利用 COSMO-RS σ 轮廓分子描述符预测水相酸碱深共晶溶剂的 pH 值。
Prediction of pH Value of Aqueous Acidic and Basic Deep Eutectic Solvent Using COSMO-RS σ Profiles' Molecular Descriptors.
机构信息
Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva Ulica 6, 10000 Zagreb, Croatia.
Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev Trg 19, 10000 Zagreb, Croatia.
出版信息
Molecules. 2022 Jul 13;27(14):4489. doi: 10.3390/molecules27144489.
The aim of this work was to develop a simple and easy-to-apply model to predict the pH values of deep eutectic solvents (DESs) over a wide range of pH values that can be used in daily work. For this purpose, the pH values of 38 different DESs were measured (ranging from 0.36 to 9.31) and mathematically interpreted. To develop mathematical models, DESs were first numerically described using σ profiles generated with the COSMOtherm software. After the DESs’ description, the following models were used: (i) multiple linear regression (MLR), (ii) piecewise linear regression (PLR), and (iii) artificial neural networks (ANNs) to link the experimental values with the descriptors. Both PLR and ANN were found to be applicable to predict the pH values of DESs with a very high goodness of fit (R2independent validation > 0.8600). Due to the good mathematical correlation of the experimental and predicted values, the σ profile generated with COSMOtherm could be used as a DES molecular descriptor for the prediction of their pH values.
本工作旨在开发一种简单易用的模型，以预测在较宽 pH 值范围内的深共熔溶剂 (DES) 的 pH 值，该模型可用于日常工作。为此，测量了 38 种不同 DES 的 pH 值（范围为 0.36 至 9.31）并进行了数学解释。为了开发数学模型，首先使用 COSMOtherm 软件生成的 σ 分布对 DES 进行数值描述。DES 描述后，使用以下模型：（i）多元线性回归（MLR），（ii）分段线性回归（PLR）和（iii）人工神经网络（ANN）将实验值与描述符联系起来。PLR 和 ANN 都被发现适用于预测 DES 的 pH 值，拟合度非常高（独立验证 R2>0.8600）。由于实验值和预测值之间具有良好的数学相关性，因此可以使用 COSMOtherm 生成的 σ 分布作为 DES 的分子描述符来预测其 pH 值。
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