4.7 Article

Estimation of the heat capacities of deep eutectic solvents

期刊

JOURNAL OF MOLECULAR LIQUIDS
卷 307, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.molliq.2020.112940

关键词

DES; Green solvent; Novel solvent; Physical property; Thermal property; Model

资金

  1. European Union Horizon 2020 Program [ERC-2016-CoG 725034]
  2. Associate Laboratory for Green ChemistryLAQV - national funds from FCT/MCTES [UID/QUI/50006/2019]
  3. Shiraz University

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Nowadays, Deep Eutectic Solvents (DESs) are considered as green solvents in many research fields. Knowledge of the physical properties of DESs can pave the way to their useful utilization. One of the important properties of a DES is its heat capacity. Besides experimental measurements, which are expensive and time consuming, it is vital to have models for the estimation of heat capacities. A generalized model is presented to estimate the heat capacities of DESs. In this regards, 505 isobaric heat capacity data points, over the wide temperature range of 278.15 to 363.15 K, from 28 DESs of different natures were used to develop the model. Up to date, this is the largest data bank investigated for isobaric heat capacities of DESs in the open literature. Based on this database, a simple, straightforward, yet precise correlation was developed to estimate the heat capacities of DESs as a function of temperature, molecular weight, critical pressure, and acentric factor. The absolute average relative deviation (AARD%) of the proposed correlation for all of the investigated data points is 4.7%, which shows that the calculated results are quite promising for such complicated systems. To the best of our knowledge, up to now, there are no generalized models for estimating the heat capacities of deep eutectic solvents, and this is the first model in the literature, therefore, it can be of great value to researchers in the field. The model has even further significance when considering that it is actually needless of any experimental data as input. This is because the physical properties used in the correlation to identify the substance are themselves obtained by group contribution methods. In practice, the only required information to use this simple and predictive model is the chemical structure. (C) 2020 Elsevier B.V. All rights reserved.

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