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Nanostructured tin (Sn) is a promising high-capacity electrode for improved performance in lithium-ion batteries for electric vehicles. In this work, Sn nanoisland growth for nanostructured electrodes assisted by the pulse laser irradiation has been investigated based on a mesoscale modeling formalism. The influence of pertinent processing conditions, such as pulse duration, heating/cooling rates, and atom flux, on the Sn nanostructure formation is specifically considered. The interaction between the adsorbed atom and the substrate, represented by the adatom diffusion barrier, is carefully studied. It is found that the diffusion barrier predominantly affects the distribution of Sn atoms. For both α-Sn and β-Sn, the averaged coordination number is larger than 3 when the diffusion barrier equals to 0.15 eV. The averaged coordination number decreases as the diffusion barrier increases. The substrate temperature, which is determined by heating/cooling rates and pulse duration, can also affect the formation of Sn nanoislands. For α-Sn, when applied low heating/cooling rates, nanoislands cannot form if the diffusion barrier is larger than 0.35 eV. [ABSTRACT FROM AUTHOR]

The modelling of habitat suitability and the prediction of the spatial distribution of species are important for the conservation of endangered species. In this paper, the evaluation of habitat suitability of the Chinese horseshoe bat (Rhinolophus sinicus) in the Wuling Mountains was studied. The global positioning system (GPS), remote sensing (RS) and geographic information system (GIS) were used to obtain ground survey data and analyse the habitat factors driving the distribution of R. sinicus. The binary logistic regression model and the Maximum Entropy Model (MAXENT model) were used to analyse the habitat suitability of R. sinicus, and the receiver operating characteristic (ROC) curve was used to evaluate the models. The results showed that both models had high prediction accuracy, but the results of the MAXENT model were more stable than those of the binary logistic regression model when the sample size changed, indicating that the MAXENT model was the optimal method. Thena jack-knife test was used to determine the importance of the influencing factors. The results showed that the main factors influencing habitat selection were annual mean temperature, annual precipitation, the nearest distance from a settlement and vegetation fractional coverage (VFC). The suitable habitat was mainly distributed in the middle region of the study area. Marginally suitable habitat was located on the edge of suitable habitat, and unsuitable habitats were mainly distributed in the southern and northwestern regions. [ABSTRACT FROM AUTHOR]