Iron doped ZnO (Fe-ZnO) nanoparticles were synthesized using two techniques that are economical as well as scalable to yield tunable properties of nanoparticles for facilitating down conversion in an absorbing layer of a solar cell. To evaluate the suitability of Fe-ZnO nanoparticles prepared by two deposition methods, we present a comparison of optical, electrical, and structural properties of Fe-ZnO using several experimental techniques. Structural properties were analyzed using transmission electron microscopy and x-ray diffraction spectroscopy (XRD) with Rietveld analysis for extracting information on compositional variations with Fe doping. The chemical composition of nanoparticles was analyzed through X-ray photoelectron spectroscopy (XPS). The optical properties of nanoparticles were studied using photoluminescence and UV-Vis absorption spectroscopy. In addition, fluorescence lifetime measurement was also performed to study the changes in an exponential decay of lifetimes. The electrical transport properties of Fe-ZnO were analyzed by impedance spectroscopy. Our studies indicate that ethanol as a solvent in a microwave method would produce smaller nanoparticles up to the size of 11 nm. In contrast, the precipitation method produces secondary phases of Fe2O3 beyond 5% doping. In addition, our studies show that the optical and electrical properties of resulting Fe-ZnO nanoparticles depend on the particle sizes and the synthesis techniques used. These new results provide insight into the role of solvents in fabricating Fe-ZnO nanoparticles by precipitation and microwave methods for photovoltaic and other applications.
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An Open Source, Iterative Dual-Tree Wavelet Background Subtraction Method Extended from Automated Diffraction Pattern Analysis to Optical Spectroscopy
Background subtraction is a general problem in spectroscopy often addressed with application-specific techniques, or methods that introduce a variety of implementation barriers such as having to specify peak-free regions of the spectrum. An iterative dual-tree complex wavelet transform-based background subtraction method (DTCWT-IA) was recently developed for the analysis of ultrafast electron diffraction patterns. The method was designed to require minimal user intervention, to support streamlined analysis of many diffraction patterns with complex overlapping peaks and time-varying backgrounds, and is implemented in an open-source computer program. We examined the performance of DTCWT-IA for the analysis of spectra acquired by a range of optical spectroscopies including ultraviolet–visible spectroscopy (UV–Vis), X-ray photoelectron spectroscopy (XPS), and surface-enhanced Raman spectroscopy (SERS). A key benefit of the method is that the user need not specify regions of the spectrum where no peaks are expected to occur. SER spectra were used to investigate the robustness of DTCWT-IA to signal-to-noise levels in the spectrum and to user operation, specifically to two of the algorithm parameter settings: decomposition level and iteration number. The single, general DTCWT-IA implementation performs well in comparison to the different conventional approaches to background subtraction for UV–Vis, XPS, and SERS, while requiring minimal input. The method thus holds the same potential for optical spectroscopy as for ultrafast electron diffraction, namely streamlined analysis of spectra with complex distributions of peaks and varying signal levels, thus supporting real-time spectral analysis or the analysis of data acquired from different sources.
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- Award ID(s):
- 1655221
- NSF-PAR ID:
- 10158826
- Date Published:
- Journal Name:
- Applied Spectroscopy
- Volume:
- 73
- Issue:
- 12
- ISSN:
- 0003-7028
- Page Range / eLocation ID:
- 1370 to 1379
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Two-dimensional (2D) ternary materials recently generated interest in optoelectronics and energy-related applications, alongside their binary counterparts. To date, only a few naturally occurring layered 2D ternary materials have been explored. The plethora of benefits owed to reduced dimensionality prompted exploration of expanding non-layered ternary chalcogenides into the 2D realm. This work presents a templating method that uses 2D transition metal dichalcogenides as initiators to be converted into the corresponding ternary chalcogenide upon addition of copper, via a solution-phase synthesis, conducted in high boiling point solvents. The process starts with preparation of VSe2nanosheets, which are next converted into Cu3VSe4sulvanite nanosheets (NSs) which retain the 2D geometry while presenting an X-ray diffraction pattern identical with the one for the bulk Cu3VSe4. Both the scanning electron microscopy and transmission microscopy electron microscopy show the presence of quasi-2D morphology. Recent studies of the sulfur-containing sulvanite Cu3VS4highlight the presence of an intermediate bandgap, associated with enhanced photovoltaic (PV) performance. The Cu3VSe4nanosheets reported herein exhibit multiple UV–Vis absorption peaks, related to the intermediate bandgaps similar to Cu3VS4and Cu3VSe4nanocrystals. To test the potential of Cu3VSe4NSs as an absorber for solar photovoltaic devices, Cu3VSe4NSs thin-films deposited on FTO were subjected to photoelectrochemical testing, showing p-type behavior and stable photocurrents of up to ~ 0.036 mA/cm2. The photocurrent shows a ninefold increase in comparison to reported performance of Cu3VSe4nanocrystals. This proves that quasi-2D sulvanite nanosheets are amenable to thin-film deposition and could show superior PV performance in comparison to nanocrystal thin-films. The obtained electrical impedance spectroscopy signal of the Cu3VSe4 NSs-FTO based electrochemical cell fits an equivalent circuit with the circuit elements of solution resistance (Rs), charge-transfer resistance (Rct), double-layer capacitance (Cdl), and Warburg impedance (W). The estimated charge transfer resistance value of 300 Ω cm2obtained from the Nyquist plot provides an insight into the rate of charge transfer on the electrode/electrolyte interface.
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This dataset contains raw data, processed data, and the codes used for data processing in our manuscript from our Fourier-transform infrared (FTIR) spectroscopy, Nuclear magnetic resonance (NMR), Raman spectroscopy, and X-ray diffraction (XRD) experiments. The data and codes for the fits of our unpolarized Raman spectra to polypeptide spectra is also included. The following explains the folder structure of the data provided in this dataset, which is also explained in the file ReadMe.txt. Browsing the data in Tree view is recommended. Folder contents Codes Raman Data Processing: The MATLAB script file RamanDecomposition.m contains the code to decompose the sub-peaks across different polarized Raman spectra (XX, XZ, ZX, ZZ, and YY), considering a set of pre-determined restrictions. The helper functions used in RamanDecomposition.m are included in the Helpers folder. RamanDecomposition.pdf is a PDF printout of the MATLAB code and output. P Value Simulation: 31_helix.ipynb and a_helix.ipynb: These two Jupyter Notebook files contain the intrinsic P value simulation for the 31-helix and alpha-helix structures. The simulation results were used to prepare Supplementary Table 4. See more details in the comments contained. Vector.py, Atom.py, Amino.py, and Helpers.py: These python files contains the class definitions used in 31_helix.ipynb and a_helix.ipynb. See more details in the comments contained. FTIR FTIR Raw Transmission.opj: This Origin data file contains the raw transmission data measured on single silk strand and used for FTIR spectra analysis. FTIR Deconvoluted Oscillators.opj: This Origin data file was generated from the data contained in the previous file using W-VASE software from J. A. Woollam, Inc. FTIR Unpolarized MultiStrand Raw Transmission.opj: This Origin data file contains the raw transmission data measured on multiple silk strands. The datasets contained in the first two files above were used to plot Figure 2a-b and the FTIR data points in Figure 4a, and Supplementary Figure 6. The datasets contained in the third file above were used to plot Supplementary Figure 3a. The datasets contained in the first two files above were used to plot Figure 2a-b, FTIR data points in Figure 4a, and Supplementary Figure 6. NMR Raw data files of the 13C MAS NMR spectra: ascii-spec_CP.txt: cross-polarized spectrum ascii-spec_DP.txt: direct-polarized spectrum Data is in ASCII format (comma separated values) using the following columns: Data point number Intensity Frequency [Hz] Frequency [ppm] Polypeptide Spectrum Fits MATLAB scripts (.m files) and Helpers: The MATLAB script file Raman_Fitting_Process_Part_1.m and Raman_Fitting_Process_Part_2.m contains the step-by-step instructions to perform the fitting process of our calculated unpolarized Raman spectrum, using digitized model polypeptide Raman spectra. The Helper folder contains two helper functions used by the above scripts. See the scripts for further instruction and information. Data aPA.csv, bPA.csv, GlyI.csv, GlyII.csv files: These csv files contain the digitized Raman spectra of poly-alanine, beta-alanine, poly-glycine-I, and poly-glycine-II. Raman_Exp_Data.mat: This MATLAB data file contains the processed, polarized Raman spectra obtained from our experiments. Variable freq is the wavenumber information of each collected spectrum. The variables xx, yy, zz, xz, zx represent the polarized Raman spectra collected. These variables are used to calculate the unpolarized Raman spectrum in Raman_Fitting_Process_Part_2.m. See the scripts for further instruction and information. Raman Raman Raw Data.mat: This MATLAB data file contains all the raw data used for Raman spectra analysis. All variables are of MATLAB structure data type. Each variable has fields called Freq and Raw, with Freq contains the wavenumber information of the measured spectra and Raw contains 5 measured Raman signal strengths. Variable XX, XZ, ZX, ZZ, and YY were used to plot and sub-peak analysis for Figure 2c-d, Raman data points in Figure 4a, Figure 5b, Supplementary Figure 2, and Supplementary Figure 7. Variable WideRange was used to plot and identify the peaks for Supplementary Figure 3b. X-Ray X-Ray.mat: This MATLAB data file contains the raw X-ray data used for the diffraction analysis in Supplementary Figure 5.more » « less
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Surface-enhanced Raman scattering (SERS) is a sensitive analytical technique capable of magnifying the vibrational intensity of molecules adsorbed onto the surface of metallic nanostructures. Various solution-based SERS-active metallic nanostructures have been designed to generate substantial SERS signal enhancements. However, most of these SERS substrates rely on the chemical aggregation of metallic nanostructures to create strong signals. While this can induce high SERS intensities through plasmonic coupling, most chemically aggregated assemblies suffer from poor signal reproducibility and reduced long-term stability. To overcome these issues, here we report for the first time the synthesis of gold core–satellite nanoparticles (CSNPs) for robust SERS signal generation. The novel CSNP assemblies consist of a 30 nm spherical gold core linked to 18 nm satellite particles via linear heterobifunctional thiol–amine terminated PEG chains. We explore the effects that the varying chain lengths have on SERS hot-spot generation, signal reproducibility and long-term activity. The chain length was varied by using PEGs with different molecular weights (1000 Da, 2000 Da, and 3500 Da). The CSNPs were characterized via UV-Vis spectrophotometry, transmission electron microscopy (TEM), ζ -potential measurements, and lastly SERS measurements. The versatility of the synthesized SERS-active CSNPs was revealed through characterization of optical stability and SERS enhancement at 0, 1, 3, 5, 7 and 14 days.more » « less
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Abstract Raman spectroscopy is widely used to identify mineral and fluid inclusions in host crystals, as well as to calculate pressure-temperature (P-T) conditions with mineral inclusion elastic thermobarometry, for example quartz-in-garnet barometry (QuiG) and zircon-in-garnet thermometry (ZiG). For thermobarometric applications, P-T precision and accuracy depend crucially on the reproducibility of Raman peak position measurements. In this study, we monitored long-term instrument stability and varied analytical parameters to quantify peak position reproducibility for Raman spectra from quartz and zircon inclusions and reference crystals. Our ultimate goal was to determine the reproducibility of calculated inclusion pressures (“Pinc”) and entrapment pressures (“Ptrap”) or temperatures (“Ttrap”) by quantifying diverse analytical errors, as well as to identify optimal measurement conditions and provide a baseline for interlaboratory comparisons. Most tests emphasized 442 nm (blue) and 532 nm (green) laser sources, although repeated analysis of a quartz inclusion in garnet additionally used a 632.8 nm (red) laser. Power density was varied from <1 to >100 mW and acquisition time from 3 to 270s. A correction is proposed to suppress interference on the ~206 cm–1 peak in quartz spectra by a broad nearby (~220 cm–1) peak in garnet spectra. Rapid peak drift up to 1 cm–1/h occurred after powering the laser source, followed by minimal drift (<0.2 cm–1/h) for several hours thereafter. However, abrupt shifts in peak positions as large as 2–3 cm–1 sometimes occurred within periods of minutes, commonly either positively or negatively correlated to changes in room temperature. An external Hg-emission line (fluorescent light) can be observed in spectra collected with the green laser and shows highly correlated but attenuated directional shifts compared to quartz and zircon peaks. Varying power density and acquisition time did not affect Raman peak positions of either quartz or zircon grains, possibly because power densities at the levels of inclusions were low. However, some zircon inclusions were damaged at higher power levels of the blue laser source, likely because of laser-induced heating. Using a combination of 1, 2, or 3 peak positions for the ~128, ~206, and ~464 cm–1 peaks in quartz to calculate Pinc and Ptrap showed that use of the blue laser source results in the most reproducible Ptrap values for all methods (0.59 to 0.68 GPa at an assumed temperature of 450 °C), with precisions for a single method as small as ±0.03 GPa (2σ). Using the green and red lasers, some methods of calculating Ptrap produce nearly identical estimates as the blue laser with similarly good precision (±0.02 GPa for green laser, ±0.03 GPa for red laser). However, using 1- and 2-peak methods to calculate Ptrap can yield values that range from 0.52 ± 0.06 to 0.93 ± 0.16 GPa for the green laser, and 0.53 ± 0.08 GPa to 1.00 ± 0.45 GPa for the red laser. Semiquantitative calculations for zircon, assuming a typical error of ±0.25 cm–1 in the position of the ~1008 cm–1 peak, imply reproducibility in temperature (at an assumed pressure) of approximately ±65 °C. For optimal applications to elastic thermobarometry, analysts should: (1) delay data collection approximately one hour after laser startup, or leave lasers on; (2) collect a Hg-emission line simultaneously with Raman spectra when using a green laser to correct for externally induced shifts in peak positions; (3) correct for garnet interference on the quartz 206 cm–1 peak; and either (4a) use a short wavelength (blue) laser for quartz and zircon crystals for P-T calculations, but use very low-laser power (<12 mW) to avoid overheating and damage or (4b) use either the intermediate wavelength (green; quartz and zircon) or long wavelength (red; zircon) laser for P-T calculations, but restrict calculations to specific methods. Implementation of our recommendations should optimize reproducibility for elastic geothermobarometry, especially QuiG barometry and ZiG thermometry.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/0003702819871330
