Enze Chen

632 citations
33 papers · 436 · h-index 11

Impact in

Papers in

    • Cellular and Composite Structures 5
    • Microstructure and Mechanical Properties of Steels 2
    • Microstructure and mechanical properties 5
    • Machine Learning in Materials Science 5

Enze Chen

29 papers receiving 432 citations

Peers

Enze Chen
Comparison fields: 5 of 82
  • Electronic, Optical and Magnetic Materials 99
  • Mechanical Engineering 154
  • Biomedical Engineering 123
  • Materials Chemistry 128
  • Automotive Engineering 27
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Citations per field
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Citations per year

Countries citing papers authored by Enze Chen

Since Specialization
Citations

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

Fields of papers citing papers by Enze Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012111
2 202251
3 202138
4 202329
5 202427
6 202224
7 202023
8 202221
9 202214
10 201912
11 202310
12 202210
13 20229
14 20108
15 20126
16 20255
17 20125
18 20244
19 20204
20 20134

About Enze Chen

Enze Chen is a scholar working on Mechanical Engineering, Materials Chemistry, Biomedical Engineering, Mechanics of Materials and Industrial and Manufacturing Engineering, having authored 33 papers that have together received 436 indexed citations. Recurring topics across this work include Microstructure and mechanical properties (5 papers), Cellular and Composite Structures (5 papers), Machine Learning in Materials Science (5 papers), Industrial Vision Systems and Defect Detection (4 papers), Microstructure and Mechanical Properties of Steels (2 papers), Plasmonic and Surface Plasmon Research (2 papers), EEG and Brain-Computer Interfaces (2 papers) and Advanced biosensing and bioanalysis techniques (2 papers). The work is most often cited by research in Electronic, Optical and Magnetic Materials (99 citations), Mechanical Engineering (154 citations), Biomedical Engineering (123 citations), Materials Chemistry (128 citations) and Automotive Engineering (27 citations). Enze Chen has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Stavros Gaitanaros, Naveen Gandra, Limei Tian, Abdennour Abbas, Srikanth Singamaneni, Mark Asta, Timofey Frolov, Artur Tamm, Tao Wang and Christian H. Liebscher. Their work appears in journals such as Journal of Neural Engineering, Measurement Science and Technology, Journal of Radioanalytical and Nuclear Chemistry, The Journal of Physical Chemistry A and MRS Bulletin.

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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