Monte Carlo study of mixed electrolytes in the primitive model

1992 ◽  
Vol 96 (10) ◽  
pp. 7656-7661 ◽  
Author(s):  
M. Bešter ◽  
V. Vlachy
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Mixed Electrolytes ◽  
Primitive Model

A Gibbs ensemble Monte Carlo study of phase coexistence in the solvent primitive model

2000 ◽  
Vol 113 (17) ◽  
pp. 7488-7491 ◽  
Author(s):  
Tamás Kristóf ◽  
Dezsö Boda ◽  
István Szalai ◽  
Douglas Henderson
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Phase Coexistence ◽  
Gibbs Ensemble ◽  
Primitive Model ◽  
Ensemble Monte Carlo ◽  
Gibbs Ensemble Monte Carlo

scholarly journals Size and charge correlations in spherical electric double layers: a case study with fully asymmetric mixed electrolytes within the solvent primitive model

RSC Advances ◽  
2020 ◽  
Vol 10 (64) ◽  
pp. 39017-39025
Author(s):  
Chandra N. Patra
Keyword(s):  
Density Functional Theory ◽  
Monte Carlo ◽  
Density Functional ◽  
Double Layers ◽  
Mixed Electrolytes ◽  
Functional Theory ◽  
Electric Double Layers ◽  
Primitive Model ◽  
Charge Correlations

Size and charge correlations in spherical electric double layers are investigated through Monte Carlo simulations and density functional theory, through a solvent primitive model representation.

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

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