Data-Driven Optimization of Photocatalytic Water Splitting for Hydrogen Production
How to cite: Cit, A.; Ahsun, A. Data-Driven Optimization of Photocatalytic Water Splitting for Hydrogen Production. Preprints 2024, 2024102341. https://doi.org/10.20944/preprints202410.2341.v1 Cit, A.; Ahsun, A. Data-Driven Optimization of Photocatalytic Water Splitting for Hydrogen Production. Preprints 2024, 2024102341. https://doi.org/10.20944/preprints202410.2341.v1
Abstract
Photocatalytic water splitting offers a promising route for sustainable hydrogen production, but its efficiency remains limited by complex interactions between material properties, operating conditions, and reaction mechanisms. This study presents a data-driven approach to optimize photocatalytic water splitting for enhanced hydrogen evolution. By integrating experimental data, density functional theory calculations, and machine learning algorithms, we identified key descriptors governing photocatalyst performance. A predictive model was developed to screen optimal catalyst compositions, morphologies, and operating conditions, leading to a significant increase in hydrogen production rates. The results demonstrate the potential of data-driven optimization to accelerate the discovery of high-performance photocatalysts, paving the way for scalable and efficient solar-driven hydrogen production.
Keywords
photocatalytic; Water splitting; Hydrogen production
Subject
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.
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