Ruixing Huang

997 total citations
31 papers, 743 citations indexed

About

Ruixing Huang is a scholar working on Pollution, Water Science and Technology and Materials Chemistry. According to data from OpenAlex, Ruixing Huang has authored 31 papers receiving a total of 743 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Pollution, 8 papers in Water Science and Technology and 7 papers in Materials Chemistry. Recurrent topics in Ruixing Huang's work include Thallium and Germanium Studies (7 papers), Nanoparticles: synthesis and applications (6 papers) and Analytical chemistry methods development (5 papers). Ruixing Huang is often cited by papers focused on Thallium and Germanium Studies (7 papers), Nanoparticles: synthesis and applications (6 papers) and Analytical chemistry methods development (5 papers). Ruixing Huang collaborates with scholars based in China, Bangladesh and United States. Ruixing Huang's co-authors include Xiaoliu Huangfu, Chengxue Ma, Jun Ma, Qiang He, Qiang He, Caihong Liu, Yanghui Xu, Jun Ma, Zhengsong Wu and Qin Ou and has published in prestigious journals such as SHILAP Revista de lepidopterología, Environmental Science & Technology and The Science of The Total Environment.

In The Last Decade

Ruixing Huang

31 papers receiving 734 citations

Peers

Ruixing Huang
Ruixing Huang
Citations per year, relative to Ruixing Huang Ruixing Huang (= 1×) peers Chengxue Ma

Countries citing papers authored by Ruixing Huang

Since Specialization
Citations

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

Fields of papers citing papers by Ruixing Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ruixing Huang. 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 Ruixing Huang. The network helps show where Ruixing Huang may publish in the future.

Co-authorship network of co-authors of Ruixing Huang

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

All Works

20 of 20 papers shown
1.
Huang, Ruixing, et al.. (2025). A framework predicting removal efficacy of antibiotic resistance genes during disinfection processes with machine learning. Journal of Hazardous Materials. 492. 138048–138048. 1 indexed citations
2.
Wang, Jingrui, et al.. (2025). Simulation, prediction and optimization of heavy metal adsorption by metal-organic frameworks with machine learning. Environmental Research. 285(Pt 4). 122612–122612. 1 indexed citations
3.
Huang, Ruixing, et al.. (2025). Machine learning-guided prediction of chlorinated/chloraminated disinfection by-product formation in drinking water treatment. Water Research. 283. 123849–123849. 2 indexed citations
4.
Wang, Jingrui, Xiaoliu Huangfu, Ruixing Huang, et al.. (2025). Evaluating degradation efficiency of pesticides by persulfate, Fenton, and ozonation oxidation processes with machine learning. Environmental Research. 277. 121548–121548. 2 indexed citations
5.
Li, Hongye, Xiaojun Zhou, Ruixing Huang, et al.. (2024). Combined toxicity of biochar with nanoplastics or silver nanoparticles toward Chlorella vulgaris. Algal Research. 78. 103418–103418. 1 indexed citations
6.
Huangfu, Xiaoliu, et al.. (2024). The application of machine learning methods for prediction of heavy metal by activated carbons, biochars, and carbon nanotubes. Chemosphere. 354. 141584–141584. 20 indexed citations
7.
Ma, Chengxue, Hongye Li, Xiaoliu Huangfu, Ruixing Huang, & Jun Ma. (2024). Photochemical transformation and immobilization of thallium in the presence of iron and arsenic: Mechanistic insights from the coupled formation of arsenate complexes. Journal of Hazardous Materials. 469. 134081–134081. 1 indexed citations
8.
Wang, Jingrui, et al.. (2024). Prediction of antibiotic sorption in soil with machine learning and analysis of global antibiotic resistance risk. Journal of Hazardous Materials. 466. 133563–133563. 33 indexed citations
9.
Huangfu, Xiaoliu, et al.. (2024). Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals. Journal of Hazardous Materials. 472. 134501–134501. 10 indexed citations
10.
Yang, Pengfei, et al.. (2023). Spontaneous buckling morphology transition of an elastic ring confined in an annular region constraint. European Journal of Mechanics - A/Solids. 100. 105026–105026. 9 indexed citations
11.
Ma, Chengxue, et al.. (2022). Light- and H2O2-Mediated Redox Transformation of Thallium in Acidic Solutions Containing Iron: Kinetics and Mechanistic Insights. Environmental Science & Technology. 56(9). 5530–5541. 23 indexed citations
12.
Huang, Ruixing, Chengxue Ma, Jun Ma, Xiaoliu Huangfu, & Qiang He. (2021). Machine learning in natural and engineered water systems. Water Research. 205. 117666–117666. 208 indexed citations
13.
Ma, Chengxue, Haijun Cheng, Ruixing Huang, et al.. (2021). Kinetics of Thallium(I) Oxidation by Free Chlorine in Bromide-Containing Waters: Insights into the Reactivity with Bromine Species. Environmental Science & Technology. 56(2). 1017–1027. 16 indexed citations
14.
Xu, Yanghui, Qin Ou, Caihong Liu, et al.. (2020). Aggregation and deposition behaviors of dissolved black carbon with coexisting heavy metals in aquatic solution. Environmental Science Nano. 7(9). 2773–2784. 26 indexed citations
15.
Xu, Yanghui, Qin Ou, Xiaojun Zhou, et al.. (2020). Impacts of carrier properties, environmental conditions and extracellular polymeric substances on biofilm formation of sieved fine particles from activated sludge. The Science of The Total Environment. 731. 139196–139196. 22 indexed citations
16.
Xu, Yanghui, Caihong Liu, Qiang He, et al.. (2020). Interpreting the role of NO3−, SO42−, and extracellular polymeric substances on aggregation kinetics of CeO2 nanoparticles: Measurement and modeling. Ecotoxicology and Environmental Safety. 194. 110456–110456. 14 indexed citations
17.
Ou, Qin, Yanghui Xu, Xiaoling Li, et al.. (2020). Interactions between activated sludge extracellular polymeric substances and model carrier surfaces in WWTPs: A combination of QCM-D, AFM and XDLVO prediction. Chemosphere. 253. 126720–126720. 41 indexed citations
18.
Huang, Ruixing, et al.. (2019). Ion specific effects of monovalent cations on deposition kinetics of engineered nanoparticles onto the silica surface in aqueous media. Environmental Science Nano. 6(9). 2712–2723. 13 indexed citations
19.
Wang, Hainan, Ruixing Huang, Chengxue Ma, et al.. (2019). Release of deposited MnO2 nanoparticles from aqueous surfaces. Journal of Environmental Sciences. 90. 234–243. 3 indexed citations
20.
Ma, Chengxue, Xiaoliu Huangfu, Qiang He, Jun Ma, & Ruixing Huang. (2018). Deposition of engineered nanoparticles (ENPs) on surfaces in aquatic systems: a review of interaction forces, experimental approaches, and influencing factors. Environmental Science and Pollution Research. 25(33). 33056–33081. 30 indexed citations

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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