Xiaobo Shi

1.5k citations
37 papers · 1.2k indexed · 1 hit paper · h-index 16

Xiaobo Shi

33 papers receiving 1.2k citations

Hit Papers

Deep Feature Learning for Medical Image Analysis with Con...3062017202620202023100200300

Peers

Xiaobo Shi
Comparison fields: 5 of 114
  • Electronic, Optical and Magnetic Materials 374
  • Materials Chemistry 736
  • Inorganic Chemistry 145
  • Health Informatics 9
  • Organic Chemistry 168
Replace Jiangsheng Yu with:
Jiangsheng Yu China
Daiki Tanaka Japan
Mário R. G. Marques Germany
Ziyuan Yang China
Hongxia Hao China
Yu Zheng China
Yuanyuan Jin China
Yuanqing Xu China
Martin Grayson United Kingdom
Jindong Chen China
Xiaobo Shi relative to Jiangsheng Yu China Jiangsheng Yu's profile →
Citations per field
00.5×5.6×
Jiangsheng Yu · 1×
Citations per year

Countries citing papers authored by Xiaobo Shi

Since Specialization
Citations

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

Fields of papers citing papers by Xiaobo Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20240
3 20246
4 20228
5 202016
6
Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Networkbreakdown →
2017306
7 20166
8 201624
9 20164
10 20166
11 20160
12 20162
13 201313
14 201216
15 20031
16 19951
17 199317
18 199334
19 199236
20 199066

About Xiaobo Shi

Xiaobo Shi is a scholar working on Materials Chemistry, Electronic, Optical and Magnetic Materials and Inorganic Chemistry, having authored 37 papers that have together received 1.2k indexed citations. Recurring topics across this work include Nanocluster Synthesis and Applications (10 papers), Organometallic Complex Synthesis and Catalysis (9 papers), 2D Materials and Applications (8 papers), Inorganic Chemistry and Materials (5 papers), MXene and MAX Phase Materials (4 papers), Perovskite Materials and Applications (4 papers), Magnetic properties of thin films (3 papers) and Magnetism in coordination complexes (3 papers). The work is most often cited by research in Electronic, Optical and Magnetic Materials (374 citations), Materials Chemistry (736 citations) and Inorganic Chemistry (145 citations). Xiaobo Shi has collaborated with scholars based in China, United States and Saudi Arabia. Frequent co-authors include Boon K. Teo, Hong Zhang, Yin Zhang⋆, Hong Zhang, Mohsen Guizani, Min Chen, Di Wu, Hong Zhang, Hong Zhang and Hong Zhang. Their work appears in journals such as Journal of the American Chemical Society, The Journal of Chemical Physics and Applied Physics Letters.

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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