Using design of experiments to understand and predict polymer microcapsule core-shell architecture

摘要

Microbubbles (MBs) have tremendous application in a number of fields and strategies to impart them with tunable properties are of great interest to the scientific community. We recently reported a robust platform to produce polymeric MBs (more appropriately termed polymeric microcapsules, PMCs) with highly tunable materials properties by controlling the self-emulsification of oil-in-water emulsions. In this study, we used design of experiments to develop a model to predict PMC internal architectures and mean particle diameter as a function of three key processing parameters: concentration of self-emulsifier (0.1% < F68 < 0.25% wt/wt), homogenization speed (1500-3000 rpm), and dilution factor (15 < DF < 30). We show that the homogenization speed and F68 concentration strongly influence both PMC morphology and size, with porous shell hollow cored PMCs being favored at low speeds and high F68 concentrations and nonporous-shelled gas-in-oil cored PMCs (g/o-PMCs) being favored at faster speeds and lower F68 concentrations. Models were subsequently validated by successfully predicting the relative percent yield and mean particle diameter of g-PMCs and g/o-PMCs for four sets of randomly selected factor settings. We anticipate that the results shown here will serve as a roadmap for other investigators interested in evaluating the utility of g-PMCs, g/o-PMCs or, combinations of the two, as novel gas carriers, diagnostic imaging agents, acoustic insulators, shock absorbers, and lightweight building materials, among many others.