Issue 29, 2024

Predicting polymer solubility from phase diagrams to compatibility: a perspective on challenges and opportunities

Abstract

Polymer processing, purification, and self-assembly have significant roles in the design of polymeric materials. Understanding how polymers behave in solution (e.g., their solubility, chemical properties, etc.) can improve our control over material properties via their processing-structure–property relationships. For many decades the polymer science community has relied on thermodynamic and physics-based models to aid in this endeavor, but all rely on disparate data sets and use-case scenarios. Hence, there are still significant challenges to predict a priori the solubility of a polymer, whether it is for selecting sustainable solvents, obtaining thermodynamic parameters for phase separation, or navigating the coexistence curve. This perspective aims to discuss the different approaches of applying computational tools to predict polymer solubility, with a significant focus on machine learning techniques to capture the rapid progress in that space. We examine challenges and opportunities that remain for creating a comprehensive solubility toolset that can accelerate the design of a broad range of applications including films, membranes, and pharmaceuticals.

Graphical abstract: Predicting polymer solubility from phase diagrams to compatibility: a perspective on challenges and opportunities

Article information

Article type
Perspective
Submitted
16 maj 2024
Accepted
06 jul 2024
First published
08 jul 2024
This article is Open Access
Creative Commons BY-NC license

Soft Matter, 2024,20, 5652-5669

Predicting polymer solubility from phase diagrams to compatibility: a perspective on challenges and opportunities

J. Ethier, E. R. Antoniuk and B. Brettmann, Soft Matter, 2024, 20, 5652 DOI: 10.1039/D4SM00590B

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements