Data-Driven Analysis of the Internal Motions of a Thermoresponsive Single Chain Nanoparticle (SCNP)

Data-driven analysis techniques are powerful methods for understanding polymer behavior in systems that extend beyond ideal conditions, and are based on the principle that low-dimensional behaviors are often embedded within the dynamics of complex systems. In this talk, the dynamics of single chain nanoparticles (SCNPs) are simulated using energy-conserving dissipative particle dynamics (eDPD) simulations. SCNPs were created by crosslinking a fraction (x_c) of monomers of a themoresponsive polymer after the coil-to-globule transition of a free, LCST polymer (e.g., PNIPAM or POEGMA). The internal motions of SCNPs were extracted using proper orthogonal decomposition (POD) above and below the LCST to determine how the monomer displacement patterns within a SCNP differ from those of a free polymer chain. We find significant distortions of the relaxation modes of the polymer that depend strongly on x_c and temperature, and which are unique to any given nanoparticle. Nevertheless, the longest relaxation times of the SCNPs converge to a common value despite each SCNP having a different set of crosslinked monomers and monomer displacement patterns/modes. Finally, the scaling of the relaxation times with relevant SCNP descriptors (e.g., chain length and crosslink density) are discussed.