Ariane Nunes Alves (@anunesalves.bsky.social)

A Solvent Selection Framework for Porous Organic Polymers 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. https://pubs.acs.org/doi/10.1021/acs.jcim.5c02163