Haoyun Wang
Impact in
- Materials Chemistry top 10%
- 2D Materials and Applications
- MXene and MAX Phase Materials
- Graphene research and applications
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- Perovskite Materials and Applications
- Advanced Memory and Neural Computing
- Gas Sensing Nanomaterials and Sensors
- Chalcogenide Semiconductor Thin Films
Papers in
Haoyun Wang
80 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 137
- Materials Chemistry 665
- Electrical and Electronic Engineering 683
- Electronic, Optical and Magnetic Materials 110
- Polymers and Plastics 73
- Biomedical Engineering 214
Countries citing papers authored by Haoyun Wang
This map shows the geographic impact of Haoyun 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 Haoyun Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haoyun Wang more than expected).
Fields of papers citing papers by Haoyun Wang
This network shows the impact of papers produced by Haoyun 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 Haoyun Wang. The network helps show where Haoyun Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Haoyun 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 | 2025 | 4 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 1 | |
| 6 | 2024 | 5 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 2 | |
| 9 | 2024 | 6 | |
| 10 | 2023 | 3 | |
| 11 | 2023 | 0 | |
| 12 | 2023 | 9 | |
| 13 | 2023 | 2 | |
| 14 | 2022 | 57 | |
| 15 | 2022 | 9 | |
| 16 | Uncertainty Quantification for Inferring Hawkes Networks | 2020 | 2 |
| 17 | 2019 | 10 | |
| 18 | Growth and physiological responses of seedlings with different leaf shapes to drought and re-watering in Pinus massoniana. | 2018 | 5 |
| 19 | Cyber physical system spatio-temporal modeling method and its application in greenhouse control. | 2015 | 2 |
| 20 | Layer Optimization for DHT-based Peer-to-Peer Network | 2011 | 1 |
About Haoyun Wang
Haoyun Wang is a scholar working on Acoustics and Ultrasonics, Speech and Hearing, Computer Networks and Communications, Pollution and Materials Chemistry, having authored 93 papers that have together received 1.3k indexed citations. Recurring topics across this work include 2D Materials and Applications (19 papers), Perovskite Materials and Applications (11 papers), MXene and MAX Phase Materials (7 papers), Peer-to-Peer Network Technologies (4 papers), Plant Stress Responses and Tolerance (4 papers), Wastewater Treatment and Nitrogen Removal (4 papers), Caching and Content Delivery (4 papers) and Plant Water Relations and Carbon Dynamics (4 papers). The work is most often cited by research in Materials Chemistry (665 citations), Electrical and Electronic Engineering (683 citations), Electronic, Optical and Magnetic Materials (110 citations), Polymers and Plastics (73 citations) and Biomedical Engineering (214 citations). Haoyun Wang has collaborated with scholars based in China, United States and Pakistan. Frequent co-authors include Tianyou Zhai, Zexin Li, Xing Zhou, Ping Chen, Lejing Pi, Dongyan Li, Dongyan Li, Xiang Xu, Peng Luo and Pengfei Wang. Their work appears in journals such as Forests, Advanced Optical Materials, ACS Nano, Advanced Materials and Advanced Functional Materials.
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.