A machine learning approach for the design of hyperbranched polymeric dispersing agents based on aliphatic polyesters for radiation-curable inks

Abstract

Polymeric dispersing agents were prepared from aliphatic polyesters consisting of δ-undecalactone (UDL) and β,δ-trimethyl-ε-caprolactones (TMCL) as biobased monomers, which were polymerized in bulk via organocatalysts. Graft copolymers were obtained by coupling of the polyesters to poly(ethylene imine) (PEI) in the bulk without using solvents. Various parameters that influence the performance of the dispersing agents in pigment-based UV-curable matrices were investigated: chemistry of the polyester (UDL or TMCL), polyester/PEI weight ratio, molecular weight of the polyesters and of PEI. The performance of the dispersing agents was modelled using machine learning in order to increase the efficiency of the dispersant design. The resulting models were presented as analytical models for the individual polyesters and the synthesis conditions for optimally performing dispersing agents were indicated as a preference for high-molecular-weight polyesters and a polyester-dependent maximum polyester/PEI weight ratio.