Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

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

Showing 26-50 of 42 citing articles:

Modeling capacitance of carbon-based supercapacitors by artificial neural networks
B.S. Reddy, P.L. Narayana, A.K. Maurya, et al.
Journal of Energy Storage (2023) Vol. 72, pp. 108537-108537
Closed Access | Times Cited: 8
Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes
Wael Z. Tawfik, Samar N. Mohammad, Kamel H. Rahouma, et al.
Physica Scripta (2023) Vol. 99, Iss. 2, pp. 026001-026001
Closed Access | Times Cited: 7
Data-Driven Optimization of Carbon Electrodes for Aqueous Supercapacitors
Runtong Pan, Mengyang Gu, Jianzhong Wu
Journal of Chemical & Engineering Data (2024)
Closed Access | Times Cited: 2
New Engineering Science Insights into the Electrode Materials Pairing of Electrochemical Energy Storage Devices
Longbing Qu, Peiyao Wang, Benyamin Motevalli, et al.
Advanced Materials (2024) Vol. 36, Iss. 35
Open Access | Times Cited: 2
A comprehensive review on MoSe2 nanostructures with an overview of machine learning techniques for supercapacitor applications
B. W. Robertson, R. Sapna, Vinod Hegde, et al.
RSC Advances (2024) Vol. 14, Iss. 51, pp. 37644-37675
Open Access | Times Cited: 2
Machine learning and data-driven material exploration for supercapacitors
Sijie Wang, Kai Huang, Cheng Lian, et al.
Elsevier eBooks (2024), pp. 353-370
Closed Access | Times Cited: 1
Potential and challenge in accelerating high-value conversion of CO2 in microbial electrosynthesis system via data-driven approach
Jiasheng Jin, Yang Wu, Peiyu Cao, et al.
Bioresource Technology (2024) Vol. 412, pp. 131380-131380
Closed Access | Times Cited: 1
Machine learning-assisted prediction, screen, and interpretation of porous carbon materials for high-performance supercapacitors
Hongwei Liu, Zhenming Cui, Zhennan Qiao, et al.
Journal of Materials Informatics (2024) Vol. 4, Iss. 4
Open Access | Times Cited: 1
Ensemble Machine Learning for Predictive Accuracy and Experimental Corroboration on transition metal-based electrodes for Supercapacitor
Arpit B. Mendhe, Bharati Panigrahy, Himanshu Sekhar Panda
Electrochimica Acta (2024), pp. 145482-145482
Closed Access | Times Cited: 1
Transient piezoresistive sensors based on tolerable, safe, and semiconductive polypyrrole/vermiculite/alginate frameworks
Pengzhen Wang, Junxuan Liang, Weiliang Tian, et al.
Science China Materials (2024) Vol. 67, Iss. 2, pp. 580-587
Open Access
Artificial Neural Network and Multiple Linear Regression Approach for Optimization of Material Composition for Sustainable Super Capacitor
Kurupati Sireesha, Balasubramanian Murugesan, P. T. Ravichandran
Lecture notes in civil engineering (2024), pp. 631-642
Closed Access
Unlocking the potential of Ti3C2 electrodes: a data-driven capacitance prediction study
Sanjay Krishna, Afkham Mir
Energy Advances (2024)
Open Access
Applications of Graphene Derivatives in All‐Solid‐State Supercapacitors
Edwin T. Mombeshora, Edigar Muchuweni, Hamed Hashemi
ChemistrySelect (2024) Vol. 9, Iss. 44
Open Access
Application of Graphenes in Supercapacitors: A Review
Yu. M. Volfkovich
Russian Journal of Electrochemistry (2024) Vol. 60, Iss. 12, pp. 1198-1227
Closed Access
“Crypton 1.0”: Accurate cyclic voltammetry forecasting of activated carbon electrode with machine learning
Adisa Jarubenjaluk, Pannapha Kullattanapratep, Apinporn Pornpipattanasiri, et al.
Chemical Engineering Journal Advances (2023) Vol. 16, pp. 100548-100548
Open Access | Times Cited: 1
An improved method of supercapacitor aging prediction based on Prophet under actual working condition
Xintong Bai, Tingrun Lin, Yu Wang, et al.
2022 IEEE 5th International Electrical and Energy Conference (CIEEC) (2023) Vol. 459, pp. 675-680
Closed Access
How False Data Affects Machine Learning Models in Electrochemistry?
Krittapong Deshsorna, Luckhana Lawtrakul, Pawin Iamprasertkun
arXiv (Cornell University) (2023)
Open Access
Machine learning in materials synthesis and characterization
Hicham Meskher
(2023) Vol. 1, Iss. 1, pp. 1-3
Open Access
Previous Page - Page 2

Scroll to top