Haibo Jiang
- Molecular Biology top 10%
- Cardiology and Cardiovascular Medicine top 10%
- Materials Chemistry
- Biomedical Engineering
- Endocrinology, Diabetes and Metabolism top 10%
- Co-authors
- Stephen G. YoungLoren G. FongC.R.M. GrovenorCuiwen HeAnne P. BeigneuxWen‐Xiong WangMatt R. KilburnPaul Guagliardo
- Topics
- Lipid Membrane Structure and Behavior (10 papers)Lipid metabolism and disorders (7 papers)Caveolin-1 and cellular processes (6 papers)
- Journals
- ScienceProceedings of the National Academy of SciencesJournal of the American Chemical Society
- Partner nations
- AustraliaUnited StatesChina
In The Last Decade
Haibo Jiang
70 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 140
- Molecular Biology 766
- Cardiology and Cardiovascular Medicine 281
- Materials Chemistry 261
- Biomedical Engineering 198
- Endocrinology, Diabetes and Metabolism 180
Countries citing papers authored by Haibo Jiang
This map shows the geographic impact of Haibo Jiang'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 Haibo Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haibo Jiang more than expected).
Fields of papers citing papers by Haibo Jiang
This network shows the impact of papers produced by Haibo Jiang. 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 Haibo Jiang. The network helps show where Haibo Jiang may publish in the future.
Co-authorship network of co-authors of Haibo Jiang
This figure shows the co-authorship network connecting the top 25 collaborators of Haibo Jiang. A scholar is included among the top collaborators of Haibo Jiang 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 Haibo Jiang. Haibo Jiang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 14 | |
| 5 | Nano-Drug Delivery Systems Based on Natural Productsbreakdown → | 74 |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 6 | |
| 9 | 6 | |
| 10 | 49 | |
| 11 | 19 | |
| 12 | 27 | |
| 13 | 13 | |
| 14 | 27 | |
| 15 | 104 | |
| 16 | 4 | |
| 17 | 34 | |
| 18 | 60 | |
| 19 | 20 | |
| 20 | 9 |
About Haibo Jiang
Haibo Jiang is a scholar working on Structural Biology, Cell Biology and Biophysics, having authored 76 papers that have together received 2.2k indexed citations. Recurring topics across this work include Lipid Membrane Structure and Behavior (10 papers), Lipid metabolism and disorders (7 papers) and Caveolin-1 and cellular processes (6 papers). The work is most often cited by research in Structural Biology (44 citations), Microbiology (121 citations) and Biophysics (85 citations). Haibo Jiang has collaborated with scholars based in Australia, United States and China. Frequent co-authors include Stephen G. Young, Loren G. Fong, C.R.M. Grovenor, Cuiwen He, Anne P. Beigneux, Wen‐Xiong Wang, Matt R. Kilburn, Paul Guagliardo, Peter Tontonoz and Maximiliano G. Gutiérrez. Their work appears in journals such as Science, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.
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.