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Two-dimensional spintronics has become a hot topic in recent years due to its advantages and potential in manipulating electron spins. In this paper, the electronic structures and magnetic properties of the Janus NbSSe monolayer are calculated using first-principles and Monte Carlo methods. Our study shows that the ground state of the material is a ferromagnetic metal. Under carrier doping, it undergoes a second-order phase transition from metal to half-metal, achieving 100% spin polarization, and enhancing or weakening ferromagnetic coupling. The value of the magnetocrystalline anisotropy energy is 570.96 μeV, and doping with an appropriate concentration of holes can transform the easy magnetization axis from in-plane to out-of-plane. Since the out-of-plane mirror symmetry is broken, we study the charge changes in the layer under the action of an external electric field. Due to the combined action of the external electric field and the built-in electric field, the layer exhibits a unique charge transfer mode. It is predicted that the Curie temperature of the material is about 156 K. When doped with 4.01 × 1013 cm−2 (0.04 holes per atom) concentration holes, the Curie temperature can reach about 350 K, indicating that the Curie temperature of the material can be reasonably controlled by regulating the carrier concentration. The coercive force calculated from the hysteresis loop is 0.01 T, and its hysteresis loss is low, showing its response to the external magnetic field. All of the above results indicate the application potential of this material in spin-electronic devices. [ABSTRACT FROM AUTHOR]

We present the explicit bonding Reaction ensemble Monte Carlo (eb-RxMC) method, designed to sample reversible bonding reactions in macromolecular systems in thermodynamic equilibrium. Our eb-RxMC method is based on the reaction ensemble method; however, its implementation differs from the latter by the representation of the reaction. In the eb-RxMC implementation, we are adding or deleting bonds between existing particles, instead of inserting or deleting particles with different chemical identities. This new implementation makes the eb-RxMC method suitable for simulating the formation of reversible linkages between macromolecules, which would not be feasible with the original implementation. To enable coupling of our eb-RxMC algorithm with molecular dynamics algorithm for the sampling of the configuration space, we biased the sampling of reactions only within a certain inclusion radius. We validated our algorithm using a set of ideally behaving systems undergoing dimerization and polycondensation reactions, for which analytical results are available. For dimerization reactions with various equilibrium constants and initial compositions, the degree of conversion measured in our simulations perfectly matched the reference values given by the analytical equations. We also showed that this agreement is not affected by the arbitrary choice of the inclusion radius or the stiffness of the harmonic bond potential. Next, we showed that our simulations can correctly match the analytical results for the distribution of the degree of polymerization and end-to-end distance of ideal chains in polycondensation reactions. Altogether, we demonstrated that our eb-RxMC simulations correctly sample both reaction and configuration spaces of these reference systems, opening the door to future simulations of more complex interacting macromolecular systems. [ABSTRACT FROM AUTHOR]

A free-energy functional is presented to explicitly take into account pair correlations between molecules in inhomogeneous fluids. The framework of classical density functional theory (DFT) is used to describe the variation in the density of molecules interacting through a Mie (generalized Lennard-Jones) potential. Grand Canonical Monte Carlo simulations are performed for the systems to validate the new functional. The statistical associating fluid theory developed for Mie fluids (SAFT-VR Mie) is selected as a reference for the homogeneous bulk limit of the DFT and is applied here to systems of spherical non-associating particles. The importance of a correct description of the pair correlations for a reliable representation of the free energy in the development of the equation of state is duly noted. Following the Barker–Henderson high-temperature expansion, an analogous formulation is proposed from the general DFT formalism to develop an inhomogeneous equivalent of the SAFT-VR Mie free energy as a functional of the one-body density. In order to make use of this new functional in adsorption studies, a non-local version of the DFT is considered, with specific weighted densities describing the effects of neighboring molecules. The computation of these quantities is possible in three-dimensional space for any pore geometry with repulsive or attractive walls. We showcase examples to validate the new functional, revealing a very good agreement with molecular simulation. The new SAFT-DFT approach is well-adapted to describe realistic complex fluids. [ABSTRACT FROM AUTHOR]

We develop a multiscale coarse-grain model of the NIST Monoclonal Antibody Reference Material 8671 (NISTmAb) to enable systematic computational investigations of high-concentration physical instabilities such as phase separation, clustering, and aggregation. Our multiscale coarse-graining strategy captures atomic-resolution interactions with a computational approach that is orders of magnitude more efficient than atomistic models, assuming the biomolecule can be decomposed into one or more rigid bodies with known, fixed structures. This method reduces interactions between tens of thousands of atoms to a single anisotropic interaction site. The anisotropic interaction between unique pairs of rigid bodies is precomputed over a discrete set of relative orientations and stored, allowing interactions between arbitrarily oriented rigid bodies to be interpolated from the precomputed table during coarse-grained Monte Carlo simulations. We present this approach for lysozyme and lactoferrin as a single rigid body and for the NISTmAb as three rigid bodies bound by a flexible hinge with an implicit solvent model. This coarse-graining strategy predicts experimentally measured radius of gyration and second osmotic virial coefficient data, enabling routine Monte Carlo simulation of medically relevant concentrations of interacting proteins while retaining atomistic detail. All methodologies used in this work are available in the open-source software Free Energy and Advanced Sampling Simulation Toolkit. [ABSTRACT FROM AUTHOR]

Diffusion-controlled deactivation of excited phenanthrene and oxidation of triplet aryl-nitrene by molecular oxygen were used to determine the energetics of oxygen jump rates in the set of glassy polymers: poly(methyl methacrylate), poly(n-butyl methacrylate), polycarbonate, polystyrene, and polysulfone. To interpret experimental results, a simple model based on the transition state theory of diffusion jump has been used. The kinetic Monte Carlo simulations of phenanthrene deactivation and nitrene oxidation were carried out in a cubic lattice that modeled a polymer matrix. The bonds of the lattice were assigned to be activation barriers for the diffusion jumps of oxygen molecules from one site of the lattice to another. The standard deviation, σ‡, and spatial correlation length, rc, of the free energy of diffusion jump have been determined. It is shown that the spatial correlation of oxygen jump rates on a nanometer scale and the entropic nature of the dynamic heterogeneity are common features of all the studied polymers. [ABSTRACT FROM AUTHOR]

Traditional Kinetic Monte Carlo (KMC) approaches, rooted in Gillespie's stochastic simulation algorithm, become computationally demanding in systems with a large range of timescales. The goal of this work is to propose and study new adaptive lattice-KMC time integration strategies for spatially non-uniform systems. To that end, two novel adaptive tau-leaping methods and their corresponding time integration strategies are developed based on the idea of the "n-fold" direct KMC method. These strategies allow for the simultaneous execution of multiple reactions, advancing time by adaptively selected coarse increments. We present numerical experiments comparing the proposed methods with existing approaches in a catalytic surface kinetics application involving ammonia decomposition. [ABSTRACT FROM AUTHOR]

Theories of small systems play an important role in the fundamental understanding of finite size effects in statistical mechanics, as well as the validation of molecular simulation results as no computer can simulate fluids in the thermodynamic limit. Previously, a shell particle was included in the isothermal–isobaric ensemble in order to resolve an ambiguity in the resulting partition function. The shell particle removed either redundant volume states or redundant translational degrees of freedom of the system and yielded quantitative differences from traditional simulations in this ensemble. In this work, we investigate the effect of including a shell particle in the canonical, grand canonical, and Gibbs ensembles. For systems comprised of a pure component ideal gas, analytical expressions for various thermodynamic properties are obtained. We also derive the Metropolis Monte Carlo simulation acceptance criteria for these ensembles with shell particles, and the results of the simulations of an ideal gas are in excellent agreement with the theoretical predictions. The system size dependence of various important ensemble averages is also analyzed. [ABSTRACT FROM AUTHOR]

Hydrofluorocarbons are a class of fluorinated molecules used extensively in residential and industrial refrigeration systems. This study examines the potential of using adsorption processes with the silicalite-1 zeolite to separate a mixture of difluoromethane (CH2F2, HFC-32) and pentafluoroethane (CF3CF2H, HFC-125) at various concentrations. Pure adsorption data were measured using a XEMIS gravimetric microbalance, whereas binary data were determined using the Integral Mass Balance method. Grand canonical Monte Carlo molecular simulations were performed with the Cassandra package. We found that the results from molecular simulations are in satisfactory agreement with experimental loading measurements. Moreover, we show that ideal adsorbed solution theory could not quantitatively match the experimental or computational measurements of binary adsorption or selectivity. Molecular simulations show that refrigerant molecules do not have a uniform distribution in the zeolite framework. [ABSTRACT FROM AUTHOR]

Stochastic lattice-gas models provide the natural framework for analysis of the surface diffusion-mediated evolution of crystalline metal nanostructures on the appropriate time scale (often 101–104 s) and length scale. Model behavior can be precisely assessed by kinetic Monte Carlo simulation, typically incorporating a rejection-free algorithm to efficiently handle the broad range of Arrhenius rates for hopping of surface atoms. The model should realistically prescribe these rates, or the associated barriers, for a diversity of local surface environments. However, commonly used generic choices for barriers fail, even qualitatively, to simultaneously describe diffusion for different low-index facets, for terrace vs step edge diffusion, etc. We introduce an alternative Unconventional Interaction–Conventional Interaction formalism to prescribe these barriers, which, even with few parameters, can realistically capture most aspects of behavior. The model is illustrated for single-component fcc metal systems, mainly for the case of Ag. It is quite versatile and can be applied to describe both the post-deposition evolution of 2D nanostructures in homoepitaxial thin films (e.g., reshaping and coalescence of 2D islands) and the post-synthesis evolution of 3D nanocrystals (e.g., reshaping of nanocrystals synthesized with various faceted non-equilibrium shapes back to 3D equilibrium Wulff shapes). [ABSTRACT FROM AUTHOR]

Policy-guided Monte Carlo is an adaptive method to simulate classical interacting systems. It adjusts the proposal distribution of the Metropolis–Hastings algorithm to maximize the sampling efficiency, using a formalism inspired by reinforcement learning. In this work, we first extend the policy-guided method to deal with a general state space, comprising, for instance, both discrete and continuous degrees of freedom, and then apply it to a few paradigmatic models of glass-forming mixtures. We assess the efficiency of a set of physically inspired moves whose proposal distributions are optimized through on-policy learning. Compared to conventional Monte Carlo methods, the optimized proposals are two orders of magnitude faster for an additive soft sphere mixture but yield a much more limited speed-up for the well-studied Kob–Andersen model. We discuss the current limitations of the method and suggest possible ways to improve it. [ABSTRACT FROM AUTHOR]

A field-supported multiphase kinetic Monte Carlo method previously applied to self-assembled trimesic acid molecular layers [Ustinov et al., Phys. Chem. Chem. Phys. 24, 26111 (2022)] was generalized to three-dimensional gas–liquid and gas–solid systems. This method allows us to calculate the thermodynamic potentials of the liquid and solid phases and then determine the parameters of the liquid–solid phase transition. In this study, the requirement that the gas phase be ideal was introduced as an additional condition. It was shown that in a two-phase system, the sum of the analytical expression for the chemical potential of an ideal gas and the external potential imposed on the gas phase exactly equals the chemical potential of the equilibrium crystal or liquid phase. For example, the coexistence of crystalline/liquid krypton and ideal gas has been considered. A comparison with previously published data has shown that the proposed approach provides the most accurate results for determining the parameters of phase transitions and fully satisfies the Gibbs–Duhem equation. This method does not impose any restrictions on the complexity or hardness of dense phases. [ABSTRACT FROM AUTHOR]

A summary of the development of Monte Carlo statistical mechanics simulations for the computation of free energies of hydration of organic molecules is followed by presentation of results with the latest version of the optimized potentials for liquid simulations–all atom force field and the TIP4P water model. Scaling of the Lennard-Jones interactions between water, oxygen, and carbon atoms by a factor of 1.25 is found to improve the accuracy of free energies of hydration for 50 prototypical organic molecules from a mean unsigned error of 1.0–1.2 to 0.4 kcal/mol. [ABSTRACT FROM AUTHOR]

This Response addresses critiques raised about the Binding Debye–Hückel (BiDH) theory [Naseri Boroujeni et al., J. Chem. Phys. 159, 154503 (2023)] by Simonin and Bernard [J.-P. Simonin and O. Bernard, J. Chem. Phys. 161, 057102 (2024)]. The critiques questioned the foundational framework of the Debye–Hückel (DH) theory, the relevance of ion pairing in primitive model fluids, and the accuracy of the BiDH model compared to mean spherical approximation model. Through a systematic rebuttal, supported by extensive literature review and comparison with Monte Carlo simulation data, this Response addresses these concerns. It demonstrates the efficacy of DH theory in describing real electrolyte solutions, validates the relevance of ion pairing in primitive model fluids, and establishes the BiDH model's accuracy in describing electrolyte properties. [ABSTRACT FROM AUTHOR]

Using a diffusion Monte Carlo technique, we calculated the phase diagram of 3He adsorbed on a first solid layer of a molecular hydrogen isotope (H2, HD, and D2) on top of graphite. The results are qualitatively similar in all cases: a two-dimensional gas spanning from the infinite dilution limit to a second-layer helium density of 0.048 ± 0.004 Å−2. That gas is in equilibrium with a 7/12 commensurate structure, more stable than any incommensurate triangular solid of similar density. These findings are in reasonably good agreement with available experimental data. [ABSTRACT FROM AUTHOR]

Vapor–Liquid Equilibria (VLE) of hydrogen (H2) and aqueous electrolyte (KOH and NaCl) solutions are central to numerous industrial applications such as alkaline electrolysis and underground hydrogen storage. Continuous fractional component Monte Carlo simulations are performed to compute the VLE of H2 and aqueous electrolyte solutions at 298–423 K, 10–400 bar, 0–8 mol KOH/kg water, and 0–6 mol NaCl/kg water. The densities and activities of water in aqueous KOH and NaCl solutions are accurately modeled (within 2% deviation from experiments) using the non-polarizable Madrid-2019 Na+/Cl− ion force fields for NaCl and the Madrid-Transport K+ and Delft Force Field of OH− for KOH, combined with the TIP4P/2005 water force field. A free energy correction (independent of pressure, salt type, and salt molality) is applied to the computed infinite dilution excess chemical potentials of H2 and water, resulting in accurate predictions (within 5% of experiments) for the solubilities of H2 in water and the saturated vapor pressures of water for a temperature range of 298–363 K. The compositions of water and H2 are computed using an iterative scheme from the liquid phase excess chemical potentials and densities, in which the gas phase fugacities are computed using the GERG-2008 equation of state. For the first time, the VLE of H2 and aqueous KOH/NaCl systems are accurately captured with respect to experiments (i.e., for both the liquid and gas phase compositions) without compromising the liquid phase properties or performing any refitting of force fields. [ABSTRACT FROM AUTHOR]

Ion partitioning between different compartments (e.g., a porous material and a bulk solution reservoir), known as Donnan equilibrium, plays a fundamental role in various contexts such as energy, environment, or water treatment. The linearized Poisson–Boltzmann (PB) equation, capturing the thermal motion of the ions with mean-field electrostatic interactions, is practically useful to understand and predict ion partitioning, despite its limited applicability to conditions of low salt concentrations and surface charge densities. Here, we investigate the Donnan equilibrium of coarse-grained dilute electrolytes confined in charged slit-pores in equilibrium with a reservoir of ions and solvent. We introduce and use an extension to confined systems of a recently developed hybrid nonequilibrium molecular dynamics/grand canonical Monte Carlo simulation method ("H4D"), which enhances the efficiency of solvent and ion-pair exchange via a fourth spatial dimension. We show that the validity range of linearized PB theory to predict the Donnan equilibrium of dilute electrolytes can be extended to highly charged pores by simply considering renormalized surface charge densities. We compare with simulations of implicit solvent models of electrolytes and show that in the low salt concentrations and thin electric double layer limit considered here, an explicit solvent has a limited effect on the Donnan equilibrium and that the main limitations of the analytical predictions are not due to the breakdown of the mean-field description but rather to the charge renormalization approximation, because it only focuses on the behavior far from the surfaces. [ABSTRACT FROM AUTHOR]

Through extensive Monte Carlo simulations, we systematically study the effect of chain stiffness on the packing ability of linear polymers composed of hard spheres in extremely confined monolayers, corresponding effectively to 2D films. First, we explore the limit of random close packing as a function of the equilibrium bending angle and then quantify the local and global order by the degree of crystallinity and the nematic or tetratic orientational order parameter, respectively. A multi-scale wealth of structural behavior is observed, which is inherently absent in the case of athermal individual monomers and is surprisingly richer than its 3D counterpart under bulk conditions. As a general trend, an isotropic to nematic transition is observed at sufficiently high surface coverages, which is followed by the establishment of the tetratic state, which in turn marks the onset of the random close packing. For chains with right-angle bonds, the incompatibility of the imposed bending angle with the neighbor geometry of the triangular crystal leads to a singular intra- and inter-polymer tiling pattern made of squares and triangles with optimal local filling at high surface concentrations. The present study could serve as a first step toward the design of hard colloidal polymers with a tunable structural behavior for 2D applications. [ABSTRACT FROM AUTHOR]

Upon osmotic compression, rotationally symmetric faceted colloidal particles can form translationally ordered, orientationally disordered rotator mesophases. This study explores the mechanism of rotator-to-crystal phase transitions where orientational order is gained in a translationally ordered phase, using rotator-phase forming truncated cubes as a testbed. Monte Carlo simulations were conducted for two selected truncations (s), one for s = 0.527 where the rotator and crystal lattices are dissimilar and one for s = 0.572 where the two phases have identical lattices. These differences set the stage for a qualitative difference in their rotator–crystal transitions, highlighting the effect of lattice distortion on phase transition kinetics. Our simulations reveal that significant lattice deviatoric effects could hinder the rotator-to-crystal transition and favor arrangements of lower packing fraction instead. Indeed, upon compression, it is found that for s = 0.527, the rotator phase does not spontaneously transition into the stable, densely packed crystal due to the high lattice strains involved but instead transitions into a metastable solid phase to be colloquially referred to as "orientational salt" for short, which has a similar lattice as the rotator phase and exhibits two distinct particle orientations having substitutional order, alternating regularly throughout the system. This study paves the way for further analysis of diffusionless transformations in nanoparticle systems and how lattice-distortion could influence crystallization kinetics. [ABSTRACT FROM AUTHOR]

We introduce a novel algorithm that leverages stochastic sampling techniques to compute the perturbative triples correction in the coupled-cluster framework. By combining elements of randomness and determinism, our algorithm achieves a favorable balance between accuracy and computational cost. The main advantage of this algorithm is that it allows for the calculation to be stopped at any time, providing an unbiased estimate, with a statistical error that goes to zero as the exact calculation is approached. We provide evidence that our semi-stochastic algorithm achieves substantial computational savings compared to traditional deterministic methods. Specifically, we demonstrate that a precision of 0.5 millihartree can be attained with only 10% of the computational effort required by the full calculation. This work opens up new avenues for efficient and accurate computations, enabling investigations of complex molecular systems that were previously computationally prohibitive. [ABSTRACT FROM AUTHOR]

Rigid bodies, made of smaller composite beads, are commonly used to simulate anisotropic particles with molecular dynamics or Monte Carlo methods. To accurately represent the particle shape and to obtain smooth and realistic effective pair interactions between two rigid bodies, each body may need to contain hundreds of spherical beads. Given an interacting pair of particles, traditional molecular dynamics methods calculate all the inter-body distances between the beads of the rigid bodies within a certain distance. For a system containing many anisotropic particles, these distance calculations are computationally costly and limit the attainable system size and simulation time. However, the effective interaction between two rigid particles should only depend on the distance between their center of masses and their relative orientation. Therefore, a function capable of directly mapping the center of mass distance and orientation to the interaction energy between the two rigid bodies would completely bypass inter-bead distance calculations. It is challenging to derive such a general function analytically for almost any non-spherical rigid body. In this study, we have trained neural nets, powerful tools to fit nonlinear functions to complex datasets, to achieve this task. The pair configuration (center of mass distance and relative orientation) is taken as an input, and the energy, forces, and torques between two rigid particles are predicted directly. We show that molecular dynamics simulations of cubes and cylinders performed with forces and torques obtained from the gradients of the energy neural-nets quantitatively match traditional simulations that use composite rigid bodies. Both structural quantities and dynamic measures are in agreement, while achieving up to 23 times speedup over traditional molecular dynamics, depending on hardware and system size. The method presented here can, in principle, be applied to any irregular concave or convex shape with any pair interaction, provided that sufficient training data can be obtained. [ABSTRACT FROM AUTHOR]

Magnetic structure factor (MSF), calculated from ground state configuration previously obtained by Monte Carlo simulation in different rectangular artificial spin ices, is employed to investigate ground state degeneracy. Our analysis considers the importance of nanoislands size to the ratio between rectangle sides in the lattice parameters via a dumbbell model. Pinch points in MSF along with residual entropy, determined for a number of different rectangular lattices with disconnected nanoislands, point out the conditions for the emergency of a gauge field, through which magnetic monopoles interact effectively. [ABSTRACT FROM AUTHOR]

Histogram-reweighting grand canonical Monte Carlo simulations are used to obtain the critical properties of lattice chains composed of solvophilic and solvophobic monomers. The model is a modification of one proposed by Larson et al. [J. Chem. Phys. 83, 2411 (1985)], lowering the "contrast" between beads of different types to prevent aggregation into finite-size micelles that would mask true phase separation between bulk high- and low-density phases. Oligomeric chains of lengths between 5 and 24 beads are studied. Mixed-field finite-size scaling methods are used to obtain the critical properties with typical relative accuracies of better than 10−4 for the critical temperature and 10−3 for the critical volume fraction. Diblock chains are found to have lower critical temperatures and volume fractions relative to the corresponding homopolymers. The addition of solvophilic blocks of increasing length to a fixed-length solvophobic segment results in a decrease of both the critical temperature and the critical volume fraction, with an eventual slow asymptotic approach to the long-chain limiting behavior. Moving a single solvophobic or solvophilic bead along a chain leads to a minimum or maximum in the critical temperature, with no change in the critical volume fraction. Chains of identical length and composition have a significant spread in their critical properties, depending on their precise sequence. The present study has implications for understanding biomolecular phase separation and for developing design rules for synthetic polymers with specific phase separation properties. It also provides data potentially useful for the further development of theoretical models for polymer and surfactant phase behavior. [ABSTRACT FROM AUTHOR]

The shape of Janus particles is directly connected to their adsorption behavior. Janus tadpole polymers offer a unique topological architecture that includes competition between entropic, enthalpic, and topological terms in the adsorption free energy; accordingly, non-trivial adsorption behavior patterns are expected. We study the surface adsorption of Janus tadpole polymers by means of Monte Carlo simulations, finding that, depending on which part of the tadpole polymers is preferentially adsorbing on the surface, very different types of behavior for both the adsorbed polymeric phase and of the brush arise. The adsorbed phase and the brush mutually influence each other, leading to a variety of phenomena such as nematic ordering of the adsorbed stiff tadpole tails and intriguing changes in the territoriality of adsorbed ring polymers on the surface. We analyze in detail our findings, revealing the mechanisms behind the organization and ordering, and opening up new possibilities to tune and control the structure of such systems. [ABSTRACT FROM AUTHOR]

We have calculated electron backscattering coefficients, η (E p) , at primary electron energies E p of 0.1–100 keV for three elemental and intermediate atomic number solids, Cr, Co and Pd, with an up-to-date Monte Carlo simulation model. A relativistic dielectric functional approach is adopted for the calculation of the electron inelastic cross section, where several different datasets of optical energy loss function (ELF) are adopted. The calculated backscattering coefficient is found to be substantially affected by the ELF, where the influence can be seen to follow the f- and ps-sum rules and the resultant energy dependence of electron inelastic mean free path. To understand the uncertainties involved in a comparison with experimental data both the theoretical uncertainty due to the elastic cross-section model and the experimental systematic error for the contaminated surfaces are investigated. A total of 192 different scattering potentials are employed for the calculation of Mott's electron elastic cross section and this theoretical uncertainty is confirmed to be small. On the other hand, the simulation of contaminated Co and Pd surfaces with several carbonaceous atomic layers can well explain the experimental data. The present results indicate that accurate backscattering coefficient data should be either measured from fully cleaned surfaces or obtained from modern Monte Carlo theoretical calculations involving reliable optical constants data. With the recent progress in the accurate measurement of optical constants by reflection electron energy loss spectroscopy technique, constructing a reliable theoretical database of electron backscattering coefficients for clean surfaces of elemental solids is highly hopeful. [ABSTRACT FROM AUTHOR]

Non-contact photothermal pump-probe methodologies such as Frequency-Domain Thermo-Reflectance (FDTR) systems facilitate the examination of thermal characteristics spanning semiconductor materials and their associated interfaces. We underscore the significance of meticulous measurements and precise error estimation attained through the analysis of both amplitude and phase data in Thermo-Reflectance (TR). The precision of the analytical estimation hinges greatly on the assumptions made before implementing the method and notably showcases a decrease in errors when both the amplitude and phase are incorporated as input parameters. We demonstrate that frequency-domain calculations can attain high precision in measurements, with error estimations in thermal conductivity (k), thermal boundary resistance (Rth), and thermal diffusivity (α) as low as approximately 2.4%, 2.5%, and 3.0%, respectively. At the outset, we evaluate the uncertainty arising from the existence of local minima when analyzing data acquired via FDTR, wherein both the phase and amplitude are concurrently utilized for the assessment of cross-plane thermal transport properties. Expanding upon data analysis techniques, particularly through advanced deep learning approaches, can significantly enhance the accuracy and precision of predictions when analyzing TR data across a spectrum of modulation frequencies. Deep learning models enhance the quality of fitting and improve the accuracy and precision of uncertainty estimation compared to traditional Monte Carlo simulations. This is achieved by providing suitable initial guesses for data fitting, thereby enhancing the overall performance of the analysis process. [ABSTRACT FROM AUTHOR]

This paper highlights the tight relationship between the picking and packing processes in warehouse management and the need to consider them as an integrated problem. The study describes and models this integrated problem as a mixed-integer programming model, to optimise overall labour costs by determining the assignment of the subsets of orders, i.e. batches, for picking and packing. To address the issue of model complexity, the paper presents a statistical-based framework for generating approximate models and selecting the optimal one through examination. Based on the examination results, a pair-swapping heuristic is additionally proposed to be combined as a hybrid algorithm. Numerical experiments based on a real-world case demonstrate the effectiveness of the framework-proposed and selected hybrid algorithm by comparison with other framework-proposed approximate models, a solver, and existing heuristics. Our findings indicate that the combined usage of integrated picking and packing processes planning and the hybrid algorithm proposed and selected within the statistical-based framework can effectively reduce the cost of warehouse management. [ABSTRACT FROM AUTHOR]

We explore the phase behavior of a binary colloidal system under external spatially periodic modulation. We perform Monte Carlo simulations on a binary mixture of big and small repulsive Lennard-Jones particles with a diameter ratio of 2:1. We characterize structure by isotropic and anisotropic pair correlation functions, cluster size distribution, bond angle distribution, order parameter, and specific heat. We observe the demixing of the species in the absence of external modulation. However, the mixing of the species gets enhanced with increasing potential strength along with the alignment of the particles transverse to the modulation. The de-mixing order parameter shows discontinuity with increasing modulation strength, characterizing a first order phase transition. The peak in specific heat increases linearly with the size of the system. We also look into the dynamical behavior of the system via computing Mean Square Displacement (MSD) along both parallel and perpendicular directions to the modulation. We observe a decrease in the diffusion coefficient for both types of particles as we increase the strength of the modulation. [ABSTRACT FROM AUTHOR]

We study important aspects of shape selectivity effects of zeolites for hydroisomerization of linear alkanes, which produces a myriad of isomers, particularly for long chain hydrocarbons. To investigate the conditions for achieving an optimal yield of branched hydrocarbons, it is important to understand the role of chemical equilibrium in these reversible reactions. We conduct an extensive analysis of shape selectivity effects of different zeolites for the hydroisomerization of C7 and C8 isomers at chemical reaction equilibrium conditions. The reaction ensemble Monte Carlo method, coupled with grand-canonical Monte Carlo simulations, is commonly used for computing reaction equilibrium of heterogeneous reactions. The computational demands become prohibitive for a large number of reactions. We used a faster alternative in which reaction equilibrium is obtained by imposing chemical equilibrium in the gas phase and phase equilibrium between the gas phase components and the adsorbed phase counterparts. This effectively mimics the chemical equilibrium distribution in the adsorbed phase. Using Henry's law at infinite dilution and mixture adsorption isotherm models at elevated pressures, we calculate the adsorbed loadings in the zeolites. This study shows that zeolites with cage or channel-like structures exhibit significant differences in selectivity for alkane isomers. We also observe a minimal impact of pressure on the gas-phase equilibrium of these reactions at typical experimental reaction temperatures 400 − 700 K . This study marks initial strides in understanding the reaction product distribution for long-chain alkanes. [ABSTRACT FROM AUTHOR]

Thermodynamic potentials play a substantial role in numerous scientific disciplines and serve as basic constructs for describing the behavior of matter. Despite their significance, comprehensive investigations of their topological characteristics and their connections to molecular interactions have eluded exploration due to experimental inaccessibility issues. This study addresses this gap by analyzing the topology of the Helmholtz energy, Gibbs energy, Grand potential, and Null potential that are associated with different isothermal boundary conditions. By employing Monte Carlo simulations in the NVT, NpT, and μVT ensembles and a molecular-based equation of state, methane, ethane, nitrogen, and methanol are investigated over a broad range of thermodynamic conditions. The predictions from the two independent methods are overall in very good agreement. Although distinct quantitative differences among the fluids are observed, the overall topology of the individual thermodynamic potentials remains unaffected by the molecular architecture, which is in line with the corresponding states principle—as expected. Furthermore, a comparative analysis reveals significant differences between the total potentials and their residual contributions. [ABSTRACT FROM AUTHOR]

Cobalt oxides are known for their excellent heat transfer properties. The main component of cobalt oxides is the CoO2 monolayer, which exhibits high-temperature superconductivity caused by strong electron–phonon coupling (EPC). We here systematically investigate the structural stability, electronic structure, and magnetism of the CoO2 monolayer using first-principles and Monte Carlo simulations. On this basis, we further study the changes in the spin energy gap, magnetic axis direction, and other properties of the CoO2 monolayer with the changes in carrier concentration. By appropriately doping the CoO2 monolayer with holes, the magnetic axis direction of the CoO2 monolayer can be reversed, thereby enhancing its potential application in the field of spin electronic devices. Monte Carlo simulation is used to study the regulation of different factors on the magnetothermal properties of the CoO2 monolayer. Through the analysis of physical parameters such as Curie temperature (TC) and bandgap, we find that the appropriate carrier concentration and magnetic field can not only regulate the magnetothermal properties of materials but also further improve the efficiency of materials in low-temperature environments. [ABSTRACT FROM AUTHOR]

We develop an adaptive scheme in the kinetic Monte Carlo simulations, where the adsorption and activation energies of all elementary steps, including the effects of other adsorbates, are evaluated "on-the-fly" by employing the neural network potentials. The configurations and energies evaluated during the simulations are stored for reuse when the same configurations are sampled in a later step. The present scheme is applied to hydrogen adsorption and diffusion on the Pd(111) and Pt(111) surfaces and the CO oxidation reaction on the Pt(111) surface. The effects of interactions between adsorbates, i.e., adsorbate–adsorbate lateral interactions, are examined in detail by comparing the simulations without considering lateral interactions. This study demonstrates the importance of lateral interactions in surface diffusion and reactions and the potential of our scheme for applications in a wide variety of heterogeneous catalytic reactions. [ABSTRACT FROM AUTHOR]

The adsorption of particles onto fluid membranes can lead to membrane-mediated interactions between particles that promote their self-assembly and lead to changes in membrane morphology. However, in contrast with rigid particles, relatively little is known about deformable particles, which introduce additional complexities due to the mutual deformability of the particles and the membrane. Here, we use Monte Carlo simulations and umbrella sampling to investigate the equilibrium properties of hinge-like particles adsorbed on membrane vesicles by means of anisotropic, attractive interactions. We vary the hinge stiffness, adhesive area fraction, patterning of adhesive regions, and number of adsorbed particles. Depending on their properties, isolated particles can conform to the vesicle, induce invaginations of the membrane, or exhibit multistable behavior in which they sample distinct classes of configurations due to the interplay of particle and membrane deformations. With two adsorbed particles, the properties of the particles can be used to promote aggregation, bias the particles to different parts of the vesicle, or stabilize the coexistence of both cases. With multiple adsorbed particles, the number and type control their organization and collective impact on the vesicle, which can adopt shapes ranging from roughly spherical to dumbbell-like and multi-lobed. Our results highlight how modifying the mechanical properties and patterned adhesion of deformable particles, which is possible with DNA nanotechnology, influences their self-assembly and the resulting shapes of both the particles and vesicles. [ABSTRACT FROM AUTHOR]

O and Nb are two representative alloying elements of Ti to form high-temperature and corrosion resistance α Ti alloys. The investigation on the thermodynamic characteristics of α Ti–O and Ti–Nb has attracted much attention in recent years. However, in this regard, a satisfied experimental technique or modeling scheme is still yet to be developed due to the appearance of a variety of oxides in Ti–O and the mechanical instability present in Ti–Nb. Herein, we combined first-principles calculations with the cluster expansion method to investigate the ground-state characteristics for α Ti–O and α Ti–Nb systems. The atomic bonding interactions in these two systems were first revealed based on the calculated electronic structures. Afterward, the Debye–Grüneisen model and Monte Carlo simulations were employed together to investigate the thermodynamic properties of α phases in these two systems, and the effect of vibrational entropy on the order–disorder transition temperatures of the phases in α Ti–O system was first examined. A good agreement with experimentally reported phase boundaries is obtained in the Ti–Nb system by handling the mechanical instabilities introduced by the highly distorted structures. In addition, the cluster expansion coefficients for the Ti–O and Ti–Nb system offer a good starting point to investigate the phase equilibrium in Ti–Nb–O ternary alloy. We also believe the insights provided here would be helpful for those who would like to seek an efficient scheme they are confident with to investigate the phase thermodynamic properties of other hcp Ti-based alloys. [ABSTRACT FROM AUTHOR]

Potential applications of previously synthesized pillared graphene oxide frameworks with phenyldiboronic acid linkers in the field of the adsorption and separation of polar protic and aprotic liquid solvents have been systematically explored using grand canonical Monte Carlo simulations. Particular attention was initially paid to the adsorption of pure liquid water, methanol, and dimethyl sulfoxide. The results obtained a significant increase in the isosteric heat of adsorption at low uptake in the case of dimethyl sulfoxide, which is about 17.9 and 26.8 kJ/mol higher than the values corresponding to methanol and water adsorption. These findings indicated that from a thermodynamic point of view, these pillared graphene oxide frameworks could be used in the separation of dimethyl sulfoxide–water liquid mixtures. Systematic grand canonical Monte Carlo simulations were then subsequently performed for dimethyl sulfoxide–water mixtures, with a low dimethyl sulfoxide concentration. The calculated values of the separation selectivity indicate that these materials could have potential applications in the separation of these mixed liquid solvents. Molecular dynamics simulations performed for a representative adsorbed mixture have revealed a substantial slowing down of the dynamics under confinement, particularly in the case of the hydrogen bonds formed between water and dimethyl sulfoxide. [ABSTRACT FROM AUTHOR]

This study introduces recommendations for conducting molecular simulations of vapor adsorption, with an emphasis on enhancing the accuracy, reproducibility, and comparability of results. The first aspect we address is consistency in the implementation of some details of typical molecular models, including tail corrections and cutoff distances, due to their significant influence on generated data. We highlight the importance of explicitly calculating the saturation pressures at relevant temperatures using methods such as Gibbs ensemble Monte Carlo simulations and illustrate some pitfalls in extrapolating saturation pressures using this method. For grand canonical Monte Carlo (GCMC) simulations, the input fugacity is usually calculated using an equation of state, which often requires the critical parameters of the fluid. We show the importance of using critical parameters derived from the simulation with the same model to ensure internal consistency between the simulated explicit adsorbate phase and the implicit bulk phase in GCMC. We show the advantages of presenting isotherms on a relative pressure scale to facilitate easier comparison among models and with experiment. Extending these guidelines to a practical case study, we evaluate the performance of various isoreticular metal–organic frameworks (MOFs) in adsorption cooling applications. This includes examining the advantages of using propane and isobutane as working fluids and identifying MOFs with a superior performance. [ABSTRACT FROM AUTHOR]

The escape dynamics of sticky particles from textured surfaces is poorly understood despite importance to various scientific and technological domains. In this work, we address this challenge by investigating the escape time of adsorbates from prevalent surface topographies, including holes/pits, pillars, and grooves. Analytical expressions for the probability density function and the mean of the escape time are derived. A particularly interesting scenario is that of very deep and narrow confining spaces within the surface. In this case, the joint effect of the entrapment and stickiness prolongs the escape time, resulting in an effective desorption rate that is dramatically lower than that of the untextured surface. This rate is shown to abide a universal scaling law, which couples the equilibrium constants of adsorption with the relevant confining length scales. While our results are analytical and exact, we also present an approximation for deep and narrow cavities based on an effective description of one-dimensional diffusion that is punctuated by motionless adsorption events. This simple and physically motivated approximation provides high-accuracy predictions within its range of validity and works relatively well even for cavities of intermediate depth. All theoretical results are corroborated with extensive Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

A Fourier–Matsubara series expansion is derived for imaginary–time correlation functions that constitutes the imaginary–time generalization of the infinite Matsubara series for equal-time correlation functions. The expansion is consistent with all known exact properties of imaginary–time correlation functions and opens up new avenues for the utilization of quantum Monte Carlo simulation data. Moreover, the expansion drastically simplifies the computation of imaginary–time density–density correlation functions with the finite temperature version of the self-consistent dielectric formalism. Its existence underscores the utility of imaginary–time as a complementary domain for many-body physics. [ABSTRACT FROM AUTHOR]

Molecular-based equations of state for describing the thermodynamics of chain molecules are often based on mean-field like arguments that reduce the problem of describing the interactions between chains to a simpler one involving only nonbonded monomers. While for dense liquids such arguments are known to work well, at low density they are typically less appropriate due to an incomplete description of the effect of chain connectivity on the local environment of the chains' monomer segments. To address this issue, we develop three semi-empirical approaches that significantly improve the thermodynamic description of chain molecules at low density. The approaches are developed for chain molecules with repulsive intermolecular forces; therefore, they could be used as reference models for developing equations of the state of real fluids based on perturbation theory. All three approaches are extensions of Wertheim's first-order thermodynamic perturbation theory (TPT1) for polymerization. The first model, referred to as TPT1-v, incorporates a second-virial correction that is scaled to zero at liquid-like densities. The second model, referred to as TPT1-y, introduces a Helmholtz-energy contribution to account for correlations between next-nearest-neighbor segments within chain molecules. The third approach, called TPT-E, directly modifies TPT1 without utilizing an additional Helmholtz energy contribution. By employing TPT1 at the core of these approaches, we ensure an accurate description of mixtures and enable a seamless extension from chains of tangentially bonded hard-sphere segments of equal size to hetero-segmented chains, fused chains, and chains of soft repulsive segments (which are influenced by temperature). The low-density corrections implemented in TPT1 are designed to preserve these good characteristics, as confirmed through comparisons with novel molecular simulation results for the pressure of various chain fluids. TPT1-v exhibits excellent transferability across different chain types, but it relies on knowing the second virial coefficient of the chain molecules, which is non-trivial to obtain and determined here using Monte Carlo simulation. The TPT1-y model, on the other hand, achieves comparable accuracy to TPT1-v while being fully predictive, requiring no input besides the geometry of the chain molecules. [ABSTRACT FROM AUTHOR]

A dynamic Monte Carlo (MC) method recently proposed by us [Nagai et al., J. Chem. Phys. 156, 154506 (2022)] to describe single-particle diffusion of a molecule in a heterogeneous space with position-dependent diffusion coefficient and free energy is generalized here to n-particle dynamics, where n molecules diffuse in heterogeneous media interacting via their intermolecular potential. Starting from the master equation, we give an algebraic proof that the dynamic MC transition probabilities proposed here produce particle trajectories that satisfy the n-particle diffusion equation with position-dependent diffusion coefficient D 0 i ( r i ) , free energy F 1 i ( r i ) , and intermolecular interactions Vij(ri, rj). The MC calculations based on this method are compared to molecular dynamics (MD) calculations for two-dimensional heterogeneous Lennard-Jones test systems, showing excellent agreement of the long-distance global diffusion coefficient between the two cases. Thus, the particle trajectories produced by the present MC transition probabilities satisfy the n-particle diffusion equation, and the diffusion equation well describes the long-distance trajectories produced by the MD calculations. The method is also an extension of the conventional equilibrium Metropolis MC calculation for homogeneous systems with a constant diffusion coefficient to the dynamics in heterogeneous systems with a position-dependent diffusion coefficient and potential. In the present method, interactions and dynamics of the real systems are coarse-grained such that the calculation cost is drastically reduced. This provides an approach for the investigation of particle dynamics in very complex and large systems, where the diffusing length is of sub-micrometer order and the diffusion time is of the order of milliseconds or more. [ABSTRACT FROM AUTHOR]

As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate ab initio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing us to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective, we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid–liquid interfaces. [ABSTRACT FROM AUTHOR]

The focus of our study is an in-depth investigation of the quantum effects associated with the surface tension and other thermodynamic properties of nanoscopic liquid drops. The behavior of drops of quantum Lennard-Jones fluids is investigated with path-integral Monte Carlo simulations, and the test-area method is used to determine the surface tension of the spherical vapor–liquid interface. As the thermal de Broglie wavelength, λB, becomes more significant, the average density of the liquid drop decreases, with the drop becoming mechanically unstable at large wavelengths. As a consequence, the surface tension is found to decrease monotonically with λB, vanishing altogether for dominant quantum interactions. Quantum effects can be significant, leading to values that are notably lower than the classical thermodynamic limit, particularly for smaller drops. For planar interfaces (with infinite periodicity in the direction parallel to the interface), quantum effects are much less significant with the same values of λB but are, nevertheless, consequential for values representative of hydrogen or helium-4 at low temperatures corresponding to vapor–liquid coexistence. Large quantum effects are found for small drops of molecules with quantum interactions corresponding to water, ethane, methanol, and carbon dioxide, even at ambient conditions. The notable decrease in the density and tension has important consequences in reducing the Gibbs free-energy barrier of a nucleating cluster, enhancing the nucleation kinetics of liquid drops and of bubble formation. This implies that drops would form at a much greater rate than is predicted by classical nucleation theory. [ABSTRACT FROM AUTHOR]

We present extensive new ab initio path integral Monte Carlo (PIMC) results for a variety of structural properties of warm dense hydrogen and beryllium. To deal with the fermion sign problem—an exponential computational bottleneck due to the antisymmetry of the electronic thermal density matrix—we employ the recently proposed [Y. Xiong and H. Xiong, J. Chem. Phys. 157, 094112 (2022); T. Dornheim et al., J. Chem. Phys. 159, 164113 (2023)] ξ-extrapolation method and find excellent agreement with the exact direct PIMC reference data where available. This opens up the intriguing possibility of studying a gamut of properties of light elements and potentially material mixtures over a substantial part of the warm dense matter regime, with direct relevance for astrophysics, material science, and inertial confinement fusion research. [ABSTRACT FROM AUTHOR]

Measurements of local density fluctuations are crucial to characterizing the interfacial properties of equilibrium fluids. A specific case that has been well-explored involves the heightened compressibility of water near hydrophobic entities. Commonly, a spatial profile of local fluctuation strength is constructed from the measurements of the mean and variance of solvent particle number fluctuations in a set of contiguous subvolumes of the system adjacent to the solvo-/hydrophobe. An alternative measure proposed by Evans and Stewart [J. Phys.: Condens. Matter 27, 194111 (2015)] defines a local compressibility profile in terms of the chemical potential derivative of the spatial number density profile. Using Grand canonical Monte Carlo simulation, we compare and contrast the efficacy of these two approaches for a Lennard-Jones solvent at spherical and planar solvophobic interfaces and SPC/E water at a hydrophobic spherical solute. Our principal findings are as follows: (i) the local compressibility profile χ(r) of Evans and Stewart is considerably more sensitive to variations in the strength of local density fluctuations than the spatial fluctuation profile F (r) and can resolve much more detailed structure; and (ii) while the local compressibility profile is essentially independent of the choice of spatial discretization used to construct the profile, the spatial fluctuation profile exhibits a strong systematic dependence on the size of the subvolumes on which the profile is defined. We clarify the origin and nature of this finite-size effect. [ABSTRACT FROM AUTHOR]

Among lattice configurations of densely packed hard ellipses, Monte Carlo simulations are used to identify the so-called parallel and diagonal lattices as the two favorable states. The free energies of these two states are computed for several system sizes employing the Einstein crystal method. An accurate calculation of the free energy difference between the two states reveals the parallel lattice as the state with the lowest free energy. The origin of the entropic difference between the two states is further elucidated by assessing the roles of the translational and rotational degrees of freedom. [ABSTRACT FROM AUTHOR]

Refractory high-entropy alloys are under consideration for applications where materials are subjected to high temperatures and levels of radiation, such as in the fusion power sector. However, at present, their scope is limited because they are highly brittle at room temperature. One suggested route to mitigate this issue is by alloying with Ti. In this theoretical study, using a computationally efficient linear-response theory based on density functional theory calculations of the electronic structure of the disordered alloys, we study the nature of atomic short-range order in these multi-component materials, as well as assessing their overall phase stability. Our analysis enables direct inference of phase transitions in addition to the extraction of an atomistic, pairwise model of the internal energy of an alloy suitable for study via, e.g., Monte Carlo simulations. Once Ti is added into either the NbMoTaW or VNbMoTaW system, we find that there is competition between chemical phase ordering and segregation. These results shed light on observed chemical inhomogeneity in experimental samples, as well as providing fundamental insight into the physics of these complex systems. [ABSTRACT FROM AUTHOR]

Statistical monitoring of warranty claims data using dynamic probability control limits has been shown to be effective in early detection of unforeseen reliability problems that emerge at the design and manufacturing phases. As the discrepancy between abnormal patterns and the normal pattern in aggregate warranty claims is usually small (especially at the early stage), we develop two new dynamic monitoring schemes that adopt CUSUM-type and EWMA-type statistics, named DyCUSUM and DyEWMA, respectively, to better address the warranty claims monitoring problem. Three effective algorithms – that is, the Monte Carlo simulation, Markov chain, and near-enumeration algorithms – are proposed to progressively determine control limits for the two schemes. In particular, comparison studies show that the near-enumeration algorithm can attain a higher approximation accuracy with a lower computational burden and is thus recommended. In-depth simulation experiments are then conducted to assess the performance of the schemes. We find that the DyEWMA scheme has superior and robust detection performance in various situations, whereas the DyCUSUM scheme is less effective and could even be ineffective in certain cases, compared with a Shewhart-type counterpart. Some specific suggestions are also provided to facilitate implementation of the proposed monitoring schemes. Improved schemes by combining the moving window approach to mitigate the 'inertia' problem is further discussed. Finally, a real case study is presented. [ABSTRACT FROM AUTHOR]

Scaling is common in empirical accounting research. It is often done to mitigate heteroscedasticity or the influence of firm size on parameter estimates. However, Barth and Clinch conclude that common diagnostic tools are ineffective in detecting various scale effects. Using analytic results and Monte Carlo simulations, we show that common forms of scaling, when misapplied, induce substantial spurious correlation via biased parameter estimates. Researchers, when uncertain about the exact functional form of scale effect, are typically better off dealing with both heteroscedasticity and the influence of larger firms using techniques other than scaling. [ABSTRACT FROM AUTHOR]

It is argued that the Binding Debye–Hückel (BiDH) model proposed by Naseri Boroujeni et al. [J. Chem. Phys. 159, 154503 (2023)] might not be appropriate for the description of Monte Carlo simulation data obtained for primitive model electrolytes. The first reason is that the original Debye–Hückel (DH) theory is of low accuracy for describing deviations from ideality in concentrated solutions of strong salts. The DH framework is thus a poor basis for building a model including association. The second reason is that the mean-spherical approximation, without assumption of association, apparently predicts Monte Carlo (MC) data for primitive electrolytes better than BiDH. Thus, the BiDH model seems to be simply a way of compensating for the deficiencies of DH theory by assuming association. [ABSTRACT FROM AUTHOR]

We present a new method to measure sub-microcurie activities of photon-emitting radionuclides in organs and lesions of small animals in vivo. Our technique, named the collimator-less likelihood fit, combines a very high sensitivity collimatorless detector with a Monte Carlo-based likelihood fit in order to estimate the activities in previously segmented regions of interest along with their uncertainties. This is done directly from the photon projections in our collimatorless detector and from the region of interest segmentation provided by an x-ray computed tomography scan. We have extensively validated our approach with 225Ac experimentally in spherical phantoms and mouse phantoms, and also numerically with simulations of a realistic mouse anatomy. Our method yields statistically unbiased results with uncertainties smaller than 20% for activities as low as ~111Bq (3nCi) and for exposures under 30 minutes. We demonstrate that our method yields more robust recovery coefficients when compared to SPECT imaging with a commercial pre-clinical scanner, specially at very low activities. Thus, our technique is complementary to traditional SPECT/CT imaging since it provides a more accurate and precise organ and tumor dosimetry, with a more limited spatial information. Finally, our technique is specially significant in extremely low-activity scenarios when SPECT/CT imaging is simply not viable.

BACKGROUND: Exposure to climate change events like wildfires can lead to health and mental health problems. While conceptual frameworks have been hypothesized describing the potential relationship between disaster exposure and substance use, the association remains under-researched and unquantified. METHODS: We constructed a quantitative portrayal of one proposed conceptual framework that focuses on the intermediary role of anxiety. We used the Monte Carlo simulation to estimate the impact of wildfire exposure on opioid misuse outcomes through increased anxiety. We searched for and extracted prior empirical evidence on the associations between wildfire anxiety and anxiety-opioid misuse. Three scenarios were devised: in S1 the impact of wildfire on opioid misuse was limited to increasing anxiety incidence; in S2 we also considered the additive role of altered anxiety phenotype; and in S3 we further considered the role of increased opioid-related consequences of pre-existing anxiety due to wildfire exposure. RESULTS: Models show that the prevalence of opioid misuse post-wildfire may rise to 6.0%-7.2% from a baseline of 5.3%. In S1, the opioid misuse prevalence ratio was 1.12 (95% uncertainty interval [UI]: 1.00 - 1.27). The two exploratory scenarios, with less stringent assumptions, yielded prevalence ratios of 1.23 (95% UI: 1.00 - 1.51) and 1.34 (95% UI: 1.11 - 1.63). CONCLUSIONS: Our modeling study suggests that exposure to wildfires may elevate opioid misuse through increasing anxiety incidence and severity. This can lead to substantial health burdens, possibly beyond the duration of the wildfire event, which may offset recent gains in opioid misuse prevention.