文献类型：期刊文献

英文题名：A QSPR study for predicting θ(LCST) and θ(UCST) in binary polymer solutions

作者：Wu, Jia-Qi[1,2,4];Gong, Xue-Qing[1,2];Wang, Qiang[4];Yan, Fangyou[4];Li, Jin-Jin[3]

机构：[1]East China Univ Sci & Technol, Sch Chem & Mol Engn, Key Lab Adv Mat, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Feringa Nobel Prize Scientist Joint Res Ctr, Ctr Computat Chem & Res Inst Ind Catalysis, Sch Chem & Mol Engn,Joint Int Res Lab Precis Chem, 130 Meilong Rd, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Sch Chem Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China;[4]Tianjin Univ Sci & Technol, Sch Chem Engn & Mat Sci, 13St 29, Tianjin 300457, Peoples R China

年份：2023

卷号：267

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20230113335489);WOS:【SCI-EXPANDED(收录号:WOS:000906633100001)】；

基金：Acknowledgments This work was supported by National Natural Science Founda-tion of China (22278319, 21808140) , Tianjin Municipal Science and Technology Bureau (20JCQNJC00090) , and Shanghai Rising-Star Program (22QA1402800) .

语种：英文

外文关键词：QSPR; Norm descriptors; Critical solution temperature; LCST; UCST

摘要：Lower critical solution temperature (LCST) and upper critical solution temperature (UCST) are key ther-modynamic properties of binary polymer solutions. In this work, quantitative structure property relation-ship (QSPR) modeling was employed to predict theta(LCST) and theta(UCST) (LCST and UCST at the limit of infinite molar mass). Based on a series of topological norm descriptors and quantum chemical norm descriptors derived exclusively from the chemical structures of polymers and solvents, four linear topological and spatial LCST-QSPR and UCST-QSPR models were developed. The accuracy, robustness, and predictability of the proposed models were evaluated in detail by various statistical parameters (e.g., R2, MAE, MRE, and RMSE) and validation approaches (e.g., leave-one-out cross validation and Y-randomized validation). Various validation techniques and statistical indicators reveal that the spatial LCST-QSPR and UCST-QSPR models established by adding quantum chemical norm descriptors show better performances. Desirable agreements (Rtraining 2 = 0.9423) between calculated and experimental theta(LCST) of 118 training set polymer solutions can be found in the spatial LCST-QSPR model as demonstrated by MRE of 2.65 %. Meanwhile, the spatial UCST-QSPR model shows the performances with MRE of 8.03 % and Rtraining 2 of 0.8826 of 87 training set polymer solutions. The comparative results with those of different models from literatures further confirm the advantages of the as-developed models. The presented QSPR models are expected for rapid and accurate prediction of the Theta(LCST) and Theta(UCST) values of various binary polymer solutions. (c) 2022 Elsevier Ltd. All rights reserved.