Fang Huang

6.0k total citations
205 papers, 5.0k citations indexed

About

Fang Huang is a scholar working on Molecular Biology, Materials Chemistry and Biomedical Engineering. According to data from OpenAlex, Fang Huang has authored 205 papers receiving a total of 5.0k indexed citations (citations by other indexed papers that have themselves been cited), including 103 papers in Molecular Biology, 48 papers in Materials Chemistry and 24 papers in Biomedical Engineering. Recurrent topics in Fang Huang's work include Lipid Membrane Structure and Behavior (19 papers), Protein Structure and Dynamics (14 papers) and Advanced Nanomaterials in Catalysis (14 papers). Fang Huang is often cited by papers focused on Lipid Membrane Structure and Behavior (19 papers), Protein Structure and Dynamics (14 papers) and Advanced Nanomaterials in Catalysis (14 papers). Fang Huang collaborates with scholars based in China, United States and Germany. Fang Huang's co-authors include Werner M. Nau, Xiaojuan Wang, Alan R. Fersht, Tongtao Yue, Baosheng Ge, Xianren Zhang, Hua He, Leonid A. Mirny, Anahita Tafvizi and Antoine M. van Oijen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nucleic Acids Research.

In The Last Decade

Fang Huang

197 papers receiving 4.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fang Huang China 39 2.5k 1.4k 726 490 473 205 5.0k
Tiantian Chen China 36 1.5k 0.6× 693 0.5× 560 0.8× 222 0.5× 291 0.6× 192 4.1k
Cláudio M. Soares Portugal 42 3.1k 1.2× 827 0.6× 397 0.5× 654 1.3× 915 1.9× 154 5.7k
Masafumi Yohda Japan 40 3.8k 1.5× 1.7k 1.2× 528 0.7× 271 0.6× 258 0.5× 257 5.5k
Hui Li China 37 1.7k 0.7× 2.4k 1.7× 516 0.7× 328 0.7× 308 0.7× 242 6.4k
Kathy Qian Luo China 37 2.3k 0.9× 2.4k 1.7× 1.5k 2.1× 157 0.3× 451 1.0× 126 6.1k
Gerardino D’Errico Italy 41 1.6k 0.6× 1.1k 0.8× 731 1.0× 380 0.8× 186 0.4× 219 5.8k
Lili Shi China 38 1.6k 0.6× 852 0.6× 662 0.9× 141 0.3× 369 0.8× 194 4.4k
Hongda Wang China 36 2.5k 1.0× 626 0.4× 609 0.8× 316 0.6× 269 0.6× 219 4.5k
Zhongyu Yang United States 38 1.5k 0.6× 2.2k 1.6× 452 0.6× 397 0.8× 122 0.3× 148 5.0k
Bin Xia China 44 2.6k 1.0× 839 0.6× 372 0.5× 622 1.3× 272 0.6× 182 5.1k

Countries citing papers authored by Fang Huang

Since Specialization
Citations

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

Fields of papers citing papers by Fang Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fang Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Fang Huang. A scholar is included among the top collaborators of Fang Huang 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 Fang Huang. Fang Huang 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.
He, Hua, Guangyong Qin, Zhenzhen Feng, et al.. (2024). Mitochondrial nanoprobe for precise cellular and drug analysis via graph Neural network. Chemical Engineering Journal. 493. 152709–152709. 4 indexed citations
2.
Han, Dongxue, et al.. (2024). Enzymatic response of heparin-protamine complex: Spectroscopic investigation and application for lung adenocarcinoma cells detection. International Journal of Biological Macromolecules. 277(Pt 2). 134307–134307.
3.
Zhang, Xue Liang, Yike Liu, Xin Wang, et al.. (2024). Light-driven methanol-to-olefins catalysis over W18O49/SAPO-34 hierarchical nanocomposite with strong photothermal effect. Journal of environmental chemical engineering. 12(5). 114099–114099. 3 indexed citations
4.
5.
6.
Shi, Zhuang, Jingyan Zhu, Xiaodan Liu, et al.. (2023). Armoring a liposome-integrated tissue factor with sacrificial CaCO3 to form potent self-propelled hemostats. Journal of Materials Chemistry B. 11(12). 2778–2788. 9 indexed citations
7.
Li, Shixin, Zengshuai Yan, Fang Huang, et al.. (2020). Design of Alanine-Rich Short Peptides as a Green Alternative of Gas Hydrate Inhibitors: Dual Methyl Group Docking for Efficient Adsorption on the Surface of Gas Hydrates. ACS Sustainable Chemistry & Engineering. 8(10). 4256–4266. 57 indexed citations
8.
Li, Shixin, Zengshuai Yan, Fang Huang, Xianren Zhang, & Tongtao Yue. (2019). How a lipid bilayer membrane responds to an oscillating nanoparticle: Promoted membrane undulation and directional wave propagation. Colloids and Surfaces B Biointerfaces. 187. 110651–110651. 3 indexed citations
9.
Li, Shixin, et al.. (2019). Size-, Aggregation-, and Oxidization-Dependent Perturbation of Methane Hydrate by Graphene Nanosheets Revealed by Molecular Dynamics Simulations. The Journal of Physical Chemistry C. 123(20). 13154–13166. 17 indexed citations
10.
Xu, Yan, Shixin Li, Zengshuai Yan, et al.. (2019). Revealing Cooperation between Knotted Conformation and Dimerization in Protein Stabilization by Molecular Dynamics Simulations. The Journal of Physical Chemistry Letters. 10(19). 5815–5822. 17 indexed citations
11.
Xu, Yan, Shixin Li, Zhen Luo, et al.. (2018). Role of Lipid Coating in the Transport of Nanodroplets across the Pulmonary Surfactant Layer Revealed by Molecular Dynamics Simulations. Langmuir. 34(30). 9054–9063. 24 indexed citations
12.
Luo, Zhen, Shixin Li, Yan Xu, et al.. (2018). The role of nanoparticle shape in translocation across the pulmonary surfactant layer revealed by molecular dynamics simulations. Environmental Science Nano. 5(8). 1921–1932. 32 indexed citations
13.
Xu, Yan, Zhen Luo, Shixin Li, et al.. (2017). Perturbation of the pulmonary surfactant monolayer by single-walled carbon nanotubes: a molecular dynamics study. Nanoscale. 9(29). 10193–10204. 42 indexed citations
14.
15.
Luo, Zhen, Shixin Li, Yan Xu, et al.. (2017). Extracting pulmonary surfactants to form inverse micelles on suspended graphene nanosheets. Environmental Science Nano. 5(1). 130–140. 24 indexed citations
16.
Yue, Tongtao, Yan Xu, Shixin Li, et al.. (2017). Ultrashort Single-Walled Carbon Nanotubes Insert into a Pulmonary Surfactant Monolayer via Self-Rotation: Poration and Mechanical Inhibition. The Journal of Physical Chemistry B. 121(13). 2797–2807. 14 indexed citations
17.
Xu, Yan, Li Deng, Hao Ren, et al.. (2017). Transport of nanoparticles across pulmonary surfactant monolayer: a molecular dynamics study. Physical Chemistry Chemical Physics. 19(27). 17568–17576. 32 indexed citations
18.
Ouyang, Fan, Mingyu Zhang, Mingxian Chen, et al.. (2016). HMGB1 induces apoptosis and EMT in association with increased autophagy following H/R injury in cardiomyocytes. International Journal of Molecular Medicine. 37(3). 679–689. 53 indexed citations
19.
Leith, Jason S., Anahita Tafvizi, Fang Huang, et al.. (2012). Sequence-dependent sliding kinetics of p53. Research Online (University of Wollongong). 2012. 1 indexed citations
20.
Huang, Fang, et al.. (2010). Phosphorylation of Conserved PIN Motifs Directs Arabidopsis PIN1 Polarity and Auxin Transport  . The Plant Cell. 22(4). 1129–1142. 224 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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