Chengxi Yao - JoVE Visualize

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Physical Chemistry Chemical Physics : PCCP|June 16, 2026

Data-driven and interpretable machine learning for performance-determining interactions governing C-C coupling and C<sub>2</sub><sup>+</sup> selectivity in Cu-catalyzed CO<sub>2</sub> electroreductionMuhammad Asif, Luqman Hakeem, Chengxi Yao

Materials (Basel, Switzerland)|July 13, 2024

Research on the Mechanism and Processability of Roll FormingCunfeng Kang, Baoxu Sun, Xinshang Zhang, et al.

RSC Advances|May 28, 2026

Machine learning-based prediction of biomass pyrolysis kinetics: integrating mechanistic modeling and compositional featuresMuhammad Asif, Luqman Hakeem, Chengxi Yao, et al.

Communications Engineering|June 9, 2026

A machine learning perspective on three decades of methanol synthesis: research framework and experimental operation insightsMo Yan, Chengxi Yao, Shihao Wu, et al.

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Showing results (1-10 of 4) with videos related to

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Physical Chemistry Chemical Physics : PCCP|June 16, 2026

Data-driven and interpretable machine learning for performance-determining interactions governing C-C coupling and C<sub>2</sub><sup>+</sup> selectivity in Cu-catalyzed CO<sub>2</sub> electroreductionMuhammad Asif, Luqman Hakeem, Chengxi Yao

Materials (Basel, Switzerland)|July 13, 2024

Research on the Mechanism and Processability of Roll FormingCunfeng Kang, Baoxu Sun, Xinshang Zhang, et al.

RSC Advances|May 28, 2026

Machine learning-based prediction of biomass pyrolysis kinetics: integrating mechanistic modeling and compositional featuresMuhammad Asif, Luqman Hakeem, Chengxi Yao, et al.

Communications Engineering|June 9, 2026

A machine learning perspective on three decades of methanol synthesis: research framework and experimental operation insightsMo Yan, Chengxi Yao, Shihao Wu, et al.

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