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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:

Synergetic effect of N/O functional groups and microstructures of activated carbon on supercapacitor performance by machine learning
Mohammad Rahimi, Mohammad Hossein Abbaspour‐Fard, Abbas Rohani
Journal of Power Sources (2022) Vol. 521, pp. 230968-230968
Closed Access | Times Cited: 68

Showing 1-25 of 68 citing articles:

Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors
Tao Wang, Runtong Pan, Murillo L. Martins, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 59
Recent advances in artificial intelligence boosting materials design for electrochemical energy storage
X.-B. Liu, Kexin Fan, Xinmeng Huang, et al.
Chemical Engineering Journal (2024) Vol. 490, pp. 151625-151625
Open Access | Times Cited: 18
Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology
Qadeer Akbar Sial, Usman Safder, Shahid Iqbal, et al.
Sustainability (2024) Vol. 16, Iss. 4, pp. 1516-1516
Open Access | Times Cited: 15
Application of activated carbon in renewable energy conversion and storage systems: a review
Zahra Teimouri, Sonil Nanda, Nicolas Abatzoglou, et al.
Environmental Chemistry Letters (2024) Vol. 22, Iss. 3, pp. 1073-1092
Closed Access | Times Cited: 15
Machine Learning‐Guided Prediction of Desalination Capacity and Rate of Porous Carbons for Capacitive Deionization
Hao Wang, Mingxi Jiang, Guangsheng Xu, et al.
Small (2024)
Closed Access | Times Cited: 14
Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review
V.A. Sawant, Rashmi Deshmukh, Chetan J. Awati
Journal of Energy Chemistry (2022) Vol. 77, pp. 438-451
Closed Access | Times Cited: 53
Sandwich-like porous MXene/Ni3S4/CuS derived from MOFs as superior supercapacitor electrode
Hao Guo, Junye Zhang, Fan Yang, et al.
Journal of Alloys and Compounds (2022) Vol. 906, pp. 163863-163863
Closed Access | Times Cited: 47
Data-driven machine learning approach for predicting the capacitance of graphene-based supercapacitor electrodes
Ahmed G. Saad, Ahmed Emad-Eldeen, Wael Z. Tawfik, et al.
Journal of Energy Storage (2022) Vol. 55, pp. 105411-105411
Closed Access | Times Cited: 42
Natural Products Derived Porous Carbons for CO2 Capture
Mobin Safarzadeh Khosrowshahi, Hossein Mashhadimoslem, Hadi Shayesteh, et al.
Advanced Science (2023) Vol. 10, Iss. 36
Open Access | Times Cited: 34
Methods, progresses, and opportunities of materials informatics
Chen Li, Kun Zheng
InfoMat (2023) Vol. 5, Iss. 8
Open Access | Times Cited: 28
Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques
Mohammad Rahimi, Hossein Mashhadimoslem, Hung Vo Thanh, et al.
Energy (2023) Vol. 283, pp. 128546-128546
Closed Access | Times Cited: 27
Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges
Swarn Jha, Matthew Yen, Yazmin Soto Salinas, et al.
Journal of Materials Chemistry A (2023) Vol. 11, Iss. 8, pp. 3904-3936
Closed Access | Times Cited: 25
Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model
Hung Vo Thanh, Sajad Ebrahimnia Taremsari, Benyamin Ranjbar, et al.
Energies (2023) Vol. 16, Iss. 5, pp. 2348-2348
Open Access | Times Cited: 23
A machine learning assisted prediction of potential biochar and its applications in anaerobic digestion for valuable chemicals and energy recovery from organic waste
Pengshuai Zhang, Tengyu Zhang, Jingxin Zhang, et al.
Carbon Neutrality (2024) Vol. 3, Iss. 1
Open Access | Times Cited: 12
Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets
Mohammed Shariq, Sathish Marimuthu, Amit Rai Dixit, et al.
Chemical Engineering Journal (2024) Vol. 484, pp. 149502-149502
Closed Access | Times Cited: 12
Sodium carboxymethyl cellulose derived carbon aerogels synthesized by zinc nitrate hexahydrate and urea for supercapacitor electrodes
Qinying Kong, Guangjie Yang, Chenweijia He, et al.
Journal of Energy Storage (2024) Vol. 86, pp. 111300-111300
Closed Access | Times Cited: 10
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
An overview, methods of synthesis and modification of carbon-based electrodes for supercapacitor
N. Rajeswari Yogamalar, Kalpana Sharma, P. Muhammed Shafi
Journal of Energy Storage (2022) Vol. 55, pp. 105727-105727
Closed Access | Times Cited: 30
Modeling and classifying the in-operando effects of wear and metal contaminations of lubricating oil on diesel engine: A machine learning approach
Mohammad Rahimi, Mohammad-Reza Pourramezan, Abbas Rohani
Expert Systems with Applications (2022) Vol. 203, pp. 117494-117494
Closed Access | Times Cited: 28
A multi-criteria decision-making (MCDM) approach to determine the synthesizing routes of biomass-based carbon electrode material in supercapacitors
Mohammad Rahimi, Hung Vo Thanh, Iman Ebrahimzade, et al.
Journal of Cleaner Production (2023) Vol. 397, pp. 136606-136606
Closed Access | Times Cited: 19
Fabrication of an asymmetric supercapacitor using a novel electrode design and introduce a robust machine learning model for its performance evaluation
Samaneh Mahmoudi Qashqay, Mohammad‐Reza Zamani‐Meymian, Ali Maleki, et al.
Journal of Power Sources (2024) Vol. 613, pp. 234911-234911
Closed Access | Times Cited: 6
Core-shell carbon@Ni2(CO3)(OH)2 particles as advanced cathode materials for hybrid supercapacitor: The key role of carbon for enhanced electrochemical properties
Damin Lee, Dong Hwan Kim, Jong Wook Roh, et al.
Journal of Energy Storage (2024) Vol. 97, pp. 112944-112944
Closed Access | Times Cited: 6
Synergistic design and synthesis of O, N Co-doped hierarchical porous carbon for enhanced supercapacitor performance
Hongwei Liu, Zhenming Cui, Yan Sun, et al.
Energy Materials (2025) Vol. 5, Iss. 3
Open Access
Exploring how base model combination affects the results of a “stacking” ensemble machine learning model: An applied study on optimization of heteroatom doped carbon data
Krittapong Deshsorn, Weekit Sirisaksoontorn, Wisit Hirunpinyopas, et al.
FlatChem (2025) Vol. 50, pp. 100827-100827
Closed Access
Insights into the specific capacitance of CNT-based supercapacitor electrodes using artificial intelligence
Wael Z. Tawfik, Mohamed Shaban, Athira Raveendran, et al.
RSC Advances (2025) Vol. 15, Iss. 5, pp. 3155-3167
Open Access
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