Structure and Phase Behavior of Mixed Self-Assembled Alkanethiolate Monolayers on Gold Nanoparticles: A Monte Carlo Study

Abstract

Configurational-bias Monte Carlo (CBMC) simulations are carried out to investigate the structure and phase behavior of self-assembled monolayers consisting of equimolar alkanethiolate mixtures chemisorbed on the surface of gold nanoparticles. The simulations probe the effects of variations in the chain length, nanoparticle curvature, and exchange of alkanethiolates between nanoparticles. The TraPPE-UA force field is used for the alkanethiolates, whereas the nanoparticle is represented by gold atoms placed on the surface of a sphere. CBMC identity exchange moves are used to enhance sampling of the spatial distribution of the different ligands and to ensure that thermodynamic equilibrium is reached. At a temperature of 298 K, mixtures differing in length by four methylene units exhibit some degree of local segregation. In contrast, the hexanethiolate/tetradecanethiolate mixture yields Janus-like arrangement when the ligands are confined to a single nanoparticle but global demixing when the ligands are allowed to distribute between two nanoparticles.

Molecular dynamics simulations of nanoscale engravings on an alkanethiol monolayer

“…4 remain separate from each other by running simulation for extra 140 ns. Nevertheless, due to the inherent limitation of a nite MD simulation time, we cannot ensure that these patterns will survive for a much longer timescale such as >1 ms. For this purpose, an efficient sampling method such as the Monte Carlo method 57,58 or accelerated MD simulation 59 might be needed.…”

Gold nanoparticles with patterned surface monolayers for nanomedicine: current perspectives

“… 2016b ). Alternatively, due to the long times required for ligands to move on the curved surface and reach their equilibrium arrangement, statistical methods such as configurational-bias Monte Carlo can be adopted (Fetisov and Siepmann 2016 ; Charchar et al. 2016 ) (Table 2 ).…”

“…( 2012 ) and Şologan et al. ( 2016b ); c Fetisov and Siepmann ( 2016 ); d Ghorai and Glotzer ( 2010 ), Heikkilä et al. ( 2012 ), Lane and Grest ( 2010 ), Van Lehn and Alexander-Katz ( 2013 ), and Velachi et al.…”

Mass spectrometry and Monte Carlo method mapping of nanoparticle ligand shell morphology

“…To further understand the suitability of the nearest neighbor descriptor and its possible limitations in comparing different predictions, a more complex descriptor, i.e., nearest neighbor distribution in the first two neighboring shells (18 neighbors) of the two models are computed and compared. The same type of analysis was reported previously 49 . As shown in Supplementary Figure 6 , in the 18 nearest neighbor distribution profile, the differences between the two structures become clearer.…”