Focal construct geometry – a novel approach to the acquisition of diffraction data

2010 ◽  
Vol 43 (2) ◽  
pp. 264-268 ◽  
Author(s):  
Keith Rogers ◽  
Paul Evans ◽  
Joseph Rogers ◽  
JerWang Chan ◽  
Anthony Dicken
Keyword(s):  
Diffraction Data ◽  
Experimental Verification ◽  
Single Point ◽  
Polycrystalline Materials ◽  
Pencil Beam ◽  
X Rays ◽  
Debye Ring ◽  
Novel Approach ◽  
Reflection Geometry ◽  
Point Detector

This paper presents the first use of a simple novel geometry that enables the measurement of diffractograms from polycrystalline materials through linear translation of a point detector. The geometry is such that intensities from all points around any Debye ring are summed to a single point, and thus coherently scattered X-rays are harvested efficiently. Data from initial experimental verification of the approach used in transmission mode are presented and the diffractograms compared with their equivalent measured using a pencil beam. Brief discussions of potential modifications in reflection geometry and applications for fibre samples are also provided.

scholarly journals Optimal Falling Track Design for Twice detonating Fuze of Double event Fuel air Explosive with High Speed

2020 ◽  
Vol 70 (4) ◽  
pp. 366-373
Author(s):  
Congliang Ye ◽  
Qi Zhang
Keyword(s):  
High Speed ◽  
High Efficiency ◽  
Single Point ◽  
Design System ◽  
Motion Trajectory ◽  
Novel Approach ◽  
Scale Experiment ◽  
Set Up ◽  
Point Center ◽  
Double Event

To prevent the initiation failure caused by the uncontrolled fuze and improve the weapon reliability in the high-speed double-event fuel-air explosive (DEFAE) application, it is necessary to study the TDF motion trajectory and set up a twice-detonating fuze (TDF) design system. Hence, a novel approach of realising the fixed single-point center initiation by TDF within the fuel air cloud is proposed. Accordingly, a computational model for the TDF motion state with the nonlinear mechanics analysis is built due to the expensive and difficult full-scale experiment. Moreover, the TDF guidance design system is programmed using MATLAB with the equations of mechanical equilibrium. In addition, by this system, influences of various input parameters on the TDF motion trajectory are studied in detail singly. Conclusively, the result of a certain TDF example indicates that this paper provides an economical idea for the TDF design, and the developed graphical user interface of high-efficiency for the weapon designers to facilitate the high-speed DEFAE missile development.

Micro Diffraction Imaging of Bulk Polycrystalline Materials

2006 ◽  
Vol 524-525 ◽  
pp. 273-278
Author(s):  
Thomas Wroblewski ◽  
A. Bjeoumikhov ◽  
Bernd Hasse
Keyword(s):  
High Spatial Resolution ◽  
Polycrystalline Materials ◽  
X Rays ◽  
X Ray Diffraction ◽  
Short Sample ◽  
X Ray ◽  
Detector Distance ◽  
Transmission Geometry ◽  
Diffraction Imaging ◽  
Position Sensitive

X-ray diffraction imaging applies an array of parallel capillaries in front of a position sensitive detector. Conventional micro channel plates of a few millimetre thickness have successfully been used as collimator arrays but require short sample to detector distances to achieve high spatial resolution. Furthermore, their limited absorption restricts their applications to low energy X-rays of around 10 keV. Progress in the fabrication of long polycapillaries allows an increase in the sample to detector distance without decreasing resolution and the use of high X-ray energies enables bulk investigations in transmission geometry.

A Novel Approach to Detect Abnormal Chest X-rays of COVID-19 Patients Using Image Processing and Deep Learning

2021 ◽  
pp. 23-41
Author(s):  
Subhagata Chattopadhyay
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Hessian Matrix ◽  
Region Of Interest ◽  
Computational Time ◽  
X Rays ◽  
Feed Forward Neural Network ◽  
Novel Approach ◽  
Input Dataset

The study proposes a novel approach to automate classifying Chest X-ray (CXR) images of COVID-19 positive patients. All acquired images have been pre-processed with Simple Median Filter (SMF) and Gaussian Filter (GF) with kernel size (5, 5). The better filter is then identified by comparing Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) of denoised images. Canny's edge detection has been applied to find the Region of Interest (ROI) on denoised images. Eigenvalues [-2, 2] of the Hessian matrix (5 × 5) of the ROIs are then extracted, which constitutes the 'input' dataset to the Feed Forward Neural Network (FFNN) classifier, developed in this study. Eighty percent of the data is used for training the said network after 10-fold cross-validation and the performance of the network is tested with the remaining 20% of the data. Finally, validation has been made on another set of 'raw' normal and abnormal CXRs. Precision, Recall, Accuracy, and Computational time complexity (Big(O)) of the classifier are then estimated to examine its performance.

2D real space visualization of d values in polycrystalline bulk materials of different hardness

2021 ◽  
Vol 54 (2) ◽  
pp. 597-603
Author(s):  
Mari Mizusawa ◽  
Kenji Sakurai
Keyword(s):  
Lattice Distortion ◽  
Structural Information ◽  
Hardness Test ◽  
Real Space ◽  
Polycrystalline Materials ◽  
X Rays ◽  
X Ray Diffraction ◽  
X Ray ◽  
Diffraction Imaging ◽  
D Values

Conventional X-ray diffraction measurements provide some average structural information, mainly on the crystal structure of the whole area of the given specimen, which might not be very uniform and may include different crystal structures, such as co-existing crystal phases and/or lattice distortion. The way in which the lattice plane changes due to strain also might depend on the position in the sample, and the average information might have some limits. Therefore, it is important to analyse the sample with good lateral spatial resolution in real space. Although various techniques for diffraction topography have been developed for single crystals, it has not always been easy to image polycrystalline materials. Since the late 1990s, imaging technology for fluorescent X-rays and X-ray absorption fine structure has been developed via a method that does not scan either a sample or an X-ray beam. X-ray diffraction imaging can be performed when this technique is applied to a synchrotron radiation beamline with a variable wavelength. The present paper reports the application of X-ray diffraction imaging to bulk steel materials with varying hardness. In this study, the distribution of lattice distortion of hardness test blocks with different hardness was examined. Via this 2D visualization method, the grains of the crystals with low hardness are large enough to be observed by X-ray diffraction contrast in real space. The change of the d value in the vicinity of the Vickers mark has also been quantitatively evaluated.

X-Ray Diffraction

2021 ◽  
pp. 408-480
Author(s):  
Kannan M. Krishnan
Keyword(s):  
Point Group ◽  
Polycrystalline Materials ◽  
X Rays ◽  
X Ray Diffraction ◽  
Symmetry Point Group ◽  
X Ray ◽  
Atomic Scattering ◽  
Scattering Factor ◽  
Diffraction Patterns ◽  
Lattice Planes

X-rays diffraction is fundamental to understanding the structure and crystallography of biological, geological, or technological materials. X-rays scatter predominantly by the electrons in solids, and have an elastic (coherent, Thompson) and an inelastic (incoherent, Compton) component. The atomic scattering factor is largest (= Z) for forward scattering, and decreases with increasing scattering angle and decreasing wavelength. The amplitude of the diffracted wave is the structure factor, F hkl, and its square gives the intensity. In practice, intensities are modified by temperature (Debye-Waller), absorption, Lorentz-polarization, and the multiplicity of the lattice planes involved in diffraction. Diffraction patterns reflect the symmetry (point group) of the crystal; however, they are centrosymmetric (Friedel law) even if the crystal is not. Systematic absences of reflections in diffraction result from glide planes and screw axes. In polycrystalline materials, the diffracted beam is affected by the lattice strain or grain size (Scherrer equation). Diffraction conditions (Bragg Law) for a given lattice spacing can be satisfied by varying θ or λ — for study of single crystals θ is fixed and λ is varied (Laue), or λ is fixed and θ varied to study powders (Debye-Scherrer), polycrystalline materials (diffractometry), and thin films (reflectivity). X-ray diffraction is widely applied.

SU-E-T-617: Commissioning the Eclipse Pencil Beam for Low Energy X-Rays

2013 ◽  
Vol 40 (6Part20) ◽  
pp. 347-347
Author(s):  
M Cherven ◽  
J Burmeister ◽  
J Rakowski ◽  
M Snyder
Keyword(s):  
Low Energy ◽  
Pencil Beam ◽  
X Rays

Quantitative texture analysis of small domains with synchrotron radiation X-rays

1999 ◽  
Vol 32 (5) ◽  
pp. 841-849 ◽  
Author(s):  
F. Heidelbach ◽  
C. Riekel ◽  
H.-R. Wenk
Keyword(s):  
Synchrotron Radiation ◽  
European Synchrotron Radiation Facility ◽  
Polymer Fibers ◽  
Polycrystalline Materials ◽  
X Rays ◽  
Bone Material ◽  
Experimental Procedure ◽  
Crystallographic Preferred Orientation ◽  
Fine Grained ◽  
Steel Wires

Quantitative analysis of crystallographic preferred orientation (texture) of very small volumes in fine-grained polycrystalline materials has been carried out with a monochromatic X-ray microbeam (≤30 µm) at the microfocus beamline of the European Synchrotron Radiation Facility (ESRF). The experimental procedure is described and illustrated with textures of rolled aluminium, aluminium and steel wires, polymer fibers and natural bone material (apatite).

scholarly journals Determination of crystallographic intensities from sparse data

IUCrJ ◽  
2015 ◽  
Vol 2 (1) ◽  
pp. 29-34 ◽  
Author(s):  
Kartik Ayyer ◽  
Hugh T. Philipp ◽  
Mark W. Tate ◽  
Jennifer L. Wierman ◽  
Veit Elser ◽  
...  
Keyword(s):  
Diffraction Data ◽  
Crystal Size ◽  
Three Dimensional ◽  
Sparse Data ◽  
Data Sets ◽  
X Rays ◽  
Diffraction Intensity ◽  
X Ray ◽  
Proof Of Principle

X-ray serial microcrystallography involves the collection and merging of frames of diffraction data from randomly oriented protein microcrystals. The number of diffracted X-rays in each frame is limited by radiation damage, and this number decreases with crystal size. The data in the frame are said to be sparse if too few X-rays are collected to determine the orientation of the microcrystal. It is commonly assumed that sparse crystal diffraction frames cannot be merged, thereby setting a lower limit to the size of microcrystals that may be merged with a given source fluence. TheEMCalgorithm [Loh & Elser (2009),Phys. Rev. E,80, 026705] has previously been applied to reconstruct structures from sparse noncrystalline data of objects with unknown orientations [Philippet al.(2012),Opt. Express,20, 13129–13137; Ayyeret al.(2014),Opt. Express,22, 2403–2413]. Here, it is shown that sparse data which cannot be oriented on a per-frame basis can be used effectively as crystallographic data. As a proof-of-principle, reconstruction of the three-dimensional diffraction intensity using sparse data frames from a 1.35 kDa molecule crystal is demonstrated. The results suggest that serial microcrystallography is, in principle, not limited by the fluence of the X-ray source, and collection of complete data sets should be feasible at, for instance, storage-ring X-ray sources.

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