kmos: A lattice kinetic Monte Carlo framework

Kinetic Monte Carlo (kMC) simulations have emerged as a key tool for microkinetic modeling in heterogeneous catalysis and other materials applications. Systems, where site-specificity of all elementary reactions allows a mapping onto a lattice of discrete active sites, can be addressed within the particularly efficient lattice kMC approach. To this end we describe the versatile kmos software package, which offers a most user-friendly implementation, execution, and evaluation of lattice kMC models of arbitrary complexity in one- to three-dimensional lattice systems, involving multiple active sites in periodic or aperiodic arrangements, as well as site-resolved pairwise and higher-order lateral interactions. Conceptually, kmos achieves a maximum runtime performance which is essentially independent of lattice size by generating code for the efficiency-determining local update of available events that is optimized for a defined kMC model. For this model definition and the control of all runtime and evaluation aspects kmos offers a high-level application programming interface. Usage proceeds interactively, via scripts, or a graphical user interface, which visualizes the model geometry, the lattice occupations and rates of selected elementary reactions, while allowing on-the-fly changes of simulation parameters. We demonstrate the performance and scaling of kmos with the application to kMC models for surface catalytic processes, where for given operation conditions (temperature and partial pressures of all reactants) central simulation outcomes are catalytic activity and selectivities, surface composition, and mechanistic insight into the occurrence of individual elementary processes in the reaction network.Comment: 21 pages, 12 figure

First-Principles Approach to Heat and Mass Transfer Effects in Model Catalyst Studies

We assess heat and mass transfer limitations in in situ studies of model catalysts with a first-principles based multiscale modeling approach that integrates a detailed description of the surface reaction chemistry and the macro-scale flow structures. Using the CO oxidation at RuO2(110) as a prototypical example we demonstrate that factors like a suppressed heat conduction at the backside of the thin single-crystal, and the build-up of a product boundary layer above the flat-faced surface play a significant role

Isospin fluctuations in spinodal decomposition

We study the isospin dynamics in fragment formation within the framework of an analytical model based on the spinodal decomposition scenario. We calculate the probability to obtain fragments with given charge and neutron number, focussing on the derivation of the width of the isotopic distributions. Within our approach this is determined by the dispersion of N/Z among the leading unstable modes, due to the competition between Coulomb and symmetry energy effects, and by isovector-like fluctuations present in the matter that undergoes the spinodal decomposition. Hence the widths exhibit a clear dependence on the properties of the Equation of State. By comparing two systems with different values of the charge asymmetry we find that the isotopic distributions reproduce an isoscaling relationship.Comment: 18 RevTex4 pages, 6 eps figure

Predictive-Quality Surface Reaction Chemistry in Real Reactor Models: Integrating First-Principles Kinetic Monte Carlo Simulations into Computational Fluid Dynamics

We present a numerical framework to integrate first-principles kinetic Monte Carlo (1p-kMC) based microkinetic models into the powerful computational fluid dynamics (CFD) package CatalyticFoam. This allows for the simultaneous account of a predictive-quality surface reaction kinetics inside an explicitly described catalytic reactor geometry. Crucial means toward an efficient and stable implementation are the exploitation of the disparate time scales of surface chemistry and gas-phase transport, as well as the reliable interpolation of irregularly gridded 1p-kMC data by means of an error-based modified Shepard approach. We illustrate the capabilities of the framework using the CO oxidation at Pd(100) and RuO2(110) model catalysts in different reactor configurations and fluid dynamic conditions as showcases. These showcases underscore both the necessity and value of having reliable treatments of the surface chemistry and flow inside integrated multiscale catalysis simulations when aiming at an atomic-scale understanding of the catalytic function in near-ambient environments. Our examples highlight how intricately this function is affected by specifics of the reactor geometry and heat dissipation channels on the one end, and on the other end by characteristics of the intrinsic catalytic activity that are only captured by treatments beyond prevalent mean-field rate equations

Spinodal decomposition of expanding nuclear matter and multifragmentation

Density fluctuations of expanding nuclear matter are studied within a mean-field model in which fluctuations are generated by an external stochastic field. Fluctuations develop about a mean one-body phase-space density corresponding to a hydrodinamic motion that describes a slow expansion of the system. A fluctuation-dissipation relation suitable for a uniformly expanding medium is obtained and used to constrain the strength of the stochastic field. The distribution of the liquid domains in the spinodal decomposition is derived. Comparison of the related distribution of the fragment size with experimental data on the nuclear multifragmentation is quite satisfactory.Comment: 19 RevTex4 pages, 6 eps figures, to appear in Phys. Rev.

A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm's applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained. The results demonstrate the algorithm's vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal

Eco-Sustainable Energy Production in Healthcare: Trends and Challenges in Renewable Energy Systems

The shift from fossil fuels to renewable energy systems represents a pivotal step toward the realization of a sustainable society. This study aims to analyze representative scientific literature on eco-sustainable energy production in the healthcare sector, particularly in hospitals. Given hospitals’ substantial electricity consumption, the adoption of renewable energy offers a reliable, low-CO2 emission solution. The COVID-19 pandemic has underscored the urgency for energy-efficient and environmentally-responsible approaches. This brief review analyzes the development of experimental, simulation, and optimization projects for sustainable energy production in healthcare facilities. The analysis reveals trends and challenges in renewable energy systems, offering valuable insights into the potential of eco-sustainable solutions in the healthcare sector. The findings indicate that hydrogen storage systems are consistently coupled with photovoltaic panels or solar collectors, but only 14% of the analyzed studies explore this potential within hospital settings. Hybrid renewable energy systems (HRES) could be used to meet the energy demands of healthcare centers and hospitals. However, the integration of HRES in hospitals and medical buildings is understudied

Impact of pairing on clustering and neutrino transport properties in low-density stellar matter

We analyze the effects of pairing correlations on the behavior of stellar matter, focusing on thermodynamical conditions close to the onset of the liquid–gas phase transition, which are characterized by quite large density fluctuations and where clustering phenomena occur. We concentrate on the neutrino transport properties and we show, within a thermodynamical treatment, that at moderate temperatures, where pairing effects are still active, the scattering of neutrinos in the nuclear medium is significantly affected by the matter superfluidity. The pairing correlations can indeed enhance neutrino trapping and reduce the energy flux carried out by neutrino emission. New hints about an important impact of pairing on the cooling mechanism, by neutrino emission, are so envisaged and therefore this study could be of relevant interest for the evolution of proto-neutron stars and in modelization of supernova explosions

Role of folic acid depletion on homocysteine serum level inchildren and adolescents with epilepsy and different MTHFR C677T genotypes.

Homocysteine (Hcy) is a sulfur-containing amino acid involved in methionine metabolism. An elevated total plasma Hcy concentration (tHcy) is a risk factor for vascular disease. The present study aimed to assess the role of antiepileptic drugs (AEDs) and C677T methylenetetrahydrofolate (MTHFR) polymorphisms on tHcy in pediatric patients with epilepsy treated for at least 6 months with various treatment regimens protocols including the newer AEDs. The study group was recruited from children and adolescents with epilepsy followed up in the Child Neuropsychiatry Clinic of the Second University of Naples, between January 2007 and March 2008. Inclusion criteria were: (1) patients with epilepsy, treated with one or more anticonvulsant drugs for at least 6 months; (2) age between 2 and 16 years. Plasma tHcy concentrations were considered elevated when they exceeded 10.4 mmol/L, and folate concentrations <3 ng/mL were considered deficient. Serum vitamin B12 levels were considered normal between 230 and 1200 pg/mL. The study group was composed of 78 patients (35 males, 43 females), aged between 3 and 15 years (mean 8.9 years). Thirtyfive patients were taking AED monotherapy, 43 polytherapy. Sixty-three healthy sex- and age-matched children and adolescents served as controls. The mean tHcy value in the patient group was higher than the mean value in the control group (12.11 7.68 mmol/L vs 7.4 4.01 mmol/L; p < 0.01). DNA analysis for the MTHFR C677T polymorphism showed the CT genotype in 46%, CC in 35% and TT in 17.8% of cases. Decreased folic acid serum levels significantly correlated with increased tHcy levels (p < 0.003). Female sex was a less significant risk factor for increased tHcy levels (p = 0.039). Our study confirms the association between hyperhomocysteinemia and epilepsy. The elevation of tHcy is essentially related to low folate levels. Correction of poor folate status, through supplementation, remains the most effective approach to normalize tHcy levels in patients on AED mono- or polytherapy