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We propose the theory for the temperature and concentration phase transformations in composite lithium grease doped with the mesogenic additives, copper(II) carboxylates valerate and isovalerate, in the range 1, 5, 10, 20 wt. %. The changes in specific electric conductivity manifested in dielectric spectroscopy in the measuring electric field of 100 Hz – 1 MHz and polarization microscopy are reliable caused by reversible transitions from the discotic to isotropic phase or at heating from room temperature to 391 K. To define critical properties of the transitions, we applied Monte Carlo simulations using the Berezinsky-Kosterlitz-Thouless model describing screw, transverse edge and longitudinal dislocations. [ABSTRACT FROM AUTHOR]

The assessment of thermal conductivity in alloy plays a crucial role in ensuring their stability and performance in thermal applications. This paper specifically focuses on simulating the thermal conductivity of complex microstructures, which involve solid solution atoms and second phases. The lattice Monte Carlo (LMC) method is employed to examine the effective thermal conductivity of the Mg-(Ce, La) model alloy. The results obtained from numerical simulation are compared to experimental data, demonstrating a good agreement between them. Additionally, the study establishes a correlation between the content of solid solution atoms and thermal resistance through r j , α = A/(1-2x). Furthermore, the impact of various factors, such as volume fraction (f β), size (Z), and morphology (F ‾) of the second phase on the effective thermal conductivity, is thoroughly investigated and summarized. Overall, this research contributes to a better understanding the thermal conductivity behavior in complex alloy microstructures. • Lattice Monte Carlo method used to simulate thermal conductivity of magnesium alloy. • Solid solution atoms and second phase are considered simultaneously. • The error of numerical simulation and experimental data is less than 5.8%. • The law of influence on the second relative thermal conductivity is provided. [ABSTRACT FROM AUTHOR]

We study the effect of helical structure on the aggregation of proteins using a simplified lattice protein model with an implicit membrane environment. A recently proposed Monte Carlo approach, which exploits the proven statistical optimality of the MBAR estimator in order to improve simulation efficiency, was used. The results show that with both two and four proteins present, the tendency to aggregate is strongly expedited by the presence of amphipathic helix (APH), whereas a transmembrane helix (TMH) slightly disfavours aggregation. When four protein molecules are present, partially aggregated states (dimers and trimers) were more common when the APH was present, compared with the cases where no helices or only the TMH is present. [ABSTRACT FROM AUTHOR]

Development of new materials capable of conducting protons in the absence of water is crucial for improving the performance, reducing the cost, and extending the operating conditions for proton exchange membrane fuel cells. We present detailed atomistic simulations showing that graphanol (hydroxylated graphane) will conduct protons anhydrously with very low diffusion barriers. We developed a deep learning potential (DP) for graphanol that has near-density functional theory accuracy but requires a very small fraction of the computational cost. We used our DP to calculate proton self-diffusion coefficients as a function of temperature, to estimate the overall barrier to proton diffusion, and to characterize the impact of thermal fluctuations as a function of system size. We propose and test a detailed mechanism for proton conduction on the surface of graphanol. We show that protons can rapidly hop along Grotthuss chains containing several hydroxyl groups aligned such that hydrogen bonds allow for conduction of protons forward and backward along the chain without hydroxyl group rotation. Long-range proton transport only takes place as new Grotthuss chains are formed by rotation of one or more hydroxyl groups in the chain. Thus, the overall diffusion barrier consists of a convolution of the intrinsic proton hopping barrier and the intrinsic hydroxyl rotation barrier. Our results provide a set of design rules for developing new anhydrous proton conducting membranes with even lower diffusion barriers.

In the present study, mixtures of gemini surfactant (T 3 H 1 S 2 H 1 T 3 ) and a conventional surfactant (A 4 B 4 ) in aqueous solutions have been investigated through coarse-grained Monte Carlo simulation. The Critical Micelle Concentration (CMC) and aggregation numbers have been calculated at different ratios of the T 3 H 1 S 2 H 1 T 3 /A 4 B 4 surfactant mixture. The mixed CMC of the binary combinations falls between those of constituent surfactants. To explain the simulation results, theoretical models like Clint, Rubingh, and Maeda have been used to evaluate the interaction parameter, micellar composition, activity coefficients and the free energies of micellization. By increasing the overall composition until it reaches 0.5, all these parameters indicate non-ideal behavior and synergistic interactions between the surfactants, which is explained in terms of reduction of repulsion between head groups of T 3 H 1 S 2 H 1 T 3 due to the presence of conventional surfactants. At higher ratios, the formation of the aggregations would be harder due to the higher concentration of A 4 B 4 . [ABSTRACT FROM AUTHOR]

This paper addresses the thermal properties of syntactic metal foam made by embedding expanded perlite particles in A356 aluminium matrix. Lattice Monte Carlo (LMC) analyses are conducted to determine the thermal characterisation of the foam. For increased accuracy, the complex geometry of the metallic foam is captured by micro-computed tomography imaging. Using the resulting detailed geometric models, the effective thermal conductivity tensor is computed with possible thermal anisotropy taken into consideration. The numerical results are verified by comparison with experimental measurements. To this end, an improved steady-state method is used to correct for thermal contact resistance. Furthermore, the effective heat capacity, average density and thermal diffusivity of perlite – metal syntactic foam are determined. [ABSTRACT FROM AUTHOR]

This paper addresses the use of the Lattice Monte Carlo method for the thermal characterization of composite materials. An optimized approach that minimizes computational time is presented. The key aspect of the approach is the avoidance of the need to model the local thermal inertia. A combined finite element and Lattice Monte Carlo analysis is conducted on a model composite for a formal verification of the effective thermal diffusivity and conductivity calculated by the optimized Lattice Monte Carlo method. The effective thermal inertia is calculated separately by making use of the energy conservation law. [ABSTRACT FROM AUTHOR]

We review the calculation of the Hoyle state of C in nuclear lattice effective field theory (NLEFT) and its anthropic implications in the nucleosynthesis of C and O in red giant stars. We also analyse the extension of NLEFT to the regime of medium-mass nuclei, with emphasis on the determination of the ground-state energies of the α nuclei O, Ne, Mg, and Si by Euclidean time projection. Finally, we discuss recent NLEFT results for the spectrum, electromagnetic properties, and α-cluster structure of O. [ABSTRACT FROM AUTHOR]

The effective thermal conductivity of Cu-Fe and Sn-Al miscibility gap alloys over a range of temperatures and volume fractions was determined using the Lattice Monte Carlo method. The Cu-Fe system was found to have an effective conductivity predictable by the Maxwell-Eucken model. The Sn-Al system was not consistent with any empirical model analysed. The microstructures of physical samples were approximated using a random growth algorithm calibrated to electron or optical microscope images. Charts of effective conductivity against temperature for a number of volume fractions are presented for the two alloys. It was determined that the Cu-Fe alloy would benefit from an interstice type microstructure and the Sn-Al would be more efficient with a hard spheres type microstructure. More general conclusions are drawn about the efficiency of the two observed microstructures. [ABSTRACT FROM AUTHOR]

In the current study, we have investigated the micellization of pure gemini surfactants and a mixture of gemini and conventional surfactants using a 3D lattice Monte Carlo simulation method. For the pure gemini surfactant system, the effects of tail length on CMC and aggregation number were studied, and the simulation results were found to be in excellent agreement with the experimental results. For a mixture of gemini and conventional surfactants, variations in the mixed CMC, interaction parameter β , and excess Gibbs free energy G E with composition revealed synergism in micelle formation. Simulation results were compared to estimations made using regular solution theory to determine the applicability of this theory for non-ideal mixed surfactant systems. A large discrepancy was observed between the behavior of parameters such as the activity coefficients f i and the excess Gibbs free energy G E and the expected behavior of these parameters as predicted by regular solution theory. Therefore, we have used the modified version of regular solution theory. This three parameter model contains two parameters in addition to the interaction parameters: the size parameter, ρ , which reflects differences in the size of components, and the packing parameter, P* , which reflects nonrandom mixing in mixed micelles. The proposed model provides a good description of the behavior of gemini and conventional surfactant mixtures. The results indicated that as the chain length of gemini surfactants in mixture is increased, the size parameter remains constant while the interaction and packing parameters increase. [ABSTRACT FROM AUTHOR]

Abstract: Advanced pore morphology (APM) foam elements have a spherical outer skin and a porous inner structure. In this study, the method of Lattice Monte Carlo is applied to determining the thermal characterisation of periodic structures formed by spherical APM foam elements. Two diameters, i.e. 5mm and 10mm spheres, are considered. To this end, micro-computed tomography data of real samples is converted into numerical calculation models. This procedure allows the accurate geometric representation of the complex internal foam geometry. Lattice Monte Carlo is then used to obtain the effective thermal conductivity of partial and syntactic structures made up of APM foam elements. Samples are analysed for variation in absolute and directional (anisotropy) thermal conductivity. [Copyright &y& Elsevier]

Abstract: We extend Nuclear Lattice Effective Field Theory (NLEFT) to medium-mass nuclei, and present results for the ground states of alpha nuclei from 4He to 28Si, calculated up to next-to-next-to-leading order (NNLO) in the EFT expansion. This computational advance is made possible by extrapolations of lattice data using multiple initial and final states. For our soft two-nucleon interaction, we find that the overall contribution from multi-nucleon forces must change sign from attractive to repulsive with increasing nucleon number. This effect is not produced by three-nucleon forces at NNLO, but it can be approximated by an effective four-nucleon interaction. We discuss the convergence of the EFT expansion and the broad significance of our findings for future ab initio calculations. [Copyright &y& Elsevier]

In this paper, canonical lattice Monte Carlo simulation technique is used to derive cluster size distribution of a surfactant solution, by which activity coefficient of a monomer in solution and in the micellar phase is estimated. To do this, correlation of cluster concentration with the aggregation number is needed. The multiple equilibrium method is used to relate cluster size distribution to the activity coefficient. The introduced idea is not only applicable to the lattice model techniques, but also to any method that can produce cluster size distribution. The method can be used as a reference to test activity coefficient data of lattice-based equations of state. Results show that the activity coefficients of monomers in micellar aggregates are much greater than unity, in accordance with the experimental results. [ABSTRACT FROM AUTHOR]

Lattice Monte Carlo simulations for the surfactant–inorganic component–water system are performed to study the morphologies formation of mesoporous materials in a flat confining space. The effects of component concentration, distance and hydrophilicity of two confining walls are considered in a systematic way and 12 different morphologies are observed. In addition to the well-known morphologies that have been found in the bulk before, such as the cylindrical, spherical and lamellar morphologies, new morphologies such as perpendicular lamellar morphology and bridge morphology are found in the flat confining space. Besides, the ternary phase diagram for the solution composition, the binary phase diagram for the surfactant concentration versus the distance of the two walls and that for different wall–surfactant interactions are given in this work. [ABSTRACT FROM PUBLISHER]

Abstract: Surfactant-enhanced remediation (SER) is an effective approach for the removal of absorbed hydrophobic organic compounds (HOCs) from contaminated soils. The solubilization of contaminants by mixed surfactants with attractive and repulsive head–head interactions was studied by measuring the micelle–water partition coefficient (K C ) and molar solubilization ratio (MSR) using the lattice Monte Carlo method. The effect of surfactant mixing on the MSR and K C of contaminants displayed the following trend: C4 >C3 >C2. Synergistic binary surfactant mixtures showed greater solubilization capacities for contaminants than the corresponding individual surfactants. Mixed micellization parameters, including the interaction parameter β, and activity coefficient f i , were evaluated with Rubingh''s approach. Synergistic mixed-surfactant systems can improve the performance of surfactant-enhanced remediation of soils and groundwater by decreasing the amount of applied surfactant and the cost of remediation. [Copyright &y& Elsevier]

Abstract: This work addresses the effective thermal conductivity of cellular metals. Analytical relations for upper and lower bonds are evaluated. Formulae for effective conductivities are developed for an accurate estimation. In addition, extensive thermal Lattice Monte Carlo analyses are performed on a range of geometries. Next to simplified model structures, real geometries are addressed that exhibit closed (thick-walled structures), interconnected (open-cell structures) or both types (thin-walled structures) of porosity. To this end, calculation models of the real materials were directly based on computed tomography (CT) data in order to achieve a high level of geometrical accuracy. [ABSTRACT FROM AUTHOR]

We have studied the behavior of binary surfactant mixtures using the Monte Carlo (MC) simulation technique with a three-dimensional lattice model of a binary surfactant mixture, in which the constituent surfactant species are represented by a series of connected grid sites. Head–head interactions, alone and along with tail–tail interactions, among identical surfactant species were varied to imitate non-ideal mixing and to manipulate the net attractions and repulsions between surfactant species. We found that the head–head and tail–tail interactions affect both the mixed critical micelle concentration and distribution of aggregates. The simulation results are analyzed in the light of the phase separation model, which considers micelles as separate bulk pseudo-phase. Our studies reveal that regular solution theories do not present a satisfactory description for such systems. The discrepancies observed between the theoretical and simulation results for the studied systems could be attributed to the nonrandom mixing effect in simulation, which is neglected in regular solution theory. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Abstract: For the first time, a newly developed Lattice Monte Carlo (LMC) method is used to determine concentration profiles for combined mass diffusion and chemical reaction. Different chemical reaction models are considered: instantaneous reaction, irreversible reaction and reversible reaction. Several simple problems are addressed so that LMC solutions can be compared to exact analytical and numerical reference solutions. Excellent agreement between analytical solutions and numerical results is obtained. In addition, a more complex scenario is considered in order to present the capability of the new method. [Copyright &y& Elsevier]

Abstract: The results are presented from the lattice canonical Monte Carlo simulation of Ising-like model of nano-structure micelles in the presence of hydrophobically modified polymers. To simulate nano-cluster formation, the simplest model of potential, like to the previous work, is used. The model is based on the hydrophobic interactions as a main driving force for nonionic micellization. Polymer structure is changed from previous one, (H4T4)5, to (H10T10)2 to remove the small length of tail in the monomer of polymer in comparison with the diameter of the micelles bound to it. Results show that this small change in arrange of head/tail sites of polymer leads to a distinct variation in micellization. [Copyright &y& Elsevier]

Abstract: Using the recently formulated Lattice Monte Carlo method this work addresses transient thermal response in composite materials. Lattice-based random walks of virtual thermal energy particles are used to obtain temperature profiles in homogeneous and simple two-phase composites and compared to finite element calculations for validation. In particular, temperature-dependent thermal properties of the base material(s) are accounted for. In the second part of the paper, transient heat transfer into several different types of cellular metals is considered. The geometric discretisation is based on computed tomography scans of real materials. In addition to acquisition of temperature profiles inside the cellular metals, the amount of energy transferred over time is determined in order to evaluate their applicability in heat sinks. [Copyright &y& Elsevier]

The influence of the presence of a curved (convex) solid wall on the conformations of long, flexible polymer chains is studied in a dense polymer system and in the athermal limit by means of lattice Monte Carlo simulations. It is found that the chain conformation entropy drives a reduction of the density at the wall, similar to the flat wall case. The chain end density is higher next to the interface compared to the bulk polymer (segregation), with the difference increasing with chain length. The wall curvature does not significantly affect the segregation. The bonds are preferentially oriented in the direction tangential to the wall. The distance from the interface over which this effect is observed is about two bond lengths. Similar results are obtained when probing the preferential orientation of chain segments. In this case, the perturbed region has a thickness on the order of the considered probing chain segment length. This suggests that experimental results on the thickness of the ‘bonded layer’ next to a wall depend on the wavelength of the radiation employed for probing. The chains are ellipsoidal in the bulk and rotate close to the surface with the large semi[hyphen]axis of the ellipsoid normal to the line connecting their center of mass with the filler center. Since there is no energetic interaction with the filler, no adsorption transition is observed, but the chains tend to wrap around the filler once the gyration radius becomes comparable to the filler radius. [Copyright &y& Elsevier]

In this work, we show that lattice Monte Carlo simulations can be used to span the time and distance scales between underlying atomistic processes and macroscopic diffusion behavior. We use ab- initio calculations of binding energies versus configuration to calculate hopping rates of vacancies for use in lattice Monte Carlo (LMC) simulations of diffusion and aggregation in silicon. The LMC simulations consider the biased nature of vacancy hop frequencies in the neighborhood of dopants, with interactions up to sixth-nearest- neighbor distances included. We use these simulations to investigate the expected macroscopic diffusion behavior, as well as the process by which dopant/defect aggregation occurs. Specific phenomena investigated include collective behavior leading to greatly enhanced diffusivity at high doping levels, the time dependence of effective diffusivity due to the formation of dopant/vacancy clusters, and dopant fluxes in the presence of a vacancy gradient. [ABSTRACT FROM AUTHOR]