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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:

DESignSolvents: an open platform for the search and prediction of the physicochemical properties of deep eutectic solvents
Valeria Odegova, Anastasia K. Lavrinenko, Timur Rakhmanov, et al.
Green Chemistry (2024) Vol. 26, Iss. 7, pp. 3958-3967
Closed Access | Times Cited: 13

Showing 13 citing articles:

Explicable Machine Learning for Predicting High-Efficiency Lignocellulose Pretreatment Solvents Based on Kamlet–Taft and Polarity Parameters
Hanwen Ge, Yuekun Bai, Rui Zhou, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 19, pp. 7578-7590
Closed Access | Times Cited: 9
Applications of Functional Polymeric Eutectogels
Alma Nicolau, Alexandra L. Mutch, Stuart C. Thickett
Macromolecular Rapid Communications (2024) Vol. 45, Iss. 21
Open Access | Times Cited: 6
Viscosity of Deep Eutectic Solvents: Predictive Modelling with Experimental Validation
Dmitriy M. Makarov, A. M. Kolker
Fluid Phase Equilibria (2024) Vol. 587, pp. 114217-114217
Closed Access | Times Cited: 4
An integrated ML model for the prediction of the melting points, phase diagrams, and eutectic points of the Type III and V deep eutectic solvents
Dian Jin, Haotian He, Li Sun, et al.
Chemical Engineering Science (2025), pp. 121245-121245
Closed Access
ChemBERTa Embeddings and Ensemble Learning for Prediction of Density and Melting Point of Deep Eutectic Solvents with Hybrid Features
Ting Wu, Peng Zhan, Weiqiu Chen, et al.
Computers & Chemical Engineering (2025), pp. 109065-109065
Closed Access
Machine Learning-Driven Web Tools for Predicting Properties of Materials and Molecules
Dmitriy M. Makarov, Pavel S. Bocharov, Michail M. Lukanov, et al.
Challenges and advances in computational chemistry and physics (2025), pp. 273-292
Closed Access
Metal extraction using deep eutectic solvents for metal recovery and environmental remediation – A review
Chongqing Wang, Zhenxing Zhou, Xiuxiu Zhang, et al.
Separation and Purification Technology (2025), pp. 132533-132533
Closed Access
Machine Learning for Predicting and Optimizing Physicochemical Properties of Deep Eutectic Solvents: Review and Perspectives
Francisco Javier López-Flores, César Ramírez‐Márquez, J. Betzabe González‐Campos, et al.
Industrial & Engineering Chemistry Research (2024)
Closed Access | Times Cited: 3
CO2 capture using choline chloride-based eutectic solvents. An experimental and theoretical investigation
Dmitriy M. Makarov, Michael A. Krestyaninov, А. А. Дышин, et al.
Journal of Molecular Liquids (2024) Vol. 413, pp. 125910-125910
Closed Access | Times Cited: 2
Machine learning-driven prediction of deep eutectic solvents’ heat capacity for sustainable process design
Amit Kumar Halder, Reza Haghbakhsh, Elisabete S.C. Ferreira, et al.
Journal of Molecular Liquids (2024) Vol. 418, pp. 126707-126707
Open Access | Times Cited: 1
Screening and Characterization of 1,8-Cineole-Based Solvents as an Alternative to Hexane for Obtaining Nonpolar Compounds from Plant-Based Milk Coproducts
Monique Martins Strieder, Felipe Sanchez Bragagnolo, J. A. Mendiola, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 43, pp. 16052-16063
Open Access
Efficient Machine-Learning-Based New Tools to Design Eutectic Mixtures and Predict Their Viscosity
Stella Christodoulou, Camille Cousseau, Emmanuelle Limanton, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 52, pp. 18537-18554
Closed Access
Morphology engineering of inorganic nanocrystals with deep eutectic solvents (DESs): Current developments and future prospects
Bhagirath Mahto, Adnan Ali, Ashok Barhoi, et al.
Coordination Chemistry Reviews (2024) Vol. 527, pp. 216406-216406
Closed Access
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