A Solvent Selection Framework for Porous Organic Polymers

TL;DR

This paper introduces a new method to choose solvents for making porous organic polymers, improving their performance in capturing CO2.

Contribution

The novel MLoc algorithm enables rapid determination of solubility parameters for new materials using UV/vis absorbance data.

Findings

MLoc algorithm uses UV/vis absorbance data to determine Hansen solubility parameters for novel porous organic polymers.

Using solvents with similar HSPs to the polymer improved CO2 uptake by 220% compared to a previous method.

A database of HSPs for 17 polymers and over 80 reactions is provided for future research.

Abstract

Selecting suitable solvents to control the morphology
and properties
of novel functional materials remains a significant challenge, especially
when there is limited or no prior knowledge of the material and its
solubility. In this work, we present a solvent selection toolkit for
functional porous organic polymers. We have developed the MLoc algorithm for the fast determination of Hansen solubility
parameters (HSPs) for novel materials. This approach requires ultraviolet
and visible (UV/vis) absorbance data, measured for a number of candidate
solvents using a standard laboratory setup. Based on these measurements, MLoc determines the HSPs for novel porous organic materials
using a centroid-location algorithm based on Hansen distance. The
results of this algorithm can guide the fine-tuning of both morphology
and carbon-capture performance of target polymers, which we illustrate
in a case study. In this example, performing the polymer synthesis
in solvents with HSPs most similar to the porous material has led
to CO2 uptake improved by 220% compared to a reported analogue
(from 2.16 to 6.95 wt %). Using MLoc, we have also
compiled a HSP database for 17 porous organic polymers, enhanced with
data for over 80 reactions, sampling different conditions, which we
present as a resource for future data-driven research in this area.

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