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:

Revealing acetylene separation performances of anion-pillared MOFs by combining molecular simulations and machine learning
Hakan Demir, Seda Keskın
Chemical Engineering Journal (2023) Vol. 464, pp. 142731-142731
Open Access | Times Cited: 13

Showing 13 citing articles:

Thiadiazole-Functionalized Th/Zr-UiO-66 for Efficient C2H2/CO2 Separation
Xiaokang Wang, Hongyan Liu, Meng Sun, et al.
ACS Applied Materials & Interfaces (2024) Vol. 16, Iss. 6, pp. 7819-7825
Closed Access | Times Cited: 5

Combining computational screening and machine learning to explore MOFs and COFs for methane purification
Hasan Can Gülbalkan, Alper Uzun, Seda Keskın
Applied Physics Letters (2024) Vol. 124, Iss. 20
Open Access | Times Cited: 5

Unveiling Cutting-edge progress in coordination chemistry of the Metal-Organic frameworks (MOFs) and their Composites: Fundamentals, synthesis Strategies, electrochemical and environmental applications
Valentine Chikaodili Anadebe, Abhinay Thakur, Chandrabhan Verma, et al.
Journal of Industrial and Engineering Chemistry (2025)
Closed Access

Metal-organic frameworks for low-concentration gases adsorption under ambient conditions: Characterization, modification, processing, shaping and applications
Hao Wang, Yufan Jiang, Rui Han, et al.
Coordination Chemistry Reviews (2025) Vol. 531, pp. 216464-216464
Closed Access

Discovery of metal-organic frameworks for efficient NF3/N2 separation by integrating high-throughput computational screening, machine learning, and experimental validation
Yanjing He, Zhi Fang, Weijiang Xue, et al.
Separation and Purification Technology (2025), pp. 132481-132481
Closed Access

A Systematic Approach for Incorporating Structural Flexibility in High-Throughput Computational Screening of Metal–Organic Frameworks for Xylene Separation
Saad Aldin Mohamed, Rui Zheng, Neng‐Xiu Zhu, et al.
Journal of the American Chemical Society (2025)
Closed Access

Molecular Fingerprint and Machine Learning Enhance High-Performance MOFs for Mustard Gas Removal
Jing Ni, Jinfeng Li, Shuhua Li, et al.
iScience (2024) Vol. 27, Iss. 6, pp. 110042-110042
Open Access | Times Cited: 1

Machine learning aided computational exploration of metal–organic frameworks with open Cu sites for the effective separation of hydrogen isotopes
Yanling Chen, Yunpan Ying, Yizhen Situ, et al.
Separation and Purification Technology (2023) Vol. 334, pp. 126001-126001
Closed Access | Times Cited: 3

Optimizing host–guest selectivity through side-chain modification of pillar[6]arenes for o-ethyltoluene/m-ethyltoluene separation
Yang Liu, Shuai Fang, Li Shao, et al.
Microporous and Mesoporous Materials (2024) Vol. 369, pp. 113006-113006
Closed Access

Ionic liquids in acetylene separation: Progress and challenges
Chao Liang, Dong Zhe Chai, Yong‐Wei Zhang, et al.
Separation and Purification Technology (2024), pp. 131234-131234
Closed Access

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