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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:

Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization
Haiping Gao, Shifa Zhong, Wenlong Zhang, et al.
Environmental Science & Technology (2021) Vol. 56, Iss. 4, pp. 2572-2581
Closed Access | Times Cited: 104

Showing 26-50 of 104 citing articles:

Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation
Huiming Cao, Peng Jian-hua, Zhen Zhou, et al.
Environmental Science & Technology (2022) Vol. 57, Iss. 46, pp. 17762-17773
Closed Access | Times Cited: 30
Machine learning strategies for the structure-property relationship of copolymers
Lei Tao, John Byrnes, Vikas Varshney, et al.
iScience (2022) Vol. 25, Iss. 7, pp. 104585-104585
Open Access | Times Cited: 28
Inkjet printing technique for membrane fabrication and modification: A review
Chen Wang, Myoung Jun Park, Youngwoo Choo, et al.
Desalination (2023) Vol. 565, pp. 116841-116841
Closed Access | Times Cited: 20
Exploring the Knowledge Attained by Machine Learning on Ion Transport across Polyamide Membranes Using Explainable Artificial Intelligence
Nohyeong Jeong, Razi Epsztein, Ruoyu Wang, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 17851-17862
Closed Access | Times Cited: 18
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
Designing desalination MXene membranes by machine learning and global optimization algorithm
Xuanchao Ma, Chengyi Lan, Haoran Lin, et al.
Journal of Membrane Science (2024) Vol. 702, pp. 122803-122803
Closed Access | Times Cited: 6
Smart materials for sustainable energy
J. Bhattacharjee, Subhasis Roy
Natural Resources Conservation and Research (2024) Vol. 7, Iss. 1, pp. 5536-5536
Open Access | Times Cited: 6
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: 6
Machine learning – Driven surface grafting of thin-film composite reverse osmosis (TFC-RO) membrane
Arash Tayyebi, Ali Alshami, Erfan Tayyebi, et al.
Desalination (2024) Vol. 579, pp. 117502-117502
Closed Access | Times Cited: 5
Molecular fingerprint-aided prediction of organic solute rejection in reverse osmosis and nanofiltration
Sangsuk Lee, Michael R. Shirts, Anthony P. Straub
Journal of Membrane Science (2024) Vol. 705, pp. 122927-122927
Closed Access | Times Cited: 5
Transforming PFAS management: A critical review of machine learning applications for enhanced monitoring and treatment
Md Hasan-Ur Rahman, Rabbi Sikder, Tanvir Ahamed Tonmoy, et al.
Journal of Water Process Engineering (2025) Vol. 70, pp. 106941-106941
Closed Access
DFT-assisted machine learning for polyester membrane design in textile wastewater recovery applications
Peng Liu, Hangbin Xu, Pengrui Jin, et al.
Water Research (2025) Vol. 279, pp. 123438-123438
Closed Access
Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds
Kai Zhang, Huichun Zhang
ACS ES&T Water (2022) Vol. 4, Iss. 3, pp. 773-783
Closed Access | Times Cited: 23
Can machine learning methods guide gas separation membranes fabrication?
Arash Tayyebi, Ali Alshami, Xue Yu, et al.
Journal of Membrane Science Letters (2022) Vol. 2, Iss. 2, pp. 100033-100033
Open Access | Times Cited: 23
Machine Learning Guided Polyamide Membrane with Exceptional Solute–Solute Selectivity and Permeance
Hao Deng, Zhiyao Luo, Joe Imbrogno, et al.
Environmental Science & Technology (2022) Vol. 57, Iss. 46, pp. 17841-17850
Closed Access | Times Cited: 23
Revealing key structural and operating features on water/salts selectivity of polyamide nanofiltration membranes by ensemble machine learning
Xuanchao Ma, Dan Lu, Jiancong Lu, et al.
Desalination (2022) Vol. 548, pp. 116293-116293
Closed Access | Times Cited: 22
Membrane Science Meets Machine Learning: Future and Potential Use in Assisting Membrane Material Design and Fabrication
Musabbir Jahan Talukder, Ali Alshami, Arash Tayyebi, et al.
Separation and Purification Reviews (2023) Vol. 53, Iss. 2, pp. 216-229
Closed Access | Times Cited: 15
Physics-informed neural network-based serial hybrid model capturing the hidden kinetics for sulfur-driven autotrophic denitrification process
Xu Zou, Hongxiao Guo, Chu-Kuan Jiang, et al.
Water Research (2023) Vol. 243, pp. 120331-120331
Closed Access | Times Cited: 15
Machine Learning-Assisted Design of Thin-Film Composite Membranes for Solvent Recovery
Mao Wang, Gui Min Shi, Daohui Zhao, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 42, pp. 15914-15924
Closed Access | Times Cited: 13
Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective
Chi Wu, Yanan Xu, Jianguang Fang, et al.
Archives of Computational Methods in Engineering (2024)
Open Access | Times Cited: 4
Integrating chemistry knowledge in large language models via prompt engineering
Hongxuan Liu, Haoyu Yin, Zhiyao Luo, et al.
Synthetic and Systems Biotechnology (2024) Vol. 10, Iss. 1, pp. 23-38
Open Access | Times Cited: 4
Predicting the Performance of Lithium Adsorption and Recovery from Unconventional Water Sources with Machine Learning
Ziyang Xu, Yihao Ding, Soyeon Caren Han, et al.
Water Research (2024) Vol. 266, pp. 122374-122374
Closed Access | Times Cited: 4
Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm
Chunhui Xie, Haoke Qiu, Lu Liu, et al.
SmartMat (2025) Vol. 6, Iss. 1
Open Access
Machine learning-based Bayesian optimization facilitates ultrafiltration process design for efficient protein purification
Qinglin Lu, Hao Zhang, Rong Fan, et al.
Separation and Purification Technology (2025), pp. 132122-132122
Closed Access
Data Driven Modeling and Design of Cellulose Acetate-Polysulfone Blend Ultrafiltration Membranes Based on Artificial Neural Networks
Elif Gungormus
Journal of environmental chemical engineering (2025), pp. 116337-116337
Closed Access

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