Shuli Yin

Ayuda Buscar en la ayuda Buscar en la ayuda Multi-objective optimization and uncertainty quantification for inductors based on neural network Xiaohan Kong [1] ; Shuli Yin [1] ; Yunyi Gong [1] ; Hajime Igarashi [1] [1] Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol.

Methanol-assisted energy-efficient water splitting over rambutan-like Au@PdRu core-shell nanocatalysts Hongjing Wang, Lin Cui, Shuli Yin, Hongjie Yu, Kai Deng, You Xu, Xin Wang, Ziqiang Wang and Liang Wang Abstract The integration of thermodynamically favorable methanol oxidation reaction (MOR) with hydrogen evolution reaction (HER) is a promising technique for energy-saving hydrogen production, and its efficiency mainly relies on the development of actively bifunctional electrocatalysts.

Highlights • AuCu nanofibers were prepared via a one-step synthesis strategy • The catalysts possess Boerdijk-Coxeter structure and abundant structural defects • The catalysts exhibit noteworthy performance for electrosynthesis of urea • The strategy provides a strategy for preparing defect-rich nanofibers Summary Carbon dioxide reduction reaction (CO2RR) is a promising technology for mitigating greenhouse gas emission and achieving carbon neutrality.