Zhuqing Zhang

4.0k total citations · 1 hit paper
87 papers, 3.2k citations indexed

About

Zhuqing Zhang is a scholar working on Molecular Biology, Materials Chemistry and Electrical and Electronic Engineering. According to data from OpenAlex, Zhuqing Zhang has authored 87 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Molecular Biology, 28 papers in Materials Chemistry and 10 papers in Electrical and Electronic Engineering. Recurrent topics in Zhuqing Zhang's work include Protein Structure and Dynamics (16 papers), RNA modifications and cancer (11 papers) and Enzyme Structure and Function (9 papers). Zhuqing Zhang is often cited by papers focused on Protein Structure and Dynamics (16 papers), RNA modifications and cancer (11 papers) and Enzyme Structure and Function (9 papers). Zhuqing Zhang collaborates with scholars based in China, United States and Canada. Zhuqing Zhang's co-authors include C.P. Wong, Yangyang Sun, Hue Sun Chan, Kyoung‐Sik Moon, Mingxin Ye, Jianfeng Shen, Luhua Lai, Robert Baines, Pulickel M. Ajayan and Stefan Wallin and has published in prestigious journals such as Chemical Reviews, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Zhuqing Zhang

83 papers receiving 3.2k citations

Hit Papers

Covalent Organic Frameworks for Batteries 2021 2026 2022 2024 2021 50 100 150 200

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Zhuqing Zhang China 31 1.3k 1.2k 615 495 391 87 3.2k
Da Wang China 31 1.1k 0.9× 681 0.6× 731 1.2× 210 0.4× 222 0.6× 106 3.1k
Jing Meng China 30 838 0.7× 573 0.5× 968 1.6× 338 0.7× 559 1.4× 114 3.2k
Wei Zheng China 37 1.5k 1.2× 1.1k 0.9× 1.2k 1.9× 180 0.4× 334 0.9× 172 4.7k
Fei Yang China 31 1.2k 0.9× 862 0.7× 1.4k 2.3× 191 0.4× 718 1.8× 183 3.7k
Xinling Liu China 27 1.6k 1.3× 519 0.4× 925 1.5× 937 1.9× 1.0k 2.6× 162 3.6k
Kang Chen China 38 1.1k 0.9× 1.5k 1.3× 392 0.6× 637 1.3× 107 0.3× 165 4.3k
Xi Zhang China 36 1.4k 1.1× 1.6k 1.3× 796 1.3× 210 0.4× 344 0.9× 144 4.4k
Yu-Xiang Zheng China 31 1.6k 1.3× 835 0.7× 1.3k 2.1× 182 0.4× 293 0.7× 215 4.1k
Weina Li China 32 895 0.7× 767 0.7× 534 0.9× 132 0.3× 156 0.4× 133 2.8k
Xiaomei Zhao China 34 813 0.6× 867 0.7× 1.6k 2.6× 240 0.5× 457 1.2× 138 4.0k

Countries citing papers authored by Zhuqing Zhang

Since Specialization
Citations

This map shows the geographic impact of Zhuqing Zhang'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 Zhuqing Zhang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhuqing Zhang more than expected).

Fields of papers citing papers by Zhuqing Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zhuqing Zhang. 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 Zhuqing Zhang. The network helps show where Zhuqing Zhang may publish in the future.

Co-authorship network of co-authors of Zhuqing Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Zhuqing Zhang. A scholar is included among the top collaborators of Zhuqing Zhang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Zhuqing Zhang. Zhuqing Zhang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Liu, Jian, et al.. (2025). Network-aware multi-step hazard prediction using temporal knowledge graphs: A chemical industry case study. Journal of Loss Prevention in the Process Industries. 99. 105787–105787.
2.
Lu, Qin, Zhuqing Zhang, Jun Wang, et al.. (2025). Inhibition of stemness and PD-L1 expression by Pien Tze Huang enhances T cell-mediated killing of colorectal cancer. Journal of Ethnopharmacology. 343. 119447–119447. 1 indexed citations
3.
4.
Zhang, Zhuqing, et al.. (2024). Prediction techniques of movie box office using neural networks and emotional mining. Scientific Reports. 14(1). 21209–21209. 2 indexed citations
5.
Zhang, Yujun, et al.. (2023). Evaluation of sequence-based predictors for phase-separating protein. Briefings in Bioinformatics. 24(4). 2 indexed citations
6.
Zhang, Zhuqing, et al.. (2023). Inhibiting Asphaltene Deposition Using Polymer-Functionalized Nanoparticles in Microfluidic Porous Media. Energy & Fuels. 37(24). 19461–19471. 7 indexed citations
7.
Zhang, Zhuqing, et al.. (2022). Physicochemical Characterization of Asphaltenes Using Microfluidic Analysis. Chemical Reviews. 122(7). 7205–7235. 31 indexed citations
8.
Lin, Yu‐Jiun, Zhuqing Zhang, & Sibani Lisa Biswal. (2022). Entrapment of Asphaltene-Stabilized Emulsions in Microfluidic Porous Media. Energy & Fuels. 36(16). 8760–8768. 7 indexed citations
9.
Wang, Xi, Peiyu Xu, Qian Li, et al.. (2022). LLPSDB v2.0: an updated database of proteins undergoing liquid–liquid phase separation in vitro. Bioinformatics. 38(7). 2010–2014. 39 indexed citations
10.
Zhang, Zhuqing, et al.. (2021). Evaluation of Asphaltene Remediation Using Microemulsion Formulations in a Porous Media Microfluidic Device. Energy & Fuels. 35(14). 11162–11170. 7 indexed citations
11.
Zhang, Zhuqing, et al.. (2020). Comparing the Coalescence Rate of Water-in-Oil Emulsions Stabilized with Asphaltenes and Asphaltene-like Molecules. Langmuir. 36(27). 7894–7900. 23 indexed citations
12.
Li, Qian, Xi Wang, Zhihui Dou, et al.. (2020). Protein Databases Related to Liquid–Liquid Phase Separation. International Journal of Molecular Sciences. 21(18). 6796–6796. 30 indexed citations
13.
Lai, Luhua, et al.. (2019). How calcium ion binding induces the conformational transition of the calmodulin N-terminal domain—an atomic level characterization. Physical Chemistry Chemical Physics. 21(36). 19795–19804. 5 indexed citations
14.
Wang, Shengxu, Zhuqing Zhang, Xiaowei Xu, et al.. (2019). Au nanoclusters/porous silica particles nanocomposites as fluorescence enhanced sensors for sensing and mapping of copper(II) in cells. Nanotechnology. 30(47). 475701–475701. 9 indexed citations
15.
Li, Qian, Xiaojun Peng, Yuanqing Li, et al.. (2019). LLPSDB: a database of proteins undergoing liquid–liquid phase separation in vitro. Nucleic Acids Research. 48(D1). D320–D327. 130 indexed citations
16.
Zhang, Zhuqing, et al.. (2018). Characterization of the structural ensembles of p53 TAD2 by molecular dynamics simulations with different force fields. Physical Chemistry Chemical Physics. 20(13). 8676–8684. 25 indexed citations
17.
Wu, Jing, Guojun Chen, Zhuqing Zhang, Ping Zhang, & Tao Chen. (2017). The low populated folding intermediate of a mutant of the Fyn SH3 domain identified by a simple model. Physical Chemistry Chemical Physics. 19(33). 22321–22328. 7 indexed citations
18.
Hu, Jie, et al.. (2017). A critical comparison of coarse-grained structure-based approaches and atomic models of protein folding. Physical Chemistry Chemical Physics. 19(21). 13629–13639. 23 indexed citations
19.
Zhang, Zhuqing, et al.. (2016). Influences of heterogeneous native contact energy and many-body interactions on the prediction of protein folding mechanisms. Physical Chemistry Chemical Physics. 18(45). 31304–31311. 5 indexed citations
20.
Zhang, Zhuqing, et al.. (2010). Meat rate and muscle nutrient composition of Varicorhinus (Onychostoma) simus under artificial aquaculture condition.. Guizhou nongye kexue. 129–133. 1 indexed citations

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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