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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 and deep learning enabled fuel sooting tendency prediction from molecular structure
Runzhao Li, J.M. Herreros, A. Tsolakis, et al.
Journal of Molecular Graphics and Modelling (2021) Vol. 111, pp. 108083-108083
Open Access | Times Cited: 9

Showing 9 citing articles:

Chemical SuperLearner (ChemSL) - An automated machine learning framework for building physical and chemical properties model
Balaji Mohan, Junseok Chang
Chemical Engineering Science (2024) Vol. 294, pp. 120111-120111
Closed Access | Times Cited: 5
Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning
Lorenz Fleitmann, Philipp Ackermann, Johannes Schilling, et al.
Energy & Fuels (2023) Vol. 37, Iss. 3, pp. 2213-2229
Open Access | Times Cited: 13
Artificial intelligence model of fuel blendings as a step toward the zero emissions optimization of a 660 MWe supercritical power plant performance
Ahsan Amjad, Waqar Muhammad Ashraf, Ghulam Moeen Uddin, et al.
Energy Science & Engineering (2023) Vol. 11, Iss. 8, pp. 2899-2911
Open Access | Times Cited: 9
Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons
Mohamed Roubehie Fissa, Yasmina Lahiouel, Latifa Khaouane, et al.
Journal of Molecular Graphics and Modelling (2023) Vol. 121, pp. 108450-108450
Closed Access | Times Cited: 5
Combustion condition predictions for C2-C4 alkane and alkene fuels via machine learning methods
Mingfei Chen, Jiaying He, Xuan Zhao, et al.
Fuel (2024) Vol. 373, pp. 132375-132375
Closed Access | Times Cited: 1
Multi-objective Design of Blending Fuel by Intelligent Optimization Algorithms
Ruichen Liu, Cong Li, Li Wang, et al.
Transactions of Tianjin University (2024) Vol. 30, Iss. 3, pp. 221-237
Closed Access
Sooting tendencies: Combustion science for designing sustainable fuels with improved properties
Lisa D. Pfefferle, Seonah Kim, Sabari Kumar, et al.
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105750-105750
Closed Access
Explainable machine learning assisted design of tailor-made fuels using conjoint fingerprints
Yong Chen, Zhiyuan Lu, Zhifeng Yao, et al.
Energy Conversion and Management (2024) Vol. 322, pp. 119118-119118
Closed Access
Soot modeling in the numerical simulation of buoyant diffusion flames and fires - A review
Shahrooz Motaghian, Tarek Beji
Fire Safety Journal (2024) Vol. 150, pp. 104279-104279
Closed Access
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