Calculation of optical trapping landscapes

Manipulation of micrometer sized particles with optical tweezers can be precisely modeled with electrodynamic theory using Mie's solution for spherical particles or the T-matrix method for more complex objects. We model optical tweezers for a wide range of parameters including size, relative refractive index and objective numerical aperture. We present the resulting landscapes of the trap stiffness and maximum applicable trapping force in the parameter space. These landscapes give a detailed insight into the requirements and possibilities of optical trapping and provide detailed information on trapping of nanometer sized particles or trapping of high index particles like diamond.

Ab initio studies of hydrogen desorption from low index magnesium hydride surface

The low index Magnesium hydride surfaces, MgH2(001) and MgH2(110), have been studied by ab intio Density Functional Theory (DFT) calculations. It was found that the MgH2(110) surface is more stable than MgH2(001) surface, which is in good agreement with the experimental observation. The H-2 desorption barriers vary depending on the crystalline surfaces that are exposed and also the specific H atom sites involved-they are found to be generally high, due to the thermodynamic stability of the MgH2, system, and are larger for the MgH2(001) surface. The pathway for recombinative desorption of one in-plane and one bridging H atom from the MgH2(110) surface was found to be the lowest energy barrier amongst those computed (172 KJ/mol) and is in good agreement with the experimental estimates. (c) 2006 Elsevier B.V. All rights reserved.

BM+-tree: A hyperplane-based index method for high-dimensional metric spaces

In this paper, we propose a novel high-dimensional index method, the BM+-tree, to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a rotary binary hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the M+-tree. Compared with the key dimension concept in the M+-tree, the binary hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Experimental results using two types of real data sets illustrate a significantly improved filtering efficiency.