Topological Analysis of Single-Chain Nanoparticle Formation Pathways

Abstract

Single-chain nanoparticles (SCNPs) are incredibly versatile nanomaterials formed from the intramolecular cross-linking of a single precursor polymer chain. A variety of applications, including catalysis and nanomedicine, require that SCNPs manifest precise structures. The dynamics in SCNP formation pathways is generally not well understood. Here, we present a characterization strategy to analyze SCNP trajectories via topological analysis. We first introduce the Euler characteristic (EC), a topological invariant, as means of capturing important folding events during SCNP formation. We perform MD simulations of 30 SCNPs of varying morphologies and record the time evolution of SCNP formation via the EC. We then apply statistical and unsupervised machine learning techniques to analyze the time evolution of the ECs. Our findings reveal that the EC effectively captures crucial topological events and correlates with the physical transformations within the SCNP system. The EC is also capable of differentiating between the formation pathways of morphologically varying SCNPs. This work provides insight into the global and local folding dynamics of SCNPs and furthers the application of topological analysis in studying macromolecular systems.