Control of Particle Size in the Self-Assembly of Amphiphilic Statistical Copolymers

A range of amphiphilic statistical copolymers is synthesized where the hydrophilic component is either methacrylic acid (MAA) or 2-(dimethylamino)ethyl methacrylate (DMAEMA) and the hydrophobic component comprises methyl, ethyl, butyl, hexyl, or 2-ethylhexyl methacrylate, which provide a broad range of partition coefficients (log ). Small-angle X-ray scattering studies confirm that these amphiphilic copolymers self-assemble to form well-defined spherical nanoparticles in an aqueous solution, with more hydrophobic copolymers forming larger nanoparticles. Varying the nature of the alkyl substituent also influenced self-assembly with more hydrophobic comonomers producing larger nanoparticles at a given copolymer composition. A model based on particle surface charge density (PSC model) is used to describe the relationship between copolymer composition and nanoparticle size. This model assumes that the hydrophilic monomer is preferentially located at the particle surface and provides a good fit to all of the experimental data. More specifically, a linear relationship is observed between the surface area fraction covered by the hydrophilic comonomer required to achieve stabilization and the log  value for the hydrophobic comonomer. Contrast variation small-angle neutron scattering is used to study the internal structure of these nanoparticles. This technique indicates partial phase separation within the nanoparticles, with about half of the available hydrophilic comonomer repeat units being located at the surface and hydrophobic comonomer-rich cores. This information enables a refined PSC model to be developed, which indicates the same relationship between the surface area fraction of the hydrophilic comonomer and the log  of the hydrophobic comonomer repeat units for the anionic (MAA) and cationic (DMAEMA) comonomer systems. This study demonstrates how nanoparticle size can be readily controlled and predicted using relatively ill-defined statistical copolymers, making such systems a viable attractive alternative to diblock copolymer nanoparticles for a range of industrial applications.