Optimization of g-C3N4 Synthesis Parameters Based on Machine Learning to Predict the Efficiency of Photocatalytic Hydrogen Production Full article
| Journal |
International Journal of Hydrogen Energy
ISSN: 0360-3199 , E-ISSN: 1879-3487 |
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| Output data | Year: 2024, Volume: 81, Pages: 193-203 Pages count : 11 DOI: 10.1016/j.ijhydene.2024.07.245 | ||||
| Tags | Photocatalysis; g-C3N4; Machine learning; Artificial intelligence; Chemical soft; Hydrogen energy | ||||
| Authors |
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| Affiliations |
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Funding (1)
| 1 | Russian Science Foundation | 24-13-00355 (124052800014-5) |
Abstract:
This study demonstrated a machine learning approach to predict the photocatalytic properties of graphitic carbon nitride (g-C3N4) depending on its synthesis parameters to enhance photocatalytic hydrogen production. In connection with the task, a database was experimentally formed to prepare g-C3N4 samples by heat treatment of nitrogen-containing precursors in air at a temperature of 450–600 °C with varying time and heating rates of the synthesis. Physicochemical analyses characterized the materials, including X-ray diffraction and low-temperature nitrogen adsorption. Several machine learning algorithms were used to process the obtained data, which showed a high-efficiency R2 above 0.9. Hyperparameters were optimized for each model using different preprocessing methods. In addition, the importance of features for selecting the most effective sample was assessed. For convenience, a web application was created with the ability to expand the database to work with machine learning models.
Cite:
Yurova V.Y.
, Potapenko K.O.
, Aliev T.A.
, Kozlova E.A.
, Skorb E.V.
Optimization of g-C3N4 Synthesis Parameters Based on Machine Learning to Predict the Efficiency of Photocatalytic Hydrogen Production
International Journal of Hydrogen Energy. 2024. V.81. P.193-203. DOI: 10.1016/j.ijhydene.2024.07.245 WOS Scopus ANCAN OpenAlex
Optimization of g-C3N4 Synthesis Parameters Based on Machine Learning to Predict the Efficiency of Photocatalytic Hydrogen Production
International Journal of Hydrogen Energy. 2024. V.81. P.193-203. DOI: 10.1016/j.ijhydene.2024.07.245 WOS Scopus ANCAN OpenAlex
Dates:
| Submitted: | May 22, 2024 |
| Accepted: | Jul 16, 2024 |
| Published online: | Jul 23, 2024 |
| Published print: | Sep 4, 2024 |
Identifiers:
| Web of science: | WOS:001279696200001 |
| Scopus: | 2-s2.0-85199295090 |
| Chemical Abstracts: | 2024:1617544 |
| Chemical Abstracts (print): | 188:184551 |
| OpenAlex: | W4400900493 |