Two-dimensional Monte Carlo Model Research Articles

Probabilistic modeling and risk characterization of the chronic aflatoxin M1 exposure of Hungarian consumers.

Aflatoxin contamination can appear in various points of the food chain. If animals are fed with contaminated feed, AFB1 is transformed—among others—to aflatoxin M1 (AFM1) metabolite. AFM1 is less toxic than AFB1, but it is still genotoxic and carcinogenic and it is present in raw and processed milk and all kinds of milk products. In this article, the chronic exposure estimation and risk characterization of Hungarian consumers are presented, based on the AFM1 contamination of milk and dairy products, and calculated with a probabilistic method, the two-dimensional Monte-Carlo model. The calculations were performed using the R plugin (mc2d package) integrated into the KNIME (Konstanz Information Miner) software. The simulations were performed using data from the 2018–2020 food consumption survey. The AFM1 analytical data were derived from the Hungarian monitoring survey and 1,985 milk samples were analyzed within the framework of the joint project of the University of Debrecen and the National Food Chain Safety Office of Hungary (NÉBIH). Limited AFM1 concentrations were available for processed dairy products; therefore, a database of AFM1 processing factors for sour milk products and various cheeses was produced based on the latest literature data, and consumer exposure was calculated with the milk equivalent of the consumed quantities of these products. For risk characterization, the calculation of hazard index (HI), Margin of Exposure, and the hepatocellular carcinoma incidence were used. The results indicate that the group of toddlers that consume a large amount of milk and milk products are exposed to a certain level of health risk. The mean estimated daily intake of toddlers is in the range of 0.008–0.221 ng kg−1 bw day−1; the 97.5th percentile exposure of toddlers is between 0.013 ng kg−1 bw day−1 and 0.379 ng kg−1 bw day−1, resulting in a HI above 1. According to our study, the exposure of older age groups does not pose an emergent health risk. Nevertheless, the presence of carcinogenic compounds should be kept to a minimum in the whole population.

A two-dimensional Monte Carlo model for pore densification in a bi-crystal via grain boundary diffusion: Effect of diffusion rate, initial pore distance, temperature, boundary energy and number of pores

A two-dimensional Monte Carlo (MC) model is introduced for simulating the evolution of the pore on a bi-crystal grain boundary via grain boundary diffusion. Simulated pore shrinkage kinetics is found to be consistent with previously reported results over variable grain boundary diffusion rates and initial pore distances while the essential characteristics of the microstructural evolution are simultaneously realized. The influence on the pore densification kinetics of grain boundary motion, boundary energy ratio, simulation temperature and pore interactions in an array is found such that pore shrinkage rate increases as the grain boundary motion, the simulation temperature and the grain boundary energy increase. The interactions of the pores are found to hinder the pore densification. The body of results signify that the more elongated the pore shape and the shaper the pore tip region are favored for the faster pore shrinkage kinetics during the simulated densification process via grain boundary diffusion.

Computer simulation of microstructure evolution in strip-cast silicon steel with cube and fibre texture

ABSTRACTThin strip casting process which is already used for non-oriented grades of electrical steel, is relatively novel processing route for grain oriented electrical steel and to achieve the cube texture through suitable annealing. In the present investigation, a two-dimensional Monte Carlo model has been formulated to study texture evolution in strip-cast and annealed Fe–3.2%Si steel with a cube texture. Both random and non-random microstructures of Fe–3.2% silicon steel and corresponding Euler angles have been used as input parameters. Results obtained from the simulation characterised the evolving texture in terms of grain size distribution and misorientation distribution functions. It has been found that randomly textured microstructures follow power law growth kinetics with evolving grain size distribution similar to normal grain growth. Microstructures with random orientation texture evolve steady state misorientation distribution functions, while those with fibre texture narrows and shifts to low angles. The simulation results based on EBSD microstructure inputs have been compared and validated with microstructures obtained from computationally generated input microstructures as well as published literature for both random and non-random orientation texture and found to be in good agreement.

Monte Carlo simulation of column growth in plasma spray physical vapor deposition process

Plasma spray-physical vapor deposition is used to produce columnar microstructure coatings under particular operating parameters. Simulations of the growth of columns were carried out through a two-dimensional Monte Carlo model. The modeling was performed using inclined vapor flux impinging onto a substrate due to shadowing effects. An incoming particle travels along a straight line and attaches itself to already deposited particles. Furthermore, the newly deposited particle will relax to a stable surrounding position along the incoming velocity direction. The modeling results predicted the linking of an oblique vapor flux and column orientation. The numerical simulations were validated in three ways. Firstly, the porosity of simulated columns was predicted and compared to that obtained in the experimental columnar microstructure of coatings. Secondly, the morphology of simulated structures is compared to that of experimental coatings produced by plasma spray physical vapor deposition. Finally, the simulated orientation of columns is compared to the experimental one.

Percolating transport and the conductive scaling relationship in lamellar block copolymers under confinement.

The topology and transport behavior of the lamellar morphology self-assembled by block copolymers in thin films are shown to depend on the length scale over which they are characterized and can be described by percolation in a network under confinement. Gold nanowires replicating the lamellar morphology were fabricated via self-assembled poly(styrene-block-methyl methacrylate) thin films and a lift-off pattern transfer process. The lamellar morphology exhibits long-range connectivity (macroscopic scale); however, characterization of electrical conduction over confined areas (5-500 μm) demonstrates a discrete probability of disconnection that arises due to the underlying network structure and a lack of self-similarity at these microscale dimensions. In particular, it is proved that the lamellar network morphology under confinement has a conductance that is nonlinear with channel length or width. The experimental results are discussed in terms of percolation theory, and a simple, two-dimensional Monte Carlo model is shown to predict the key trends in the network topology and conductance in lamellar block copolymers, including the dependencies on composition, extent of spatial confinement, and confinement geometry. These results highlight the need to exquisitely control or engineer the self-assembled nanostructured pathways formed by block copolymers to ensure consistent device performance for any application that depends upon percolating material, ionic, or electrical transport, especially when confined in any dimension. It is also concluded that the two most promising approaches for enhancing conductivity in block copolymer materials may be achieved either at the limits of (1) perfectly oriented, single-crystalline or (2) high defect density, polycrystalline microphase separated morphologies and that nanostructured systems with intermediate defect densities would be detrimental to transport in confined systems.

Simulation of eutectic solidification structures of binary alloys: a multiparticle diffusion limited aggregation model

The (isothermal) eutectic solidification structures of binary alloys have been simulated using a two-dimensional monte carlo model. The model takes into account simultaneous diffusion of all liquid atoms, incorporates the energy contributions of like and unlike nearest and next-nearest neighbor bonds in the solid on solidification, and allows surface rearrangement at the solid-liquid interface. It has been demonstrated that results obtained from this model exhibit phenomena observed during eutectic solidification in practice, for example, the passage of the growth rate through a maximum upon decreasing temperature. Particular attention has been devoted to the effect of growth parameters, such as the next nearest neighbor interaction in the solid and the surface relaxtion length, on the morphology of the growing solid phase.

A two-dimensional Monte Carlo model for thin film growth by oblique evaporation: simulation of two-component systems for the example of CoCr

Starting from the well-known idea of atomic shadowing during thin film growth ( i.e. ballistic aggregation) a two-dimensional computer model is developed which includes surface relaxation by thermally induced surface diffusion and atomic attraction for a two-component system. An attempt was made to link the model interaction energies to real bonding energies in CoCr thin films. The model was applied to investigate structural as well as local configuration changes in the aggregates during systematic parameter variations.