Statistical Copolymerization-Induced Self-Assembly

Abstract

Statistical copolymers have been extensively used in chemical industries and our daily lives, owing to their ease of synthesis and functionalization. However, self-assembly based on statistical copolymers has been haunted by high interfacial energy, poor stability, and low concentration. We proposed the statistical copolymerizationinduced self-assembly (stat-PISA) as a general strategy for one-step preparing stable statistical copolymer assemblies with high solids content. The concept was demonstrated through a model dispersion polymerization system comprising a charged hydrophilic monomer and a core-forming monomer, producing spherical micelles via a spinodal decomposition mechanism with an interconnected network intermediate. The stat-PISA was tunable by varying the fraction of charged monomer, the polymer chain length, and the solids content. The statistical copolymer micelles were demonstrated to be a potential Pickering emulsifier with superior stabilizing performances compared to their block copolymer counterparts. The general applicability of stat-PISA was demonstrated by preparing statistical copolymer assemblies with varying surface charges and chemical compositions. Particularly, this strategy is feasible for conventional free radical polymerization, promising for industrial scale-up.