Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells
We introduce a theoretical model for the photocurrent-voltage (I-V) characteristics designed to elucidate the interfacial phenomena in photoelectrochemical cells (PECs). This model investigates the sources of voltage losses and the distribution of photocurrent across the semiconductor–electrolyte in...
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| Format: | Article |
| Language: | English |
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2024-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/21/5313 |
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| author | Niranjan Sunderraj Shankar Raman Dhanushkodi Ramesh Kumar Chidambaram Bohdan Węglowski Dorota Skrzyniowska Mathias Schmid Michael William Fowler |
| author_facet | Niranjan Sunderraj Shankar Raman Dhanushkodi Ramesh Kumar Chidambaram Bohdan Węglowski Dorota Skrzyniowska Mathias Schmid Michael William Fowler |
| author_sort | Niranjan Sunderraj |
| collection | DOAJ |
| description | We introduce a theoretical model for the photocurrent-voltage (I-V) characteristics designed to elucidate the interfacial phenomena in photoelectrochemical cells (PECs). This model investigates the sources of voltage losses and the distribution of photocurrent across the semiconductor–electrolyte interface (SEI). It calculates the whole exchange current parameter to derive cell polarization data at the SEI and visualizes the potential drop across n-type cells. The I-V model’s simulation outcomes are utilized to distinguish between the impacts of bulk recombination and space charge region (SCR) recombination within semiconductor cells. Furthermore, we develop an advanced deep neural network model to analyze the electron–hole transfer dynamics using the I-V characteristic curve. The model’s robustness is evaluated and validated with real-time experimental data, demonstrating a high degree of concordance with observed results. |
| format | Article |
| id | doaj-art-22964659b0b04e33b5b9efe9c22f4cca |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-22964659b0b04e33b5b9efe9c22f4cca2024-11-08T14:35:17ZengMDPI AGEnergies1996-10732024-10-011721531310.3390/en17215313Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical CellsNiranjan Sunderraj0Shankar Raman Dhanushkodi1Ramesh Kumar Chidambaram2Bohdan Węglowski3Dorota Skrzyniowska4Mathias Schmid5Michael William Fowler6Dhanushkodi Research Group, Department of Chemical Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDhanushkodi Research Group, Department of Chemical Engineering, Vellore Institute of Technology, Vellore 632014, IndiaAutomotive Research Center, School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, IndiaInstitute of Thermal Power Engineering, Cracow University of Technology, 31-864 Cracow, PolandInstitute of Thermal Power Engineering, Cracow University of Technology, 31-864 Cracow, PolandZHAW School of Engineering, ICP—Institute of Computational Physics, Technikumstrasse 71, CH-8401 Winterthur, SwitzerlandDepartment of Chemical Engineering, University of Waterloo, Waterloo, ON N2L3G1, CanadaWe introduce a theoretical model for the photocurrent-voltage (I-V) characteristics designed to elucidate the interfacial phenomena in photoelectrochemical cells (PECs). This model investigates the sources of voltage losses and the distribution of photocurrent across the semiconductor–electrolyte interface (SEI). It calculates the whole exchange current parameter to derive cell polarization data at the SEI and visualizes the potential drop across n-type cells. The I-V model’s simulation outcomes are utilized to distinguish between the impacts of bulk recombination and space charge region (SCR) recombination within semiconductor cells. Furthermore, we develop an advanced deep neural network model to analyze the electron–hole transfer dynamics using the I-V characteristic curve. The model’s robustness is evaluated and validated with real-time experimental data, demonstrating a high degree of concordance with observed results.https://www.mdpi.com/1996-1073/17/21/5313photochemical cellspace charge widthrecombinationI-V model and electron hole transfer |
| spellingShingle | Niranjan Sunderraj Shankar Raman Dhanushkodi Ramesh Kumar Chidambaram Bohdan Węglowski Dorota Skrzyniowska Mathias Schmid Michael William Fowler Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells Energies photochemical cell space charge width recombination I-V model and electron hole transfer |
| title | Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells |
| title_full | Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells |
| title_fullStr | Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells |
| title_full_unstemmed | Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells |
| title_short | Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells |
| title_sort | development of semi empirical and machine learning models for photoelectrochemical cells |
| topic | photochemical cell space charge width recombination I-V model and electron hole transfer |
| url | https://www.mdpi.com/1996-1073/17/21/5313 |
| work_keys_str_mv | AT niranjansunderraj developmentofsemiempiricalandmachinelearningmodelsforphotoelectrochemicalcells AT shankarramandhanushkodi developmentofsemiempiricalandmachinelearningmodelsforphotoelectrochemicalcells AT rameshkumarchidambaram developmentofsemiempiricalandmachinelearningmodelsforphotoelectrochemicalcells AT bohdanweglowski developmentofsemiempiricalandmachinelearningmodelsforphotoelectrochemicalcells AT dorotaskrzyniowska developmentofsemiempiricalandmachinelearningmodelsforphotoelectrochemicalcells AT mathiasschmid developmentofsemiempiricalandmachinelearningmodelsforphotoelectrochemicalcells AT michaelwilliamfowler developmentofsemiempiricalandmachinelearningmodelsforphotoelectrochemicalcells |