Shiting Wang
Impact in
- Filtration and Separation top 5%
- Chemical and Physical Properties in Aqueous Solutions
- Process Chemistry and Technology top 10%
- Carbon dioxide utilization in catalysis
Papers in
-
- Carbon dioxide utilization in catalysis 4
-
- CO2 Reduction Techniques and Catalysts 4
- Co-authors
- Chiou‐Ying Yang (2 shared papers)Wen‐Hsin Hsieh (2 shared papers)I‐Ting Kuo (2 shared papers)Lai‐Kwan Chau (2 shared papers)Hsing‐Ying Lin (2 shared papers)Zhifeng Dai (4 shared papers)Yubing Xiong (4 shared papers)Yuxia Liu (3 shared papers)
- Journals
- Swarm and Evolutionary Computation (2 papers)Journal of Macromolecular Science Part B (2 papers)Small (2 papers)New Journal of Chemistry (2 papers)Journal of CO2 Utilization (2 papers)
- Partner nations
- ChinaUnited KingdomTaiwan
In The Last Decade
Shiting Wang
17 papers receiving 347 citations
Peers
Comparison fields: 5 of 74
- Filtration and Separation 43
- Process Chemistry and Technology 45
- Biophysics 23
- Biomedical Engineering 135
- Materials Chemistry 140
Countries citing papers authored by Shiting Wang
This map shows the geographic impact of Shiting 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 Shiting Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shiting Wang more than expected).
Fields of papers citing papers by Shiting Wang
This network shows the impact of papers produced by Shiting 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 Shiting Wang. The network helps show where Shiting Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Shiting Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 114 | |
| 2 | 2019 | 59 | |
| 3 | 2016 | 36 | |
| 4 | 2013 | 36 | |
| 5 | 2021 | 18 | |
| 6 | 2021 | 16 | |
| 7 | 2019 | 16 | |
| 8 | 2023 | 13 | |
| 9 | 2022 | 11 | |
| 10 | 2018 | 9 | |
| 11 | 2024 | 6 | |
| 12 | 2022 | 5 | |
| 13 | 2024 | 4 | |
| 14 | 2021 | 3 | |
| 15 | 2014 | 2 | |
| 16 | 2024 | 1 | |
| 17 | 2023 | 1 | |
| 18 | 2025 | 0 |
About Shiting Wang
Shiting Wang is a scholar working on Process Chemistry and Technology, Renewable Energy, Sustainability and the Environment, Computational Theory and Mathematics, Catalysis and Biomedical Engineering, having authored 18 papers that have together received 350 indexed citations. Recurring topics across this work include CO2 Reduction Techniques and Catalysts (4 papers), Advanced Multi-Objective Optimization Algorithms (4 papers), Carbon dioxide utilization in catalysis (4 papers), Metaheuristic Optimization Algorithms Research (3 papers), Evolutionary Algorithms and Applications (2 papers), Carbon Dioxide Capture Technologies (2 papers), Dielectric materials and actuators (2 papers) and Advanced Sensor and Energy Harvesting Materials (2 papers). The work is most often cited by research in Filtration and Separation (43 citations), Process Chemistry and Technology (45 citations), Biophysics (23 citations), Biomedical Engineering (135 citations) and Materials Chemistry (140 citations). Shiting Wang has collaborated with scholars based in China, United Kingdom and Taiwan. Frequent co-authors include Chiou‐Ying Yang, Wen‐Hsin Hsieh, I‐Ting Kuo, Lai‐Kwan Chau, Hsing‐Ying Lin, Zhifeng Dai, Yubing Xiong, Yuxia Liu, De-Man Han and Chu Chu. Their work appears in journals such as Swarm and Evolutionary Computation, Journal of Macromolecular Science Part B, Small, New Journal of Chemistry and Journal of CO2 Utilization.
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.