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Electron Diffraction Data (electron + diffraction_data)
Selected AbstractsSolving the crystal structures of zeolites using electron diffraction data.ACTA CRYSTALLOGRAPHICA SECTION A, Issue 2 2008The maximum-entropy and likelihood method for solving zeolite crystal structures from electron diffraction data is modified to use potential-map-density histograms as an additional figure of merit. The experimental histogram is compared to an idealized one (based on known zeolite structures) using Pearson and Spearman correlation coefficients. These supplement the use of log-likelihood estimates as figures of merit to select the optimal solution from a collection of phase sets. The method has been applied with success to seven zeolite and one inorganic crystal structures that have varying associated data quality. The technique works easily even with two-dimensional data sets of less than 50 unique diffraction data and a resolution of less than 2,Å. The method is very fast, and the computer time needed on a modest PC was never more than a few minutes. [source] Precession electron diffraction 1: multislice simulationACTA CRYSTALLOGRAPHICA SECTION A, Issue 6 2006C. S. Own Precession electron diffraction (PED) is a method that considerably reduces dynamical effects in electron diffraction data, potentially enabling more straightforward solution of structures using the transmission electron microscope. This study focuses upon the characterization of PED data in an effort to improve the understanding of how experimental parameters affect it in order to predict favorable conditions. A method for generating simulated PED data by the multislice method is presented and tested. Data simulated for a wide range of experimental parameters are analyzed and compared to experimental data for the (Ga,In)2SnO4 (GITO) and ZSM-5 zeolite (MFI) systems. Intensity deviations between normalized simulated and kinematical data sets, which are bipolar for dynamical diffraction data, become unipolar for PED data. Three-dimensional difference plots between PED and kinematical data sets show that PED data are most kinematical for small thicknesses, and as thickness increases deviations are minimized by increasing the precession cone semi-angle ,. Lorentz geometry and multibeam dynamical effects explain why the largest deviations cluster about the transmitted beam, and one-dimensional diffraction is pointed out as a strong mechanism for deviation along systematic rows. R factors for the experimental data sets are calculated, demonstrating that PED data are less sensitive to thickness variation. This error metric was also used to determine the experimental specimen thickness. R1 (unrefined) was found to be about 12 and 15% for GITO and MFI, respectively. [source] Structure of Ti2P solved by three-dimensional electron diffraction data collected with the precession technique and high-resolution electron microscopyACTA CRYSTALLOGRAPHICA SECTION A, Issue 2 2003Xiaodong Zou The crystal structure of Ti2P has been analysed using electron diffraction and high-resolution electron-microscopy techniques. A new unit cell was found, the compound is hexagonal with a = 19.969,(1) and c = 3.4589,(1),Å. The structure was first solved in space group in projection using direct methods on electron diffraction data from the [001] zone axis. A three-dimensional solution was obtained using again direct methods but on a three-dimensional set of electron diffraction data recorded with the precession technique. Ti2P is a distorted Fe2P structure and, based on high-resolution images, it is possible to explain that the tripling of the unit cell is due to the ordering of P vacancies that reduces the symmetry to . [source] Determining Molecular Structures and Conformations Directly from Electron Diffraction using a Genetic AlgorithmCHEMPHYSCHEM, Issue 2 2006Scott Habershon Dr. Abstract A global optimization strategy, based upon application of a genetic algorithm (GA), is demonstrated as an approach for determining the structures of molecules possessing significant conformational flexibility directly from gas-phase electron diffraction data. In contrast to the common approach to molecular structure determination, based on trial-and-error assessment of structures available from quantum chemical calculations, the GA approach described here does not require expensive quantum mechanical calculations or manual searching of the potential energy surface of the sample molecule, relying instead upon simple comparison between the experimental and calculated diffraction pattern derived from a proposed trial molecular structure. Structures as complex as all- trans retinal and p -coumaric acid, both important chromophores in photosensing processes, may be determined by this approach. In the examples presented here, we find that the GA approach can determine the correct conformation of a flexible molecule described by 11 independent torsion angles. We also demonstrate applications to samples comprising a mixture of two distinct molecular conformations. With these results we conclude that applications of this approach are very promising in elucidating the structures of large molecules directly from electron diffraction data. [source] | ||||||||||