A hybrid molecular dynamics (MD)/kinetic Monte Carlo (KMC) model is developed for atomistic modeling of fluorine ion implantation and diffusion in AlGaN/GaN heterostructures. The MD simulation reveals the F distribution profiles and the corresponding defect profiles, and most importantly, the potential energies of fluorine ions in the III-nitride material system. Using the results from the MD simulation, the diffusion process is simulated with KMC method, and the modeling results are validated by the secondary-ion-mass-spectrum (SIMS) measurement. The surface effect on the fluorine's stability and its improvement by passivation are also successfully modeled.
We use an electrostatic model to study the average kinetic energy of ions ejected from the pure Coulomb explosions of methane clusters (CA4)n (light atom A=H and D). It is found that the ratio of the average kinetic energy of the ions to their initial average electrostatic potential energy is irrelevant to the cluster size. This finding implies that as long as the ratio is given, the average kinetic energies of the ions can be simply estimated from their initial average electrostatic potential energies, rather than from the time-consuming simulations. The ratios for the different charge states of carbon ions are presented.
Urban air quality is subject to the increasing pressure of urbanization, and, consequently. the potentia I impact of air quality changes must be addressed. A Bayesian hierarchical model was developed in this paper for urban air quality predication. Literature data on three pollutants and four external driving factors in Xiamen City, China, were studied. The air quality model structure and prior distributions of model parameters were determined by multivariate statistical methods, including correlation analysis, classification and regression trees (CART), hierarchical cluster analysis (CA), and discriminant analysis (DA). A multiple linear regression (MLR) equation was proposed to measure the relationship between pollutant concentrations and driving variables; and Bayesian hierarchical model was introduced for parameters estimation and uncertainty analysis. Model fit between the observed data and the modeled values was demonstrated, with mean and median values and two credible levels (2.5% and 97.5%). The average relative errors between the observed data and the mean values of SO(2), NO(x), and dust fall were 6.81%, 6.79%, and 3.52%, respectively. (c) 2008 Elsevier Ltd. All rights reserved.