Jafar Ai

226 papers receiving 6.7k citations

Peers

Jafar Ai
Comparison fields: 5 of 148
  • Biomaterials 2.6k
  • Rehabilitation 586
  • Genetics 793
  • Developmental Neuroscience 243
  • Molecular Medicine 286
Replace Masoud Soleimani with:
Masoud Soleimani Iran
Jiang Peng China
Hang Lin United States
Suk Ho Bhang South Korea
Lianfu Deng China
Dimitrios I. Zeugolis Ireland
Cunyi Fan China
Jianwu Dai China
Masoud Soleimani Iran
Valeria Chiono Italy
Jafar Ai relative to Masoud Soleimani Iran Masoud Soleimani's profile →
Citations per field
00.5×1.5×2.2×
Masoud Soleimani · 1×
Citations per year

Countries citing papers authored by Jafar Ai

Since Specialization
Citations

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

Fields of papers citing papers by Jafar Ai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jafar Ai, 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 Jafar Ai Line = papers co-authored together Jafar Ai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 228 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011289
2 2019152
3 2020149
4 2017140
5 2014138
6 2019137
7 2018134
8 2016129
9 2015106
10 2011105
11 2020104
12 201796
13 201593
14 202091
15 201783
16 201382
17 201781
18 201780
19 201373
20 200668

About Jafar Ai

Jafar Ai is a scholar working on Biomaterials, Biomedical Engineering, Surgery, Molecular Biology and Genetics, having authored 228 papers that have together received 6.8k indexed citations. Recurring topics across this work include Electrospun Nanofibers in Biomedical Applications (62 papers), Tissue Engineering and Regenerative Medicine (54 papers), Bone Tissue Engineering Materials (51 papers), Mesenchymal stem cell research (43 papers), Nerve injury and regeneration (32 papers), Silk-based biomaterials and applications (18 papers), Wound Healing and Treatments (17 papers) and 3D Printing in Biomedical Research (17 papers). The work is most often cited by research in Biomaterials (2.6k citations), Rehabilitation (586 citations), Genetics (793 citations), Developmental Neuroscience (243 citations) and Molecular Medicine (286 citations). Jafar Ai has collaborated with scholars based in Iran, United States and United Kingdom. Frequent co-authors include Somayeh Ebrahimi‐Barough, Mahmoud Azami, Nasrin Lotfibakhshaiesh, Shiva Asadpour, Mehdi Khanmohammadi, Saeed Farzamfar, Hamid Yeganeh, Saeid Kargozar, Arman Ai and Hossein Ghanbari. Their work appears in journals such as Molecular Neurobiology, Journal of Biomedical Materials Research Part A, Materials Science and Engineering C, Journal of Biomedical Materials Research Part B Applied Biomaterials and Artificial Cells Nanomedicine and Biotechnology.

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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