Search Results (31)

In the present work, we address the theory of the lattice-gas model to the study of intercalation materials by using a novel kinetic Monte Carlo (kMC) algorithm for the simulation of an electrochemical method of everyday use in R&D laboratories: constant-current chrono-potentiometric measurements. The main aim of the present approach is to show how to use these atomistic simulations to study intercalation materials used as electrodes in alkali-ion batteries under galvanostatic conditions. The framework can be applied to related areas. To accomplish this, we explain the electrochemical background, linking the continuum scale with the microscopic events of discrete simulations. A comprehensive theoretical approach developed in a previous work is used as a reference for this aim. The galvanostatic kMC algorithm proposed is explained in detail and is subject to validation tests. The present work may serve as a basis for future implementations of kMC under galvanostatic conditions to study phenomena beyond the applicability of simulations on the continuum scale. Full article

In chemical vapor deposition (CVD)-mediated graphene growth, copper foil serves as both a catalyst for methane decomposition and as a substrate for graphene nucleation and growth. Due to the low solubility of carbon in copper and the ease of transferring graphene from its surface, copper—particularly the Cu(111) facet—is widely favored for high-quality, monolayer graphene synthesis. In this article, the thermodynamic processes involved in methane dissociation and graphene nucleation on the Cu(111) surface were investigated using density functional theory (DFT). Molecular dynamics simulations were performed for structural optimization and to evaluate the reaction energies. Additionally, the average adsorption energies (ΔEad) of carbon clusters with varying atomic numbers on the Cu(111) surface were calculated. The graphene growth process was further modeled using the kinetic Monte Carlo (KMC) method to simulate carbon atom migration and nucleation dynamics. Thermodynamic analysis based on equilibrium component data was conducted to examine the influence of key operational parameters—temperature, pressure, and the CH₄/H₂ partial pressure ratio—on the graphene deposition rate. Full article

This paper presents a method for modeling ReRAM in TCAD and validating its accuracy for neuromorphic systems. The data obtained from TCAD are used to analyze the accuracy of the neuromorphic system. The switching behaviors of ReRAM are implemented using the kinetic Monte Carlo (KMC) approach. Realistic ReRAM characteristics are obtained through the use of the trap-assisted tunneling (TAT) model and thermal equations. HfO₂-Al₂O₃-based ReRAM offers improved switching behaviors compared to HfO₂-based ReRAM. The variation in conductance depends on the structure of the ReRAM. The conductance extracted from TCAD is validated in the neuromorphic system using the MNIST (Modified National Institute of Standards and Technology) dataset. Full article

The widespread use of clay minerals and clays in environmental engineering, industry, medicine, and cosmetics largely stems from their adsorption properties and surface charge, as well as their ability to react with water. The dissolution and growth of minerals as a function of pH are closely related to acid–base reactions at their surface sites and their surface charge. The vivid tapestry of different types of surface sites across different types of clay minerals generates difficulties in experimental studies of structure–property relationships. The aim of this paper is to demonstrate how a mesoscale stochastic kinetic Monte Carlo (kMC) approach altogether with atomistic acid-base models and empirical data can be used for understanding the mechanisms of dissolution and surface charge behavior of clay minerals. The surface charge is modeled based on equilibrium equations for de/protonated site populations, which are defined by the pH and site-specific acidity constants (pK_as). Lowered activation energy barriers for these sites in de/protonated states introduce pH-dependent effects into the dissolution kinetics. The V-shaped curve observed in laboratory experiments is reproduced with the new kMC model. A generic rate law for clay mineral dissolution as a function of pH is derived from this study. Thus, the kMC approach can be used as a hypothesis-testing tool for the verification of acid–base models for clay and other minerals and their influence on the kinetics of mineral dissolution and growth. Full article

Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. The objectives for optimization include the maximal similarity of molar mass distributions (MMDs) compared to the target MMDs, a minimal reaction time, and maximal monomer conversion. The method is tested for vinyl acetate radical polymerizations and can be adopted to other monomers. The data for the optimization procedure are generated by an in-house-developed kinetic Monte-Carlo (kMC) simulator for a selected recipe search space. The proposed reverse engineering algorithm comprises several steps: kMC simulations for the selected recipe search space to derive initial data, performing MOO for a targeted MMD, and the identification of the Pareto optimal space. The last step uses a weighted sum optimization function to calculate the weighted score of each candidate polymerization condition. To decrease the execution time, clustering of the search space based on MMDs is applied. The performance of the proposed approach is tested for various target MMDs. The suggested MOO-based reverse engineering provides multiple recipe candidates depending on competing objectives. Full article

Secondary reactions in radical polymerization pose a challenge when creating kinetic models for predicting polymer structures. Despite the high impact of these reactions in the polymer structure, their effects are difficult to isolate and measure to produce kinetic data. To this end, we used solvation-corrected M06-2X/6-311+G(d,p) ab initio calculations to predict a complete and consistent data set of intrinsic rate coefficients of the secondary reactions in acrylate radical polymerization, including backbiting, β-scission, radical migration, macromonomer propagation, mid-chain radical propagation, chain transfer to monomer and chain transfer to polymer. Two new approaches towards computationally predicting rate coefficients for secondary reactions are proposed: (i) explicit accounting for all possible enantiomers for reactions involving optically active centers; (ii) imposing reduced flexibility if the reaction center is in the middle of the polymer chain. The accuracy and reliability of the ab initio predictions were benchmarked against experimental data via kinetic Monte Carlo simulations under three sufficiently different experimental conditions: a high-frequency modulated polymerization process in the transient regime, a low-frequency modulated process in the sliding regime at both low and high temperatures and a degradation process in the absence of free monomers. The complete and consistent ab initio data set compiled in this work predicts a good agreement when benchmarked via kMC simulations against experimental data, which is a technique never used before for computational chemistry. The simulation results show that these two newly proposed approaches are promising for bridging the gap between experimental and computational chemistry methods in polymer reaction engineering. Full article

During Fischer–Tropsch synthesis, O atoms are dissociated on the surface of Fe-based catalysts. However, most of the dissociated O would be removed as H₂O or CO₂, which results in a low atom economy. Hence, a comprehensive study of the O removal pathway as formic acid has been investigated using the combination of density functional theory (DFT) and kinetic Monte Carlo (kMC) to improve the economics of Fischer–Tropsch synthesis on Fe-based catalysts. The results show that the optimal pathway for the removal of dissociated O as formic acid is the OH pathway, of which the effective barrier energy (0.936 eV) is close to that of the CO activation pathway (0.730 eV), meaning that the removal of dissociated O as formic acid is possible. The main factor in an inability to form formic acid is the competition between the formic acid formation pathway and other oxygenated compound formation pathways (H₂O, CO₂, methanol-formaldehyde); the details are as follows: 1. If the CO is hydrogenated first, then the subsequent reaction would be impossible due to its high effective Gibbs barrier energy. 2. If CO reacts first with O to become CO₂, it is difficult for it to be hydrogenated further to become HCOOH because of the low adsorption energy of CO₂. 3. When the CO + OH pathway is considered, OH would react easily with H atoms to form H₂O due to the hydrogen coverage effect. Finally, the removal of dissociated O to formic acid is proposed via improving the catalyst to increase the CO₂ adsorption energy or CO coverage. Full article

Metakaolin (MK) is a high-quality, reactive nanomaterial that holds promising potential for large-scale use in improving the sustainability of cement and concrete production. It can replace cement due to its pozzolanic reaction with calcium hydroxide and water to form cementitious compounds. Therefore, understanding the dissolution mechanism is crucial to fully comprehending its pozzolanic reactivity. In this study, we present an approach for computing the activation energies required for the dissolution of metakaolin (MK) silicate units at far-from-equilibrium conditions using the improved dimer method (IDM) and the transition-state theory (TST) within density functional theory (DFT). Four different models were prepared to calculate the activation energies required for breaking oxo-bridging bonds between silicate or aluminate units. Our results showed that the activation energy for breaking the oxo-bridging bond to a silicate neighbor is higher than that to an aluminate neighbor due to the ionic interaction. However, for complete silicate tetrahedra dissolution, a higher activation energy is required for breaking the oxo-bridging bond to the aluminate neighbor compared to the silicate neighbor. The findings provide methodology for missing input data to predict the mesoscopic dissolution rate, e.g., by the atomistic kinetic Monte Carlo (KMC) upscaling approach. Full article

The growing demand for various types of paper highlights the importance of optimizing the kraft pulping process to achieve desired paper properties. This work proposes a novel multiscale model to optimize the kraft pulping process and obtain desired paper properties. The model combines mass and energy balance equations with a layered kinetic Monte Carlo (kMC) algorithm to predict the degradation of wood chips, the depolymerization of cellulose, and the spatio-temporal evolution of the Kappa number and cellulose degree of polymerization ($DP$). A surrogate LSTM-ANN model is trained on data generated from the multiscale model under different operating conditions, dealing with both time-varying and time-invariant inputs, and an LSTM-ANN-based model predictive controller is designed to achieve desired set-point values of the Kappa number and cellulose $DP$ while considering process constraints. The results show that the LSTM-ANN-based controller is able to drive the process to desired set-point values with the use of a computationally faster surrogate model with high accuracy and low offset. Full article

The requirements for the efficient replacement of fossil fuel, combined with the growing energy crisis, places focus on hydrogen production. Efficient and cost-effective electrocatalysts are needed for H₂ production, and novel strategies for their discovery must be developed. Here, we utilized Kinetic Monte Carlo (KMC) simulations to demonstrate that hydrogen evolution reaction (HER) can be boosted via hydrogen spillover to the support when the catalyst surface is largely covered by adsorbed hydrogen under operating conditions. Based on the insights from KMC, we synthesized a series of reduced graphene-oxide-supported catalysts and compared their activities towards HER in alkaline media with that of corresponding pure metals. For Ag, Au, and Zn, the support effect is negative, but for Pt, Pd, Fe, Co, and Ni, the presence of the support enhances HER activity. The HER volcano, constructed using calculated hydrogen binding energies and measured HER activities, shows a positive shift of the strong binding branch. This work demonstrates the possibilities of metal–support interface engineering for producing effective HER catalysts and provides general guidelines for choosing novel catalyst–support combinations for electrocatalytic hydrogen production. Full article

Cement clinkers containing mainly belite (β-C₂S as a model crystal), replacing alite, offer a promising solution for the development of environmentally friendly solutions to reduce the high level of CO₂ emissions in the production of Portland cement. However, the much lower reactivity of belite compared to alite limits the widespread use of belite cements. Therefore, this work presents a fundamental atomistic computational approach for comprehending and quantifying the mesoscopic forward dissolution rate of β-C₂S, applied to two reactive crystal facets of (100) and ($\overline{1}00$). For this, an atomistic kinetic Monte Carlo (KMC) upscaling approach for cement clinker was developed. It was based on the calculated activation energies (ΔG*) under far-from-equilibrium conditions obtained by a molecular dynamic simulation using the combined approach of ReaxFF and metadynamics, as described in the Part 1 paper in this Special Issue. Thus, the individual atomistic dissolution rates were used as input parameters for implementing the KMC upscaling approach coded in MATLAB to study the dissolution time and morphology changes at the mesoscopic scale. Four different cases and 21 event scenarios were considered for the dissolution of calcium atoms (Ca) and silicate monomers. For this purpose, the (100) and ($\overline{1}00$) facets of a β-C₂S crystal were considered using periodic boundary conditions (PBCs). In order to demonstrate the statistical nature of the KMC approach, 40 numerical realizations were presented. The major findings showed a striking layer-by-layer dissolution mechanism in the case of an ideal crystal, where the total dissolution rate was limited by the much slower dissolution of the silicate monomer compared to Ca. The introduction of crystal defects, namely cutting the edges at two crystal boundaries, increased the overall average dissolution rate by a factor of 519. Full article

A major concern in the modern cement industry is considering how to minimize the CO₂ footprint. Thus, cements based on belite, an impure clinker mineral (CaO)₂SiO₂ (C₂S in cement chemistry notation), which forms at lower temperatures, is a promising solution to develop eco-efficient and sustainable cement-based materials, used in enormous quantities. The slow reactivity of belite plays a critical role, but the dissolution mechanisms and kinetic rates at the atomistic scale are not known completely yet. This work aims to understand the dissolution behavior of different facets of β-C₂S providing missing input data and an upscaling modeling approach to connect the atomistic scale to the sub-micro scale. First, a combined ReaxFF and metadynamics-based molecular dynamic approach are applied to compute the atomistic forward reaction rates (R_D) of calcium (Ca) and silicate species of (100) facet of β-C₂S considering the influence of crystal facets and crystal defects. To minimize the huge number of atomistic events possibilities, a generalized approach is proposed, based on the systematic removal of nearest neighbors’ crystal sites. This enables us to tabulate data on the forward reaction rates of most important atomistic scenarios, which are needed as input parameters to implement the Kinetic Monte Carlo (KMC) computational upscaling approach. The reason for the higher reactivity of the (100) facet compared to the (010) is explained. Full article

Aging reactions due to heat and radiation cause chain scissions and cross-linking in cross-linked polyethylene (XLPE). We have developed theoretical and numerical graph models to study the evolution of the gel fraction and network properties during aging as well as the mass loss during thermogravimetric analysis (TGA). Our analytical and kinetic Monte Carlo (KMC) based models that combine degradation reactions and a simple vaporization model can quantitatively predict TGA curves for aged XLPE. Fitting the model to experimental TGA data yields the number of scission reactions, showing that thermogravimetry combined with our models can present a nondestructive aging characterization tool for lifetime prediction. Full article

We have developed a self-consistent model for predicting the velocity of 1/2[111] screw dislocation in binary iron–carbon alloys gliding by a high-temperature Peierls mechanism. The methodology of modelling includes: (i) Kinetic Monte-Carlo (kMC) simulation of carbon segregation in the dislocation core and determination the total carbon occupancy of the core binding sites; (ii) Determination of kink-pair formation enthalpy of a screw dislocation in iron—carbon alloy; (iii) KMC simulation of carbon drag and determination of maximal dislocation velocity at which the atmosphere of carbon atoms can follow a moving screw dislocation; (iv) Self consistent calculation of the average velocity of screw dislocation in binary iron–carbon alloys gliding by a high-temperature kink-pair mechanism under a constant strain rate. We conduct a quantitative analysis of the conditions of stress and temperature at which screw dislocation glide in iron–carbon alloy is accomplished by a high-temperature kink-pair mechanism. We estimate the dislocation velocity at which the screw dislocation breaks away from the carbon cloud and thermally-activated smooth dislocation propagation is interrupted by sporadic bursts of dislocation activity. Full article

Portlandite, as a most soluble cement hydration reaction product, affects mechanical and durability properties of cementitious materials. In the present work, an atomistic kinetic Monte Carlo (KMC) upscaling approach is implemented in MATLAB code in order to investigate the dissolution time and morphology changes of a hexagonal platelet portlandite crystal. First, the atomistic rate constants of individual Ca dissolution events are computed by a transition state theory equation based on inputs of the computed activation energies (ΔG*) obtained through the metadynamics computational method (Part 1 of paper). Four different facets (100 or $\overline{1}00$, 010 or 0$\overline{1}$0, $\overline{1}10$ or $1\overline{1}0$, and 001 or 00$\overline{1}$) are considered, resulting in a total of 16 different atomistic event scenarios. Results of the upscaled KMC simulations demonstrate that dissolution process initially takes place from edges, sides, and facets of 010 or 0$\overline{1}$0 of the crystal morphology. The steady-state dissolution rate for the most reactive facets (010 or 0$\overline{1}0$) was computed to be 1.0443 mol/(s cm²); however, 0.0032 mol/(s cm²) for $\overline{1}10$ or $1\overline{1}0$, 2.672 × 10⁻⁷ mol/(s cm²) for 001 or 00$\overline{1}$, and 0.31 × 10⁻¹⁶ mol/(s cm²) for 100 or $\overline{1}00$ were represented in a decreasing order for less reactive facets. Obtained upscaled dissolution rates between each facet resulted in a huge (16 orders of magnitude) difference, reflecting the importance of crystallographic orientation of the exposed facets. Full article