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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 for membrane design and discovery
Haoyu Yin, Muzi Xu, Zhiyao Luo, et al.
Green Energy & Environment (2022) Vol. 9, Iss. 1, pp. 54-70
Open Access | Times Cited: 49

Showing 1-25 of 49 citing articles:

Machine learning for membrane design in energy production, gas separation, and water treatment: a review
Ahmed I. Osman, Mahmoud Nasr, Mohamed Farghali, et al.
Environmental Chemistry Letters (2024) Vol. 22, Iss. 2, pp. 505-560
Open Access | Times Cited: 23
Localized assembly in biological activity: Origin of life and future of nanoarchitectonics
Jingwen Song, Kohsaku Kawakami, Katsuhiko Ariga
Advances in Colloid and Interface Science (2025) Vol. 339, pp. 103420-103420
Closed Access | Times Cited: 1
Machine learning in gas separation membrane developing: Ready for prime time
Jing Wang, Kai Tian, Dongyang Li, et al.
Separation and Purification Technology (2023) Vol. 313, pp. 123493-123493
Closed Access | Times Cited: 37
Different applications of machine learning approaches in materials science and engineering: Comprehensive review
Yan Cao, Ali Taghvaie Nakhjiri, Mahdi Ghadiri
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108783-108783
Closed Access | Times Cited: 11
Polymeric porous membranes as solid support and protective material in microextraction processes: A review
Lutfi Andre Yahya, Marek Tobiszewski, Paweł Kubica, et al.
TrAC Trends in Analytical Chemistry (2024) Vol. 173, pp. 117651-117651
Open Access | Times Cited: 9
Scalable Preparation of Ultraselective and Highly Permeable Fully Aromatic Polyamide Nanofiltration Membranes for Antibiotic Desalination
Haohao Liu, Lijun Liang, Feng Tian, et al.
Angewandte Chemie International Edition (2024) Vol. 63, Iss. 23
Closed Access | Times Cited: 8
Machine learning for the advancement of membrane science and technology: A critical review
Gergő Ignácz, Lana Bader, Aron K. Beke, et al.
Journal of Membrane Science (2024) Vol. 713, pp. 123256-123256
Open Access | Times Cited: 8
Analysis of PEM and AEM electrolysis by neural network pattern recognition, association rule mining and LIME
M. Erdem Günay, Niyazi Alper Tapan
Energy and AI (2023) Vol. 13, pp. 100254-100254
Open Access | Times Cited: 20
Machine Learning for Heavy Metal Removal from Water: Recent Advances and Challenges
Xiangzhou Yuan, Jie Li, Juin Yau Lim, et al.
ACS ES&T Water (2023) Vol. 4, Iss. 3, pp. 820-836
Closed Access | Times Cited: 16
Sustainable valorisation of food waste into engineered biochars for CO2 capture towards a circular economy
Wenhui Jia, Shuangjun Li, Junyao Wang, et al.
Green Chemistry (2024) Vol. 26, Iss. 4, pp. 1790-1805
Closed Access | Times Cited: 6
Scalable Preparation of Ultraselective and Highly Permeable Fully Aromatic Polyamide Nanofiltration Membranes for Antibiotic Desalination
Haohao Liu, Lijun Liang, Feng Tian, et al.
Angewandte Chemie (2024) Vol. 136, Iss. 23
Closed Access | Times Cited: 5
Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review
Izabela Rojek, Dariusz Mikołajewski, A. Mroziński, et al.
Electronics (2024) Vol. 13, Iss. 16, pp. 3338-3338
Open Access | Times Cited: 5
MOF membranes for gas separations
Yiming Zhang, Hang Yin, Lingzhi Huang, et al.
Progress in Materials Science (2025), pp. 101432-101432
Closed Access
Ultrahigh antifouling ultrafiltration membrane based on Ag NPs photoreduction modified PVP/BiOCl nanoflower for efficient membrane fouling removal
Chunmei Gao, Wenjing Tan, Baogui Liang, et al.
Environmental Research (2025) Vol. 270, pp. 120842-120842
Closed Access
Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane
Nadeem Baig, Sani I. Abba, Jamilu Usman, et al.
Environmental Science Advances (2023) Vol. 2, Iss. 10, pp. 1446-1459
Open Access | Times Cited: 15
A critical methodological revisit on group-contribution based property prediction of ionic liquids with machine learning
P.-L. Cao, Jiahui Chen, Guzhong Chen, et al.
Chemical Engineering Science (2024) Vol. 298, pp. 120395-120395
Closed Access | Times Cited: 4
An Overview of Recent Advances in Inorganic Nanomaterials and MOFs in Modification of Forward Osmosis Membranes for Industrial Wastewater Treatment
Pravin R. Gulave, Sadanand Y Guhe
Journal of Inorganic and Organometallic Polymers and Materials (2024)
Closed Access | Times Cited: 4
Comprehensive sensitivity and mechanism analysis of fuel cell performance under varying operating conditions using RF–Sobol–DRT approach
Bowen Liang, Huanxia Wei, Meng-Zhu Shen, et al.
Energy Conversion and Management (2025) Vol. 326, pp. 119486-119486
Closed Access
Recent progress on the development of non-fluorinated proton exchange membrane-A review
Peng Song, Yi Zhang, Xue Zhang, et al.
Green Energy & Environment (2025)
Open Access
Hydro-environmental predictive management of sub-surface salinization in arid nearshore-coastal saline aquifer using deep learning and SHAP analysis
Fahad Jibrin Abdu, Shaibu Isah, Jamilu Usman, et al.
Research Square (Research Square) (2025)
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
Zeolite-Based Poly(vinylidene fluoride) Ultrafiltration Membrane: Characterization and Molecular Weight Cut-Off Estimation with Support Vector Regression Modelling
Mieow Kee Chan, Syee Jia Tan, Andrew T.H. Yeow, et al.
Membranes (2024) Vol. 14, Iss. 4, pp. 91-91
Open Access | Times Cited: 3
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
Machine learning-assisted performance prediction from the synthesis conditions of nanofiltration membranes
Bhaumik Sutariya, Pulak Sarkar, Pankaj D. Indurkar, et al.
Separation and Purification Technology (2024) Vol. 354, pp. 128960-128960
Closed Access | Times Cited: 3
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
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