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:

Performance Analysis of Anode-Supported Solid Oxide Fuel Cells: A Machine Learning Approach
Mohammad Hossein Golbabaei, Mohammadreza Saeidi Varnoosfaderani, Arsalan Zare, et al.
Materials (2022) Vol. 15, Iss. 21, pp. 7760-7760
Open Access | Times Cited: 9

Showing 9 citing articles:

A breakthrough in creep lifetime prediction: Leveraging machine learning and service data
Arsalan Zare, Reza Khadem Hosseini
Scripta Materialia (2024) Vol. 245, pp. 116037-116037
Closed Access | Times Cited: 9

Machine learning prediction and characterization of sigma-free high-entropy alloys
Mohammad Sajad Mehranpour, Ali Koushki, Seyed Soroush Karimi Madahi, et al.
Materials Characterization (2024) Vol. 212, pp. 113937-113937
Closed Access | Times Cited: 9

Leveraging machine learning in porous media
Mostafa Delpisheh, Benyamin Ebrahimpour, Abolfazl Fattahi, et al.
Journal of Materials Chemistry A (2024) Vol. 12, Iss. 32, pp. 20717-20782
Open Access | Times Cited: 8

Fuel Cells: A Technical, Environmental, and Economic Outlook
Ilham Sebbani, Mohammed Karim Ettouhami, Mouaad Boulakhbar
Cleaner Energy Systems (2025) Vol. 10, pp. 100168-100168
Open Access | Times Cited: 1

A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks
Mohsen Shayanmehr, Sepehr Aarabi, Ahad Ghaemi, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access

Machine Learning for the Optimization and Performance Prediction of Solid Oxide Electrolysis Cells: A Review
Mahmoud Makki Abadi, Mohammad Mehdi Rashidi
Processes (2025) Vol. 13, Iss. 3, pp. 875-875
Open Access

AI-enabled materials discovery for advanced ceramic electrochemical cells
Idris Temitope Bello, Ridwan Taiwo, Oladapo Christopher Esan, et al.
Energy and AI (2023) Vol. 15, pp. 100317-100317
Open Access | Times Cited: 10

Smart paradigm to predict copper surface area of Cu/ZnO/Al2O3 catalyst based on synthesis parameters
Soheil Saffary, Mansoureh Rafiee, Mohammadreza Saeidi Varnoosfaderani, et al.
Process Safety and Environmental Protection (2023) Vol. 191, pp. 604-616
Closed Access | Times Cited: 7

Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters
Mohammad Hossein Golbabaei, Mohammadreza Saeidi Varnoosfaderani, Farshid Hemmati, et al.
RSC Advances (2024) Vol. 14, Iss. 22, pp. 15178-15199
Open Access | Times Cited: 1

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