Xiang Huang

Apr 16, 2024 | onlinelibrary.wiley.com | Fuli Ma |Xiang Huang |Junyong Zhou |Ning Zhang

CONFLICT OF INTEREST STATEMENT The authors declare that there are no conflicts of interest. Supporting Information Filename Description pce14913-sup-0001-figures.docx11.1 MB Supporting information. pce14913-sup-0002-Supplementary_Table_S1.xlsx14.3 KB Supporting information. pce14913-sup-0003-Supplementary_table_S2.xlsx10.2 KB Supporting information. REFERENCES , , , , , et al.

Feb 1, 2024 | chemistry-europe.onlinelibrary.wiley.com | Xiang Huang |Hu Xu

Identifying a universal activity descriptor for metal oxides, akin to the d-band center for transition metals, remains a significant challenge in catalyst design, largely due to the intricate electronic structures of metal oxides. This review highlights a major advancement in formulating the number of excess electrons (NEE) as an activity descriptor for oxygen evolution reaction (OER) on reducible metal oxide surfaces.

Jan 18, 2024 | pubs.acs.org | Jing Zhou |Xiang Huang |Xin Yu |Liu Yang

Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Org. Chem. 2024, XXXX, XXXJing ZhouLaboratory of Asymmetric Synthesis, College of Chemistry and Environmental Engineering, Chongqing University of Arts and Sciences, 319 Honghe Avenue, Yongchuan, Chongqing 402160, P. R. China, Xiang HuangLaboratory of Asymmetric Synthesis, College of Chemistry and Environmental Engineering, Chongqing University of Arts and Sciences, 319 Honghe Avenue, Yongchuan, Chongqing 402160, P. R.

Jan 18, 2024 | dx.doi.org | Jing Zhou |Xiang Huang |Xin Yu |Liu Yang

Oct 14, 2023 | nature.com | Xiang Huang |C. Zhao |Hong Wang

AbstractThe efficient and economical exploitation of polymers with high thermal conductivity (TC) is essential to solve the issue of heat dissipation in organic devices. Currently, the experimental preparation of functional polymers with high TC remains a trial-and-error process due to the multi-degrees of freedom during the synthesis and characterization process. Polymer informatics equips machine learning (ML) as a powerful engine for the efficient design of polymers with desired properties.