Harnessing Zinc Stannate for Sustainable Energy and Environment Solutions: Advances in Photocatalytic, Piezocatalytic, and Piezo-photocatalytic Technologies

Nano Energy, Journal Year: 2024, Volume and Issue: unknown, P. 110518 - 110518

Published: Nov. 1, 2024

Language: Английский

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Rational Design of Carbon‐Based Electrocatalysts for H₂O₂ Production by Machine Learning and Structural Engineering

Advanced Energy Materials, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Abstract Electrochemical synthesis of hydrogen peroxide (H 2 O ) via two‐electron oxygen reduction reaction (2e − ORR) represents an economically viable alternative to conventional anthraquinone processes. While noble metal catalysts have dominated this field, carbon‐based materials are emerging as promising alternatives due their low cost, abundant reserves, and tunable properties. This mini‐review summarizes recent advances in computational methods, particularly the integration density functional theory (DFT) with machine learning (ML), accelerate rational design electrocatalysts by enabling rapid screening structure‐training predictions. Meanwhile, optimization strategies systematically investigated, focusing on four key aspects: atomic‐level heterochromatic doping, defect engineering, microenvironment control, morphological design. Despite significant progress achieving high selectivity activity, challenges remain scaling these for industrial applications. Moving H will require multidisciplinary efforts combining advanced situ characterization techniques, modeling, process engineering develop robust suitable diverse operating conditions.

Language: Английский

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Task Decomposition Strategy Based on Machine Learning for Boosting Performance and Identifying Mechanisms in Heterogeneous Activation of Peracetic Acid Process

Water Research, Journal Year: 2024, Volume and Issue: 267, P. 122521 - 122521

Published: Sept. 26, 2024

Language: Английский

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Reverse design of high-detonation-velocity organic energetic compounds based on an accurate BPNN with wide applicability

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 9, 2024

Key factors affecting detonation velocity ( D ) are identified with machine learning (2% error), and high- energetic compounds designed.

Language: Английский

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