Version 1
: Received: 20 July 2024 / Approved: 21 July 2024 / Online: 22 July 2024 (09:46:13 CEST)
How to cite:
Sunderraj, N.; Dhanushkodi, S.; Chidambaram, R. K.; Węglowski, B.; Skrzyniowska, D.; Schmid, M.; Fowler, M. W. Development of Semi Empirical and Machine Learning Models for Photo-Electrochemical Cells. Preprints2024, 2024071663. https://doi.org/10.20944/preprints202407.1663.v1
Sunderraj, N.; Dhanushkodi, S.; Chidambaram, R. K.; Węglowski, B.; Skrzyniowska, D.; Schmid, M.; Fowler, M. W. Development of Semi Empirical and Machine Learning Models for Photo-Electrochemical Cells. Preprints 2024, 2024071663. https://doi.org/10.20944/preprints202407.1663.v1
Sunderraj, N.; Dhanushkodi, S.; Chidambaram, R. K.; Węglowski, B.; Skrzyniowska, D.; Schmid, M.; Fowler, M. W. Development of Semi Empirical and Machine Learning Models for Photo-Electrochemical Cells. Preprints2024, 2024071663. https://doi.org/10.20944/preprints202407.1663.v1
APA Style
Sunderraj, N., Dhanushkodi, S., Chidambaram, R. K., Węglowski, B., Skrzyniowska, D., Schmid, M., & Fowler, M. W. (2024). Development of Semi Empirical and Machine Learning Models for Photo-Electrochemical Cells. Preprints. https://doi.org/10.20944/preprints202407.1663.v1
Chicago/Turabian Style
Sunderraj, N., Mathias Schmid and Michael William Fowler. 2024 "Development of Semi Empirical and Machine Learning Models for Photo-Electrochemical Cells" Preprints. https://doi.org/10.20944/preprints202407.1663.v1
Abstract
We propose a theoretical Photocurrent - Voltage characteristic (PC-V) model to assess the interfacial phenom-ena for a photo-electrochemical cell (PEC). The origin of voltage deficits and the distribution of the photocur-rent across the semiconductor-electrolyte interface (SEI) are reported for the cell. The model predicts the hole exchange current parameter to extract the cell polarization data at the SEI. The potential drop across the SEI across the cell is mapped for the n-type cells. The simulation results of the Pc-V model are used to differentiate the effect of the bulk and space charge region (SCR) recombination in the semiconductor cells. A deep neural network model is developed to assess the electron-hole transfer mechanism using the Pc-V characteristic curve. The applicability of the model is tested and validated with the real time data. The results show good agreement with the experimental data.
Keywords
Photochemical cell,; Pc-V model; electron-hole transfer; recombination; Space charge width
Subject
Engineering, Chemical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
