Combining dynamic Monte Carlo with machine learning to study nanoparticle translocation
Combining dynamic Monte Carlo with machine learning to study nanoparticle translocation
复制标题
将动态蒙特卡罗与机器学习相结合来研究纳米颗粒易位
DOI：
10.1039/d2sm00431c
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发表时间：
2022
期刊：
影响因子：
3.4
通讯作者：
Hore, Michael J.
中科院分区：
文献类型：
--
作者：
Vieira, Luiz Fernando;Weinhofer, Alexandra C.;Oltjen, William C.;Yu, Cindy;de Souza Mendes, Paulo Roberto;Hore, Michael J.
纳米颗粒移位的电阻脉冲感测（RPS）测量具有提供关于单颗粒水平特征（诸如直径或迁移率）以及系综平均值的信息的能力。然而，解释这些测量结果是复杂的，需要了解纳米粒子在密闭空间中的动力学，以及纳米粒子在纳米孔内破坏离子传输的方式。在这里，我们结合联合收割机动态蒙特卡罗（DMC）模拟与机器学习（ML）和泊松-能斯特-普朗克计算，同时模拟纳米粒子的动力学和离子传输过程中的数百个独立的粒子易位作为纳米粒子的大小，电泳迁移率和纳米孔长度的函数。DMC模拟的使用使我们能够明确地研究布朗运动和纳米颗粒/纳米孔特性对易位信号的振幅和持续时间的影响。模拟结果与实验RPS测量进行了验证，并发现在定量协议。
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.