Teng Long

1.0k citations
37 papers · 800 indexed · 1 hit paper · h-index 13
Topics
Machine Learning in Materials Science (7 papers)Magnetic properties of thin films (5 papers)Magnetic Properties and Applications (5 papers)
Journals
SHILAP Revista de lepidopterologíaJournal of Applied PhysicsChemistry of Materials
Partner nations
ChinaGermanyNorway

In The Last Decade

Teng Long

35 papers receiving 764 citations

Hit Papers

Liquid Metal‐Based Transient Circuits for Flexible and Re...2019202620212023201950100150200250

Peers

Teng Long
Comparison fields: 5 of 76
  • Materials Chemistry 314
  • Biomedical Engineering 305
  • Electrical and Electronic Engineering 243
  • Mechanical Engineering 137
  • Electronic, Optical and Magnetic Materials 119
Replace Seung Hyun Lee with:
Seung Hyun Lee South Korea
Andrew J. Pascall United States
Jin Woo Kim South Korea
Qingming Chen China
Julie Hamilton United States
Zhiqiang Yu China
Erin Antono United States
James L. Hedrick United States
Xuemei Li China
Teng Long relative to Seung Hyun Lee South Korea Seung Hyun Lee's profile →
Citations per field
00.5×
Seung Hyun Lee · 1×
Citations per year

Countries citing papers authored by Teng Long

Since Specialization
Citations

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

Fields of papers citing papers by Teng Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Teng Long

This figure shows the co-authorship network connecting the top 25 collaborators of Teng Long. A scholar is included among the top collaborators of Teng Long 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 Teng Long. Teng Long 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
#WorkIndexed citations
1 0
2 6
3 0
4 2
5 9
6 4
7 5
8 11
9 1
10 6
11 18
12 7
13 16
14 105
15
CCDCGAN: Inverse design of crystal structures
1
16 7
17 21
18
Liquid Metal‐Based Transient Circuits for Flexible and Recyclable Electronicsbreakdown →
279
19 6
20 12

About Teng Long

Teng Long is a scholar working on Electronic, Optical and Magnetic Materials, Materials Chemistry and Earth-Surface Processes, having authored 37 papers that have together received 800 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Magnetic properties of thin films (5 papers) and Magnetic Properties and Applications (5 papers). The work is most often cited by research in Catalysis (55 citations), Polymers and Plastics (100 citations) and Biomedical Engineering (305 citations). Teng Long has collaborated with scholars based in China, Germany and Norway. Frequent co-authors include Stephan Handschuh‐Wang, Xuechang Zhou, Chen Shen, Tiansheng Gan, Xiaohu Zhou, Hongbin Zhang, Yi Xiao, Yixuan Zhang, D. B. Dove and Nuno M. Fortunato. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of Applied Physics and Chemistry of Materials.

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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