Kovács Attila, Neyts Erik C, Cornet Iris, Wijnants Marc, Billen Pieter
Department of Chemistry/Biochemistry, iPRACS Research Group, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium.
Department of Chemistry, PLASMANT Research Group, NANOLab Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium.
ChemSusChem. 2020 Aug 7;13(15):3789-3804. doi: 10.1002/cssc.202000286. Epub 2020 Jun 22.
Natural deep eutectic solvents (NADES) are mixtures of naturally derived compounds with a significantly decreased melting point owing to specific interactions among the constituents. NADES have benign properties (low volatility, flammability, toxicity, cost) and tailorable physicochemical properties (by altering the type and molar ratio of constituents); hence, they are often considered to be a green alternative to common organic solvents. Modeling the relation between their composition and properties is crucial though, both for understanding and predicting their behavior. Several efforts have been made to this end. This Review aims at structuring the present knowledge as an outline for future research. First, the key properties of NADES are reviewed and related to their structure on the basis of the available experimental data. Second, available modeling methods applicable to NADES are reviewed. At the molecular level, DFT and molecular dynamics allow density differences and vibrational spectra to be interpreted, and interaction energies to be computed. Additionally, properties at the level of the bulk medium can be explained and predicted by semi-empirical methods based on ab initio methods (COSMO-RS) and equation of state models (PC-SAFT). Finally, methods based on large datasets are discussed: models based on group-contribution methods and machine learning. A combination of bulk-medium and dataset modeling allows qualitative prediction and interpretation of phase equilibria properties on the one hand, and quantitative prediction of melting point, density, viscosity, surface tension, and refractive index on the other. Multiscale modeling, combining molecular and macroscale methods, is expected to strongly enhance the predictability of NADES properties and their interaction with solutes, and thus yield truly tailorable solvents to accommodate (bio)chemical reactions.
天然深共熔溶剂（NADES）是由天然衍生化合物组成的混合物，由于其成分之间的特定相互作用，熔点显著降低。NADES具有良好的性质（低挥发性、低可燃性、低毒性、低成本）和可定制的物理化学性质（通过改变成分的类型和摩尔比）；因此，它们常被视为常见有机溶剂的绿色替代品。然而，对其组成与性质之间的关系进行建模，对于理解和预测它们的行为都至关重要。为此已经做出了多项努力。本综述旨在梳理当前的知识，为未来的研究提供一个框架。首先，根据现有的实验数据，对NADES的关键性质进行综述，并将其与结构相关联。其次，对适用于NADES的现有建模方法进行综述。在分子层面，密度泛函理论（DFT）和分子动力学能够解释密度差异和振动光谱，并计算相互作用能。此外，基于从头算方法（COSMO-RS）和状态方程模型（PC-SAFT）的半经验方法，可以解释和预测体相介质层面的性质。最后，讨论基于大数据集的方法：基于基团贡献法和机器学习的模型。体相介质建模和数据集建模相结合，一方面能够对相平衡性质进行定性预测和解释，另一方面能够对熔点、密度、粘度、表面张力和折射率进行定量预测。结合分子和宏观尺度方法的多尺度建模，有望大幅提高NADES性质及其与溶质相互作用的可预测性，从而产生真正可定制的溶剂，以适应（生物）化学反应。
