Ground state study of simple atoms within a nano-scale box

Ground state energies for confined hydrogen (H) and helium (He) atoms, inside a penetrable/impenetrable compartment have been calculated using Diffusion Monte Carlo (DMC) method. Specifically, we have investigated spherical and ellipsoidal encompassing compartments of a few nanometer size. The potential is held fixed at a constant value on the surface of the compartment and beyond. The dependence of ground state energy on the geometrical characteristics of the compartment as well as the potential value on its surface has been thoroughly explored. In addition, we have investigated the cases where the nucleus location is off the geometrical centre of the compartment.Comment: 9 pages, 5 eps figures, Revte

Spin polarization effect in 2D and Q2D electron gas

We use the constructed spin-dependent static local field functions to calculate the plasmon dispersion of two dimensional spin polarized electron gas (2D SPEG) over a range of electron densities at arbitrarily spin polarization. We also investigate how the finite width of electron layer will affect the plasmon frequency and inverse static dielectric function of 2D SPEG. Our results show that the effect of finite thickness on plasmon dispersion and inverse dielectric function becomes considerable even at high densities in 2D SPEG

Improving Infiltration Prediction by Point-based PTFs for Some Soils in Southern of Iran

Abstract Modeling soil water infiltration at the field scale with ruler of calcareous, saline and sodic conditions is important for a better understanding of infiltration processes in these soils and future of infiltration modeling. The aim of the present study was to derive and evaluate soil water infiltration models for some calcareous, saline and sodic soils in Marvdasht plain, southern of Iran. The infiltration data was measured in 72 locations at the regional scale with 3 replications. In each location, the basic soil properties were also measured. The multiple linear regression (MLR) and feed-forward multilayer perceptron artificial neural networks (ANN) model were used to estimate cumulative soil water infiltration at different time. The results performance of water infiltration models such as Kostiakov, Kostiakov–Lewis, USDA-NRCS, Philip, Horton and Green-Ampt models according to the mean R2, ME, RMSE and SDRMSE indices for all soils showed the Kostiakov–Lewis model provided the most accurate predictions. Moreover, the results showed that the derived ANN models at different times with a R2 of 0.438-0.661 and a RMSE of 0.977-17.111 performed better than MLR model. There would be great interest to improve the cumulative soil water infiltration in site-specific soil utilization, management and protection of the environment by MLR and ANN methods.</jats:p