Combining dynamic Monte Carlo with machine learning to study nanoparticle translocation

Area of Science:

• Nanotechnology
• Physical Chemistry
• Computational Science

Background:

• Resistive pulse sensing (RPS) offers single-particle analysis but interpretation is complex.
• Understanding nanoparticle dynamics and ion disruption in nanopores is crucial for RPS data analysis.

Purpose of the Study:

• To develop a method for interpreting complex resistive pulse sensing measurements.
• To simulate nanoparticle dynamics and ion transport during translocation events.

Main Methods:

• Dynamic Monte Carlo (DMC) simulations were used to model nanoparticle dynamics.
• Poisson-Nernst-Planck calculations simulated ion transport.
• Machine learning (ML) integrated these methods for comprehensive analysis.

Main Results:

• Simulations accurately captured the effects of Brownian motion, nanoparticle size, mobility, and nanopore length on translocation signals.
• The combined simulation approach provided quantitative agreement with experimental RPS measurements.
• This method allows for detailed analysis of single nanoparticle characteristics.

Conclusions:

• The integrated DMC, ML, and Poisson-Nernst-Planck approach provides a robust framework for interpreting RPS data.
• This computational method enhances the understanding of nanoparticle translocation dynamics and ion transport.
• The findings validate the simulation approach against experimental data, paving the way for advanced nanoparticle characterization.