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Optimization of g-C3N4 Synthesis Parameters Based on Machine Learning to Predict the Efficiency of Photocatalytic Hydrogen Production Научная публикация

Журнал International Journal of Hydrogen Energy
ISSN: 0360-3199 , E-ISSN: 1879-3487
Вых. Данные Год: 2024, Том: 81, Страницы: 193-203 Страниц : 11 DOI: 10.1016/j.ijhydene.2024.07.245
Ключевые слова Photocatalysis; g-C3N4; Machine learning; Artificial intelligence; Chemical soft; Hydrogen energy
Авторы Yurova Veronika Yu. 1 , Potapenko Kseniya O. 2 , Aliev Timur A. 1 , Kozlova Ekaterina A. 2 , Skorb Ekaterina V. 1
Организации
1 ITMO University, 197101, Saint Petersburg, Russia
2 Federal Research Center G. K. Boreskov Institute of Catalysis, Siberian Branch of the Russian Academy of Sciences, 630090, Novosibirsk, Russia

Информация о финансировании (1)

1 Российский научный фонд 24-13-00355 (124052800014-5)

Реферат: 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.
Библиографическая ссылка: 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 OpenAlex
Даты:
Поступила в редакцию: 22 мая 2024 г.
Принята к публикации: 16 июл. 2024 г.
Опубликована online: 23 июл. 2024 г.
Опубликована в печати: 4 сент. 2024 г.
Идентификаторы БД:
Web of science: WOS:001279696200001
Scopus: 2-s2.0-85199295090
OpenAlex: W4400900493
Цитирование в БД:
БД Цитирований
Scopus 3
Web of science 3
Альметрики: