Literature DB >> 33919063

Off-Lattice Monte-Carlo Approach for Studying Nucleation and Evaporation Phenomena at the Molecular Scale.

Panagiotis E Theodorakis1, Yongjie Wang1, Aiqiang Chen2, Bin Liu2.   

Abstract

Droplet nucleation and evaporation are ubiquitous in nature and many technological applications, such as phase-change cooling and boiling heat transfer. So far, the description of these phenomena at the molecular scale has posed challenges for modelling with most of the models being implemented on a lattice. Here, we propose an off-lattice Monte-Carlo approach combined with a grid that can be used for the investigation of droplet formation and evaporation. We provide the details of the model, its implementation as Python code, and results illustrating its dependence on various parameters. The method can be easily extended for any force-field (e.g., coarse-grained, all-atom models, and external fields, such as gravity and electric field). Thus, we anticipate that the proposed model will offer opportunities for a wide range of studies in various research areas involving droplet formation and evaporation and will also form the basis for further method developments for the molecular modelling of such phenomena.

Entities:  

Keywords:  Monte-Carlo simulation; droplet; evaporation; nucleation; off-lattice models

Year:  2021        PMID: 33919063     DOI: 10.3390/ma14092092

Source DB:  PubMed          Journal:  Materials (Basel)        ISSN: 1996-1944            Impact factor:   3.623


  21 in total

1.  Drying-mediated self-assembly of nanoparticles.

Authors:  Eran Rabani; David R Reichman; Phillip L Geissler; Louis E Brus
Journal:  Nature       Date:  2003-11-20       Impact factor: 49.962

2.  The evaporation/condensation transition of liquid droplets.

Authors:  Luis G MacDowell; Peter Virnau; Marcus Müller; Kurt Binder
Journal:  J Chem Phys       Date:  2004-03-15       Impact factor: 3.488

3.  Modelling the superspreading of surfactant-laden droplets with computer simulation.

Authors:  Panagiotis E Theodorakis; Erich A Müller; Richard V Craster; Omar K Matar
Journal:  Soft Matter       Date:  2015-12-28       Impact factor: 3.679

4.  Accurate statistical associating fluid theory for chain molecules formed from Mie segments.

Authors:  Thomas Lafitte; Anastasia Apostolakou; Carlos Avendaño; Amparo Galindo; Claire S Adjiman; Erich A Müller; George Jackson
Journal:  J Chem Phys       Date:  2013-10-21       Impact factor: 3.488

5.  Superspreading: mechanisms and molecular design.

Authors:  Panagiotis E Theodorakis; Erich A Müller; Richard V Craster; Omar K Matar
Journal:  Langmuir       Date:  2015-02-18       Impact factor: 3.882

6.  Control of Stratification in Drying Particle Suspensions via Temperature Gradients.

Authors:  Yanfei Tang; Gary S Grest; Shengfeng Cheng
Journal:  Langmuir       Date:  2019-03-13       Impact factor: 3.882

7.  Stratification of drying particle suspensions: Comparison of implicit and explicit solvent simulations.

Authors:  Yanfei Tang; Gary S Grest; Shengfeng Cheng
Journal:  J Chem Phys       Date:  2019-06-14       Impact factor: 3.488

8.  Kinetic Monte Carlo simulation of the classical nucleation process.

Authors:  A Filipponi; P Giammatteo
Journal:  J Chem Phys       Date:  2016-12-07       Impact factor: 3.488

9.  Stratification in Drying Films Containing Bidisperse Mixtures of Nanoparticles.

Authors:  Yanfei Tang; Gary S Grest; Shengfeng Cheng
Journal:  Langmuir       Date:  2018-06-05       Impact factor: 3.882

10.  Martini 3: a general purpose force field for coarse-grained molecular dynamics.

Authors:  Paulo C T Souza; Riccardo Alessandri; Jonathan Barnoud; Sebastian Thallmair; Ignacio Faustino; Fabian Grünewald; Ilias Patmanidis; Haleh Abdizadeh; Bart M H Bruininks; Tsjerk A Wassenaar; Peter C Kroon; Josef Melcr; Vincent Nieto; Valentina Corradi; Hanif M Khan; Jan Domański; Matti Javanainen; Hector Martinez-Seara; Nathalie Reuter; Robert B Best; Ilpo Vattulainen; Luca Monticelli; Xavier Periole; D Peter Tieleman; Alex H de Vries; Siewert J Marrink
Journal:  Nat Methods       Date:  2021-03-29       Impact factor: 28.547

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