Number Of Diffraction Spots Research Articles

Diffraction-based automated crystal centering

A fully automated procedure for detecting and centering protein crystals in the X-ray beam of a macromolecular crystallography beamline has been developed. A cryo-loop centering routine that analyzes video images with an edge detection algorithm is first used to determine the dimensions of the loop holding the sample; then low-dose X-rays are used to record diffraction images in a grid over the edge and face plane of the loop. A three-dimensional profile of the crystal based on the number of diffraction spots in each image is constructed. The derived center of mass is then used to align the crystal to the X-ray beam. Typical samples can be accurately aligned in approximately 2-3 min. Because the procedure is based on the number of ;good' spots as determined by the program Spotfinder, the best diffracting part of the crystal is aligned to the X-ray beam.

3D grainmap generation with the algebraic reconstruction technique from 3DXRD data

In this paper the algebraic reconstruction technique is applied to X-ray diffraction data and some properties of the technique are explored. Three dimensional grainmaps generated by this procedure are obtained, indicating that it is indeed a practical procedure for generating grainmaps. Also presented are results implying that the grainmap quality may not be increased simply by increasing the number of diffraction spots included in the algorithm – in some cases, increasing the number of spots may even have a detrimental effect on the grainmap quality due to errors in determining the diffraction geometry being propagated through the algorithm.

Thermophoretic Collection and Analysis of Submicrometer Ag Particles Emitted from a Graphite Tube-Type Electrothermal Vaporizer

Using thermophoretic collection with a cooled Cu probe, particles generated in an electrothermal vaporizer (ETV) from a AgNO(3) solution sample have been collected and analyzed using transmission electron microscopy and high-energy electron diffraction (HEED). Ag particles are spherical and, interestingly, have diameters falling into one of four size regimes, <5, 15, 25, and >40 nm. An extremely large number (estimated at >10(8)) of particles with diameters of <100 nm are collected. For an aqueous AgNO(3) solution deposited in the ETV, the HEED pattern of the collected aerosol particles exiting the pulse heated ETV matched body centered cubic Ag((s)), and the large number of diffraction spots suggests that particles are composed of microcrystalline domains. The features of the particles confirm earlier predictions of homonucleation as the primary particle formation mechanism. "Groupings" of apparently disconnected particles were a unique feature seen from the pulse-heated vaporization of the dried sample. This morphology is unique and does not appear like any other clustering or aggregation of particles reported elsewhere. It is not clear what causes this particle grouping, although the absence of these groupings when a 700 °C thermal pretreatment step was introduced suggests that the AgNO(3) decomposition plays a role in their formation. It is suggested that the particles formed from homonucleation are created very near the graphite surface and are cooled quite rapidly to form the solid silver particles. A mechanism is presented to explain the appearance of silver in the gas phase at temperatures below the vaporization temperature for silver metal.

The influence of the divergence of X-rays on the determination of volume of diffracting particles

The relation was investigated of the number of diffraction spots from unit volume of a polycrystal to the mean instrumental divergenceα m of X-ray beam. Experimental evidence proves this dependence to be decreasing, in contradiction to the generally accepted theoretical basis of the method. These experimental results mean an increasing effective volume of diffracting particles with increasing instrumental divergence. Thus the application of the quantityα m itself distorts the true dependence of number of spots on instrumental divergence. We shall prove this statement and shall apply the previous experimental results to infer more detailed ideas on the mentioned dependence, while using the instrumental divergenceα instead of the mean valueα m .