Combining dynamic Monte Carlo with machine learning to study nanoparticle translocation.

Resistive pulse sensing (RPS) measurements of nanoparticle translocation have the ability to provide information on single-particle level characteristics, such as diameter or mobility, as well as ensemble averages. However, interpreting these measurements is complex and requires an understanding of nanoparticle dynamics in confined spaces as well as the ways in which nanoparticles disrupt ion transport while inside a nanopore. Here, we combine Dynamic Monte Carlo (DMC) simulations with Machine Learning (ML) and Poisson-Nernst-Planck calculations to simultaneously simulate nanoparticle dynamics and ion transport during hundreds of independent particle translocations as a function of nanoparticle size, electrophoretic mobility, and nanopore length. The use of DMC simulations allowed us to explicitly investigate the effects of Brownian motion and nanoparticle/nanopore characteristics on the amplitude and duration of translocation signals. Simulation results were verified with experimental RPS measurements and found to be in quantitative agreement.

Translocation of soft phytoglycogen nanoparticles through solid-state nanochannels.

Phytoglycogen nanoparticles are soft, naturally-derived nanomaterials with a highly uniform size near 35 nm. Their interior is composed of a highly-branched polysaccharide core that contains more than 200% of its dry mass in water. In this work, we measure the translocation of phytoglycogen particles by observing blockade events they create when occluding solid-state nanochannels with diameters between 60 and 100 nm. The translocation signals are interpreted using Poisson-Nernst-Planck calculations with a "hardness parameter" that describes the extent to which solvent can penetrate through the interior of the particles. Theory and experiment were found to be in quantitative agreement, allowing us to extract physical characteristics of the particles on a per particle basis.