Monte Carlo Molecular Dynamics Research Articles

Investigation on the methane emissions from permeation of urban gas polyethylene pipes under the background of hydrogen-mixed natural gas transportation

Methane, a more potent greenhouse gas than carbon dioxide, aggravates the greenhouse effect of ecological environment through its emissions. When transporting hydrogen-mixed natural gas through urban polyethylene pipes, methane will be emitted due to the permeation characteristic of polyethylene pipe material. Therefore, it is crucial to pay attention to the methane emissions from permeation of polyethylene pipes for protecting the environment and climate. In this paper, the solubility and diffusion coefficients of methane within hydrogen-mixed natural gas in polyethylene pipe materials are calculated through the Monte Carlo method and molecular dynamics simulation to determine the permeation coefficient of methane. Simultaneously, the methane emissions and equivalent carbon dioxide emissions of the hydrogen-mixed natural gas in polyethylene pipes are investigated. Results indicate the methane solubility coefficient decreases with increasing temperature and hydrogen mixing ratio. The methane diffusion and permeation coefficients increase with the rising temperature and pressure, and decreasing hydrogen mixing ratio. The pressure exerts minor influence while the temperature and hydrogen mixing ratio have more significant impact. That is, the lower the temperature and the higher the hydrogen mixing ratio, the less methane emissions caused by permeation. Therefore, the effective reduction of methane emissions can be achieved by adding the insulation layer to mitigate the impact of high temperature and increasing the hydrogen mixing ratio. For example, for the polyethylene pipe with the operating pressure of 4 bar and the size specification of SDR11, when the temperature drops from 300 K to 260 K, the methane emissions of hydrogen-mixed natural gas with hydrogen mixing ratios of 0 vol%, 20 vol%, 50 vol% and 80 vol% will be drastically reduced by 73.3%, 74.4%, 77.2% and 78.5% respectively, and when 20 vol%∼80 vol% hydrogen are mixed in the natural gas at 260 K, 300 K and 310 K, the methane emissions will be effectively reduced by 26.7%∼85.6%, 23.3%∼82.1% and 23.9%∼82.4% respectively. The findings of this study can serve as guidance for reducing the methane emissions from permeation of polyethylene pipes transporting hydrogen-mixed natural gas.

Atomic cluster expansion interatomic potential for defects and thermodynamics of Cu–W system

The unique properties exhibited in immiscible metals, such as excellent strength, hardness, and radiation-damage tolerance, have stimulated the interest of many researchers. As a typical immiscible metal system, the Cu–W nano-multilayers combine the plasticity of copper and the strength of tungsten, making it a suitable candidate for applications in aerospace, nuclear fusion engineering, and electronic packaging, etc. To understand the atomistic origin of the defects (e.g., vacancies, free surfaces, grain boundaries, and stacking faults and thermodynamical properties), we developed an accurate machine learning interatomic potential for Cu–W based on the atomic cluster expansion (ACE) method. The Cu–W ACE potential can faithfully reproduce the fundamental properties of Cu and W predicted by density functional theory (DFT) calculations. Moreover, the thermodynamical properties, such as the melting point, coefficient of thermal expansion, diffusion coefficient, and equation of the state curve of the Cu–W solid solution, are calculated and compared against DFT and experiments. Monte Carlo molecular dynamics simulations performed with the Cu–W ACE potential predict the experimentally observed phase separation and uphill diffusion phenomena. Our findings not only provide an accurate ACE potential for describing the Cu–W immiscible system but also shed light on understanding the atomistic mechanism during the Cu–W nano-multilayers formation process.

Mechanism study of replacing adsorbed O2 molecules in different pore sizes under different CO2/N2 inert gas conditions

Injecting inert gases into the goaf is a crucial method for inhibiting coal spontaneous combustion. Research has verified that the co-injection of CO2 and N2 provides a more effective control measure compared to a single gas, but the microscopic mechanism underlying the synergistic displacement of O2 by CO2 and N2 remains unclear. Injecting inert gases into the goaf is a crucial method for inhibiting coal spontaneous combustion. Research has verified that the co-injection of CO2 and N2 provides a more effective control measure compared to a single gas, but the microscopic mechanism underlying the synergistic displacement of O2 by CO2 and N2 remains unclear. Here, for the constructed multi-scale aperture slit model, the Grand Canonical Monte Carlo simulation (GCMC) and Molecular Dynamics (MD) simulation are used to investigate the mechanism of displacing adsorbed O2 molecules under different CO2/N2 inert gas conditions. The results indicate that CO2 and N2 aggregated on opposing sides of the coal molecules and dispersed within the slit layer, respectively. At 1 nm pore size, the relative concentration of O2 for optimal displacement was 1.054. The absolute value of the average binding energy is small when the CO2:N2 gas ratio is 80:20 and 40:60 at 2 nm and 4 nm apertures, respectively. This suggests that as the aperture size increases, the displacement effect on O2 shifts towards a higher proportion of N2, with a more conspicuous replacement effect as the aperture widens. The smaller pores ranging from 1-2 nm predominantly feature CO2 displacing adsorbed state O2, while larger pores exceeding 2 nm primarily N2 facilitate the transportation of free state O2.

Adsorption of Red 141 and methylene blue by cuttlebone: experimental and molecular dynamics study

AbstractThe current study explores the removal of two organic dyes: Reactive Red 141, an anionic dye, and Methylene Blue, a cationic one, via adsorption onto a novel animal-derived biomaterial known as Cuttlebone (Sepia Officinalis). Before conducting experiments, an analysis of the biomaterial was performed. Subsequently, a sequence of experiments was undertaken to investigate the impact of different parameters on adsorption capacity. These parameters included mass of the adsorbent, pH level, duration of contact, and initial concentration of the dye. Findings indicate that Cuttlebone exhibits a more pronounced adsorption affinity for the anionic dye Reactive Red 141 compared to the cationic dye Methylene Blue (MB). The examination of adsorption isotherms for the respective adsorbent/adsorbate systems reveals that the adsorption behavior differs. Specifically, the adsorption of Red 141 on Cuttlebone conforms to the Langmuir model, while the uptake of MB on Cuttlebone shows a superior agreement with the Freundlich model. A peak adsorption capacity of 129.87 mg/g was noted for Red 141, while for MB, it was observed to be 23.86 mg/g. To elucidate the mechanism, the adsorptive characteristics of Red 141 and MB were validated using various methods, including Monte Carlo simulation (MC) and Molecular Dynamics simulation (MD). The results of MC and MD modeling demonstrate that Red 141 is significantly adsorbed onto calcium carbonate via the chemisorption phenomenon. Graphical abstract

Atomistic investigation of effect of alloying on mechanical properties and microstructural evolution of ternary FeCo-X (X = V, Nb, Mo, W)

This atomistic simulation study delves into the impact of V, Nb, Mo, and W on the mechanical properties of equiatomic FeCo, employing the modified embedded atom method (MEAM). An analysis of individual effects on antiphase boundary (APB) energies reveals a consistent reduction along preferred slip planes, except for Nb. Monte Carlo-molecular dynamics (MC-MD) simulations were used to explore the diffusion behavior of these solutes, highlighting their dynamic interactions and preference to migrate into the grain boundaries (GB).Tensile simulations conducted on nanocrystalline (NC) models oriented in different directions unveil comparable stress–strain curves, displaying continuous yielding with a humpy yield curve that varies with the straining orientation. Notably, W emerged as the most effective addition enhancing the ultimate tensile strength (UTS). Microcrack nucleation development differ depending on the straining direction. In binary FeCo and in the alloy with Mo additions, void development was observed at grain boundary (GB) triple junctions, while with V and W additions, it occurred at the intersection of a slip band and a GB, with limited propagation in both scenarios. In contrast, Nb additions show enhanced stress accommodation through slip band formation within grains, preventing microcrack development. These discoveries offer valuable insights on the impact of alloying elements on the mechanical behavior of ternary FeCo-X (X = V, Nb, Mo, W) alloys.

Machine Learning Approach for Predicting the Hole Mobility of the Perovskite Solar Cells

AbstractTraditional computational approaches such as Monte Carlo simulation, molecular dynamics, and density functional theory (DFT) have contributed to understanding the role of hole mobility in the development of suitable hole transporting materials (HTMs) for perovskite solar cell efficiency. However, these methods often involve significant computational expenses, thereby limiting the number of feasible studies and hindering the extraction of valuable structure−property guidelines for a rational design of novel HTMs. To address these challenges, this study proposes an ultrafast predictive model that balances prediction accuracy and time efficiency, a critical aspect for predicting the hole mobility of HTMs for perovskite optimization. The model, which leverages the Random Forest learning algorithm, enables comprehensive and rapid analysis of photovoltaic features taken from previous experiments/literature and processed with the RDKit Python library. Notably, recognizing the challenges associated with high correlation in the dataset, a method to improve the calculation of feature importance in random forests is applied. The results highlight bertz_ct, chi1, and logP as key predictive features of HTM performance. However, the model underscores the need to uncover additional predictive features based on structure–property relationships to predict the optimized hole mobility. This approach significantly accelerates the discovery process, outperforming prevalent statistical methods in the prediction of hole mobility and HTM performance. This research signifies a pivotal step toward cost‐effective, accelerated stability and efficiency of HTMs, with implications for the advancement of optoelectronic devices.

Modeling of Magnetic Films: A Scientific Perspective.

Magnetic thin-film modeling stands as a dynamic nexus of scientific inquiry and technological advancement, poised at the vanguard of materials science exploration. Leveraging a diverse suite of computational methodologies, including Monte Carlo simulations and molecular dynamics, researchers meticulously dissect the intricate interplay governing magnetism and thin-film growth across heterogeneous substrates. Recent strides, notably in multiscale modeling and machine learning paradigms, have engendered a paradigm shift in predictive capabilities, facilitating a nuanced understanding of thin-film dynamics spanning disparate spatiotemporal regimes. This interdisciplinary synergy, complemented by avantgarde experimental modalities such as in situ microscopy, promises a tapestry of transformative advancements in magnetic materials with far-reaching implications across multifaceted domains including magnetic data storage, spintronics, and magnetic sensing technologies. The confluence of computational modeling and experimental validation heralds a new era of scientific rigor, affording unparalleled insights into the real-time dynamics of magnetic films and bolstering the fidelity of predictive models. As researchers chart an ambitiously uncharted trajectory, the burgeoning realm of magnetic thin-film modeling burgeons with promise, poised to unlock novel paradigms in materials science and engineering. Through this intricate nexus of theoretical elucidation and empirical validation, magnetic thin-film modeling heralds a future replete with innovation, catalyzing a renaissance in technological possibilities across diverse industrial landscapes.

Quantum symmetrization transition in superconducting sulfur hydride from quantum Monte Carlo and path integral molecular dynamics

AbstractWe study the structural phase transition, originally associated with the highest superconducting critical temperature Tc measured in high-pressure sulfur hydride. A quantitative description of its pressure dependence has been elusive for any ab initio theory attempted so far, raising questions on the actual mechanism leading to the maximum of Tc. Here, we estimate the critical pressure of the hydrogen bond symmetrization in the Im$$\bar{3}$$ 3 ¯ m structure, by combining density functional theory and quantum Monte Carlo simulations for electrons with path integral molecular dynamics for quantum nuclei. We find that the Tc maximum corresponds to pressures where local dipole moments dynamically form on the hydrogen sites, as precursors of the ferroelectric Im$$\bar{3}$$ 3 ¯ m-R3m transition, happening at lower pressures. For comparison, we also apply the self-consistent harmonic approximation, whose ferroelectric critical pressure lies in between the ferroelectric transition estimated by path integral molecular dynamics and the local dipole formation. Nuclear quantum effects play a major role in a significant reduction (≈50 GPa) of the classical ferroelectric transition pressure at 200 K and in a large isotope shift (≈25 GPa) upon hydrogen-to-deuterium substitution of the local dipole formation pressure, in agreement with the corresponding change in the Tc maximum location.

Investigation of the microscopic mechanism of H2O adsorption by low-rank coal slit pores through GCMC and MD simulations

This study investigates the influence of pore structure on H2O adsorption in low-rank coal (LRC) models at a molecular level, employing variously sized LRC slit-pore models·H2O adsorption behavior is analyzed across different pore sizes ranging from 0.001 to 100 kPa fugacity, by employing the Grand canonical Monte Carlo (GCMC) method and molecular dynamics (MD) simulations to explore saturated adsorption kinetics. The findings indicate a positive correlation between H2O adsorption levels and both fugacity and pore diameter. Furthermore, at constant pore diameter, the adsorption heat increases exponentially with increasing adsorption levels. Notably, H2O adsorption in LRC slit pores exhibits multilayer adsorption characteristics. First, adsorption occurs as monomolecular layers on the LRC surface, transitioning to secondary adsorption sites formed by previously adsorbed H2O molecules with increasing fugacity, ultimately saturating the pores. This adsorption process exhibits a correlation with a shift in potential energy distribution from high to low potential energy directions. Moreover, the ordering of H2O molecule arrangements between hydrogen atoms, oxygen atoms, and H2O molecules decreases with increasing pore diameters. Larger pore diameters correspond to increased H2O molecule diffusion, the stronger interaction between the LRC slit pores and H2O molecules but reduced interaction with individual H2O molecules. Overall, this study establishes a theoretical foundation for further studies on coal pore wetting and offer valuable insights for studies aimed at reducing dust and preventing gas outbursts.

Understanding the pore size distribution of Ca(OH)2 for effective desulfurization under low-to-medium temperatures

The pore size distribution plays a crucial role in the gas-solid desulfurization reaction of Ca(OH)2, yet its impact on desulfurization performance is not fully understood, especially in low-to-medium temperature range (100–400 °C). This study examined the diffusion and adsorption characteristics of SO2 in Ca(OH)2 pores with different sizes by Monte Carlo Simulation and Molecular Dynamics Methods. The reaction kinetics of desulfurization for samples with different pore structures were analyzed based on TGA data and linear regression. It is found that the adsorption of SO2 on Ca(OH)2 was mainly induced by electrostatic interaction. Pores with the size of 5–15 nm were most favorable for SO2 adsorption, with the maximum adsorption heat of 464.83 kJ/mol at 5 nm. When the pore size increased, the diffusion of SO2 was improved, but the distribution of SO2 became more scattered, resulting in an adverse effect on the desulphurization reaction. The desulphurization process can be divided into the surface reaction controlled stage and product layer diffusion controlled stage. The desulphurization ability was primarily contributed by the reaction controlled stage, in which Ca2+ migration resistance was the least. Pore structure was a crucial factor in determining the duration of the reaction controlled stage. The presence of 15–30 nm pores was of significant importance for this stage. Yet in the diffusion controlled stage, pores size of ≥30 nm was much more important than other sizes to improve the Ca2+ diffusion. This study shows that a reasonable pore size combination facilitates the diffusion of SO2 and its reaction with Ca2+.

Atomistic Study on Defect–Grain Boundary Interactions in TiVTa Concentrated Solid–Solution Alloys

The elemental segregation behaviors and interactions between point defects and symmetrical tilt grain boundaries (GBs) in TiVTa concentrated solid–solution alloys (CSAs) have been studied through hybrid Monte Carlo/molecular dynamics (MC/MD) simulations. A pure V model, a random TiVTa CSA with randomly distributed elements, and an equilibrated TiVTa CSA with Ti segregation were constructed to investigate the influence of chemical disorder and local elemental segregation on defect–GB interactions. For defect–GB interactions, GBs interact more strongly with interstitials than with vacancies. Compared with the pure V, the vacancy absorption length scale of GBs is greater, whereas the interstitial absorption length scale of GBs is shorter in TiVTa CSAs due to the chemical fluctuation and local lattice distortion. This means a higher recombination efficiency of point defects in TiVTa CSAs. The elemental (Ti) segregation in TiVTa CSAs can further enhance the sink strength of GBs towards interstitials, while simultaneously reducing their sink strength towards vacancies. Consequently, the preference effects of GBs towards interstitials and vacancies are amplified in the equilibrated CSA due to local ordering, thereby reducing efficient defect annihilation around GBs. These results provide fundamental insights into the irradiation defect dynamics of CSAs with body-centered cubic (bcc) structure.

A data-guided approach for the evaluation of zeolites for hydrogen storage with the aid of molecular simulations.

This study employs a data-guided approach to evaluate zeolites for hydrogen storage, utilizing molecular simulations. The development of efficient and practical hydrogen storage materials is crucial for advancing clean energy technologies. Zeolites have shown promise as potential candidates due to their unique porous structure and tunable properties. However, the selection and design of suitable zeolites for hydrogen storage remain challenging. Therefore, this work aims to address this materials science question by utilizing molecular simulations and data-guided approaches to evaluate zeolites' performance for hydrogen storage. The results obtained from this study provide valuable insights into the evaluation of zeolites for hydrogen storage. Through molecular simulations, we analyze the adsorption behavior of hydrogen molecules in various zeolite structures. The performance of different zeolite frameworks in terms of hydrogen storage capacity, adsorption energy, and diffusion properties is assessed. Linde type A zeolite (LTA) had the highest capacity with a hydrogen capacity of 4.8wt% out of the 233 investigated zeolites. Furthermore, we investigate the influence of different factors such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) on the hydrogen storage performance of zeolites. The results show that Di, D, and M have a negative effect on the percentage weight capacity, while GSA and VSA have the highest positive contribution to the percentage weight. This study, therefore, provides new insights into the factors that affect their hydrogen storage capacity by exhibiting the importance of considering multiple factors when evaluating the performance of zeolites and demonstrates the potential of combining different computational methods to provide a more comprehensive understanding of materials. The current study contributes to the understanding of zeolite-based materials for hydrogen storage applications, aiding in the development of more efficient and practical hydrogen storage systems. Computational techniques were employed to investigate the hydrogen storage properties of zeolites. Molecular simulations were performed using classical force fields and molecular dynamics methods. The calculations were carried out at a force field level of theory with the GGA functional. To accurately capture the thermodynamics and kinetics of hydrogen adsorption, enhanced sampling techniques such as Monte Carlo simulations and molecular dynamics with metadynamics were utilized. We employed Grand Canonical Monte Carlo (GCMC) simulations to model hydrogen adsorption in zeolite structures for hydrogen storage. Our approach involved performing a substantial number of Monte Carlo steps (10,000) to ensure system equilibration and precise results. We defined a cutoff distance for particle interactions as 12.5 Ǻ and considered 0.000e framework charge per cell and 0.000e sorbate charge in energy calculations. The choice of an appropriate simulation cell size (50 × 50 × 50) Ǻ was crucial, mirroring real-world conditions. We specified lower and upper fugacity values (1 to 10atm) to capture the range of gas pressures in the simulations. These methodical steps collectively enabled us to accurately model hydrogen adsorption within zeolites, forming the core of our hydrogen storage evaluation. In this research, we utilized DFT calculations to thoroughly investigate the interactions between zeolites and hydrogen. We employed pseudopotentials to describe electron behavior in zeolite systems, choosing them in line with DFT norms and basis set compatibility. Our simulation cell design replicated zeolite periodicity and eliminated boundary effects. Pre-geometry optimization was performed with HyperChem29, ensuring stable conformations with strict convergence criteria. We utilized 6-31 + G(d) and LanL2DZ basis sets for light and heavy atoms, aligning with field standards for computational efficiency and precision. A machine learning algorithm was used to rank the importance of various structural features such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) and how they affect the capacity of the zeolites. Machine learning analysis was performed with the Scikit-learn library, an open-source Python tool. We employed a range of machine learning models, including SVMs, random forests, and neural networks, primarily for data analysis and feature extraction. Pearson correlation analysis, a classical statistical technique, was used to evaluate linear relationships between variables and assess the strength and direction of these relationships. It served as a complementary tool to understand the interplay of variables in our dataset, distinguishing it from machine learning algorithms. Further quantum chemical calculations were also performed to calculate the adsorption energy, global reactivity electronic descriptors, and natural bond orbital analysis in order to provide insights into the interaction of the zeolites with hydrogen. The simulations and data analysis were performed using BIOVIA material studio software, Gaussian, and Origin Pro software.

From groove to hillocks – Atomic-scale simulations of swift heavy ion grazing impacts on CaF2

Surface nanopatterning of CaF2 by swift heavy ions irradiation under oblique angles is studied with a combination of the event-by-event Monte Carlo particle transport model and molecular dynamics simulations. The model describes the electronic system excitation and energy transfer to the lattice followed by the atomic response. The approach allowed us to simulate the kinetics of the electronic ensemble excited by a grazing ion demonstrating that the presence of the surface does not reduce the energy of the lattice as expected. On the contrary, the track core temperature near the surface is slightly higher than in the bulk, because electrons reflected from the surface bring a part of the energy back to the core.The formation kinetics of entire grazing ion tracks is studied revealing the mechanisms of various surface nanostructures formation. Depending on the penetration depth, the ion produces a groove bordered by hillocks, a single chain of nanohillocks, a huge hillock at the end of the rift, and a single continuous structure afterward. The critical depth of the material expulsion from the surface equals approximately to the transient molten zone radius (∼4–4.5 nm). The simulated structures are in reasonable agreement with the available experimental data.

Theoretical study of copper corrosion prevention by terpenoid green inhibitors of thymol, carvacrol, and thymohydroquinone

Electronic parameters have been used to predict the corrosion inhibition performance of the green inhibitors of thymol, carvacrol, and thymohydroquinone against copper. Molecular modeling was carried out at the density functional and ab initio levels and Monte Carlo molecular dynamics. Tymohydroquinone can prevent copper corrosion better than the other two monoterpenoids. Molecular dynamics studies show that corrosion inhibitors are adsorbed on the metal surface, thereby inhibiting corrosion from occurring on the metal surface.

Enthalpies and entropies of hydration from Monte Carlo simulations.

The changes in free energy, enthalpy, and entropy for transfer of a solute from the gas phase into solution are the fundamental thermodynamic quantities that characterize the solvation process. Owing to the development of methods based on free-energy perturbation theory, computation of free energies of solvation has become routine in conjunction with Monte Carlo (MC) statistical mechanics and molecular dynamics (MD) simulations. Computation of the enthalpy change and by inference the entropy change is more challenging. Two methods are considered in this work corresponding to direct averaging for the solvent and solution and to computing the temperature derivative of the free energy in the van't Hoff approach. The application is for neutral organic solutes in TIP4P water using long MC simulations to improve precision. Definitive results are also provided for pure TIP4P water. While the uncertainty in computed free energies of hydration is ca. 0.05 kcal mol-1, it is ca. 0.4 kcal mol-1 for the enthalpy changes from either van't Hoff plots or the direct method with sampling for 5 billion MC configurations. Partial molar volumes of hydration are also computed by the direct method; they agree well with experimental data with an average deviation of 3 cm3 mol-1. In addition, the results permit breakdown of the errors in the free energy changes from the OPLS-AA force field into their enthalpic and entropic components. The excess hydrophobicity of organic solutes is enthalpic in origin.

Molecular Simulation of Nafion Swelling in the Cathode Catalyst Layer of Polymer Electrolyte Fuel Cells

As the world moves towards carbon neutrality, polymer electrolyte fuel cells (PEFCs) have emerged as a vital technology in recent years. The cathode catalyst layer of PEFCs is characterized by its porous structure, which promotes the production of water molecules from oxygen and protons through chemical reactions. The ionomer thin films, predominantly composed of Nafion, are critical in regulating the transport and mobility of molecules and ions in the cathode catalyst layer. Sulfonic groups in the side chains of Nafion enable accumulation of water molecules, causing the ionomer thin films to swell.Previous studies have examined the swelling of Nafion under wet conditions[1][2]. However, precise swelling mechanisms remain unclear. While privious research has analyzed the relationship between relative humidity and the amount of water within bulk Nafion samples[3], the swelling behavior in films with thickness of several nanometers differs significantly, necessitating further investigation[4]. This study therefore investigates the swelling mechanisms and the properties of ionomer thin films used in the cathode catalyst layer of PEFCs. This study aims to establish guidelines for designing the molecular structures of ionomers by elucidating the mechanisms underlying ionomer swelling in the cathode catalyst layer. Grand canonical Monte Carlo (GCMC) simulations and molecular dynamics (MD) simulations were employed to reproduce swelling in Nafion thin films and examine the impact of Nafion's structure on water content and swelling. These molecular simulation techniques enable the elucidation of the influence exerted by polymer structure on water content and swelling at the molecular scale.In this study, we firstly created a simulation model of a Nafion system using LAMMPS[5], the DREIDING Force Field[6], and Mulliken charges[7] in which parameter settings were referred from literature values[8]. The Nafion system contains 10 Nafion molecules with a degree of polymerization of 10, water molecules, and 100 oxonium ions for electrical neutrality. The density of 1.9 g/cm3 achieved during simulations closely agrees with literature values[8].To reproduce Nafion swelling under humid conditions, GCMC simulations and MD simulations were employed. The rationale behind utilizing GCMC simulations lies in the fact that they enable the insertion of water molecules into the Nafion system under constant chemical potential conditions, thereby inducing Nafion swelling. Subsequent to the GCMC simulations, MD simulations were performed under the NPT ensemble, which facilitated the system to reach equilibrium. This alternating cycle of GCMC and MD simulations is repeated to realize the final swelling state. Preliminary results indicate lower water content values than those experimentally measured under similar conditions. Possible causes include inadequate force field parameters that lead to polymer morphology different from the actual Nafion structure.Future work will explore the causes of these discrepancies and examine the effects of Nafion's structure on water content and swelling under various conditions. This will involve testing alternative force field parameters and initial structures to better represent the molecular-level structure of ionomer thin films on carbon support particles in the cathode catalyst. Additionally, we will investigate how different relative humidity conditions, temperatures, and ionomer compositions influence swelling behavior, which could provide insights into optimizing PEFCs performance under various operating environments.[1] G. C. Abuin et al., J. Membr. Sci. 428, 507 (2013).[2] J. Catalano et al., Int. J. Hydrog. Energy 37, 6308 (2012).[3] T. A. Zawadzinski et al., J. Electrochem. Soc. 140, 1981 (1993).[4] M. A. Modestino et al., Macromolecules 46, 867 (2013).[5] A. P. Thompson et al., Comp. Phys. Comm. 271, 10817 (2022).[6] S. L. Mayo et al., J. Phys. Chem. 94, 8897 (1990).[7] S. S. Jang et al., J. Phys. Chem. B 108, 3149 (2004).[8] Y. Kurihara et al., J. Power Sources 414, 263 (2019).Fig. 1 An ionomer model composed of Nafion molecules, oxonium ions, and water molecules. The red and white spheres represent oxygen and hydrogen atoms making up water molecules. The size of the water molecules is emphasized for clarity. Figure 1

Effects of P segregation on deformation mechanism in Ni-P nanocrystalline by atomic simulations

By using hybrid Monte Carlo-molecular dynamics (MC-MD) simulations, the segregation behavior of solute P atoms in the grain boundaries (GBs) of nanocrystalline (NC) Ni-P alloy was studied under different temperatures. The effects of P segregation on the mechanical properties and quantitative deformation mechanism of the Ni-P alloy were also investigated at high temperatures (800 K). The results show that the temperature strongly influences the segregation behavior of P atoms. Uniform P segregation occurs along GBs at 300 K, while at elevated temperatures (e.g., 1000 K), P atoms are concentrated at triple junctions (TJs), forming Ni-P precipitates. The uniform grain boundary segregation of P leads to Ni-P alloy with larger grain sizes and higher strength, while Ni-P precipitates at TJs result in alloy with smaller grain size, higher yield and flow stresses, which could be attributed to the Zener pinning effect. Moreover, quantitative analysis of the deformation mechanisms reveals that for NC Ni-P alloy with small grain size, Ni-P precipitates show significant effect on the competition between intracrystalline deformation and grain boundary deformation. A low percentage of elastic strain on the GBs was observed in the elastic deformation stage. The plastic deformation process of the non-segregated sample with grain size of 5 nm is dominated by grain boundary deformation, whereas for non-segregated samples of 10 and 15 nm, the strain is accommodated by both intragranular dislocation slip and grain boundary deformation. Importantly, Ni-P precipitates can reduce the degree of grain boundary deformation in alloys with small grains and thereby stabilize the GBs. The less grain boundary deformation caused by uniform grain boundary segregation of P and the more dislocation slip play a major role in improving the mechanical properties of the alloy with large grains. The results and findings of this study provide further insights into the segregation-induced strengthening mechanisms of NC Ni-P alloys.

A review of multiscale modelling approaches for understanding catalytic ammonia synthesis and decomposition

Ammonia plays a crucial role in our lives as it is widely used, for example, in the synthesis of fertilizers, the production of carbon-free hydrogen for fuel cells, or electricity generation. However, the conventional Haber-Bosch process for ammonia synthesis has several drawbacks, such as requiring high temperatures and pressures, consuming 1–2% of the world's energy supply. While significant progress has been made in performing the reaction under milder conditions, the subject has experienced a second renaissance with the advent of multiscale modeling and improvements in computational capabilities.Multiscale modeling at various levels, including density functional theory (DFT), transition state theory (TST), kinetic Monte Carlo method (KMC), microkinetic modeling, molecular dynamics (MD), and computational fluid dynamics (CFD), allows us to describe the reaction system at all levels simultaneously. In this review, we focus on the treatment of ammonia synthesis, decomposition, or oxidation using multiscale models, ranging from the study of quantum-level interactions to the optimization of reactor design.Recently, significant progress has been made in modeling. Current modeling tools, while well developed, still have weaknesses in their linkage. DFT /TST is often coupled with KMC and MD or microkinetic data are fed into CFD while a comprehensive model is lacking. The potential for further progress through the use of multiscale models is enormous, which could lead to more efficient catalyst and process design.

Anticorrosion performance of nicotinic acid and nicotinic acid hydrazide blended with optimized potassium iodide against Q235 steel corrosion in acid-chloride medium

The most sorted corrosion-inhibiting additives are composed of compounds that are non-toxic, cheap, and soluble in an aqueous medium without requiring organic solvents or surfactants. Nicotinic acid (NTAD) and nicotinic acid hydrazide (NADH) are naturally derived compounds that are non-toxic, cheap, and very soluble in aqueous media. Hence, the present study investigates their anticorrosion performances in an equal mixture of 0.25 M H2SO4 and 0.5 M NaCl medium which mimics the acidic-chloride medium often encountered in typical industrial descaling and pickling processes. The study considered both the individual compounds and their blend with KI at different concentrations. The anticorrosion as well as the synergistic effects were evaluated with combined electrochemical and gravimetric methods. The low inhibition efficiency (%) approaching 50% and 70% were achieved with the individual NTAD and NADH, respectively, while enhanced inhibition efficiencies reaching 96% and 97% respectively, were achieved when blended respective inhibitors with an optimal KI concentration. The adsorption of the inhibitor molecules followed Langmuir isotherm with the Kads and ΔG0 increasing with the addition of KI. The positive synergistic index values confirmed synergism. The overall anticorrosion process as well as the synergistic effect of KI were validated with complementing intensive spectroscopy (FTIR and XPS analysis) and imaging (SEM and AFFM) methods. The experimental data collaborated with DFT computational and Monte Carlo molecular dynamics simulation studies in divulging the inhibition mechanisms of NTAD and NADH without and with KI blending.

Interpreting gas sorption isotherms in glassy polymers using a Bayesian framework: A view on parameter uncertainty propagation into mixture sorption predictions

Investigation of mixed-gas sorption is necessary for robust design and optimization of membrane-based processes. While sorption models for glassy polymers are well-established, they often deviate from observed mixture data. In doing so, however, parameters of these models are typically estimated via traditional least-squares optimization methods, and so parametric uncertainty is often ignored in mixture sorption predictions. As an alternative, we use Bayesian Inference (BI) to estimate probability distributions for sorption models' parameters and thus provide statistically meaningful mixture sorption forecasts that reflect parameter uncertainty rigorously. To this end, we exploit molecular sorption simulations, combining Grand Canonical Monte Carlo (GCMC) and Equilibrium Molecular Dynamics (EMD), to focus on two different glassy polymer systems (i.e., single- and mixed-gas sorption of CO2/CH4 in a fluorinated polyimide and CH4/H2 in a polymer of intrinsic microporosity) and three popular mixture sorption models (the Dual-Mode Sorption model, Non-Equilibrium Thermodynamics for Glassy Polymers model, and the Ideal Adsorbed Solution Theory) to demonstrate the benefits of this technique for uncertainty quantification and propagation in membrane applications. We show that observed sorption data at typical working pressures (e.g., 0−25atm) are often insufficient to accurately estimate model parameters, and consequently, these models often fail to represent observed mixture data adequately. Furthermore, we show that sorption data (e.g., high-pressure data), able to capture intrinsic isotherm nonlinearities of sorption models, are critical to considerably improve parameter inference from collective model-data fits and thus accurately predict mixture sorption.