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A Solvent Selection Framework for Porous Organic Polymers
Xue Fang1,2, Ulzhalgas Karatayeva1, John D Worth1,3
1School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, U.K.
Journal of Chemical Information and Modeling
|November 4, 2025
Summary
A new algorithm, MLoc, quickly determines Hansen Solubility Parameters (HSPs) for novel porous organic polymers. This aids solvent selection, significantly improving material morphology and carbon capture performance.
Area of Science:
- Materials Science
- Polymer Chemistry
- Chemical Engineering
Background:
- Solvent selection for novel functional materials is challenging due to limited solubility data.
- Controlling material morphology and properties requires precise solvent selection.
- Porous organic polymers (POPs) offer potential for applications like carbon capture but require tailored synthesis.
Purpose of the Study:
- To develop a rapid method for determining Hansen Solubility Parameters (HSPs) for novel porous organic polymers.
- To create a solvent selection toolkit to guide the synthesis of functional porous organic polymers.
- To demonstrate the impact of HSP-guided solvent selection on material morphology and carbon capture performance.
Main Methods:
- Development of the MLoc algorithm for rapid HSP determination.
- Utilizing UV/vis absorbance data from candidate solvents.
- Application of a centroid-location algorithm based on Hansen distance.
- Experimental validation through a case study on porous organic polymer synthesis.
Main Results:
- MLoc accurately determines HSPs for novel porous organic materials.
- Solvent selection based on MLoc-derived HSPs significantly enhanced CO2 uptake by 220% in a case study.
- A comprehensive HSP database for 17 porous organic polymers and over 80 reactions was compiled.
Conclusions:
- The MLoc algorithm provides a fast and effective method for solvent selection in porous organic polymer synthesis.
- Optimizing solvent selection through HSP matching is crucial for tuning material morphology and enhancing functional properties like carbon capture.
- The developed toolkit and database serve as valuable resources for data-driven research in porous organic polymer science.
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