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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:

Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models
Tengyi Zhu, Yu Zhang, Cuicui Tao, et al.
The Science of The Total Environment (2022) Vol. 857, pp. 159348-159348
Closed Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Covalent organic framework membrane for efficient removal of emerging trace organic contaminants from water
Tongtong Liu, Yayun Zhang, Zhen Shan, et al.
Nature Water (2023) Vol. 1, Iss. 12, pp. 1059-1067
Open Access | Times Cited: 42
Research on the factors influencing nanofiltration membrane fouling and the prediction of membrane fouling
Wenjing Zheng, Yan Chen, Xiaohu Xu, et al.
Journal of Water Process Engineering (2024) Vol. 59, pp. 104876-104876
Closed Access | Times Cited: 28
Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater
Jamilu Usman, Sani I. Abba, Nadeem Baig, et al.
ACS Applied Materials & Interfaces (2024) Vol. 16, Iss. 13, pp. 16271-16289
Closed Access | Times Cited: 24
A new S-scheme heterojunction of 1D ZnGa2O4/ZnO nanofiber for efficient photocatalytic degradation of TC-HCl
Wenhui Chen, Tianxin Kang, Fenqi Du, et al.
Environmental Research (2023) Vol. 232, pp. 116388-116388
Closed Access | Times Cited: 29
Understanding Rejection Mechanisms of Trace Organic Contaminants by Polyamide Membranes via Data-Knowledge Codriven Machine Learning
Hejia Wang, Jin Zeng, Ruobin Dai, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 13, pp. 5878-5888
Closed Access | Times Cited: 9
Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives
Stefano Cairone, Shadi W. Hasan, Kwang‐Ho Choo, et al.
The Science of The Total Environment (2024) Vol. 944, pp. 173999-173999
Open Access | Times Cited: 8
Performance investigation of osmotically assisted reverse osmosis using explainable machine learning models: A comparative study
Sung Ho Chae, Seokyoon Moon, Seok Won Hong, et al.
Desalination (2024) Vol. 583, pp. 117647-117647
Open Access | Times Cited: 7
Machine learning toward improving the performance of membrane-based wastewater treatment: A review
Panchan Dansawad, Yanxiang Li, Yize Li, et al.
Advanced Membranes (2023) Vol. 3, pp. 100072-100072
Open Access | Times Cited: 16
Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest
Ram Proshad, Md. Abdur Rahim, Mahfuzur Rahman, et al.
The Science of The Total Environment (2024) Vol. 951, pp. 175746-175746
Closed Access | Times Cited: 5
Interpretable Machine Learning Models Delivering a New Perspective for the Reaction Mechanism between Organic Pollutants and Oxidative Radicals
Yiqiu Wu, Zhixiang Wang, Guangfei Yu, et al.
Environmental Science & Technology (2025)
Closed Access
The decontaminant mechanism of polyamide membranes for sulfamethoxazole: The insights from combined machine learning and molecular modelling
Zihang Zhao, Dan Lu, Ming Wu, et al.
Chemical Engineering Science (2025), pp. 121293-121293
Closed Access
Prediction and mechanism analysis of octanol-air partition coefficient for persistent organic pollutants based on machine learning models
Zhenpeng Xu, Hongxia Zhao, Jinyang Wang, et al.
Journal of environmental chemical engineering (2025) Vol. 13, Iss. 2, pp. 115741-115741
Closed Access
Strengthening nanofiltration membrane: Strategies for enhanced antifouling performance
Lulu Liu, Yuan Liu, Jiachen Huang, et al.
Chemical Engineering Journal (2025), pp. 160964-160964
Closed Access
Experiments and machine learning-based modeling for haloacetic acids rejection by nanofiltration: Influence of solute properties and operating conditions
Feifei Wang, Weikang Wang, Hongjie Wang, et al.
The Science of The Total Environment (2023) Vol. 883, pp. 163610-163610
Closed Access | Times Cited: 13
Ensemble machine learning reveals key structural and operational features governing ion selectivity of polyamide nanofiltration membranes
Dan Lu, Xuanchao Ma, Jiancong Lu, et al.
Desalination (2023) Vol. 564, pp. 116748-116748
Closed Access | Times Cited: 13
Micropollutant rejection by nanofiltration membranes: A mini review dedicated to the critical factors and modelling prediction
Rui Xu, Zeqian Zhang, Chenning Deng, et al.
Environmental Research (2023) Vol. 244, pp. 117935-117935
Open Access | Times Cited: 12
Metadata and feature importance analyses of membrane capacitive deionization models: Is a water treatment artificial intelligence panacea possible?
Sung Ho Chae, Seok Won Hong, Moon Son
Desalination (2024) Vol. 585, pp. 117784-117784
Open Access | Times Cited: 4
Application of machine learning in predicting the apparent diffusion coefficient of Se(IV) in compacted bentonite
Xiaoqiong Shi, Junlei Tian, Jiacong Shen, et al.
Journal of Radioanalytical and Nuclear Chemistry (2024) Vol. 333, Iss. 11, pp. 5811-5821
Closed Access | Times Cited: 4
Gradient boosting decision tree algorithms for accelerating nanofiltration membrane design and discovery
Weijia Gong, Hangbin Xu, Jinyan Lu, et al.
Desalination (2024) Vol. 592, pp. 118072-118072
Closed Access | Times Cited: 4
A holistic framework for improving the prediction of reverse osmosis membrane performance using machine learning
Areej Mohammed, Hussam Alshraideh, Fatima Alsuwaidi
Desalination (2023) Vol. 574, pp. 117253-117253
Closed Access | Times Cited: 11
Data-driven future for nanofiltration: Escaping linearity
Gergő Ignácz, Aron K. Beke, György Székely
Journal of Membrane Science Letters (2023) Vol. 3, Iss. 1, pp. 100040-100040
Open Access | Times Cited: 10
Predicting the boron removal of reverse osmosis membranes using machine learning
Sukarno Sukarno, Jeng Yi Chong, Gao Cong
Desalination (2024) Vol. 586, pp. 117854-117854
Open Access | Times Cited: 3
Deep learning with improved hybrid neuro-turning for predictive control of flux based on experimental DCMD module design of water desalination system
Osman Shamet, Sani I. Abba, Jamil Usman, et al.
Journal of Water Process Engineering (2024) Vol. 65, pp. 105835-105835
Closed Access | Times Cited: 3
Machine Learning Models for Predicting the Rejection of Organic Pollutants by Forward Osmosis and Reverse Osmosis Membranes and Unveiling the Rejection Mechanisms
Adel Tayara, Chii Shang, Jing Zhao, et al.
Water Research (2024) Vol. 266, pp. 122363-122363
Closed Access | Times Cited: 3
Enhanced desalination with polyamide thin-film membranes using ensemble ML chemometric methods and SHAP analysis
Jamilu Usman, Sani I. Abba, Fahad Jibrin Abdu, et al.
RSC Advances (2024) Vol. 14, Iss. 43, pp. 31259-31273
Open Access | Times Cited: 3
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