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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:

Machine learning for flow batteries: opportunities and challenges
Tianyu Li, Changkun Zhang, Xianfeng Li
Chemical Science (2022) Vol. 13, Iss. 17, pp. 4740-4752
Open Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations
Juran Noh, Hieu A. Doan, Heather Job, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 14
Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects
Shanling Ji, Jianxiong Zhu, Yaxin Yang, et al.
Small Methods (2024) Vol. 8, Iss. 7
Closed Access | Times Cited: 13
Machine learning for battery research
Zheng Wei, Qiu He, Yan Zhao
Journal of Power Sources (2022) Vol. 549, pp. 232125-232125
Closed Access | Times Cited: 58
Modelling and Estimation of Vanadium Redox Flow Batteries: A Review
Thomas Puleston, Alejandro Clemente, Ramon Costa‐Castelló, et al.
Batteries (2022) Vol. 8, Iss. 9, pp. 121-121
Open Access | Times Cited: 44
Electrospun porous carbon nanofiber-based electrodes for redox flow batteries: Progress and opportunities
Zheng Han, Tidong Wang, Yichong Cai, et al.
Carbon (2024) Vol. 222, pp. 118969-118969
Closed Access | Times Cited: 7
Machine learning applications in nanomaterials: Recent advances and future perspectives
Liang Yang, Hong Wang, Deying Leng, et al.
Chemical Engineering Journal (2024), pp. 156687-156687
Closed Access | Times Cited: 7
Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research
Muthuraja Velpandian, Suddhasatwa Basu
Indian Chemical Engineer (2025), pp. 1-18
Closed Access
Artificial intelligence-driven rechargeable batteries in multiple fields of development and application towards energy storage
Zheng Li, Shuqing Zhang, Hao Huang, et al.
Journal of Energy Storage (2023) Vol. 73, pp. 108926-108926
Closed Access | Times Cited: 14
A Review of Capacity Decay Studies of All‐vanadium Redox Flow Batteries: Mechanism and State Estimation
Yupeng Wang, Anle Mu, Wuyang Wang, et al.
ChemSusChem (2024) Vol. 17, Iss. 14
Closed Access | Times Cited: 5
Strategies for Improving Solubility of Redox‐Active Organic Species in Aqueous Redox Flow Batteries: A Review
Xiao Wang, Rajeev K. Gautam, Jianbing Jiang
Batteries & Supercaps (2022) Vol. 5, Iss. 11
Open Access | Times Cited: 19
High-Throughput Electrochemical Characterization of Aqueous Organic Redox Flow Battery Active Material
Eric M. Fell, Michael J. Aziz
Journal of The Electrochemical Society (2023) Vol. 170, Iss. 10, pp. 100507-100507
Open Access | Times Cited: 10
Redox-active molecules for aqueous electrolytes of energy storage devices: A review on fundamental aspects, current progress, and prospects
Ming Chen, Ri Chen, Igor Zhitomirsky, et al.
Materials Science and Engineering R Reports (2024) Vol. 161, pp. 100865-100865
Closed Access | Times Cited: 3
A critical review on operating parameter monitoring/estimation, battery management and control system for redox flow batteries
Haochen Zhu, Chen Yin, Mengyue Lu, et al.
Journal of Energy Storage (2024) Vol. 102, pp. 114029-114029
Closed Access | Times Cited: 3
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
Bohayra Mortazavi
Advanced Energy Materials (2024)
Open Access | Times Cited: 3
Digitization of flow battery experimental process research and development
Changyu Chen, Gaole Dai, Yuechen Gao, et al.
Energy Materials (2024) Vol. 4, Iss. 2
Open Access | Times Cited: 2
Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance
Yucheng Fu, Amanda A. Howard, Chao Zeng, et al.
ACS Energy Letters (2024) Vol. 9, Iss. 6, pp. 2767-2774
Open Access | Times Cited: 2
Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery
Lina Tang, Puiki Leung, Qian Xu, et al.
ChemElectroChem (2024)
Open Access | Times Cited: 2
Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks
Rostislav Fedorov, Ganna Gryn’ova
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 15, pp. 4796-4814
Closed Access | Times Cited: 7
Machine learning-driven advanced development of carbon-based luminescent nanomaterials
Diva Addini Maghribi Muyassiroh, Fitri Aulia Permatasari, Ferry Iskandar
Journal of Materials Chemistry C (2022) Vol. 10, Iss. 46, pp. 17431-17450
Closed Access | Times Cited: 11
High-Throughput Virtual Screening of Quinones for Aqueous Redox Flow Batteries: Status and Perspectives
Abhishek Khetan
Batteries (2022) Vol. 9, Iss. 1, pp. 24-24
Open Access | Times Cited: 10
Organic redox flow batteries in non-aqueous electrolyte solutions
Seongmo Ahn, Ariyeong Yun, Donghwi Ko, et al.
Chemical Society Reviews (2024)
Closed Access | Times Cited: 1
High-Throughput Electrochemical Characterization of Aqueous Organic Redox Flow Battery Active Material
Eric M. Fell, Michael J. Aziz
(2023)
Open Access | Times Cited: 4
Machine Learning for Battery Research
Yan Zhao, Zheng Wei, Qiu He
SSRN Electronic Journal (2022)
Closed Access | Times Cited: 6
Machine Learning Assisted Prediction of the Mass Activity of Platinum-Based Anode Catalysts Supported on a Variety of Carbon Nanostructures for Direct Methanol Fuel Cells
Ganesan Raman
(2024)
Closed Access
Alkaline zinc-based flow battery: chemical stability, morphological evolution, and performance of zinc electrode with ionic liquid
Tianyong Mao, Jing Dai, Meiqing Xin, et al.
Frontiers of Materials Science (2024) Vol. 18, Iss. 1
Closed Access
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