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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-based assessment of the impact of the manufacturing process on battery electrode heterogeneity
Marc Duquesnoy, Iker Boyano, Larraitz Ganborena, et al.
Energy and AI (2021) Vol. 5, pp. 100090-100090
Open Access | Times Cited: 45

Showing 1-25 of 45 citing articles:

A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+
Julia Amici, Pietro Asinari, Elixabete Ayerbe, et al.
Advanced Energy Materials (2022) Vol. 12, Iss. 17
Open Access | Times Cited: 134
Electrode fabrication process and its influence in lithium-ion battery performance: State of the art and future trends
Renato Gonçalves, S. Lanceros‐Méndez, Carlos M. Costa
Electrochemistry Communications (2022) Vol. 135, pp. 107210-107210
Open Access | Times Cited: 61
Roadmap on Li-ion battery manufacturing research
Patrick S. Grant, David Greenwood, Kunal Pardikar, et al.
Journal of Physics Energy (2022) Vol. 4, Iss. 4, pp. 042006-042006
Open Access | Times Cited: 48
Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities
Franco M. Zanotto, Diana Zapata Dominguez, Elixabete Ayerbe, et al.
Batteries & Supercaps (2022) Vol. 5, Iss. 9
Open Access | Times Cited: 47
Systematic analysis of the impact of slurry coating on manufacture of Li-ion battery electrodes via explainable machine learning
Mona Faraji Niri, C. D. Reynolds, Luis A. Román‐Ramírez, et al.
Energy storage materials (2022) Vol. 51, pp. 223-238
Open Access | Times Cited: 44
Progress of machine learning in materials design for Li-Ion battery
Prasshanth C.V., Arun Kumar Lakshminarayanan, R. Brindha, et al.
Next Materials (2024) Vol. 2, pp. 100145-100145
Open Access | Times Cited: 10
Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art
Joseph Omakor, Suruz Miah, Hicham Chaoui
IEEE Access (2024) Vol. 12, pp. 77903-77931
Open Access | Times Cited: 7
Machine learning for optimised and clean Li-ion battery manufacturing: Revealing the dependency between electrode and cell characteristics
Mona Faraji Niri, Kailong Liu, Geanina Apachitei, et al.
Journal of Cleaner Production (2021) Vol. 324, pp. 129272-129272
Open Access | Times Cited: 49
Quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence
Mona Faraji Niri, Kailong Liu, Geanina Apachitei, et al.
Energy and AI (2021) Vol. 7, pp. 100129-100129
Open Access | Times Cited: 49
Image-based 3D characterization and reconstruction of heterogeneous battery electrode microstructure
Vinit Nagda, Artem Kulachenko, Stefan B. Lindström
Computational Materials Science (2023) Vol. 223, pp. 112139-112139
Open Access | Times Cited: 17
Machine Learning in Lithium‐Ion Battery Cell Production: A Comprehensive Mapping Study
Sajedeh Haghi, Marc Francis V. Hidalgo, Mona Faraji Niri, et al.
Batteries & Supercaps (2023) Vol. 6, Iss. 7
Open Access | Times Cited: 16
Spherical agglomeration for local control of electrode microstructure: Generation of structured agglomerates
Kunal Pardikar, Jediah Capindale, Kate Pitt, et al.
Powder Technology (2025), pp. 120688-120688
Closed Access
Integration of lithium-ion battery recycling into manufacturing through digitalization: A perspective
Imelda Cardenas-Sierra, Utkarsh Vijay, Frédéric Aguesse, et al.
Journal of Power Sources (2025) Vol. 631, pp. 236158-236158
Open Access
Image-matching in electrode production of lithium-ion batteries for marker-free tracking and tracing applications
Johannes Lindenblatt, Jen Schneider, Alessandro Sommer, et al.
Deleted Journal (2025), pp. 100049-100049
Open Access
Comparison and assessment of machine learning approaches in manufacturing applications
K. Ramesh, M. N. Indrajith, Y. S. Prasanna, et al.
Industrial Artificial Intelligence (2025) Vol. 3, Iss. 1
Open Access
Estimation of Li‐Ion Degradation Test Sample Sizes Required to Understand Cell‐to‐Cell Variability**
Philipp Dechent, Samuel Greenbank, Felix Hildenbrand, et al.
Batteries & Supercaps (2021) Vol. 4, Iss. 12, pp. 1821-1829
Open Access | Times Cited: 38
Machine learning-inspired battery material innovation
Man‐Fai Ng, Yongming Sun, Zhi Wei Seh
Energy Advances (2023) Vol. 2, Iss. 4, pp. 449-464
Open Access | Times Cited: 14
Concept for Digital Product Twins in Battery Cell Production
Achim Kampker, Heiner Hans Heimes, Benjamin Dorn, et al.
World Electric Vehicle Journal (2023) Vol. 14, Iss. 4, pp. 108-108
Open Access | Times Cited: 12
A critical review of future aspects of digitalization next generation Li-ion batteries manufacturing process
Pradeep Kumar Dammala, Kamil Burak Dermenci, Anish Raj K, et al.
Journal of Energy Storage (2023) Vol. 74, pp. 109209-109209
Open Access | Times Cited: 12
Data-Based Interpretable Modeling for Property Forecasting and Sensitivity Analysis of Li-ion Battery Electrode
Kailong Liu, Qiao Peng, Kang Li, et al.
Automotive Innovation (2022) Vol. 5, Iss. 2, pp. 121-133
Open Access | Times Cited: 20
Effect of coating operating parameters on electrode physical characteristics and final electrochemical performance of lithium-ion batteries
Luis A. Román‐Ramírez, Geanina Apachitei, Mona Faraji Niri, et al.
International journal of energy and environmental engineering (2022) Vol. 13, Iss. 3, pp. 943-953
Open Access | Times Cited: 15
Machine learning for investigating the relative importance of electrodes’ N:P areal capacity ratio in the manufacturing of lithium-ion battery cells
Mona Faraji Niri, Geanina Apachitei, Michael Lain, et al.
Journal of Power Sources (2022) Vol. 549, pp. 232124-232124
Open Access | Times Cited: 15
Microstructurally resolved modeling of all solid-state batteries: Latest progresses, opportunities, and challenges
Mohammed Alabdali, Franco M. Zanotto, Virginie Viallet, et al.
Current Opinion in Electrochemistry (2022) Vol. 36, pp. 101127-101127
Open Access | Times Cited: 14
From Battery Manufacturing to Smart Grids: Towards a Metaverse for the Energy Sciences**
Alejandro A. Franco, Émilie Loup‐Escande, Goran Loiseaux, et al.
Batteries & Supercaps (2022) Vol. 6, Iss. 1
Open Access | Times Cited: 13
The ARTISTIC Battery Manufacturing Digitalization Initiative: From Fundamental Research to Industrialization
Javier F. Troncoso, Franco M. Zanotto, Diego E. Galvez‐Aranda, et al.
Batteries & Supercaps (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 2
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