Combining dynamic Monte Carlo with machine learning to study nanoparticle translocation-Reference-Cited by-同舟云学术

Combining dynamic Monte Carlo with machine learning to study nanoparticle translocation

Published:2022 Issue:28 Volume:18 Page:5218-5229
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

Vieira Luiz Fernando¹²³,Weinhofer Alexandra C.¹^ORCID,Oltjen William C.¹,Yu Cindy⁴,de Souza Mendes Paulo Roberto²^ORCID,Hore Michael J. A.¹^ORCID

Affiliation:

1. Department of Macromolecular Science & Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA

2. Department of Mechanical Engineering, Pontifícia Universidade Católica do Rio de Janeiro, Rua Marquês de São Vicente 225, Rio de Janeiro, RJ 22451-900, Brazil

3. Instituto Nacional de Tecnologia, Ministry of Science, Technology & Innovation, Av. Venezuela, 82 – Rio de Janeiro, RJ 20081-312, Brazil

4. Hathaway Brown School, 19600 North Park Blvd., Shaker Heights, OH 44122, USA

Abstract

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.

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Division of Materials Research

Case Western Reserve University

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SM/D2SM00431C

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5. Gold Nanorod Translocations and Charge Measurement through Solid-State Nanopores