New Molecular Simulation Method To Determine Both Aluminum and Cation Location in Cationic Zeolites

The knowledge of aluminum distribution in zeolites is a difficult task due to limitations in experimental measurements. In the present paper, we propose a new methodology to simultaneously determined aluminum atoms distribution as well as the extraframework cation location in a given experimental structure of the framework and thus allows comparison of different synthesis routes. Aluminum mean distribution is obtained over a great number of configurations that are generated during the course of the simulations at finite temperature. The obtained aluminum atom repartition is in agreement with the experimental and model data available. The consequences of aluminum distribution on solid properties such as extraframework Na⁺ cation location have been analyzed and successfully compared with the available information for different zeolite topologies. The proposed methodology can be used as a powerful complementary tool for aluminum location on X-Ray or neutron experimental structure determinations

A Kinetic Monte Carlo Simulation Study of Synthesis Variables and Diffusion Coefficients in Early Stages of Silicate Oligomerization

A kinetic Monte Carlo (kMC) approach combined with density functional theory (DFT) calculations is used to examine the effects of molecular diffusion and synthesis parameters (pH 7–12) as well as initial monomer concentration (0.01–1 mol L^–1) for a silicate oligomerization model. To implement this approach, we have adapted the open source kMC SPPARKS software in order to simulate the early stages involved in zeolite formation and more generally kinetically driven reactional systems, using a variety of lattice models (fcc/octahedral/tetrahedral, i.e., fcc/oct/tet). First, these adaptations were validated with kinetically driven reactional systems of the “Lodka” model, providing an excellent match with the analytical solution of the reactive system. The calculations reveal that both the lattice complexity and the diffusion coefficients of species have an impact on the steady state concentration (ssc) of oligomers in solution. Second, the approach is further applied to the early stages of silicate oligomerization using chemical pathways (activation barriers and associated prefactors) taken from the literature. Besides the expected impact of key input parameters (amplitudes of energy barriers, influence of water molecules in reaction pathways, pH, etc.), we demonstrate the impact of diffusion (viscosity of the clear solution) on the ssc of silicate oligomers. Considering that the kMC model is limited by the frequencies of reactional rare events, we find that when diffusional frequencies are much larger than reactional ones, the system diffuses instead of reacting. In that respect our calculations suggest that the magnitude of the diffusion coefficient determines the relative ssc of cyclic vs linear oligomers with a transition regime around of 10^–14 m² s^–1 under the reaction conditions studied here (pH 9, 350 K, and initial concentration of 1 mol L^–1)

Transferable Force Field for Equilibrium and Transport Properties in Linear and Branched Monofunctional and Multifunctional Amines. II. Secondary and Tertiary Amines

Following the same philosophy of our previous force field for primary amines (<i>J. Phys. Chem. B</i> <b>2011</b>, <i>115</i>, 14617), we present an extension for secondary and tertiary amines using the anisotropic united atom (AUA4) approach. The force field is developed to predict the phase equilibrium and transport properties of secondary and tertiary amines. The transferability was studied for an important set of molecules including as secondary amines dimethylamine, diethylamine, di-<i>n</i>-propylamine, di-iso-propylamine, and di-iso-butylamine. We have also tested diethylenetriamine, a multifunctional molecule which includes two primary and one secondary amino groups. For tertiary amines, we have included simulations for trimethylamine, triethylamine, tri-<i>n</i>-propylamine, and methyldiethylamine. Monte Carlo simulations in the Gibbs ensemble were carried out to study thermodynamic properties such as equilibrium densities, vaporization enthalpies, and vapor pressures. Critical coordinates (critical density and critical temperature) and normal boiling points were also calculated. The shear viscosity coefficients were studied for dimethyl, diethyl, di-<i>n</i>-propyl, trimethyl, triethyl, and tri-<i>n</i>-propylamine at different temperatures using molecular dynamics in the isothermal isobaric ensemble. Our results show a very good agreement with experimental values for all the studied molecules for both thermodynamic and transport properties, demonstrating the transferability of our force field

A Transferable Force Field for Primary, Secondary, and Tertiary Alkanolamines

Due to the importance of alkanolamines as solvents in several industrial processes and the absence of a dedicated transferable force field for them, we have developed an anisotropic united-atom (AUA4) force field for primary, secondary, and tertiary alkanolamines. In addition to correctly reproducing the experimental densities, additional properties for six different molecules have been verified at different temperatures including vaporization enthalpies, vapor pressures, normal boiling points, critical temperatures, and critical densities. A qualitative analysis of the radial distribution function of pure monoethanolamine has also been carried out. Furthermore, the viscosity coefficients were also calculated as a function of temperature and found to be in good agreement with experimental data. Finally, and perhaps most strikingly, the prediction of the excess enthalpies of alkanolamines in aqueous solutions has been found to be in excellent qualitative agreement with experimental data

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites

The generalization of high-throughput synthesis has recently allowed the discovery of thousands of new porous materials, generating a large amount of information, with the development of specialized databases. Widespread access to databases enabled an increase in algorithms and models for property prediction and in silico design of materials. The structural information on materials still needs to be rationalized by the inclusion of descriptors to ease the characterization of solids. This is essential for in silico screening to potential applications based on machine learning (ML) approaches. Indeed, at the forefront of a real revolution in the selection and design of porous materials for many industrial applications, the use of appropriate descriptors to encode solid material properties (topology, porosity, and surface chemistry) is one of the fundamental aspects of the development of ML-based models. Our analysis of the literature reveals a lack of descriptors based on the potential energy surface (PES) of crystalline materials embedding crucial information such as the porosity, the topology, and the surface chemistry. In this work, we introduce new PES-based descriptors including the surface probability distribution of the local mean curvature (KH), the electrostatic-PES distribution (σe), as well as the local electrostatic-potential gradient surface probability distribution (∇σe). Our descriptors allow the classification of zeolites as well as its characterization by self-containing standard morphological and topological information (pore diameter, tortuosity, surface chemistry, etc.). We illustrate their usage to generate accurate ML-based models of the isosteric heat of adsorption of CO2 on purely siliceous zeolites of the IZA database and ion-exchanged zeolites in the function of the Si/Al ratio for the case of LTA topology

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites

The generalization of high-throughput synthesis has recently allowed the discovery of thousands of new porous materials, generating a large amount of information, with the development of specialized databases. Widespread access to databases enabled an increase in algorithms and models for property prediction and in silico design of materials. The structural information on materials still needs to be rationalized by the inclusion of descriptors to ease the characterization of solids. This is essential for in silico screening to potential applications based on machine learning (ML) approaches. Indeed, at the forefront of a real revolution in the selection and design of porous materials for many industrial applications, the use of appropriate descriptors to encode solid material properties (topology, porosity, and surface chemistry) is one of the fundamental aspects of the development of ML-based models. Our analysis of the literature reveals a lack of descriptors based on the potential energy surface (PES) of crystalline materials embedding crucial information such as the porosity, the topology, and the surface chemistry. In this work, we introduce new PES-based descriptors including the surface probability distribution of the local mean curvature (KH), the electrostatic-PES distribution (σe), as well as the local electrostatic-potential gradient surface probability distribution (∇σe). Our descriptors allow the classification of zeolites as well as its characterization by self-containing standard morphological and topological information (pore diameter, tortuosity, surface chemistry, etc.). We illustrate their usage to generate accurate ML-based models of the isosteric heat of adsorption of CO2 on purely siliceous zeolites of the IZA database and ion-exchanged zeolites in the function of the Si/Al ratio for the case of LTA topology

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites

The generalization of high-throughput synthesis has recently allowed the discovery of thousands of new porous materials, generating a large amount of information, with the development of specialized databases. Widespread access to databases enabled an increase in algorithms and models for property prediction and in silico design of materials. The structural information on materials still needs to be rationalized by the inclusion of descriptors to ease the characterization of solids. This is essential for in silico screening to potential applications based on machine learning (ML) approaches. Indeed, at the forefront of a real revolution in the selection and design of porous materials for many industrial applications, the use of appropriate descriptors to encode solid material properties (topology, porosity, and surface chemistry) is one of the fundamental aspects of the development of ML-based models. Our analysis of the literature reveals a lack of descriptors based on the potential energy surface (PES) of crystalline materials embedding crucial information such as the porosity, the topology, and the surface chemistry. In this work, we introduce new PES-based descriptors including the surface probability distribution of the local mean curvature (KH), the electrostatic-PES distribution (σe), as well as the local electrostatic-potential gradient surface probability distribution (∇σe). Our descriptors allow the classification of zeolites as well as its characterization by self-containing standard morphological and topological information (pore diameter, tortuosity, surface chemistry, etc.). We illustrate their usage to generate accurate ML-based models of the isosteric heat of adsorption of CO2 on purely siliceous zeolites of the IZA database and ion-exchanged zeolites in the function of the Si/Al ratio for the case of LTA topology

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites

The generalization of high-throughput synthesis has recently allowed the discovery of thousands of new porous materials, generating a large amount of information, with the development of specialized databases. Widespread access to databases enabled an increase in algorithms and models for property prediction and in silico design of materials. The structural information on materials still needs to be rationalized by the inclusion of descriptors to ease the characterization of solids. This is essential for in silico screening to potential applications based on machine learning (ML) approaches. Indeed, at the forefront of a real revolution in the selection and design of porous materials for many industrial applications, the use of appropriate descriptors to encode solid material properties (topology, porosity, and surface chemistry) is one of the fundamental aspects of the development of ML-based models. Our analysis of the literature reveals a lack of descriptors based on the potential energy surface (PES) of crystalline materials embedding crucial information such as the porosity, the topology, and the surface chemistry. In this work, we introduce new PES-based descriptors including the surface probability distribution of the local mean curvature (KH), the electrostatic-PES distribution (σe), as well as the local electrostatic-potential gradient surface probability distribution (∇σe). Our descriptors allow the classification of zeolites as well as its characterization by self-containing standard morphological and topological information (pore diameter, tortuosity, surface chemistry, etc.). We illustrate their usage to generate accurate ML-based models of the isosteric heat of adsorption of CO2 on purely siliceous zeolites of the IZA database and ion-exchanged zeolites in the function of the Si/Al ratio for the case of LTA topology

Simulations of Interfacial Tension of Liquid–Liquid Ternary Mixtures Using Optimized Parametrization for Coarse-Grained Models

In this work, liquid–liquid systems are studied by means of coarse-grained Monte Carlo simulations (CG-MC) and Dissipative Particle Dynamics (DPD). A methodology is proposed to reproduce liquid–liquid equilibrium (LLE) and to provide variation of interfacial tension (IFT), as a function of the solute concentration. A key step is the parametrization method based on the use of the Flory–Huggins parameter between DPD beads to calculate solute/solvent interactions. Parameters are determined using a set of experimental compositional data of LLE, following four different approaches. These approaches are evaluated, and the results obtained are compared to analyze advantages/disadvantages of each one. These methodologies have been compared through their application on six systems: water/benzene/1,4-dioxane,water/chloroform/acetone, water/benzene/acetic acid, water/benzene/2-propanol, water/hexane/acetone, and water/hexane/2-propanol. CG-MC simulations in the Gibbs (NVT) ensemble have been used to check the validity of parametrization approaches for LLE reproduction. Then, CG-MC simulations in the osmotic (μ_soluteN_solventP_<i>zz</i>T) ensemble were carried out considering the two liquid phases with an explicit interface. This step allows one to work at the same bulk concentrations as the experimental data by imposing the precise bulk phase compositions and predicting the interface composition. Finally, DPD simulations were used to predict IFT values for different solute concentrations. Our results on variation of IFT with solute concentration in bulk phases are in good agreement with experimental data, but some deviations can be observed for systems containing hexane molecules

Molecular Modeling of Diffusion Coefficient and Ionic Conductivity of CO₂ in Aqueous Ionic Solutions

Mass diffusion coefficients of CO₂/brine mixtures under thermodynamic conditions of deep saline aquifers have been investigated by molecular simulation. The objective of this work is to provide estimates of the diffusion coefficient of CO₂ in salty water to compensate the lack of experimental data on this property. We analyzed the influence of temperature, CO₂ concentration,and salinity on the diffusion coefficient, the rotational diffusion, as well as the electrical conductivity. We observe an increase of the mass diffusion coefficient with the temperature, but no clear dependence is identified with the salinity or with the CO₂ mole fraction, if the system is overall dilute. In this case, we notice an important dispersion on the values of the diffusion coefficient which impairs any conclusive statement about the effect of the gas concentration on the mobility of CO₂ molecules. Rotational relaxation times for water and CO₂ increase by decreasing temperature or increasing the salt concentration. We propose a correlation for the self-diffusion coefficient of CO₂ in terms of the rotational relaxation time which can ultimately be used to estimate the mutual diffusion coefficient of CO₂ in brine. The electrical conductivity of the CO₂–brine mixtures was also calculated under different thermodynamic conditions. Electrical conductivity tends to increase with the temperature and salt concentration. However, we do not observe any influence of this property with the CO₂ concentration at the studied regimes. Our results give a first evaluation of the variation of the CO₂–brine mass diffusion coefficient, rotational relaxation times, and electrical conductivity under the thermodynamic conditions typically encountered in deep saline aquifers