M. Wang
Impact in
- Water Science and Technology top 10%
- Adsorption and biosorption for pollutant removal
-
- Phosphorus and nutrient management
Papers in ⓘ
-
- Topic Modeling 2
- Machine Learning in Healthcare 2
- Co-authors
- Jihui Li (1 shared paper)Yucang Zhang (1 shared paper)Sen Liu (1 shared paper)Xinghua Xue (1 shared paper)Shuang Xu (1 shared paper)Jingfang Zhang (1 shared paper)Shaofu Lin (3 shared papers)Jingzhu Wang (1 shared paper)
- Journals
- Computer Physics Communications (1 paper)Annual Review of Nuclear and Particle Science (1 paper)Environmental Science & Technology (1 paper)Complex & Intelligent Systems (1 paper)Bioresource Technology (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
M. Wang
12 papers receiving 208 citations
Peers
Comparison fields: 5 of 63
- Water Science and Technology 121
- Industrial and Manufacturing Engineering 37
- Organic Chemistry 44
- Health Informatics 2
- Pollution 16
Countries citing papers authored by M. Wang
This map shows the geographic impact of M. 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 M. Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Wang more than expected).
Fields of papers citing papers by M. Wang
This network shows the impact of papers produced by M. 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 M. Wang. The network helps show where M. Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside M. 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 | 2019 | 150 | |
| 2 | 2023 | 16 | |
| 3 | 2018 | 8 | |
| 4 | 2024 | 7 | |
| 5 | 2021 | 6 | |
| 6 | 2011 | 6 | |
| 7 | 2022 | 4 | |
| 8 | 2021 | 4 | |
| 9 | 2020 | 3 | |
| 10 | 2024 | 2 | |
| 11 | 1996 | 2 | |
| 12 | 2021 | 2 | |
| 13 | 2024 | 0 | |
| 14 | 2024 | 0 | |
| 15 | 2024 | 0 | |
| 16 | 2025 | 0 | |
| 17 | 2024 | 0 |
About M. Wang
M. Wang is a scholar working on Artificial Intelligence, Health, Toxicology and Mutagenesis, Molecular Biology, Sociology and Political Science and Ecology, having authored 17 papers that have together received 210 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (2 papers), Topic Modeling (2 papers), Machine Learning in Healthcare (2 papers), Infection Control and Ventilation (1 paper), Aluminum toxicity and tolerance in plants and animals (1 paper), ECG Monitoring and Analysis (1 paper), Particle Detector Development and Performance (1 paper) and Medical Imaging Techniques and Applications (1 paper). The work is most often cited by research in Water Science and Technology (121 citations), Industrial and Manufacturing Engineering (37 citations), Organic Chemistry (44 citations), Health Informatics (2 citations) and Pollution (16 citations). M. Wang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Jihui Li, Yucang Zhang, Sen Liu, Xinghua Xue, Shuang Xu, Jingfang Zhang, Shaofu Lin, Jingzhu Wang, Daniel Johnson and I. Polyakov. Their work appears in journals such as Computer Physics Communications, Annual Review of Nuclear and Particle Science, Environmental Science & Technology, Complex & Intelligent Systems and Bioresource 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.