Application of the Bethe-Peierls approximation to a lattice-gas model of adsorption on mesoporous materials

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

Effect of Confinement on Capillary Phase Transition in Granular Aggregates

Using a 3D mean-field lattice-gas model, we analyze the effect of confinement on the nature of capillary phase transition in granular aggregates with varying disorder and their inverse porous structures obtained by interchanging particles and pores. Surprisingly, the confinement effects are found to be much less pronounced in granular aggregates as opposed to porous structures. We show that this discrepancy can be understood in terms of the surface-surface correlation length with a connected path through the fluid domain, suggesting that this length captures the true degree of confinement. We also find that the liquid-gas phase transition in these porous materials is of second order nature near capillary critical temperature, which is shown to represent a true critical temperature, i.e., independent of the degree of disorder and the nature of the solid matrix, discrete or continuous. The critical exponents estimated here from finite-size scaling analysis suggest that this transition belongs to the 3D random field Ising model universality class as hypothesized by F. Brochard and P.G. de Gennes, with the underlying random fields induced by local disorder in fluid-solid interactions.

Thermodynamics of the one-dimensional parallel Kawasaki model: exact solution and mean-field approximations

The adsorption isotherm for the recently proposed parallel Kawasaki (PK) lattice-gas model [Phys. Rev. E 88, 062144 (2013)] is calculated exactly in one dimension. To do so, a third-order difference equation for the grand-canonical partition function is derived and solved analytically. In the present version of the PK model, the attraction and repulsion effects between two neighboring particles and between a particle and a neighboring empty site are ruled, respectively, by the dimensionless parameters ϕ and θ. We discuss the inflections induced in the isotherms by situations of high repulsion, the role played by finite lattice sizes in the emergence of substeps, and the adequacy of the two most widely used mean-field approximations in lattice gases, namely, the Bragg-Williams and the Bethe-Peierls approximations.

A computational study of the reconstruction of amorphous mesoporous materials from gas adsorption isotherms and structure factors via evolutionary optimization

A general method for the three-dimensional reconstruction of mesoporous materials by evolutionary optimization against target data is developed. The method is applied specifically in reconstruction of amorphous material models using gas adsorption data, structure factor data, or a combination of both. A recently introduced lattice-gas approach is used to model adsorption in these calculations, and a high-pass limited Fourier representation is used to facilitate evolution of large-scale structures during the optimization. Reconstructions are made of several material models which mimic real materials obtained either by phase separation and etching or by sol-gel processing. Analysis of the reconstructions provides considerable insight into the type and quantity of structural information probed by gas adsorption and small-angle scattering experiments. We find that reconstructions based only on structure factors tend to underestimate the mean pore size. We also find that in many cases excellent reconstructions can be obtained using only adsorption-branch data, and that in all cases reconstructions based jointly on both types of data are superior to those based only on one, suggesting that these measures contain "complementary" information. It is also found that in most cases the use of desorption data is not warranted, and that the use of adsorption data taken at many temperatures will not improve reconstructions. The reproducibility of the method is shown to be satisfactory. The method can be computationally expensive if gas adsorption data are used, but it is easily parallelized, and therefore results can still be obtained in reasonable time. Finally, the possible application of this approach to real systems, including templated porous materials, is discussed.

Freezing of fluids confined in a disordered nanoporous structure

Freezing of a simple fluid in a disordered nanoporous carbon is studied using molecular simulations. Only partial crystallization occurs, and the confined phase is composed of crystalline and amorphous nanodomains. This freezing behavior departs strongly from that for nanopores of simple geometry. We present a method for analyzing the freezing in such disordered materials in terms of a transition in the average size and number of crystalline clusters. The results provide a basis for the interpretation of experiments on freezing in such materials, particularly 1H-NMR and scattering experiments.

Differential capacitance of liquid/liquid interfaces--a lattice gas model approach

A lattice gas model formalism under mean field approximation is developed for the analysis of differential capacitance pertaining to liquid/liquid interfaces. The interfacial profiles for the two solvent mole fractions are chosen and ionic charge densities are estimated by minimizing the Helmholtz free energy. The dependence of the differential capacitance on various interaction energies, electrolyte concentrations and dielectric constants is indicated. The influence of the solvent density profile is analyzed and deviations from the predictions of Gouy-Chapman theory are pointed out.