Chun‐Shan Wang

3.5k total citations · 1 hit paper
116 papers, 3.0k citations indexed

About

Chun‐Shan Wang is a scholar working on Polymers and Plastics, Mechanical Engineering and Materials Chemistry. According to data from OpenAlex, Chun‐Shan Wang has authored 116 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Polymers and Plastics, 51 papers in Mechanical Engineering and 32 papers in Materials Chemistry. Recurrent topics in Chun‐Shan Wang's work include Synthesis and properties of polymers (58 papers), Epoxy Resin Curing Processes (49 papers) and Silicone and Siloxane Chemistry (25 papers). Chun‐Shan Wang is often cited by papers focused on Synthesis and properties of polymers (58 papers), Epoxy Resin Curing Processes (49 papers) and Silicone and Siloxane Chemistry (25 papers). Chun‐Shan Wang collaborates with scholars based in Taiwan, China and United States. Chun‐Shan Wang's co-authors include Jeng‐Yueh Shieh, Tsung-Han Ho, Tsu‐Shang Leu, Hann‐Jang Hwang, Yih‐Min Sun, Huarui Wu, Ming‐Chun Lee, Jiuxi Li, Chunjiang Zhao and Guifa Teng and has published in prestigious journals such as Scientific Reports, Polymer and The Journal of Organic Chemistry.

In The Last Decade

Chun‐Shan Wang

110 papers receiving 2.8k citations

Hit Papers

A cucumber leaf disease severity classification method ba... 2021 2026 2022 2024 2021 50 100 150

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chun‐Shan Wang Taiwan 31 2.1k 1.3k 797 551 405 116 3.0k
Yujun Li China 26 302 0.1× 427 0.3× 503 0.6× 441 0.8× 157 0.4× 140 2.1k
Wei Jiang China 28 419 0.2× 498 0.4× 298 0.4× 186 0.3× 190 0.5× 134 2.4k
T. Radhakrishnan India 22 507 0.2× 268 0.2× 496 0.6× 128 0.2× 91 0.2× 100 1.4k
Yan Wu China 31 896 0.4× 304 0.2× 478 0.6× 179 0.3× 523 1.3× 127 2.9k
Cheng Zhou China 29 1.1k 0.5× 169 0.1× 772 1.0× 343 0.6× 92 0.2× 139 2.7k
Matthew Wilding United Kingdom 22 955 0.5× 286 0.2× 211 0.3× 97 0.2× 61 0.2× 55 1.7k
Rong Lü Japan 28 358 0.2× 103 0.1× 264 0.3× 283 0.5× 201 0.5× 90 2.0k
Yulong Chen China 24 392 0.2× 282 0.2× 895 1.1× 178 0.3× 89 0.2× 169 2.4k
Shuai Zhang China 26 614 0.3× 712 0.5× 527 0.7× 279 0.5× 33 0.1× 109 1.9k
Jorge López‐Beceiro Spain 23 522 0.3× 269 0.2× 857 1.1× 111 0.2× 44 0.1× 75 1.9k

Countries citing papers authored by Chun‐Shan Wang

Since Specialization
Citations

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

Fields of papers citing papers by Chun‐Shan Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chun‐Shan Wang. 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 Chun‐Shan Wang. The network helps show where Chun‐Shan Wang may publish in the future.

Co-authorship network of co-authors of Chun‐Shan Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Chun‐Shan Wang. A scholar is included among the top collaborators of Chun‐Shan Wang 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 Chun‐Shan Wang. Chun‐Shan Wang 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
1.
Wang, Chun‐Shan, Yuxuan Zhu, Dongyu Chen, et al.. (2025). Yield Diagnosis and Tuning for Emerging Semiconductors During Research Stage. IEEE Access. 13. 78915–78927. 1 indexed citations
3.
Zhang, Lijie, et al.. (2024). Chinese named entity recognition for agricultural diseases based on entity-related visual prompts injection. Computers and Electronics in Agriculture. 227. 109493–109493. 5 indexed citations
4.
Wang, Chun‐Shan, et al.. (2023). A Cucumber Leaf Disease Severity Grading Method in Natural Environment Based on the Fusion of TRNet and U-Net. Agronomy. 14(1). 72–72. 10 indexed citations
5.
Sun, Wei, et al.. (2023). Veg-DenseCap: Dense Captioning Model for Vegetable Leaf Disease Images. Agronomy. 13(7). 1700–1700. 7 indexed citations
6.
Zhao, Chunjiang, et al.. (2022). A Vegetable Leaf Disease Identification Model Based on Image-Text Cross-Modal Feature Fusion. Frontiers in Plant Science. 13. 918940–918940. 13 indexed citations
7.
Wang, Chun‐Shan, Ji Zhou, Yan Zhang, et al.. (2022). A Plant Disease Recognition Method Based on Fusion of Images and Graph Structure Text. Frontiers in Plant Science. 12. 731688–731688. 12 indexed citations
8.
Wang, Bin, Yan Zhang, Chun‐Shan Wang, & Guifa Teng. (2022). Droplet Deposition Distribution Prediction Method for a Six-Rotor Plant Protection UAV Based on Inverse Distance Weighting. Sensors. 22(19). 7425–7425. 6 indexed citations
9.
Wang, Chun‐Shan, et al.. (2022). A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS. Agronomy. 12(10). 2555–2555. 15 indexed citations
10.
Wang, Chun‐Shan, Ji Zhou, Chunjiang Zhao, et al.. (2021). Few-shot vegetable disease recognition model based on image text collaborative representation learning. Computers and Electronics in Agriculture. 184. 106098–106098. 37 indexed citations
11.
Lin, Yann‐Rong, et al.. (2018). Subtropical adaptation of a temperate plant (Brassica oleracea var. italica) utilizes non-vernalization-responsive QTLs. Scientific Reports. 8(1). 13609–13609. 17 indexed citations
12.
Hwang, Hann‐Jang, et al.. (2008). Flame retardancy and dielectric properties of dicyclopentadiene‐based benzoxazine cured with a phosphorus‐containing phenolic resin. Journal of Applied Polymer Science. 110(4). 2413–2423. 30 indexed citations
13.
Hwang, Hann‐Jang, et al.. (2006). Dielectric and thermal properties of dicyclopentadiene containing bismaleimide and cyanate ester. Part IV. Polymer. 47(4). 1291–1299. 71 indexed citations
14.
Shieh, Jeng‐Yueh, et al.. (2004). Synthesis and characterization of novel polyesterimides. Journal of Applied Polymer Science. 94(2). 730–738. 6 indexed citations
15.
Shieh, Jeng‐Yueh & Chun‐Shan Wang. (2000). Synthesis and properties of novel phosphorus-containing hardener for epoxy resins. Journal of Applied Polymer Science. 78(9). 1636–1644. 45 indexed citations
16.
Wang, Chun‐Shan & Jeng‐Yueh Shieh. (1999). Phosphorus-containing epoxy resin for an electronic application. Journal of Applied Polymer Science. 73(3). 353–361. 106 indexed citations
17.
Wang, Chun‐Shan, et al.. (1998). Novel bismaleimide with naphthalene side group. 1. From 1-naphthaldehyde and 2,6-dimethylaniline. Polymer. 39(13). 2921–2927. 65 indexed citations
18.
Wang, Chun‐Shan, et al.. (1997). Synthesis and properties of flourine-containing polyimides. Journal of Applied Polymer Science. 66(4). 609–617. 31 indexed citations
19.
Wang, Chun‐Shan, et al.. (1994). Poly(ethylene naphthalate) formation 1. transesterification of dimethylnaphthalate with ethylene glycol. Journal of Polymer Research. 1(2). 131–139. 4 indexed citations
20.
Tou, James C. & Chun‐Shan Wang. (1972). Mass spectrometric and pyrolytic studies of diphenylarsine derivatives. Journal of Organometallic Chemistry. 34(1). 141–152. 7 indexed citations

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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