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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:

Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF4]/MOF Composites for CO2/N2 Separation
Hilal Daglar, Hasan Can Gülbalkan, Nitasha Habib, et al.
ACS Applied Materials & Interfaces (2023) Vol. 15, Iss. 13, pp. 17421-17431
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Recent development of metal-organic frameworks and their composites in electromagnetic wave absorption and shielding applications
Kexin Wei, Yang Shi, Xin Tan, et al.
Advances in Colloid and Interface Science (2024) Vol. 332, pp. 103271-103271
Closed Access | Times Cited: 40
Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery
Chunhua Li, Luqian Bao, Yixin Ji, et al.
Coordination Chemistry Reviews (2024) Vol. 514, pp. 215888-215888
Closed Access | Times Cited: 18
Recent progress on advanced solid adsorbents for CO2 capture: From mechanism to machine learning
Mobin Safarzadeh Khosrowshahi, Amirhossein Afshari Aghajari, Mohammad Rahimi, et al.
Materials Today Sustainability (2024) Vol. 27, pp. 100900-100900
Closed Access | Times Cited: 17
MOF membranes for gas separations
Yiming Zhang, Hang Yin, Lingzhi Huang, et al.
Progress in Materials Science (2025), pp. 101432-101432
Closed Access | Times Cited: 3
Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications
Hilal Daglar, Hasan Can Gülbalkan, Gokhan Onder Aksu, et al.
Advanced Materials (2024)
Open Access | Times Cited: 15
Ionic Liquid-Functionalized Metal–Organic Frameworks/Covalent–Organic Frameworks for CO2 Capture and Conversion
Shangqing Chen, Ningyuan Wang, Huaidong Zhang, et al.
Industrial & Engineering Chemistry Research (2024) Vol. 63, Iss. 8, pp. 3443-3464
Closed Access | Times Cited: 12
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: 12
Machine learning-assisted computational exploration of the optimal loading of IL in IL/COF composites for carbon dioxide capture
Tongan Yan, Minman Tong, Dahuan Liu, et al.
Journal of Materials Chemistry A (2023) Vol. 11, Iss. 27, pp. 14911-14920
Closed Access | Times Cited: 16
Accelerated Discovery of Metal–Organic Frameworks for CO2 Capture by Artificial Intelligence
Hasan Can Gülbalkan, Gokhan Onder Aksu, Goktug Ercakir, et al.
Industrial & Engineering Chemistry Research (2023) Vol. 63, Iss. 1, pp. 37-48
Open Access | Times Cited: 14
Navigating micro- and nano-motors/swimmers with machine learning: Challenges and future directions
Jueyi Xue, Hamid Alinejad‐Rokny, Kang Liang
ChemPhysMater (2024) Vol. 3, Iss. 3, pp. 273-283
Open Access | Times Cited: 4
Multi-criteria computational screening of [BMIM][DCA]@MOF composites for CO2 capture
Mengjia Sheng, Xiang Zhang, Hongye Cheng, et al.
Green Chemical Engineering (2024)
Open Access | Times Cited: 4
Metal–Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review
Kevizali Neikha, Амрит Пузари
Langmuir (2024) Vol. 40, Iss. 42, pp. 21957-21975
Closed Access | Times Cited: 4
Targeted metal–organic framework discovery goes digital: machine learning’s quest from algorithms to atom arrangements
Maryam Chafiq, Abdelkarim Chaouiki, Young Gun Ko
Advanced Composites and Hybrid Materials (2024) Vol. 7, Iss. 6
Closed Access | Times Cited: 4
Interfacial polymerization of COF-polyamide composite membranes modified with ionic liquids for CO2 separations
Yanting Tang, Qingnan Wang, Xiaohe Tian, et al.
Journal of Membrane Science (2024), pp. 123557-123557
Closed Access | Times Cited: 4
Assessing CO2 Separation Performances of IL/ZIF-8 Composites Using Molecular Features of ILs
Hasan Can Gülbalkan, Alper Uzun, Seda Keskın
Carbon Capture Science & Technology (2025) Vol. 14, pp. 100373-100373
Open Access
Toward rational design of ionic liquid/Metal-Organic Framework composites for efficient gas separations: Combining molecular modeling, machine learning, and experiments to move beyond trial-and-error
Nitasha Habib, Hasan Can Gülbalkan, Ahmet Safa Aydogdu, et al.
Coordination Chemistry Reviews (2025) Vol. 539, pp. 216707-216707
Closed Access
Bi-functionalized ionic liquid/metal-organic framework composite for low-concentration CO2 cycloaddition reaction under atmospheric pressure
Liwei Sun, Meilin Yin, Shaokun Tang
Journal of environmental chemical engineering (2023) Vol. 11, Iss. 5, pp. 110843-110843
Closed Access | Times Cited: 10
Preparation of larger MXene layers and research progress in the field of gas adsorption and separation
Peng Zu, Xiujing Xing, Haohan Wan, et al.
Separation and Purification Technology (2023) Vol. 327, pp. 125010-125010
Closed Access | Times Cited: 10
CO2 gas-liquid equilibrium study and machine learning analysis in MEA-DMEA blended amine solutions
Haonan Liu, Francesco Barzagli, Li Luo, et al.
Separation and Purification Technology (2024) Vol. 356, pp. 130024-130024
Open Access | Times Cited: 3
A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning
Min Cheng, Zhiyuan Zhang, Shihui Wang, et al.
Frontiers of Environmental Science & Engineering (2023) Vol. 17, Iss. 12
Closed Access | Times Cited: 9
Accelerating Metal–Organic Framework Selection for Type III Porous Liquids by Synergizing Machine Learning and Molecular Simulation
Lisha Sheng, Yi Wang, Xinzhu Mou, et al.
ACS Applied Materials & Interfaces (2023) Vol. 15, Iss. 48, pp. 56253-56264
Closed Access | Times Cited: 7
MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal–Organic Frameworks
Wenxuan Li, Yizhen Situ, Lifeng Ding, et al.
ACS Applied Materials & Interfaces (2023) Vol. 15, Iss. 51, pp. 59887-59894
Closed Access | Times Cited: 7
In Silico-Directed Design and Experimental Validation of an IL/UiO-66 Nanocomposite with Exceptional CO2 Selectivity across a Wide Pressure Range
Özce Durak, Ahmet Safa Aydogdu, Nitasha Habib, et al.
ACS Applied Nano Materials (2024)
Closed Access | Times Cited: 2
Solvent‐Directed Construction of a Nanoporous Metal‐Organic Framework with Potential in Selective Adsorption and Separation of Gas Mixtures Studied by Grand Canonical Monte Carlo Simulations
Saeideh Salimi, Kamran Akhbari, S. Morteza F. Farnia, et al.
ChemPlusChem (2023) Vol. 89, Iss. 1
Closed Access | Times Cited: 3
Toward Greener Flow Assurance: Review of Experimental and Computational Methods in Designing and Screening Kinetic Hydrate Inhibitors
Yang Liu, Huiyun Mu, Xiaofang Lv, et al.
Energy & Fuels (2024) Vol. 38, Iss. 18, pp. 17191-17223
Closed Access
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