Solubility prediction for ill-defined polymers: A critical comparison of Hansen parameters and COSMO-RS backfitting

Selecting solvents for polymeric materials is challenging because the target solute is rarely a single well-defined molecular species, but rather a system with chemical, physical, and formulation complexity. To address this issue, this short review compares two “inverse-method” workflows in which solvent-screening data are used to reconstruct effective descriptors of such ill-defined solutes. Hansen solubility parameters (HSP) provide a pragmatic representation of solubility by translating solvent-screening data into a spherical compatibility domain, but their predictive scope remains constrained by the “like dissolves like” paradigm.. COSMO-RS backfitting, a largely overlooked inverse extension of COSMO-RS, uses the same experimental logic to reconstruct an effective σ-profile, enabling solvent ranking in a thermodynamic framework without requiring an explicit polymer structure. Using cellulose triacetate as a stringent benchmark, the review shows that the two approaches are best viewed as sequentially complementary: HSP provides a first operational and visually intuitive map, whereas COSMO-RS backfitting offers greater predictive value for ranking new pure solvents when geometric similarity is no longer sufficiently discriminating.