The chart shows the distribution of publications by all Research.com ranked scientists in the field of Chemistry in 2026. The highlighted bar marks where Yangguang Li sits on this spectrum.
This scientist: 413 publications — 82nd percentile
82% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 1,295 publications or more.
The chart shows the D-index (discipline H-index) distribution of Chemistry scientists ranked by Research.com in 2026. The highlighted bar marks where Yangguang Li sits on this spectrum.
This scientist: 89 D-Index — 88th percentile
88% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 159 D-Index or more.
Yangguang Li is affiliated with Northeast Normal University in China. Their research encompasses a range of topics primarily within materials science and engineering, with significant activity in subfields such as materials chemistry, electrical and electronic engineering, renewable energy, sustainability and the environment, as well as computer vision and pattern recognition.
Their scholarly output highlights main areas of work including advanced photocatalysis techniques, electrocatalysts for energy conversion, polyoxometalates synthesis and applications, advanced battery technologies research, advancements in battery materials, covalent organic framework applications, and metal-organic frameworks.
Yangguang Li has published extensively, with frequent contributions appearing in venues such as:
Their recent published papers include:
Frequent co-authors collaborating with Yangguang Li include:
Their scientific contributions predominantly focus on the development and application of advanced materials for energy-related technologies, particularly in photocatalysis and electrocatalysis fields, as well as in the study and modification of frameworks at the molecular level. The interdisciplinary nature of their work bridges chemistry, materials science, and electronic engineering, with additional involvement in computer vision methodologies for data-efficient learning models.
Yuan-Yuan Ma;Cai-Xia Wu;Xiao-Jia Feng;Hua-Qiao Tan
Heping Ma;Bailing Liu;Bin Li;Liming Zhang
Hai-Yan An;En-Bo Wang;Dong-Rong Xiao;Yang-Guang Li
Xin-Long Wang;Chao Qin;En-Bo Wang;Zhong-Min Su
Xinlong Wang;Chao Qin;Enbo Wang;Yangguang Li
Xin-Bao Han;Yang-Guang Li;Zhi-Ming Zhang;Hua-Qiao Tan
Xin-Long Wang;Chao Qin;En-Bo Wang;Yang-Guang Li
Xin-Bao Han;Zhi-Ming Zhang;Zhi-Ming Zhang;Zhi-Ming Zhang;Teng Zhang;Yang-Guang Li
Li Chen;Li Chen;Wei-Lin Chen;Xin-Long Wang;Yang-Guang Li
Ai-Xue Yan;Shuang Yao;Yang-Guang Li;Zhi-Ming Zhang
Fei-Yang Yu;Zhong-Ling Lang;Li-Ying Yin;Kun Feng
Mei Yuan;Yangguang Li;Enbo Wang;Chungui Tian
Yuan-Yuan Ma;Zhong-Ling Lang;Li-Kai Yan;Yong-Hui Wang
Dong-Rong Xiao;En-Bo Wang;Hai-Yan An;Yang-Guang Li
Huaqiao Tan;Yangguang Li;Zhiming Zhang;Chao Qin
Gang Yan;Caixia Wu;Huaqiao Tan;Xiaojia Feng;Xiaojia Feng
Hai Fu;Chao Qin;Ying Lu;Zhi-Ming Zhang
Xiaojian Yang;Xiaojia Feng;Xiaojia Feng;Huaqiao Tan;Hongying Zang
Wenguang Xi;Gang Yan;Zhongling Lang;Yuanyuan Ma
Xiuli Wang;Yaqin Guo;Yangguang Li;Enbo Wang
Yangguang Li;Na Hao;Ying Lu;Enbo Wang
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