Models for the solubility calculation of a CO2/polymer system: A review. (December 2020)

• Record Type: Journal Article
• Authors: Li, Mengshan; Zhang, Jiale; Zou, Yanying; Wang, Fan; Chen, Bingsheng; Guan, Lixin; Wu, Yan
• Is Part Of: Materials today communications. Volume 25(2020)
• Publication Date: 2020-12
• Subjects: Multiscale -- Computational chemistry -- Molecular dynamics -- Solubility big data; Materials science -- Periodicals; 620.11
• DOI: 10.1016/j.mtcomm.2020.101277
• Languages: English
• ISSNs: 2352-4928

Abstract

Graphical abstract: Highlights: multiscale solubility model is surveyed. The multiscale model can be used for reference in many fields. Solubility big data combined with deep learning and AI will be expanded. Abstract: Multiscale models are modeled at different time and spatial scales to achieve the spans among the micro-, meso-, and macroscales. The multiscale study of solubility is the most effective way to reveal its essence, which is widely used in the extraction, separation, material modification, and preparation of new materials. In this article, the progress of solubility-calculation models for CO2 /polymer systems at the micro-, meso-, and macroscales is surveied. The research situation of the thermodynamic-calculation model and modern computer simulation at the macroscale is discussed, and the calculation model of molecular dynamics and particle dynamics at the micro- and mesoscales are analyzed. Focus is also given on the development trend of solubility-calculation models at different scales, particularly the mechanism and interpretability of solubility calculation at the macroscopic scale. The possible development directions of solubility big data, such as acquisition, storage, search, sharing, mining, and analysis under the background of big data, are proposed. The authors further describe the development directions of particle dynamics, coarse-grained method, and translink method on the basis of evolutionary algorithm at the micro- and mesoscales. The multiscale methods mentioned in this paper can serve as a reference in many fields, such as polymer self-assembly, phase rheological property, phase separation of block copolymers, amphiphilic molecular self-assembly into membranes, transformation and separation of vesicle morphology, and kinetic analysis. Multiscale research of solubility big data combined with deep learning and artificial intelligence may become important research directions in the future.