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

Author

Listed:
  • Niranjan Sunderraj

    (Dhanushkodi Research Group, Department of Chemical Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Shankar Raman Dhanushkodi

    (Dhanushkodi Research Group, Department of Chemical Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Ramesh Kumar Chidambaram

    (Automotive Research Center, School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Bohdan Węglowski

    (Institute of Thermal Power Engineering, Cracow University of Technology, 31-864 Cracow, Poland)

  • Dorota Skrzyniowska

    (Institute of Thermal Power Engineering, Cracow University of Technology, 31-864 Cracow, Poland)

  • Mathias Schmid

    (ZHAW School of Engineering, ICP—Institute of Computational Physics, Technikumstrasse 71, CH-8401 Winterthur, Switzerland)

  • Michael William Fowler

    (Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada)

Abstract

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.

Suggested Citation

  • Niranjan Sunderraj & Shankar Raman Dhanushkodi & Ramesh Kumar Chidambaram & Bohdan Węglowski & Dorota Skrzyniowska & Mathias Schmid & Michael William Fowler, 2024. "Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells," Energies, MDPI, vol. 17(21), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5313-:d:1506560
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    References listed on IDEAS

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    1. Giosuè Giacoppo & Stefano Trocino & Carmelo Lo Vecchio & Vincenzo Baglio & María I. Díez-García & Antonino Salvatore Aricò & Orazio Barbera, 2023. "Numerical 3D Model of a Novel Photoelectrolysis Tandem Cell with Solid Electrolyte for Green Hydrogen Production," Energies, MDPI, vol. 16(4), pages 1-12, February.
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