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Structure–properties relationships of deep eutectic solvents formed between choline chloride and carboxylic acids: Experimental and computational study. J Mol Struct 2023. [DOI: 10.1016/j.molstruc.2022.134283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Lemaoui T, Boublia A, Darwish AS, Alam M, Park S, Jeon BH, Banat F, Benguerba Y, AlNashef IM. Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks. ACS OMEGA 2022; 7:32194-32207. [PMID: 36120015 PMCID: PMC9475633 DOI: 10.1021/acsomega.2c03458] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 08/15/2022] [Indexed: 05/17/2023]
Abstract
Studies on deep eutectic solvents (DESs), a new class of "green" solvents, are attracting increasing attention from researchers, as evidenced by the rapidly growing number of publications in the literature. One of the main advantages of DESs is that they are tailor-made solvents, and therefore, the number of potential DESs is extremely large. It is essential to have computational methods capable of predicting the physicochemical properties of DESs, which are needed in many industrial applications and research. Surface tension is one of the most important properties required in many applications. In this work, we report a relatively generalized artificial neural network (ANN) for predicting the surface tension of DESs. The database used can be considered comprehensive because it contains 1571 data points from 133 different DES mixtures in 520 compositions prepared from 18 ions and 63 hydrogen bond donors in a temperature range of 277-425 K. The ANN model uses molecular parameter inputs derived from the conductor-like screening model for real solvents (S σ-profiles). The training and testing results show that the best performing ANN architecture consisted of two hidden layers with 15 neurons each (9-15-15-1). The proposed ANN was excellent in predicting the surface tension of DESs, as R 2 values of 0.986 and 0.977 were obtained for training and testing, respectively, with an overall average absolute relative deviation of 2.20%. The proposed models represent an initiative to promote the development of robust models capable of predicting the properties of DESs based only on molecular parameters, leading to savings in investigation time and resources.
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Affiliation(s)
- Tarek Lemaoui
- Laboratoire
de Biopharmacie Et Pharmacotechnie (LPBT), Ferhat Abbas Setif 1 University, 19000 Setif, Algeria
- Research
and Innovation Center on CO2 and Hydrogen (RICH Center), Khalifa University of Science and Technology, 127788 Abu Dhabi, United Arab Emirates
| | - Abir Boublia
- Laboratoire
de Physico-Chimie des Hauts Polymères (LPCHP), Département
de Génie des Procédés, Faculté de Technologie, Université Ferhat ABBAS Sétif-1, 19000 Sétif, Algeria
- Research
and Innovation Center on CO2 and Hydrogen (RICH Center), Khalifa University of Science and Technology, 127788 Abu Dhabi, United Arab Emirates
| | - Ahmad S. Darwish
- Center
for Membrane and Advanced Water Technology (CMAT), Khalifa University, P.O. Box 127788, 127788 Abu Dhabi, United Arab
Emirates
- Department
of Chemical Engineering, Khalifa University
of Science and Technology, 127788 Abu Dhabi, United Arab Emirates
| | - Manawwer Alam
- Department
of Chemistry, College of Science, King Saud
University, P.O. Box 2455, 11451 Riyadh, Saudi Arabia
| | - Sungmin Park
- Department
of Civil and Environmental Engineering, Hanyang University, 222-Wangsimni-ro, Seongdong-gu, 04763 Seoul, Republic of Korea
| | - Byong-Hun Jeon
- Department
of Earth Resources and Environmental Engineering, Hanyang University, 04763 Seoul, Republic of Korea
| | - Fawzi Banat
- Center
for Membrane and Advanced Water Technology (CMAT), Khalifa University, P.O. Box 127788, 127788 Abu Dhabi, United Arab
Emirates
- Department
of Chemical Engineering, Khalifa University
of Science and Technology, 127788 Abu Dhabi, United Arab Emirates
| | - Yacine Benguerba
- Laboratoire
de Biopharmacie Et Pharmacotechnie (LPBT), Ferhat Abbas Setif 1 University, 19000 Setif, Algeria
| | - Inas M. AlNashef
- Center
for Membrane and Advanced Water Technology (CMAT), Khalifa University, P.O. Box 127788, 127788 Abu Dhabi, United Arab
Emirates
- Department
of Chemical Engineering, Khalifa University
of Science and Technology, 127788 Abu Dhabi, United Arab Emirates
- Research
and Innovation Center on CO2 and Hydrogen (RICH Center), Khalifa University of Science and Technology, 127788 Abu Dhabi, United Arab Emirates
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Boublia A, Lemaoui T, Abu Hatab F, Darwish AS, Banat F, Benguerba Y, AlNashef IM. Molecular-Based Artificial Neural Network for Predicting the Electrical Conductivity of Deep Eutectic Solvents. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Carolina Gipiela Corrêa Dias M, Oliveira Farias F, Cazelato Gaioto R, Kaspchak E, Conceição da Costa M, Igarashi-Mafra L, Mafra MR. Thermophysical characterization of deep eutectic solvents composed by D-sorbitol, xylitol or D(+)xylose as hydrogen bond donors. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118801] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Bergua F, Castro M, Muñoz-Embid J, Lafuente C, Artal M. L-menthol-based eutectic solvents: Characterization and application in the removal of drugs from water. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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