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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning approaches to predict the apparent rate constants for aqueous organic compounds by ferrate
Shanshan Zheng, Wanqian Guo, Hao Lü, et al.
Journal of Environmental Management (2022) Vol. 329, pp. 116904-116904
Closed Access | Times Cited: 8

Showing 8 citing articles:

Practical Machine Learning Model Selection and Interpretation for Organophosphorus Flame Retardancy in Epoxy Resin
Jiajun Li, Bin Zou, Amirbek Bekeshev, et al.
Polymer Degradation and Stability (2025) Vol. 234, pp. 111209-111209
Closed Access | Times Cited: 1
Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals
Youheng Liang, Xiaoliu Huangfu, Ruixing Huang, et al.
Journal of Hazardous Materials (2024) Vol. 472, pp. 134501-134501
Closed Access | Times Cited: 6
Unveiling similarities and differences in oxidation processes of oxidants and derived reactive oxygen species through machine learning interpretation of oxidation rate constants
Zuhong Lin, Siyuan Pan, Haipu Li, et al.
Separation and Purification Technology (2024), pp. 126649-126649
Closed Access | Times Cited: 4
Unveiling the fate of metal leaching in bimetal-catalyzed Fenton-like systems: pivotal role of aqueous matrices and machine learning prediction
Wenrui Jia, Ying Li, Chuchu Chen, et al.
Journal of Hazardous Materials (2024) Vol. 477, pp. 135291-135291
Closed Access | Times Cited: 4
Prediction of Chlorination Degradation Rate of Emerging Contaminants Based on Machine Learning Models
Yufan Du, Ting Tang, Dehao Song, et al.
Environmental Pollution (2025), pp. 125976-125976
Closed Access
Electrodeposited FeNi3 alloy on 3D-printed monoliths as a stable catalyst for ofloxacin degradation in wastewater
Yu Yang, Xinlu Liu, Fei Huang, et al.
Chemical Engineering Journal (2025) Vol. 512, pp. 162408-162408
Closed Access
Innovative Feature Engineering Driven by Chemical Category in Machine Learning for Optimizing the Prediction of Hydroxyl Radical Reaction Rate Constants
Guosen Zhang, Yujie Cheng, Guoqing Zhang, et al.
ACS ES&T Engineering (2025)
Closed Access
Developing a hybrid model for predicting the reaction kinetics between chlorine and micropollutants in water
Jing Zhao, Chii Shang, Ran Yin
Water Research (2023) Vol. 247, pp. 120794-120794
Closed Access | Times Cited: 6
Graph Neural Network Integrating Self-Supervised Pretraining for Precise and Interpretable Prediction of Micropollutant Treatability by HO•-Based Advanced Oxidation Processes
Jingyi Zhu, Yuanxi Huang, Lingjun Bu, et al.
ACS ES&T Engineering (2024)
Closed Access | Times Cited: 1
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