the subsequent chapters, Chapter 4 and Chapter 5, properties of two types of nanomaterials are explored with SE-MExS: transition metal dichalcogenides (TMDs) and plasmonic nanoparticles. In situ monitoring enables observation of the electroablation process of

TMDs to monolayer sheets (Chapter 4) as well as the surface modification of plasmonic nanoparticles (Chapter 5). 112

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Appendix A: Spectroelectrochemical Cell Design and Assembly

In order to conduct electrochemical experiments in a consistent manner and to collect optical data immediately after, or even during, experiments, a spectroelectrochemical cell was designed as seen in figure 5. The cell design is as follows. The total height of the cell has to remain less than 3 mm. Because dark field (DF) optical microscopy is one of the main forms of characterization in this project, it is critical enough such that the condenser and objective can be placed the appropriate distance apart for DF data collection (DF details in SI, section 2.2.1). Inside the spectroelectrochemical cell, there is enough space for each of the three electrodes to be positioned such that they are not in contact with any other electrode, using silicone spacers as separators.

From the bottom going upward, the design is as follows. Similar to the experiments reported in the main text, the working electrode (WE) is an ITO-coated glass coverslip. 133

The NPs are placed on the ITO coating effectively making the NPs the WE. This electrode is connected to the potentiostat using Ag wire. A spacer is placed above the WE to separate the WE from the other electrodes. Then the other two electrodes – the reference electrode (RE) and the counter electrode (CE) – are placed sequentially with spacers in between, again to ensure the electrodes are not in contact with one another. The

REs to be used in these spectroelectrochemical studies are quasi-REs (Ag or Pt), as regular REs are too large to be used in such a small electrochemical cell. A Pt mesh will be used as the CE. The spectroelectrochemical cell described here was modeled after ones made by Byers et al.6 and Chirea et al.8

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Appendix B: Single-Particle, Hyperspectral, and Time-Lapse Data Analysis Code

B.1. Single-Particle

B.1.1. Analysis n = [1:53]; % INPUT the number of particles studied file = cellstr({'20150813_ITO_AuDecH6_beforeElectroplating'... '20150814_ITO_AuDecH6_afterElectroplating_25mV'... '20150814_ITO_AuDecH6_afterElectroplating_50mV'... '20150814_ITO_AuDecH6_afterElectroplating_75mV'}); % INPUT the base file name

%% LAMP for m = 1:length(file); % "m" stands for "media" cd ../ cd(strcat(num2str(pwd), '/CSV')); filetitle = strcat(file(m), '_Lamp'); filename = char(strcat(num2str(pwd), '/', filetitle, '.csv'));

cd ../ cd(strcat(num2str(pwd), '/Matlab')) delimiter = ','; formatSpec = '%f%f%[^\n\r]'; fileID = fopen(filename,'r'); dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'EmptyValue' ,NaN, 'ReturnOnError', false); fclose(fileID);

W_all = dataArray{:, 1}; I_all = dataArray{:, 2}; L = length(W_all); ROI = L./1024;

cellStart = (ROI - 2) * 1024 + 1025; cellEnd = cellStart + 1023; W_L{m} = W_all(cellStart:cellEnd, 1); I_L{m} = I_all(cellStart:cellEnd, 1); end

%% DARK for m = 1:length(file); % "m" stands for "media" cd ../ cd(strcat(num2str(pwd), '/CSV')); filetitle = strcat(file(m), '_Dark'); filename = char(strcat(num2str(pwd), '/', filetitle, '.csv'));

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W_all = dataArray{:, 1}; I_all = dataArray{:, 2}; L = length(W_all); ROI = L./1024;

cellStart = (ROI - 2) * 1024 + 1025; cellEnd = cellStart + 1023; W_D{m} = W_all(cellStart:cellEnd, 1); I_D{m} = I_all(cellStart:cellEnd, 1); end

%% BACKGROUND cd ../ cd(strcat(num2str(pwd), '/CSV')); for m = 1:length(file); % "m" stands for "media" filename = char(strcat(num2str(pwd), '/', file(m), '_I_B_mean')); delimiter = ','; formatSpec = '%f%f%[^\n\r]'; fileID = fopen(filename,'r'); dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'EmptyValue' ,NaN, 'ReturnOnError', false); fclose(fileID);

I_B_mean{m} = dataArray{:, 1}; end cd ../ cd(strcat(num2str(pwd), '/Matlab'));

%% Normalized Spectrum = (Particle - Background)/(Lamp - Detector) for m = 1:length(file); % "m" stands for "media" for l = 1:length(n); cd ../ cd(strcat(num2str(pwd), '/CSV')); filetitle = strcat(file(m), '_Particle', num2str(n(l))); filename = char(strcat(num2str(pwd), '/', filetitle, '.csv'));

W_all = dataArray{:, 1}; I_all = dataArray{:, 2}; L = length(W_all); ROI = L./1024;

cellStart = (ROI - 2) * 1024 + 1; cellEnd = cellStart + 1023; W_P{m}{l} = W_all(cellStart:cellEnd, 1); I_P{m}{l} = I_all(cellStart:cellEnd, 1);

I_numerator{m}{l} = I_P{m}{l} - I_B_mean{m}; I_denominator{m}{l} = I_L{m} - I_D{m}; I_fit = I_numerator{m}{l}./I_denominator{m}{l}; I_max = max(I_fit(254:642)); I_normalized{m}{l} = I_fit./I_max; end end

%% PLOT shape = {'k', 'r', 'b', 'g', 'c'}; cd ../ mkdir('Images/Particle_Plots') cd (strcat(num2str(pwd), '/Images/Particle_Plots')); for l = 1:length(n); figure() for m = 1:length(file); % "m" stands for "media" plot(W_P{m}{l}, I_normalized{m}{l}, shape{m}, 'LineWidth',3); hold on axis([500 800 0 1.1])

title(strcat('Particle_', num2str(n(l))), 'fontsize', 20, 'Interpreter', 'none') xlabel('Wavelength (nm)', 'fontsize', 20) ylabel('Normalized scattering (a.u.)', 'fontsize', 20) legend(file, 'location', 'southeast', 'Interpreter', 'none') set(gca,'FontSize',16);

figurename = strcat('Particle_', num2str(l)); % Change this based on the parameters of the study print('-djpeg', figurename); savefig(figurename) end end

clearvars filename delimiter formatSpec fileID dataArray ans W_all I_all L ROI cellStart cellEnd m n filetitle file; close all

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B.1.2. Background Average

function varargout = Background_template_GUI_take2(varargin) %% Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @Background_template_GUI_take2_OpeningFcn, ... 'gui_OutputFcn', @Background_template_GUI_take2_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end

%% --- Executes just before Background_template_GUI_take2 is made visible. function Background_template_GUI_take2_OpeningFcn(hObject, eventdata, handles, varargin) handles.output = hObject; guidata(hObject, handles);

%% --- Outputs from this function are returned to the command line. function varargout = Background_template_GUI_take2_OutputFcn(hObject, eventdata, handles) varargout{1} = handles.output;

%% --- Executes on selection change in listbox1. function listbox1_Callback(hObject, eventdata, handles) %set(listbox1,'Max',2,'Min',0);

index_selected = get(handles.listbox1,'Value') cla

l = handles.l;

for l = 1:l W_B = handles.W_B; I_B_temp = handles.I_B_temp;

allBackgroundPlot = plot(W_B{l}, I_B_temp{l}, 'k',... % plots particle 'l' 138

'LineWidth',3); end

for p = 1:length(index_selected) this_plot = index_selected(p) W_B_plot{p} = W_B{this_plot} I_B_plot{p} = I_B_temp{this_plot} plotAllSelected = plot (W_B_plot{p}, I_B_plot{p},'r','LineWidth',3); hold on end

handles.I_B_plot = I_B_plot; guidata(hObject, handles);

plot_selected = get(handles.listbox1, 'value') plots_allowed = get(handles.listbox1, 'string') data_2_plot = plots_allowed(plot_selected,:) %% --- Executes during object creation, after setting all properties. function listbox1_CreateFcn(hObject, eventdata, handles) if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end function edit1_Callback(hObject, eventdata, handles) n = str2double(get(hObject,'string')); for l = 1:n; ParticleNum{l} = strcat('Background_', num2str(l)); end set(handles.listbox1,'String', [ParticleNum]);

n = [1:n]; handles.n = n; guidata(hObject, handles); function edit1_CreateFcn(hObject, eventdata, handles) if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end

%% --- Executes on button press in pushbutton1. function pushbutton1_Callback(hObject, eventdata, handles) cd ../ cd (strcat(num2str(pwd), '/CSV')); [dark path]= uigetfile('*.csv'); set(handles.edit2,'String',[dark])

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base = dark(1:end-9); handles.base = base; handles.path = path; guidata(hObject, handles);

set(handles.edit3,'String',[base])

n = handles.n; for l = 1:length(n); filetitle1 = strcat(base, {'_Particle '}, num2str(n(l))); filetitle2 = strcat(base, {'_Particle'}, num2str(n(l))); filename1 = char(strcat(num2str(pwd), '/', filetitle1, '.csv')); filename2 = char(strcat(num2str(pwd), '/', filetitle2, '.csv')); doesexist = exist (filename1, 'file'); if doesexist == 2; movefile(filename1,filename2); end end

particle_template = strcat(cellstr(base), '_Particle');

cd ../ cd (strcat(num2str(pwd), '/Matlab'));

for l = 1:length(n); % for loop to access each particle cd ../ % goes back into main directory cd (strcat(num2str(pwd), '/CSV')); % goes into the CSV directory filename = char(strcat(num2str(pwd), '/',... % creates the filename based on current directory, strcat(base,'_Particle', num2str(n(l))),... base file name, and particle number '.csv'));

cd ../ % goes back into main directory cd(strcat(num2str(pwd), '/Matlab')) % goes into the Matlab folder delimiter = ','; % defines how the data is separated in the CSV file formatSpec = '%f%f%[^\n\r]'; % NOT SURE (formats the data somehow...) fileID = fopen(filename,'r'); % NOT SURE (opens the filename, don't know about r); dataArray = textscan(fileID, formatSpec,... % NOT SURE (creates a data array with data from CSV 'Delimiter', delimiter, 'EmptyValue' ,NaN,... file 'ReturnOnError', false); 140

fclose(fileID); % closes the open file

W_all = dataArray{:, 1}; % wavelength defined as first column of data I_all = dataArray{:, 2}; % intensity defined as second column of data L = length(W_all); % determines total number of data points ROI = L./1024; % determines the number of ROI's (each ROI = 1024)

cellStart_B = (ROI - 1) * 1024 + 1; % determines first cell of background ROI cellEnd_B = cellStart_B + 1023; % determines last cell of background ROI

W_B{l} = W_all(cellStart_B:cellEnd_B, 1); % populates with designated cells from dataArray handles.W_B = W_B; guidata(hObject, handles); I_B_temp{l} = I_all(cellStart_B:cellEnd_B, 1); % populates with designated cells from dataArray handles.I_B_temp = I_B_temp; guidata(hObject, handles);

allBackgroundPlot = plot(W_B{l}, I_B_temp{l}, 'k',... % plots particle 'l' 'LineWidth',3);

handles.l = l; handles.W_B = W_B; handles.I_B_temp = I_B_temp; guidata(hObject, handles);

hold on % keeps previous plots so all plot on one figure end % ends for loop accessing each particle

function edit2_Callback(hObject, eventdata, handles)

%% --- Executes during object creation, after setting all properties. function edit2_CreateFcn(hObject, eventdata, handles) if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end

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function edit3_Callback(hObject, eventdata, handles)

%% --- Executes during object creation, after setting all properties. function edit3_CreateFcn(hObject, eventdata, handles) if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end

% --- Executes on button press in pushbutton2_createAvgFile. function pushbutton2_createAvgFile_Callback(hObject, eventdata, handles) base = handles.base; I_B_plot = handles.I_B_plot; W_B = handles.W_B; W_B = cell2mat(W_B); W_B = W_B(:,1); for h = 1:length(I_B_plot); I_B_concat = horzcat(I_B_plot{h}); % concatenates all I_B_good end

I_B_mean = mean(I_B_concat, 2); % takes the average of all I_B_concat I_B_mean = [W_B I_B_mean]; cd ../ cd(strcat(num2str(pwd), '/CSV')) outputFileTitle = char(strcat(base, '_I_B_mean', '.csv')); % creates name of CSV file for the background output csvwrite(outputFileTitle, I_B_mean); % write CSV file for I_B_mean path = handles.path; set(handles.edit5,'String',[path])

function edit5_Callback(hObject, eventdata, handles)

%% --- Executes during object creation, after setting all properties. function edit5_CreateFcn(hObject, eventdata, handles) if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end 142

% --- Executes on button press in pushbutton7. function pushbutton7_Callback(hObject, eventdata, handles) close all

% --- Executes on button press in pushbutton8. function pushbutton8_Callback(hObject, eventdata, handles)

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B.2. Hyperspectral

B.2.1. Make Spectrum Image function [lamda, row1, column1, rowlast, columnlast, xyzbinmatrix, map, mymtx] = HYP1_ak_makeSpectrumImage_v2(baseFileName,numSteps, xNum, energyNum, xbinsize, ybinsize, zbinsize);

%% Variables - modify as needed baseFileName = 'expt80_20171013_hyp'; numSteps = 150; xNum = 1023; energyNum = 649; xbinsize = 3; ybinsize = 1; zbinsize = 1;

%% Code starts here disp(strcat('Start HYP1_ak_makeSpectrumImage ---->', datestr(now))) [mymtx, lamda, row1, column1, rowlast, columnlast, xyzbinmatrix] = MSI_a_importData(numSteps,baseFileName,xNum,energyNum,xbinsize ,ybinsize, zbinsize); disp(strcat('End MSI_a_importData ---->', datestr(now))) map = MSI_b_plotIntegratedSpectrumImage(xyzbinmatrix, baseFileName); disp(strcat('End MSI_b_plotIntegratedSpectrumImage ---->', datestr(now))) disp(strcat('End HYP1_ak_makeSpectrumImage ---->', datestr(now))) end

function [mymtx, lamda, row1, column1, rowlast, columnlast, xyzbinmatrix] = MSI_a_importData(numSteps,baseFileName,xNum,energyNum,xbinsize ,ybinsize, zbinsize) modifyFileName(numSteps, baseFileName) yNum = numSteps; mymtx = zeros(xNum, yNum, energyNum); row1 = 4; column1 = 1; rowlast = row1+xNum-1; columnlast = column1+energyNum-1; n = [1:numSteps]; cd ../ cd(strcat(num2str(pwd),'/CSV')) for i = 1:length(n); fileName = strcat(baseFileName, num2str(i), '.csv'); data = csvread(fileName,row1,column1,[row1, column1, rowlast, columnlast]); mymtx(:,i,:) = data(:,:); %data coming in as data(energy,x) 144

end empty = isempty(findstr(baseFileName, 'reverse')); if empty == 0 % this means the sample is reversed mymtx = flip(mymtx,2); end lamda = csvread(fileName,row1-1,column1,[row1-1,column1, row1- 1,columnlast]); cd ../ cd(strcat(num2str(pwd),'/Matlab')) [xyzbinmatrix] = akBinToResizeImage(mymtx,xbinsize ,ybinsize, zbinsize); end

function map = MSI_b_plotIntegratedSpectrumImage(xyzbinmatrix, baseFileName)

[xNum yNum energyNum] = size(xyzbinmatrix); map = zeros(xNum, yNum); cmax=0; for x = 1:xNum for y = 1:yNum thisEnergySum = sum(xyzbinmatrix(x,y,:)); map(x,y)=thisEnergySum; if thisEnergySum > cmax cmax = thisEnergySum; end end end

%cmax = 0.4*cmax; cmax = 3e7; imagesc(map); colormap jet axis equal [x y z] = size(xyzbinmatrix); axis([0 y 0 x]) colorbar %caxis([0 cmax]); fileName = baseFileName(1:end); cd ../ mkdir('Matlab output'); cd(strcat(num2str(pwd),'/Matlab output')); savefig(strcat(fileName, '_integradedSpectrumImage', '.fig')); print('-djpeg',strcat(fileName, '_integradedSpectrumImage', '.jpg')); cd ../ cd(strcat(num2str(pwd),'/Matlab')); end

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function modifyFileName(numSteps, baseFileName) cd ../ cd(strcat(num2str(pwd),'/CSV')) n = [1:numSteps]; for l = 1:length(n); currentName = char(strcat(baseFileName, {' '}, num2str(n(l)))); newName = char(strcat(baseFileName, num2str(n(l)))); currentPath = char(strcat(num2str(pwd), '/', currentName, '.csv')); newPath = char(strcat(num2str(pwd), '/', newName, '.csv')); doesexist = exist (currentPath, 'file'); if doesexist == 2; movefile(currentPath,newPath); end end cd ../ cd(strcat(num2str(pwd),'/Matlab')) end

function [mtxBinned] = akBinToResizeImage(mtxToBin,xbinsize ,ybinsize, zbinsize)

[xsize,ysize,zsize]=size(mtxToBin); if((floor(xsize/xbinsize)*xbinsize~= xsize) || (floor(ysize/ybinsize)*ybinsize~= ysize) || (floor(zsize/zbinsize)*zbinsize~= zsize)) disp( 'warning: trimming matrix') end trimmed_matrix=mtxToBin(1:floor(xsize/xbinsize)*xbinsize,1:floor(ysize/ ybinsize)*ybinsize,1:floor(zsize/zbinsize)*zbinsize); [xsize,ysize,zsize]=size(trimmed_matrix); xbinmatrix=zeros(xsize/xbinsize,ysize,zsize); for i=1:1:xbinsize xbinmatrix=xbinmatrix+trimmed_matrix(i:xbinsize:xsize,:,:); end xybinmatrix=zeros(xsize/xbinsize,ysize/ybinsize,zsize); for i=1:1:ybinsize xybinmatrix=xybinmatrix+xbinmatrix(:,i:ybinsize:ysize,:); end mtxBinned=zeros(xsize/xbinsize,ysize/ybinsize,zsize/zbinsize); for i=1:1:zbinsize mtxBinned=mtxBinned+xybinmatrix(:,:,i:zbinsize:zsize); end end

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B.2.2. Import Lamp/Dark function [lampDataSmoothedBinnedMtx darkDataSmoothedBinnedMtx] = HYP2_ak_importLampDark_v1(fileNameLamp,fileNameDark,row1, column1, rowlast, columnlast,lamda,xNum,b,energyNum,numSteps,xbinsize ,ybinsize, zbinsize);

%% Variables - COPY and PASTE into command window (modify as needed) b = 10; fileNameLamp = 'expt80_sample79b_20171013_hyp_lamp.csv'; fileNameDark = 'expt80_sample79b_20171013_hyp_dark.csv';

%% CODE STARTS HERE disp(strcat('Start HYP2_ak_importLampDark ---->', datestr(now))) [lampDataSmoothedBinnedMtx darkDataSmoothedBinnedMtx] = ILM_a_LampDark(fileNameLamp,fileNameDark,row1, column1, rowlast, columnlast,lamda,xNum,b,energyNum,numSteps,xbinsize ,ybinsize, zbinsize); disp(strcat('End HYP2_ak_importLampDark ---->', datestr(now))) end

function [lampDataSmoothedBinnedMtx darkDataSmoothedBinnedMtx] = ILM_a_LampDark(fileNameLamp,fileNameDark,row1, column1, rowlast, columnlast,lamda,xNum,b,energyNum,numSteps,xbinsize ,ybinsize, zbinsize); cd ../ cd(strcat(num2str(pwd),'/CSV')); lampData = csvread(fileNameLamp,row1,column1,[row1, column1, rowlast, columnlast]); darkData = csvread(fileNameDark,row1,column1,[row1, column1, rowlast, columnlast]); cd ../ cd(strcat(num2str(pwd),'/Matlab')); lampDataSmoothed = akSmooth(lampData,b,xNum,energyNum,lamda); lampDataSmoothedMtx = repmat(lampDataSmoothed,[1 1 numSteps]); lampDataSmoothedMtx = permute(lampDataSmoothedMtx,[1 3 2]); [lampDataSmoothedBinnedMtx] = akBinToResizeImage(lampDataSmoothedMtx,xbinsize ,ybinsize, zbinsize); darkDataSmoothed = akSmooth(darkData,b,xNum,energyNum,lamda); darkDataSmoothedMtx = repmat(darkDataSmoothed,[1 1 numSteps]); darkDataSmoothedMtx = permute(darkDataSmoothedMtx,[1 3 2]); [darkDataSmoothedBinnedMtx] = akBinToResizeImage(darkDataSmoothedMtx,xbinsize ,ybinsize, zbinsize); end

function [mtxBinned] = akBinToResizeImage(mtxToBin,xbinsize ,ybinsize, zbinsize); 147

function dataSmoothed = akSmooth(dataToSmooth,b,xNum,energyNum,lamda); c=1/(sqrt(2*pi*.37)); d=2*.37^2; dataToSmooth = dataToSmooth'; [energyNum numPositions] = size(dataToSmooth); dataSmoothed=[]; for k = 1:numPositions dataSmoothed_this = []; y = dataToSmooth(:,k); for i = 1:energyNum numerator = 0; denominator = 0; for j = 1:energyNum t = (lamda(i) - lamda(j))/b; numerator = numerator + y(j)*c*exp(-t^2/d); denominator = denominator + c*exp(-t^2/d); end dataSmoothed_this(i,1) = numerator/denominator; end dataSmoothed = [dataSmoothed dataSmoothed_this]; end

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dataSmoothed=dataSmoothed'; end

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B.2.3. Plot Spectra function [mathedData] = HYP3_ak_plotSpectra_v2(positionTopLeft,positionBottomRight,xyzbinmatrix ,BK,b,xNum,energyNum,lamda,lampDataSmoothedBinnedMtx,darkDataSmoothedBi nnedMtx,rangeHigh,rangeLow,baseFileName);

%% Variables - COPY and PASTE into command window (modify as needed) rangeLow = 500; rangeHigh = 900; positionTopLeft = [33 125]; positionBottomRight = [35 127];

%% CODE STARTS HERE disp(strcat('Start HYP4_ak_plotSpectra ---->', datestr(now))) [dataBinned totalPixelsBinned positionsToBin] = PS_a_binPositions(positionTopLeft,positionBottomRight,xyzbinmatrix); disp(strcat('End PS_a_binPositions ---->', datestr(now))) backgroundBinned_Smoothed = PS_b_binnedBKaverage(totalPixelsBinned,xyzbinmatrix,BK,b,xNum,energyNum ,lamda); disp(strcat('End PS_b_binnedBKaverage ---->', datestr(now))) [mathedData mathedData_Smoothed mathedData_Smoothed_Norm yPeaks lamdaPeaks indexHigh indexLow]= PS_c_matrixMath(dataBinned, backgroundBinned_Smoothed,lampDataSmoothedBinnedMtx,darkDataSmoothedBin nedMtx,positionsToBin,b,xNum,energyNum,lamda,rangeHigh,rangeLow); disp(strcat('End PS_c_matrixMath ---->', datestr(now))) PS_d_plotSingleParticle(mathedData_Smoothed,lamda,indexLow,indexHigh,yP eaks,lamdaPeaks,baseFileName,positionTopLeft,positionBottomRight,mathed Data); disp(strcat('End PS_d_plotSingleParticle ---->', datestr(now))) disp(strcat('End HYP4_ak_plotSpectra ---->', datestr(now))) end

function [dataBinned totalPixelsBinned positionsToBin] = PS_a_binPositions(positionTopLeft,positionBottomRight,xyzbinmatrix); positionsToBin = []; dataToBin = []; numStepBin = [positionTopLeft(1):positionBottomRight(1)]; xNumBin = [positionTopLeft(2):positionBottomRight(2)]; for p = 1:length(numStepBin) numStepPosition_this = numStepBin(p); for q = 1:length(xNumBin) xNumPosition_this = xNumBin(q); positionsToBin_this = [numStepPosition_this xNumPosition_this]; positionsToBin = [positionsToBin; positionsToBin_this]; dataToBin_this = xyzbinmatrix(xNumPosition_this,numStepPosition_this,:); dataToBin = [dataToBin; dataToBin_this]; end end dataToBin = squeeze(dataToBin); dataBinned = sum(dataToBin,1); 150

totalPixelsBinned = length(positionsToBin); end

function backgroundBinned_Smoothed = PS_b_binnedBKaverage(totalPixelsBinned,xyzbinmatrix,BK,b,xNum,energyNum ,lamda); positionsToAvg_BK = cell2mat({BK.Position}'); backgroundToAvg = []; for h = 1:length(positionsToAvg_BK) backgroundToAvg_this = squeeze(xyzbinmatrix(positionsToAvg_BK(h,2),positionsToAvg_BK(h,1),:)); backgroundToAvg_this = backgroundToAvg_this'; backgroundToAvg = [backgroundToAvg; backgroundToAvg_this]; end backgroundAvg = mean(backgroundToAvg,1); backgroundToBin = zeros(totalPixelsBinned,length(backgroundAvg)); for j = 1:totalPixelsBinned backgroundToBin(j,:)=backgroundAvg; end backgroundBinned = sum(backgroundToBin,1); backgroundBinned_Smoothed = akSmooth(backgroundBinned,b,xNum,energyNum,lamda); end

function [mathedData mathedData_Smoothed mathedData_Smoothed_Norm yPeaks lamdaPeaks indexHigh indexLow]= PS_c_matrixMath(dataBinned, backgroundBinned_Smoothed,lampDataSmoothedBinnedMtx,darkDataSmoothedBin nedMtx,positionsToBin,b,xNum,energyNum,lamda,rangeHigh,rangeLow); lampData = squeeze(lampDataSmoothedBinnedMtx(positionsToBin(2),positionsToBin(1),: )); darkData = squeeze(darkDataSmoothedBinnedMtx(positionsToBin(2),positionsToBin(1),: )); numerator = dataBinned-backgroundBinned_Smoothed; denominator = lampData'-darkData'; mathedData = numerator./denominator; mathedData_Smoothed = akSmooth(mathedData,b,xNum,energyNum,lamda); [mathedData_Smoothed_Norm yPeaks lamdaPeaks indexHigh indexLow] = akNormPeaks(mathedData,mathedData_Smoothed,rangeHigh,rangeLow,lamda); end

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function PS_d_plotSingleParticle(mathedData_Smoothed,lamda,indexLow,indexHigh,yP eaks,lamdaPeaks,baseFileName,positionTopLeft,positionBottomRight,mathed Data); figure() plot(lamda,mathedData) title({baseFileName,... strcat('FROM_',num2str(positionTopLeft(1)),',',num2str(positionTopLeft( 2))),... strcat('TO_',num2str(positionBottomRight(1)),',',num2str(positionBottom Right(2)))},... 'interpreter','none') xlabel('Wavelength (nm)', 'fontsize', 20); ylabel('Scattering (a.u.)', 'fontsize', 20); set(gca, 'fontsize', 15); axis([lamda(indexLow) lamda(indexHigh) 0 max(yPeaks)*1.2]) legend(strcat(num2str(round(lamdaPeaks,1)),' nm')) cd ../ mkdir(strcat(num2str(pwd),'/Matlab output')); cd (strcat(num2str(pwd),'/Matlab output')); dataToSave = [lamda' mathedData_Smoothed']; titleHere = strcat(baseFileName,'_',...

'FROM_',num2str(positionTopLeft(1)),',',num2str(positionTopLeft(2)),'_' ,...

'TO_',num2str(positionBottomRight(1)),',',num2str(positionBottomRight(2 ))); print('-djpeg',strcat(titleHere,'.jpg')); savefig(strcat(titleHere,'.fig')); csvwrite(strcat(titleHere,'.csv'),dataToSave) cd ../ cd (strcat(num2str(pwd),'/Matlab')); end

denominator = 0; for j = 1:energyNum t = (lamda(i) - lamda(j))/b; numerator = numerator + y(j)*c*exp(-t^2/d); denominator = denominator + c*exp(-t^2/d); end dataSmoothed_this(i,1) = numerator/denominator; end dataSmoothed = [dataSmoothed dataSmoothed_this]; end dataSmoothed=dataSmoothed'; end

function [dataNormalized yPeaks lamdaPeaks indexHigh indexLow] = akNormPeaks(dataToNormalize,dataToNormalize_smoothed,rangeHigh,rangeLow ,lamda); indexHigh = find(lamda>(rangeHigh-1) & lamda<(rangeHigh+1)); indexHigh = indexHigh(1); indexLow = find(lamda>(rangeLow-1) & lamda<(rangeLow+1)); indexLow = indexLow(1); dataNormalized=[]; yPeaks=[]; lamdaPeaks=[];

[numberPositions energyNum] = size(dataToNormalize); for d = 1:numberPositions dataToNormalize_smoothed_this = dataToNormalize_smoothed(d,:); [yPeak_this, xPeak_temp] = max(dataToNormalize_smoothed_this(indexLow:indexHigh)); xPeak = indexLow+xPeak_temp-1; lamdaPeak_this = lamda(xPeak);

dataToNormalize_this = dataToNormalize(d,:); dataNormalized_this = dataToNormalize_this/yPeak_this;

dataNormalized=[dataNormalized dataNormalized_this']; yPeaks=[yPeaks yPeak_this]; lamdaPeaks=[lamdaPeaks lamdaPeak_this]; end dataNormalized=dataNormalized'; end

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B.3. Time-Lapse

B.3.1. Make Spectrum Image function [mymtx lamda row1 column1 rowlast columnlast] = TL1_makeSpectrumImage_v4(exptName,tNum,xNum,energyNum);

%% Variables - COPY and PASTE into command window (modify as needed) exptName = 'expt78h'; tNum = 68; xNum = 1023; energyNum = 647;

%% CODE STARTS HERE

[mymtx lamda row1 column1 rowlast columnlast ] = MSI_a_importTimeLapseData(exptName,tNum,xNum,energyNum); disp('End MSI_a_importTimeLapseData') MSI_b_plotIntegratedSpectrumImage(mymtx, exptName); disp('End_MSI_b_plotIntegratedSpectrumImage') end

function [mymtx lamda row1 column1 rowlast columnlast fileName] = MSI_a_importTimeLapseData(exptName,tNum,xNum,energyNum); mymtx = zeros(xNum, tNum, energyNum); row1 = 4; column1 = 1; rowlast = row1+xNum-1; columnlast = column1+energyNum-1; fileName = findFilenames(exptName); cd ../ cd(strcat(num2str(pwd),'/CSV')) for i = 1:tNum; data = csvread(fileName{i,1},row1,column1,[row1, column1, rowlast, columnlast]); mymtx(:,i,:) = data(:,:); %data coming in as data(energy,x)... i think... end lamda = csvread(fileName{1,1},row1-1,column1,[row1-1,column1, row1- 1,columnlast]); cd ../ cd(strcat(num2str(pwd),'/Matlab'))

%% Run function findFilenames function fileName = findFilenames(exptName);

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cd ../ cd(strcat(num2str(pwd),'/CSV'))

MyFolderInfo = dir; fileName_all = cellstr({MyFolderInfo.name}'); fileName = []; for n = 1:length(fileName_all) k = findstr(char(fileName_all(n)), char(exptName)); TF = isempty(k);

p1 = findstr(char(fileName_all(n)),'lamp'); p2 = findstr(char(fileName_all(n)),'LAMP'); p3 = findstr(char(fileName_all(n)),'Lamp'); TF_lamp1 = isempty(p1); TF_lamp2 = isempty(p2); TF_lamp3 = isempty(p3);

q1 = findstr(char(fileName_all(n)),'dark'); q2 = findstr(char(fileName_all(n)),'DARK'); q3 = findstr(char(fileName_all(n)),'Dark'); TF_dark1 = isempty(q1); TF_dark2 = isempty(q2); TF_dark3 = isempty(q3);

if all([TF_lamp1 ~= 0, ... TF_lamp2 ~= 0, ... TF_lamp3 ~= 0, ... TF_dark1 ~= 0, ... TF_dark2 ~= 0, ... TF_dark3 ~= 0, ... TF == 0]) fileName = [fileName fileName_all(n)]; end end fileName = fileName'; cd ../ cd(strcat(num2str(pwd),'/Matlab')) end end

function MSI_b_plotIntegratedSpectrumImage(mymtx, exptName);

[xNum yNum energyNum] = size(mymtx); map = zeros(xNum, yNum); cmax=0; for x = 1:xNum for y = 1:yNum thisEnergySum = sum(mymtx(x,y,:)); map(x,y)=thisEnergySum; 155

if thisEnergySum > cmax cmax = thisEnergySum; end end end cmax = 0.4*cmax; % cmax = 3e7; imagesc(map); colormap jet % axis equal [x y z] = size(mymtx); axis([0 y 0 x]) colorbar % caxis([0 cmax]); xlabel('acquisition'); ylabel('position');

cd ../ fileName = strcat(exptName, '_TL_SI'); newLocation = 'Matlab plots'; mkdir(newLocation); cd(strcat(num2str(pwd),'/',newLocation)); savefig(strcat(fileName,'.fig')); print('-djpeg',strcat(fileName,'.jpg')); cd ../ cd(strcat(num2str(pwd),'/Matlab')); end

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B.3.2. Matrix Math function [bkgdAvgMtx lampDataSmoothedMtx darkDataSmoothedMtx mathedMtx] = TL2_matrixMath_v4(BK,b,mymtx,lamda,xNum,tNum,energyNum,fileNameLamp,fil eNameDark,row1, column1, rowlast, columnlast);

%% VARIABLES b = 10; fileNameLamp = 'expt78h_20170912_timeLapse_lamp.csv'; fileNameDark = 'expt78h_20170912_timeLapse_dark.csv'; % BK needs to be imported from SI

%% CODE STARTS HERE [bkgdAvgMtx] = MM_a_backgroundAverage(BK,b,mymtx,lamda,xNum,tNum,energyNum); disp('End MM_a_backgroundAverage') [lampDataSmoothedMtx darkDataSmoothedMtx] = MM_b_LampDark(fileNameLamp,fileNameDark,row1, column1, rowlast, columnlast,lamda,xNum,b,energyNum,tNum); disp('End MM_b_LampDark') [mathedMtx] = MM_c_mathMtx(mymtx,bkgdAvgMtx,lampDataSmoothedMtx,darkDataSmoothedMtx); disp('End MM_c_mathMtx') end

function [bkgdAvgMtx] = MM_a_backgroundAverage(BK,b,mymtx,lamda,xNum,tNum,energyNum); bkgdAvgMtx_timeDep = []; for n = 1:tNum bkgdMtx_temp = cell2mat({BK.Position}'); bkgdMtx(:,1) = bkgdMtx_temp(:,2); bkgdMtx(:,2) = n;

bkgdData = []; for a=1:length(bkgdMtx) xVal = bkgdMtx(a,1); yVal = bkgdMtx(a,2); thisData = mymtx(xVal,yVal,:); bkgdData = [bkgdData thisData]; end

bkgdAvg = permute(squeeze(mean(bkgdData,2)),[2 1]); bkgdAvgSmoothed = akSmooth(bkgdAvg,b,xNum,energyNum,lamda); bkgdAvgMtx_timeDep = [bkgdAvgMtx_timeDep; bkgdAvgSmoothed]; end for m = 1:xNum bkgdAvgMtx(m,:,:)=bkgdAvgMtx_timeDep; end end

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function [lampDataSmoothedMtx darkDataSmoothedMtx] = MM_b_LampDark(fileNameLamp,fileNameDark,row1, column1, rowlast, columnlast,lamda,xNum,b,energyNum,tNum); cd ../ cd(strcat(num2str(pwd),'/CSV')); lampData = csvread(fileNameLamp,row1,column1,[row1, column1, rowlast, columnlast]); darkData = csvread(fileNameDark,row1,column1,[row1, column1, rowlast, columnlast]); cd ../ cd(strcat(num2str(pwd),'/Matlab')); lampDataSmoothed = akSmooth(lampData,b,xNum,energyNum,lamda); lampDataSmoothedMtx = repmat(lampDataSmoothed,[1 1 tNum]); lampDataSmoothedMtx = permute(lampDataSmoothedMtx,[1 3 2]); darkDataSmoothed = akSmooth(darkData,b,xNum,energyNum,lamda); darkDataSmoothedMtx = repmat(darkDataSmoothed,[1 1 tNum]); darkDataSmoothedMtx = permute(darkDataSmoothedMtx,[1 3 2]); end

function [mathedMtx] = MM_c_mathMtx(mymtx,bkgdAvgMtx,lampDataSmoothedMtx,darkDataSmoothedMtx); numerator = mymtx - bkgdAvgMtx; denominator = lampDataSmoothedMtx - darkDataSmoothedMtx; mathedMtx = numerator./denominator; end

dataSmoothed_this(i,1) = numerator/denominator; end dataSmoothed = [dataSmoothed dataSmoothed_this]; end dataSmoothed=dataSmoothed'; end

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B.3.3. Check Locations function [data2bePlotted_Smoothed_CL data2bePlotted_SmoothedNormalized_CL yPeaks_CL lamdaPeaks_CL] = TL3_checkLocations_v4(xNum,tNum,energyNum,PM,mathedMtx,b,lamda,rangeHig h,rangeLow,exptName); %% VARIABLES rangeLow = 500; rangeHigh = 800; % import PM

%% CODE STARTS HERE [positionmatrix] = CL_a_positionMtx(PM); disp('End_CL_a_positionMtx') [data2bePlotted]=CL_b_extractData(positionmatrix,mathedMtx); disp('End CL_b_extractData') [data2bePlotted_Smoothed_CL data2bePlotted_SmoothedNormalized_CL yPeaks_CL lamdaPeaks_CL] = CL_c_plotData(data2bePlotted,positionmatrix,b,xNum,energyNum,lamda,rang eHigh,rangeLow,exptName); disp('End CL_c_plotData') end

function [positionmatrix] = CL_a_positionMtx(PM); positionmatrix_temp =cell2mat({PM.Position}'); positionmatrix(:,1) = positionmatrix_temp(:,2); positionmatrix(:,2) = 4; end

function [data2bePlotted]=CL_b_extractData(positionmatrix,mathedMtx); data2bePlotted=[]; for a=1:length(positionmatrix) x = positionmatrix(a,1); y = positionmatrix(a,2); data2bePlotted_this = mathedMtx(x,y,:); data2bePlotted = [data2bePlotted data2bePlotted_this]; end data2bePlotted = squeeze(data2bePlotted); end

function [data2bePlotted_Smoothed_CL data2bePlotted_SmoothedNormalized_CL yPeaks_CL lamdaPeaks_CL] = CL_c_plotData(data2bePlotted,positionmatrix,b,xNum,energyNum,lamda,rang eHigh,rangeLow,exptName); data2bePlotted_Smoothed_CL = akSmooth(data2bePlotted,b,xNum,energyNum,lamda); [data2bePlotted_SmoothedNormalized_CL yPeaks_CL lamdaPeaks_CL indexHigh indexLow] = akNormPeaks(data2bePlotted_Smoothed_CL,rangeHigh,rangeLow,lamda); 160

legendNames=cell(length(positionmatrix),1); for a=1:length(positionmatrix) plot(lamda,data2bePlotted_Smoothed_CL(a,:)); legendNames{a}=num2str(positionmatrix(a)); hold on end titleHere = strcat(exptName,'_all positions of interest'); title(titleHere,'interpreter','none'); xlabel('Wavelength (nm)', 'fontsize', 20); ylabel('Scattering (a.u.)', 'fontsize', 20); set(gca, 'fontsize', 15); legend(legendNames); axis([lamda(indexLow) lamda(indexHigh) 0 max(yPeaks_CL)*1.2]) figure() for a=1:length(positionmatrix) plot(lamda,data2bePlotted_SmoothedNormalized_CL(a,:)); legendNames{a}=num2str(positionmatrix(a)); hold on end titleHere = strcat(exptName,'_all positions of interest_normalized'); title(titleHere,'interpreter','none'); xlabel('Wavelength (nm)', 'fontsize', 20); ylabel('Scattering (a.u.)', 'fontsize', 20); set(gca, 'fontsize', 15); legend(legendNames); axis([lamda(indexLow) lamda(indexHigh) 0 1.2]) cd ../ mkdir(strcat(num2str(pwd),'/Matlab plots')); cd(strcat(num2str(pwd),'/Matlab plots')); print('-djpeg',strcat(exptName, '_all positions of interest.jpg')); savefig(strcat(exptName, '_all positions of interest.fig')); cd ../ cd(strcat(num2str(pwd),'/Matlab')); end

numerator = 0; denominator = 0; for j = 1:energyNum t = (lamda(i) - lamda(j))/b; numerator = numerator + y(j)*c*exp(-t^2/d); denominator = denominator + c*exp(-t^2/d); end dataSmoothed_this(i,1) = numerator/denominator; end dataSmoothed = [dataSmoothed dataSmoothed_this]; end dataSmoothed=dataSmoothed'; end

function [dataNormalized yPeaks lamdaPeaks indexHigh indexLow] = akNormPeaks(dataToNormalize,rangeHigh,rangeLow,lamda); indexHigh = find(lamda>(rangeHigh-1) & lamda<(rangeHigh+1)); indexHigh = indexHigh(1); indexLow = find(lamda>(rangeLow-1) & lamda<(rangeLow+1)); indexLow = indexLow(1); dataNormalized=[]; yPeaks=[]; lamdaPeaks=[];

[numberPositions energyNum] = size(dataToNormalize); for d = 1:numberPositions dataToNormalize_this = dataToNormalize(d,:); [yPeak_this, xPeak_temp] = max(dataToNormalize_this(indexLow:indexHigh)); xPeak = indexLow+xPeak_temp-1; lamdaPeak_this = lamda(xPeak); dataNormalized_this = dataToNormalize_this/yPeak_this;

dataNormalized=[dataNormalized dataNormalized_this']; yPeaks=[yPeaks yPeak_this]; lamdaPeaks=[lamdaPeaks lamdaPeak_this]; end dataNormalized=dataNormalized'; end

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B.3.4 Plot One Location function [data2bePlotted_Smoothed data2bePlotted_SmoothedNormalized yPeaks lamdaPeaks position] = TL4_plot1Location_v5(position,tNumStart,tNumEnd,xNum,tNum,energyNum,lam da,rangeHigh,rangeLow,exptName,mathedMtx,b,plotColor); %% VARIABLES position = [971:975]; tNumStart = 51; tNumEnd = 68; rangeLow = 500; rangeHigh = 900; plotColor='b';

%% CODE STARTS HERE [positionmatrix_Time positionmatrix_Pos] = P1L_a_positionMatrix(position,tNumStart,tNumEnd); disp('End_P1L_a_positionMatrix') [data2bePlotted] = P1L_b_extractData(positionmatrix_Time,positionmatrix_Pos,position,mathe dMtx); disp('End_P1L_b_extractData') [data2bePlotted_Smoothed data2bePlotted_SmoothedNormalized yPeaks lamdaPeaks] = P1L_c_plotData(positionmatrix_Time,b,xNum,energyNum,lamda,rangeHigh,ran geLow,exptName,position,data2bePlotted,plotColor); disp('End_P1L_c_plotData') P1L_d_writeCSV(data2bePlotted_SmoothedNormalized,lamda,position,exptNam e); disp('End P1L_d_writeCSV') end

function [positionmatrix_Time positionmatrix_Pos] = P1L_a_positionMatrix(position,tNumStart,tNumEnd); positionmatrix_Time(:,1) = [tNumStart:tNumEnd]; positionmatrix_Pos = zeros(length(positionmatrix_Time),length(position)); for p = 1:length(position); positionmatrix_Pos(:,p) = position(p); end end

function [data2bePlotted] = P1L_b_extractData(positionmatrix_Time,positionmatrix_Pos,position,mathe dMtx); data2bePlotted=[]; for f = 1:length(positionmatrix_Time) extractedData = []; for e = 1:length(position) x = positionmatrix_Pos(f,e); y = positionmatrix_Time(f); extractedData_this = mathedMtx(x,y,:); extractedData_this = squeeze(extractedData_this); extractedData = [extractedData extractedData_this]; end 163

if length(position) == 1; extractedData = extractedData'; else extractedData = sum(extractedData'); end data2bePlotted = [data2bePlotted; extractedData]; end end

function [data2bePlotted_Smoothed data2bePlotted_SmoothedNormalized yPeaks lamdaPeaks] = P1L_c_plotData(positionmatrix_Time,b,xNum,energyNum,lamda,rangeHigh,ran geLow,exptName,position,data2bePlotted,plotColor); data2bePlotted_Smoothed = akSmooth(data2bePlotted,b,xNum,energyNum,lamda); [data2bePlotted_SmoothedNormalized yPeaks lamdaPeaks indexHigh indexLow] = akNormPeaks(data2bePlotted_Smoothed,data2bePlotted_Smoothed,rangeHigh,r angeLow,lamda); figure() for a=1:length(positionmatrix_Time) plot(lamda,data2bePlotted(a,:)); hold on end %position = position(1); cd ../ mkdir(strcat(num2str(pwd),'/Matlab plots/position_',num2str(position(1)),'-',num2str(position(end)))); cd(strcat(num2str(pwd),'/Matlab plots/position_',num2str(position(1)),'-',num2str(position(end)))); titleHere = strcat(exptName,'_position_',num2str(position(1)),'- ',num2str(position(end))); title(titleHere,'interpreter','none'); xlabel('Wavelength (nm)', 'fontsize', 20); ylabel('Scattering (a.u.)', 'fontsize', 20); set(gca, 'fontsize', 15); axis([lamda(indexLow) lamda(indexHigh) 0 max(yPeaks)*1.2]) print('-djpeg',strcat(titleHere,'.jpg')); savefig(strcat(titleHere,'.fig')); figure() for a=1:length(positionmatrix_Time) plot(lamda,data2bePlotted_SmoothedNormalized(a,:)); hold on end %position = position(1); titleHere = strcat(exptName,'_position_',num2str(position(1)),'- ',num2str(position(end)),'_normalized'); title(titleHere,'interpreter','none'); xlabel('Wavelength (nm)', 'fontsize', 20); ylabel('Scattering (a.u.)', 'fontsize', 20); set(gca, 'fontsize', 15); axis([lamda(indexLow) lamda(indexHigh) 0 1.2]) 164

print('-djpeg',strcat(titleHere,'.jpg')); savefig(strcat(titleHere,'.fig'));

figure() scatter([1:length(lamdaPeaks)],lamdaPeaks,plotColor,'filled'); titleHere = strcat(exptName,'_position_',num2str(position(1)),'- ',num2str(position(end))); plotTitle = strcat(titleHere,'_LSPR peak wavelength vs acquisition'); title(plotTitle,'interpreter','none'); xlabel('Acquisition #', 'fontsize', 20); ylabel('LSPR Peak Wavelength (nm)', 'fontsize', 20); set(gca,'FontSize', 15); print('-djpeg', strcat(plotTitle,'.jpg')); savefig(strcat(plotTitle,'.fig')); cd ../ cd ../ cd(strcat(num2str(pwd),'/Matlab')); end

function P1L_d_writeCSV(data2bePlotted_SmoothedNormalized,lamda,position,exptNam e); dataToSave = [lamda' data2bePlotted_SmoothedNormalized']; cd ../ mkdir(strcat(num2str(pwd),'/Matlab plots/position_',num2str(position(1)),'-',num2str(position(end)))); cd(strcat(num2str(pwd),'/Matlab plots/position_',num2str(position(1)),'-',num2str(position(end)))); filename = strcat(exptName,'_position_',num2str(position(1)),'- ',num2str(position(end)),'_dataForPeakFit.csv'); csvwrite(filename,dataToSave); cd ../ cd ../ cd(strcat(num2str(pwd),'/Matlab')) end

dataSmoothed_this = []; y = dataToSmooth(:,k); for i = 1:energyNum numerator = 0; denominator = 0; for j = 1:energyNum t = (lamda(i) - lamda(j))/b; numerator = numerator + y(j)*c*exp(-t^2/d); denominator = denominator + c*exp(-t^2/d); end dataSmoothed_this(i,1) = numerator/denominator; end dataSmoothed = [dataSmoothed dataSmoothed_this]; end dataSmoothed=dataSmoothed'; end

dataToNormalize_this = dataToNormalize(d,:); dataNormalized_this = dataToNormalize_this/yPeak_this;

dataNormalized=[dataNormalized dataNormalized_this']; yPeaks=[yPeaks yPeak_this]; lamdaPeaks=[lamdaPeaks lamdaPeak_this]; end dataNormalized=dataNormalized'; end