Jun Gao

437 papers receiving 11.4k citations

Peers

Jun Gao
Comparison fields: 5 of 148
  • Filtration and Separation 1.6k
  • Catalysis 2.7k
  • Process Chemistry and Technology 650
  • Fluid Flow and Transfer Processes 1.1k
  • Molecular Medicine 620
Replace Inas M. AlNashef with:
Inas M. AlNashef Saudi Arabia
Wolfgang Arlt Germany
Jason P. Hallett United Kingdom
Huizhou Liu China
Farouq S. Mjalli Oman
E. L. Cussler United States
Ирина Смирнова Germany
Mohamed Kheireddine Aroua Malaysia
Xiangping Zhang China
Jianji Wang China
Jun Gao relative to Inas M. AlNashef Saudi Arabia Inas M. AlNashef's profile →
Citations per field
00.5×11.7×
Inas M. AlNashef · 1×
Citations per year

Countries citing papers authored by Jun Gao

Since Specialization
Citations

This map shows the geographic impact of Jun Gao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jun Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Gao more than expected).

Fields of papers citing papers by Jun Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Gao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jun Gao. The network helps show where Jun Gao may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun Gao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Gao Line = papers co-authored together Jun Gao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 454 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019308
2 2018235
3 2003172
4 2004166
5 2002140
6 2018121
7 2009113
8 2001105
9 2019103
10 2001100
11 201799
12 200398
13 200096
14 202096
15 201892
16 202092
17 201791
18 201990
19 199786
20 202085

About Jun Gao

Jun Gao is a scholar working on Mechanical Engineering, Biomedical Engineering, Catalysis, Control and Systems Engineering and Materials Chemistry, having authored 454 papers that have together received 11.5k indexed citations. Recurring topics across this work include Ionic liquids properties and applications (108 papers), Process Optimization and Integration (86 papers), Chemical and Physical Properties in Aqueous Solutions (85 papers), Phase Equilibria and Thermodynamics (73 papers), Thermodynamic properties of mixtures (66 papers), Extraction and Separation Processes (63 papers), Advanced Control Systems Optimization (39 papers) and Crystallization and Solubility Studies (27 papers). The work is most often cited by research in Filtration and Separation (1.6k citations), Catalysis (2.7k citations), Process Chemistry and Technology (650 citations), Fluid Flow and Transfer Processes (1.1k citations) and Molecular Medicine (620 citations). Jun Gao has collaborated with scholars based in China, United States and France. Frequent co-authors include Yinglong Wang, Dongmei Xu, Lianzheng Zhang, Zhaoyou Zhu, Yixin Ma, Peizhe Cui, Barbara J. Frisken, Zhishan Zhang, Zhibing Hu and Fusheng Liu. Their work appears in journals such as Journal of Chemical & Engineering Data, The Journal of Chemical Thermodynamics, Journal of Molecular Liquids, Separation and Purification Technology and Process Safety and Environmental Protection.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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