Jin Da Tan

743 total citations · 1 hit paper
9 papers, 591 citations indexed

About

Jin Da Tan is a scholar working on Organic Chemistry, Materials Chemistry and Electrical and Electronic Engineering. According to data from OpenAlex, Jin Da Tan has authored 9 papers receiving a total of 591 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Organic Chemistry, 3 papers in Materials Chemistry and 2 papers in Electrical and Electronic Engineering. Recurrent topics in Jin Da Tan's work include Radical Photochemical Reactions (4 papers), Machine Learning in Materials Science (3 papers) and Innovative Microfluidic and Catalytic Techniques Innovation (2 papers). Jin Da Tan is often cited by papers focused on Radical Photochemical Reactions (4 papers), Machine Learning in Materials Science (3 papers) and Innovative Microfluidic and Catalytic Techniques Innovation (2 papers). Jin Da Tan collaborates with scholars based in Singapore, China and France. Jin Da Tan's co-authors include Jie Wu, Haolin Wu, Hairong Tao, Li‐Zhu Wu, Hong‐Ping Deng, Quan Zhou, Jiawei Rong, Ganglong Cui, Wengang Xu and Wei‐Hai Fang and has published in prestigious journals such as Angewandte Chemie International Edition, Advanced Functional Materials and ACS Energy Letters.

In The Last Decade

Jin Da Tan

9 papers receiving 584 citations

Hit Papers

Eosin Y as a Direct Hydrogen‐Atom Transfer Photocatalyst ... 2018 2026 2020 2023 2018 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jin Da Tan Singapore 7 461 110 72 70 49 9 591
Konstantin Poscharny Germany 4 723 1.6× 74 0.7× 37 0.5× 121 1.7× 80 1.6× 5 796
Alexandra C. Sun United States 10 238 0.5× 53 0.5× 56 0.8× 34 0.5× 29 0.6× 14 454
Anthony R. Allen United States 5 342 0.7× 41 0.4× 37 0.5× 52 0.7× 31 0.6× 5 478
Maximilian Lübbesmeyer Germany 10 313 0.7× 45 0.4× 72 1.0× 23 0.3× 57 1.2× 16 375
Ming-Cheng Yang China 8 409 0.9× 58 0.5× 96 1.3× 81 1.2× 26 0.5× 11 510
Pablo García‐Losada Spain 8 251 0.5× 41 0.4× 29 0.4× 36 0.5× 40 0.8× 17 369
Mindaugas Šiaučiulis United Kingdom 7 455 1.0× 41 0.4× 45 0.6× 14 0.2× 41 0.8× 9 532
Julius Hillenbrand Germany 13 448 1.0× 34 0.3× 33 0.5× 28 0.4× 46 0.9× 18 507
Sebastian Govaerts United Kingdom 8 558 1.2× 62 0.6× 39 0.5× 82 1.2× 78 1.6× 10 661
Mathieu Morin Canada 8 442 1.0× 53 0.5× 47 0.7× 38 0.5× 48 1.0× 11 530

Countries citing papers authored by Jin Da Tan

Since Specialization
Citations

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

Fields of papers citing papers by Jin Da Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jin Da Tan. 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 Jin Da Tan. The network helps show where Jin Da Tan may publish in the future.

Co-authorship network of co-authors of Jin Da Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Jin Da Tan. A scholar is included among the top collaborators of Jin Da Tan 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 Jin Da Tan. Jin Da Tan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Tan, Jin Da, Sze Yu Tan, Yee‐Fun Lim, et al.. (2024). Multi-objective synthesis optimization and kinetics of a sustainable terpolymer. Digital Discovery. 3(12). 2628–2636. 2 indexed citations
2.
Tan, Jin Da, Balamurugan Ramalingam, Vijila Chellappan, et al.. (2024). Generative Design and Experimental Validation of Non-Fullerene Acceptors for Photovoltaics. ACS Energy Letters. 9(10). 5240–5250. 1 indexed citations
3.
Tan, Jin Da, Balamurugan Ramalingam, Swee Liang Wong, et al.. (2023). Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization. Journal of Chemical Information and Modeling. 63(15). 4560–4573. 10 indexed citations
4.
Tan, Jin Da, Balamurugan Ramalingam, Riko I Made, et al.. (2022). An object-oriented framework to enable workflow evolution across materials acceleration platforms. Matter. 5(10). 3124–3134. 14 indexed citations
5.
Bash, Daniil, Vijila Chellappan, Swee Liang Wong, et al.. (2021). Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites. Advanced Functional Materials. 31(36). 34 indexed citations
6.
Xiao, Pin, Tingting Yang, Xiaojuan Dai, et al.. (2019). Neutral‐Eosin‐Y‐Photocatalyzed Silane Chlorination Using Dichloromethane. Angewandte Chemie. 131(36). 12710–12714. 11 indexed citations
7.
Xiao, Pin, Tingting Yang, Xiaojuan Dai, et al.. (2019). Neutral‐Eosin‐Y‐Photocatalyzed Silane Chlorination Using Dichloromethane. Angewandte Chemie International Edition. 58(36). 12580–12584. 65 indexed citations
8.
Rong, Jiawei, Haolin Wu, Quan Zhou, et al.. (2018). Eosin Y as a Direct Hydrogen‐Atom Transfer Photocatalyst for the Functionalization of C−H Bonds. Angewandte Chemie. 130(28). 8650–8654. 87 indexed citations
9.
Rong, Jiawei, Haolin Wu, Quan Zhou, et al.. (2018). Eosin Y as a Direct Hydrogen‐Atom Transfer Photocatalyst for the Functionalization of C−H Bonds. Angewandte Chemie International Edition. 57(28). 8514–8518. 367 indexed citations breakdown →

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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