Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells

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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Bibliographic Details
Main Authors: Niranjan Sunderraj, Shankar Raman Dhanushkodi, Ramesh Kumar Chidambaram, Bohdan Węglowski, Dorota Skrzyniowska, Mathias Schmid, Michael William Fowler
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Energies
Subjects:
photochemical cell
space charge width
recombination
I-V model and electron hole transfer
Online Access:https://www.mdpi.com/1996-1073/17/21/5313
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Summary: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.
ISSN:1996-1073

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