Development of Solvent Selection Methods for Functional Materials Preparation

Abstract

This thesis addresses the challenges of quantitative solvent selection for novel functional materials preparation, with a focus on porous organic polymers (POPs). The goal has been to develop novel algorithms and practical tools for solvent selection that align with the needs of experimental research. These workflows aim to balance interpretability, reliance on explicit structural information, computational and experimental costs, and to achieve transferability across different systems, moving towards an ideal solvent selection method.

Primarily founded on Hansen solubility theory, the research systematically investigates the effects of solvents on CO2 uptake performance, morphology, reactivity and supramolecular interactions. The research has led to the development of a number Python programs (MLoc, SolvPred and SolvPred+) during this PhD. MLoc allows for the prediction of Hansen solubility parameters (HSPs) of novel materials; SolvPred is capable of predicting multi-solvent mixtures to construct targeted solvent environments; and SolvPred+ expands the solvent design criteria beyond HSPs.

The results demonstrate the capability of these tools, achieving both mechanistic interpretation of solvent-dependent properties and systematic optimisation of solvent–solute interactions. The generalisabitliy of these methodologies has been validated on distinct materials beyond the model systems. This work provides novel data-driven strategies for solvent optimisation in experimental research, with the potential to accelerate material discovery across diverse fields.

Date of Award	4 Feb 2025
Original language	English
Awarding Institution	University of Bristol
Sponsors	China Scholarship Council
Supervisor	Charl F J Faul (Supervisor) & Natalie Fey (Supervisor)