Lingna Chen

1.8k citations
57 papers · 1.5k · h-index 19

Impact in

Papers in

Lingna Chen

57 papers receiving 1.5k citations

Peers

Lingna Chen
Comparison fields: 5 of 118
  • Horticulture 15
  • Endocrinology 54
  • Electrical and Electronic Engineering 608
  • Electronic, Optical and Magnetic Materials 175
  • Automotive Engineering 113
Replace Stephan Schröder with:
Stephan Schröder Germany
Yingyue Li China
Pengfei Zhang China
Lou China
Zhongyang Zhao China
Yiqian Li China
Guangyun Li China
Xiaoqing Liang China
Haitao Ding China
Lingna Chen relative to Stephan Schröder Germany Stephan Schröder's profile →
Citations per field
00.5×2×4×6×8×9.4×
Stephan Schröder · 1×
Citations per year

Countries citing papers authored by Lingna Chen

Since Specialization
Citations

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

Fields of papers citing papers by Lingna Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 57 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017222
2 2018155
3 2019126
4 2012104
5 201595
6 201991
7 201463
8 201751
9 202339
10 202138
11 201637
12 201232
13 201925
14 201423
15 202023
16 202322
17 201322
18 202119
19 201518
20 201716

About Lingna Chen

Lingna Chen is a scholar working on Electrical and Electronic Engineering, Plant Science, Materials Chemistry, Molecular Biology and Computer Vision and Pattern Recognition, having authored 57 papers that have together received 1.5k indexed citations. Recurring topics across this work include Graphene research and applications (7 papers), Geophysical Methods and Applications (5 papers), Plant Molecular Biology Research (5 papers), Bamboo properties and applications (5 papers), AI in cancer detection (5 papers), 2D Materials and Applications (4 papers), Advanced Neural Network Applications (4 papers) and Advancements in Battery Materials (4 papers). The work is most often cited by research in Horticulture (15 citations), Endocrinology (54 citations), Electrical and Electronic Engineering (608 citations), Electronic, Optical and Magnetic Materials (175 citations) and Automotive Engineering (113 citations). Lingna Chen has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Shulei Chou, Mingzhe Chen, Zhe Hu, Shi Xue Dou, Qinfen Gu, Qiannan Liu, Jing Li, Zhaofa Zeng, Cai Liu and Shun Wang. Their work appears in journals such as BMC Plant Biology, Advanced Materials, Advanced Energy Materials, Journal of Applied Biomedicine and PLoS ONE.

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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