Masa‐aki Kakimoto

13.8k citations
442 papers · 11.9k indexed · 2 hit papers · h-index 54

Masa‐aki Kakimoto

440 papers receiving 11.6k citations

Hit Papers

Machine-learning-assis...3552001202620092017250500750

Peers

Masa‐aki Kakimoto
Comparison fields: 5 of 119
  • Polymers and Plastics 7.7k
  • Organic Chemistry 3.8k
  • Materials Chemistry 4.7k
  • Mechanical Engineering 2.4k
  • Surfaces, Coatings and Films 363
Replace Mitsuru Ueda with:
Mitsuru Ueda Japan
Paul Smith Switzerland
Martin D. Hager Germany
Hartmut Komber Germany
Won Ho Jo South Korea
Wei Zhang China
Toshio Masuda Japan
Mark Dadmun United States
Yoshio Imai Japan
Wei Tang China
Masa‐aki Kakimoto relative to Mitsuru Ueda Japan Mitsuru Ueda's profile →
Citations per field
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Mitsuru Ueda · 1×
Citations per year

Countries citing papers authored by Masa‐aki Kakimoto

Since Specialization
Citations

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

Fields of papers citing papers by Masa‐aki Kakimoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Masa‐aki Kakimoto. 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 Masa‐aki Kakimoto. The network helps show where Masa‐aki Kakimoto may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1
Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithmbreakdown →
2019355
2 201818
3 20186
4 201811
5 20187
6 20180
7 201721
8 20176
9 20156
10 200997
11 200925
12 20062
13 20031
14 20036
15 20026
16 19951
17 199514
18 19901
19 198718
20
指向性2-アゾニア〔3,3〕シグマトロープ転位による炭素-炭素結合の生成 新しいピロリジン合成
19791

About Masa‐aki Kakimoto

Masa‐aki Kakimoto is a scholar working on Polymers and Plastics, Organic Chemistry and Process Chemistry and Technology, having authored 442 papers that have together received 11.9k indexed citations. Recurring topics across this work include Synthesis and properties of polymers (274 papers), Silicone and Siloxane Chemistry (94 papers), Dendrimers and Hyperbranched Polymers (83 papers), Epoxy Resin Curing Processes (81 papers), Synthetic Organic Chemistry Methods (47 papers), Conducting polymers and applications (34 papers), Organic Electronics and Photovoltaics (28 papers) and Fuel Cells and Related Materials (25 papers). The work is most often cited by research in Polymers and Plastics (7.7k citations), Organic Chemistry (3.8k citations) and Materials Chemistry (4.7k citations). Masa‐aki Kakimoto has collaborated with scholars based in Japan, South Korea and United States. Frequent co-authors include Yoshio Imai, Mitsutoshi Jikei, Teruaki Hayakawa, Yoshiyuki Oishi, Yuta Nabae, Atsushi Morikawa, N. N. Maldar, Yoshitake Iyoku, Hwa‐Jin Jeong and Jisung Park. Their work appears in journals such as Journal of Polymer Science Part A Polymer Chemistry, Polymer Journal, Macromolecules, High Performance Polymers and Journal of Photopolymer Science and Technology.

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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