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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:

Rigorous hybrid machine learning approaches for interfacial tension modeling in brine-hydrogen/cushion gas systems: Implication for hydrogen geo-storage in the presence of cushion gas
Mohammad Behnamnia, Negin Mozafari, Abolfazl Dehghan Monfared
Journal of Energy Storage (2023) Vol. 73, pp. 108995-108995
Closed Access | Times Cited: 20

Showing 20 citing articles:

Prediction of hydrogen−brine interfacial tension at subsurface conditions: Implications for hydrogen geo-storage
Mostafa Hosseini, Yuri Leonenko
International Journal of Hydrogen Energy (2024) Vol. 58, pp. 485-494
Open Access | Times Cited: 17
Artificial intelligence-driven assessment of salt caverns for underground hydrogen storage in Poland
Reza Derakhshani, Leszek Lankof, Amin GhasemiNejad, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
Machine Learning-Based Interfacial Tension Equations for (H2 + CO2)-Water/Brine Systems over a Wide Range of Temperature and Pressure
Minjunshi Xie, Mingshan Zhang, Zhehui Jin
Langmuir (2024) Vol. 40, Iss. 10, pp. 5369-5377
Closed Access | Times Cited: 7
Estimation the pH of CO2-saturated NaCl solutions using gene expression programming: Implications for CO2 sequestration
Mohammad Rasool Dehghani, Parmida Seraj Ebrahimi, Moein Kafi, et al.
Results in Engineering (2025) Vol. 25, pp. 104047-104047
Closed Access
Advanced Smart Models for Predicting Interfacial Tension in Brine-Hydrogen/Cushion Gas Systems: Implication for Hydrogen Geo-Storage
Fahd Mohamad Alqahtani, Mohamed Riad Youcefi, Menad Nait Amar, et al.
Energy & Fuels (2025)
Closed Access
Long-term stability forecasting for energy storage salt caverns using deep learning-based model
Kai Zhao, Shinong Yu, Louis Ngai Yuen Wong
Energy (2025), pp. 134854-134854
Closed Access
Artificial intelligence in geoenergy: bridging petroleum engineering and future-oriented applications
Sungil Kim, Tea-Woo Kim, Suryeom Jo
Journal of Petroleum Exploration and Production Technology (2025) Vol. 15, Iss. 2
Open Access
Application of Ensemble Learning Paradigms in Predicting Interfacial Tension of H2/Cushion Gas Systems and the Implications on Subsurface H2 Storage
Joshua Nsiah Turkson, Muhammad Aslam Md Yusof, Bennet Nii Tackie-Otoo, et al.
International Petroleum Technology Conference (2025)
Closed Access
Data-driven modelling to predict interfacial tension of hydrogen–brine system: Implications for underground hydrogen storage
Niyi B. Ishola, Afeez Gbadamosi, Nasiru Salahu Muhammed, et al.
Results in Engineering (2025), pp. 104608-104608
Open Access
A two-step failure identification approach using a stochastic optimization-based ensemble learning model for beams without pristine data
Long Viet Ho, Thanh Bui-Tien, Magd Abdel Wahab
Engineering Structures (2025) Vol. 334, pp. 120253-120253
Closed Access
Key players in renewable energy and artificial intelligence research
Rolando Eslava Zapata, Verenice Sánchez Castillo, Emma Juaneda Ayensa
EAI Endorsed Transactions on Energy Web (2024) Vol. 11
Open Access | Times Cited: 2
Estimating Oil Recovery Efficiency of Carbonated Water Injection with Supervised Machine Learning Paradigms and Implications for Uncertainty Analysis
Joshua Nsiah Turkson, Muhammad Aslam Md Yusof, Ingebret Fjelde, et al.
(2024)
Closed Access | Times Cited: 2
Data-Driven Interfacial Tension Modeling of Quaternary Aqueous H2 Systems Using Sequential and Parallel Ensemble Learning Techniques and the Implications on H2 Geo-Storage
Joshua Nsiah Turkson, Bennet Nii Tackie-Otoo, Victor Darkwah-Owusu, et al.
(2024)
Closed Access | Times Cited: 2
Smart Computational Algorithms for the Prediction of Interfacial Tension between Water and Hydrogen – Insights into Underground Hydrogen Storage
Shams Kalam, Mohammad Rasheed Khan, Muhammad Arif
Day 3 Wed, February 23, 2022 (2024)
Closed Access | Times Cited: 1
Transient Pressure Prediction in Large-Scale Underground Natural Gas Storage: A Deep Learning Approach and Case Study
Hongyang Chu, Liang Zhang, Huimin Lu, et al.
Energy (2024) Vol. 311, pp. 133411-133411
Closed Access | Times Cited: 1
Predicting interfacial tension in brine-hydrogen/cushion gas systems under subsurface conditions: Implications for hydrogen geo-storage
Mostafa Hosseini, Yuri Leonenko
International Journal of Hydrogen Energy (2024) Vol. 91, pp. 1394-1406
Open Access | Times Cited: 1
Estimating Oil Recovery Efficiency of Carbonated Water Injection with Supervised Machine Learning Paradigms and Implications for Uncertainty Analysis
Joshua Nsiah Turkson, Muhammad Aslam Md Yusof, Ingebret Fjelde, et al.
Chemometrics and Intelligent Laboratory Systems (2024), pp. 105303-105303
Closed Access | Times Cited: 1
Modeling interfacial tension in hydrogen-water/brine systems for optimizing underground hydrogen storage
Ahmad Azadivash
International Journal of Hydrogen Energy (2024) Vol. 100, pp. 1385-1401
Closed Access | Times Cited: 1
Artificial intelligence-based framework for precise prediction of asphaltene particle aggregation kinetics in petroleum recovery
Ali Sharifzadegan, Mohammad Behnamnia, Abolfazl Dehghan Monfared
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 2
Modeling Interfacial Tension in Hydrogen-Water/Brine Systems for Optimizing Underground Hydrogen Storage
Ahmad Azadivash
(2024)
Closed Access
Enhanced petrophysical evaluation through machine learning and well logging data in an Iranian oil field
Bahareh Rezaei Mirghaed, Abolfazl Dehghan Monfared, A.A. Ranjbar
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
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