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Empirical Modeling of Electron Transport in Fe/Ti Layered Double Hydroxide Using Exponential, Gaussian and Mixed Gauss–Exponential Distribution

[Image: see text] Fe/Ti-layered double hydroxide (LDH) has been hydrothermally prepared and characterized using X-ray diffraction, scanning electron microscopy, atomic force microscopy, Fourier transform infrared spectroscopy, and UV–visible diffuse reflectance spectroscopy for evaluation of its str...

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Autores principales: Roy Chowdhury, Priyadarshi, Verma, Vivek, Medhi, Himani, Bhattacharyya, Krishna G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2019
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6648460/
https://www.ncbi.nlm.nih.gov/pubmed/31460158
http://dx.doi.org/10.1021/acsomega.9b01345
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author Roy Chowdhury, Priyadarshi
Verma, Vivek
Medhi, Himani
Bhattacharyya, Krishna G.
author_facet Roy Chowdhury, Priyadarshi
Verma, Vivek
Medhi, Himani
Bhattacharyya, Krishna G.
author_sort Roy Chowdhury, Priyadarshi
collection PubMed
description [Image: see text] Fe/Ti-layered double hydroxide (LDH) has been hydrothermally prepared and characterized using X-ray diffraction, scanning electron microscopy, atomic force microscopy, Fourier transform infrared spectroscopy, and UV–visible diffuse reflectance spectroscopy for evaluation of its structure, morphology, and optical properties. The purpose of doping Ti(4+) with Fe(3+) toward the synthesis of Fe/Ti LDH is to extend the absorption of the nanomaterial to longer wavelength, which is known to exhibit higher electron transport performance. To provide a practical realization, electron transport modeling across the band gap has been interpreted using exponential, Gaussian, and mixed Gauss–exponential distribution. The conduction band energy (E(C)) has been calculated by using the observed values of band gap (E(g)) and ξ-potential of the LDH. A detailed study has been undertaken to investigate the pattern of theoretical density of the LDH on the basis of unknown (E(C) = 0) and known (calculated) values of E(C). Fermi–Dirac statistics has been used extensively for estimating the occupancy probability of electron (e(–))–hole (h(+)) pair formation within the valence and conduction bands, respectively, with different temperatures, as well as for given energy levels. Monte Carlo simulations have also been performed to evaluate the suitability of the choice of the model, on the basis of the probability of availability of e(–)s within the conduction band. To provide a practical realization of the suggested models, electronic transition across the band gap of Fe/Ti LDH has been extensively investigated.
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spelling pubmed-66484602019-08-27 Empirical Modeling of Electron Transport in Fe/Ti Layered Double Hydroxide Using Exponential, Gaussian and Mixed Gauss–Exponential Distribution Roy Chowdhury, Priyadarshi Verma, Vivek Medhi, Himani Bhattacharyya, Krishna G. ACS Omega [Image: see text] Fe/Ti-layered double hydroxide (LDH) has been hydrothermally prepared and characterized using X-ray diffraction, scanning electron microscopy, atomic force microscopy, Fourier transform infrared spectroscopy, and UV–visible diffuse reflectance spectroscopy for evaluation of its structure, morphology, and optical properties. The purpose of doping Ti(4+) with Fe(3+) toward the synthesis of Fe/Ti LDH is to extend the absorption of the nanomaterial to longer wavelength, which is known to exhibit higher electron transport performance. To provide a practical realization, electron transport modeling across the band gap has been interpreted using exponential, Gaussian, and mixed Gauss–exponential distribution. The conduction band energy (E(C)) has been calculated by using the observed values of band gap (E(g)) and ξ-potential of the LDH. A detailed study has been undertaken to investigate the pattern of theoretical density of the LDH on the basis of unknown (E(C) = 0) and known (calculated) values of E(C). Fermi–Dirac statistics has been used extensively for estimating the occupancy probability of electron (e(–))–hole (h(+)) pair formation within the valence and conduction bands, respectively, with different temperatures, as well as for given energy levels. Monte Carlo simulations have also been performed to evaluate the suitability of the choice of the model, on the basis of the probability of availability of e(–)s within the conduction band. To provide a practical realization of the suggested models, electronic transition across the band gap of Fe/Ti LDH has been extensively investigated. American Chemical Society 2019-06-18 /pmc/articles/PMC6648460/ /pubmed/31460158 http://dx.doi.org/10.1021/acsomega.9b01345 Text en Copyright © 2019 American Chemical Society This is an open access article published under a Creative Commons Attribution (CC-BY) License (http://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html) , which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
spellingShingle Roy Chowdhury, Priyadarshi
Verma, Vivek
Medhi, Himani
Bhattacharyya, Krishna G.
Empirical Modeling of Electron Transport in Fe/Ti Layered Double Hydroxide Using Exponential, Gaussian and Mixed Gauss–Exponential Distribution
title Empirical Modeling of Electron Transport in Fe/Ti Layered Double Hydroxide Using Exponential, Gaussian and Mixed Gauss–Exponential Distribution
title_full Empirical Modeling of Electron Transport in Fe/Ti Layered Double Hydroxide Using Exponential, Gaussian and Mixed Gauss–Exponential Distribution
title_fullStr Empirical Modeling of Electron Transport in Fe/Ti Layered Double Hydroxide Using Exponential, Gaussian and Mixed Gauss–Exponential Distribution
title_full_unstemmed Empirical Modeling of Electron Transport in Fe/Ti Layered Double Hydroxide Using Exponential, Gaussian and Mixed Gauss–Exponential Distribution
title_short Empirical Modeling of Electron Transport in Fe/Ti Layered Double Hydroxide Using Exponential, Gaussian and Mixed Gauss–Exponential Distribution
title_sort empirical modeling of electron transport in fe/ti layered double hydroxide using exponential, gaussian and mixed gauss–exponential distribution
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6648460/
https://www.ncbi.nlm.nih.gov/pubmed/31460158
http://dx.doi.org/10.1021/acsomega.9b01345
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