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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:

Artificial intelligence-based prediction of hydrogen adsorption in various kerogen types: Implications for underground hydrogen storage and cleaner production
Hung Vo Thanh, Zhenxue Dai, Zhengyang Du, et al.
International Journal of Hydrogen Energy (2024) Vol. 57, pp. 1000-1009
Closed Access | Times Cited: 25

Showing 25 citing articles:

A comprehensive review of underground hydrogen storage: Insight into geological sites (mechanisms), economics, barriers, and future outlook
Grace Oluwakemisola Taiwo, Olusegun Stanley Tomomewo, Babalola Aisosa Oni
Journal of Energy Storage (2024) Vol. 90, pp. 111844-111844
Closed Access | Times Cited: 31
Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state
Behnam Amiri-Ramsheh, Aydin Larestani, Saeid Atashrouz, et al.
Results in Engineering (2025), pp. 104035-104035
Open Access | Times Cited: 2
Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach
Hung Vo Thanh, Mohammad Rahimi, Suparit Tangparitkul, et al.
International Journal of Hydrogen Energy (2024) Vol. 83, pp. 1-12
Closed Access | Times Cited: 8
Low-Carbon Advancement through Cleaner Production: A Machine Learning Approach for Enhanced Hydrogen Storage Predictions in Coal Seams
Yongjun Wang, Hung Vo Thanh, Hemeng Zhang, et al.
Renewable Energy (2025), pp. 122342-122342
Closed Access | Times Cited: 1
Identification of Stable Intermetallic Compounds for Hydrogen Storage via Machine Learning
A. S. Athul, Aswin V. Muthachikavil, Venkata Sudheendra Buddhiraju, et al.
Energy Storage (2025) Vol. 7, Iss. 1
Closed Access | Times Cited: 1
Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression models
Abdulrahman Al‐Fakih, Abbas Mohamed Al-Khudafi, Ardiansyah Koeshidayatullah, et al.
Geothermal Energy (2025) Vol. 13, Iss. 1
Open Access | Times Cited: 1
Estimating the hydrogen adsorption in depleted shale gas reservoirs for kerogens in underground hydrogen storage using machine learning algorithms
Grant Charles Mwakipunda, Mouigni Baraka Nafouanti, AL-Wesabi Ibrahim, et al.
Fuel (2025) Vol. 388, pp. 134534-134534
Closed Access | Times Cited: 1
Technical challenges and opportunities of hydrogen storage: A comprehensive review on different types of underground storage
Guangyao Leng, Wei Yan, Zhangxin Chen, et al.
Journal of Energy Storage (2025) Vol. 114, pp. 115900-115900
Closed Access | Times Cited: 1
Advanced machine learning-based modeling of interfacial tension in the crude oil-brine-diethyl ether system: Insights into the effects of temperature and salinity
Amir Mohammadi Khanghah, Mahsa Parhizgar Keradeh, Alireza Keshavarz, et al.
Journal of Molecular Liquids (2024) Vol. 404, pp. 124861-124861
Closed Access | Times Cited: 7
Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks
Umar Ashraf, Aqsa Anees, Hucai Zhang, et al.
Geomechanics and Geophysics for Geo-Energy and Geo-Resources (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 6
Breaking barriers: Novel approaches to proton-conducting oxide materials
Muhammad Tayyab, Sajid Rauf, Abdul Zeeshan Khan, et al.
Ceramics International (2024) Vol. 50, Iss. 20, pp. 40526-40552
Closed Access | Times Cited: 5
AI-ML techniques for green hydrogen: A comprehensive review
Mamta Motiramani, P.S. Solanki, Vikram Patel, et al.
Next Energy (2025) Vol. 8, pp. 100252-100252
Closed Access
Improving wettability estimation in carbonate formation using machine learning algorithms: Implications for underground hydrogen storage applications
Grant Charles Mwakipunda, AL-Wesabi Ibrahim, Allou Koffi Franck Kouassi, et al.
International Journal of Hydrogen Energy (2025) Vol. 111, pp. 781-797
Closed Access
Enhanced Prediction and Uncertainty Analysis for Hydrogen production rate in Depleted Oil and Gas Reservoirs Using Advanced Machine Learning Techniques
Zhengyang Du, Lulu Xu, Shangxian Yin, et al.
Geoenergy Science and Engineering (2025), pp. 213795-213795
Closed Access
Artificial intelligence and robotics in the hydrogen lifecycle: A systematic review
Paulina Quintanilla, Ayman Elhalwagy, Lijia Duan, et al.
International Journal of Hydrogen Energy (2025) Vol. 113, pp. 801-817
Open Access
Revolutionizing hydrogen storage: Predictive modeling of hydrogen-brine interfacial tension using advanced machine learning and optimization technique
Hung Vo Thanh
International Journal of Hydrogen Energy (2025) Vol. 128, pp. 406-424
Closed Access
Machine learning-based analytical approach for mechanical analysis of composite hydrogen storage tanks under internal pressure
Y. Qarssis, Mourad Nachtane, Ayoub Karine, et al.
International Journal of Hydrogen Energy (2024) Vol. 89, pp. 1440-1453
Closed Access | Times Cited: 3
Size engineering of porous CuWO4–CuO heterojunction for enhanced hydrogen evolution in alkaline media
Xiaoyu Chen, Xiaodong Li, Zhilin Chen, et al.
Journal of Physics and Chemistry of Solids (2024) Vol. 192, pp. 112079-112079
Closed Access | Times Cited: 2
State-of-the-art review on hydrogen’s production, storage, and potential as a future transportation fuel
Sonanki Keshri, S. Sudha, Abhishek Keshav Sharan Saxena
Environmental Science and Pollution Research (2024)
Closed Access | Times Cited: 2
Machine learning and LSSVR model optimization for gasification process prediction
Wei Cong
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 6, pp. 5991-6018
Closed Access | Times Cited: 2
Shear wave velocity prediction based on 1DCNN-BiLSTM network with attention mechanism
Gang Feng, Wenqing Liu, Zhe Yang, et al.
Frontiers in Earth Science (2024) Vol. 12
Open Access | Times Cited: 1
Separate-layer injection scheme optimization based on integrated injection information with artificial neural network and residual network
Lizhi Yan, Hongbing Zhang, Zhang Dailu, et al.
Journal of energy resources technology. (2024) Vol. 1, Iss. 1
Closed Access | Times Cited: 1
Review of computational advances in tailoring magnesium-hydrogen interactions: Atomistic simulations meet machine learning
Katarina Batalović, Bojana Paskaš Mamula, Mirjana Medić Ilić, et al.
International Journal of Hydrogen Energy (2024) Vol. 90, pp. 114-133
Closed Access | Times Cited: 1
Machine-learning based prediction of hydrogen/methane mixture solubility in brine
Farag M. A. Altalbawy, Mustafa Jassim Al-saray, Krunal Vaghela, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Prediction and mechanism of underground hydrogen storage in nanoporous media: Coupling molecular simulation, pore-scale simulation and machine learning
Han Wang, Ke Hu, Weipeng Fan, et al.
International Journal of Hydrogen Energy (2024) Vol. 101, pp. 303-312
Closed Access | Times Cited: 1
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