Transformative strategies in photocatalyst design: merging computational methods and deep learning

Journal of Materials Informatics, Год журнала: 2024, Номер 4(4)

Опубликована: Дек. 31, 2024

Photocatalysis is a unique technology that harnesses solar energy through in-situ processes, operating without the need for external inputs. It integral to advancing environmental, energy, chemical, and carbon-neutral objectives, promoting dual goals of pollution control carbon reduction. However, conventional approach photocatalyst design faces challenges such as inefficiency, high costs, low success rates, highlighting integrating modern technologies seeking new paradigms. Here, we demonstrate comprehensive overview transformative strategies in design, combining computational materials science with deep learning technologies. The review covers fundamental principles followed by examination methods workflow deep-learning-assisted design. Deep approaches are extensively reviewed, focusing on discovery novel photocatalysts, microstructure property optimization, approaches, application exploration, mechanistic insights into photocatalysis. Finally, highlight synergy between multidimensional computation learning, while discussing future directions development. This offers summary offering not only enhance development photocatalytic but also expand practical applications photocatalysis various domains.

Язык: Английский

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Research on Binary Mixed VOCs Gas Identification Method Based on Multi-Task Learning

Sensors, Год журнала: 2025, Номер 25(8), С. 2355 - 2355

Опубликована: Апрель 8, 2025

Traditional volatile organic compounds (VOCs) detection models separate component identification and concentration prediction, leading to low feature utilization limited learning in small-sample scenarios. Here, we realize a Residual Fusion Network based on multi-task (MTL-RCANet) implement prediction of VOCs. The model integrates channel attention mechanisms cross-fusion modules enhance extraction capabilities task synergy. To further balance the tasks, dynamic weighted loss function is incorporated adjust weights dynamically according training progress each task, thereby enhancing overall performance model. proposed network achieves an accuracy 94.86% R2 score 0.95. Comparative experiments reveal that using only 35% total data length as input yields excellent performance. Moreover, effectively information across significantly improving efficiency compared single-task learning.

Язык: Английский

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Smart VOCs Recognition System Based on Single Gas Sensor and Multi-task Deep Learning Model

Sensors and Actuators B Chemical, Год журнала: 2025, Номер unknown, С. 137853 - 137853

Опубликована: Апрель 1, 2025

Язык: Английский

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Regulation of receptor function in NiCo2O4-SnO2 heterojunction for H2S detection at room temperature

Ceramics International, Год журнала: 2024, Номер unknown

Опубликована: Окт. 1, 2024

Язык: Английский

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