A synchrotron X-ray diffraction study of strain and microstrain distributions in [alpha]-Zr caused by hydride precipitation
Abstract not provided
Shiman, Oksana V.↗
SEARCH · Engineering Papers
Results for “diffraction”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Powder X-ray diffraction of escitalopram oxalate oxalic acid hydrate, (C[subscript 20]H[subscript 21
Explore the source record for details and available documents.
Kaduk, James A.↗
Melting and refreezing of zirconium observed using ultrafast x-ray diffraction
Abstract not provided
Radousky, Harry B.↗
Development of a High-Rate Front-End ASIC for X-Ray Spectroscopy and Diffraction Applications
We developed a new front-end application-specific integrated circuit (ASIC) to upgrade the Maia X-ray microprobe. The ASIC instruments 32 configurable channels that perform either positive or negative charge amplification, pulse shaping, peak amplitude, and time extraction along with buffered analog storage. At a gain of 3.6 V/fC, 1-μs peaking time, and a temperature of 248 K, an electronic resolution of 13 and 10 e- rms was measured with and without a silicon drift detector (SDD) sensor, respectively. A spectral resolution of 170-eV full-width at half-maximum (FWHM) at 5.9 keV was obtained with an 55Fe source. The channel linearity was better than ± 1% with rate capabilities up to 40 kcps. The ASIC was fabricated in a commercial 250-nm process with a footprint of 6.3 mm × 3.9 mm and dissipates 167 mW of static power.
Vernon, Emerson↗
A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy
An image dataset of 10 different size molecules, where each molecule has 2,000 structural variants, is generated from the 2D cross-sectional projection of Molecular Dynamics trajectories. The purpose of this dataset is to provide a benchmark dataset for the increasing need of machine learning, deep learning and image processing on the study of scattering, imaging and microscopy.
BASIC BIOLOGICAL SCIENCES,OTHER INSTRUMENTATION↗
Using In-Situ Laboratory and Synchrotron-Based X-ray Diffraction for Lithium-Ion Batteries Characterization: A Review on Recent Developments
Explore the source record for details and available documents.
99 GENERAL AND MISCELLANEOUS↗
Diffraction-Based Optical Switch
Method and system for controllably redirecting a light beam, having a central wavelength lambda, from a first light-receiving site to a second light-receiving site. A diffraction grating is attached to or part of a piezoelectric substrate, which is connected to one or two controllable voltage difference sources. When a substrate voltage difference is changed and the diffraction grating length in each of one or two directions is thereby changed, at least one of the diffraction angle, the diffraction order and the central wavelength is controllably changed. A diffracted light beam component, having a given wavelength, diffraction angle and diffraction order, that is initially received at a first light receiving site (e.g., a detector or optical fiber) is thereby controllably shifted or altered and can be received at a second light receiving site. A polynomially stepped, chirped grating is used in one embodiment. In another embodiment, an incident light beam, having at least one of first and second wavelengths, lambda1 and lambda2, is received and diffracted at a first diffraction grating to provide a first diffracted beam. The first diffracted beam is received and diffracted at a second diffraction grating to produce a second diffracted beam. The second diffracted beam is received at a light-sensitive transducer, having at least first and second spaced apart light detector elements that are positioned so that, when the incident light beam has wavelength lambda1 or lambda2 (lambda1 not equal to lambda2), the second diffracted beam is received at the first element or at the second element, respectively; change in a selected physical parameter at the second grating can also be sensed or measured. A sequence of spaced apart light detector elements can be positioned along a linear or curvilinear segment with equal or unequal spacing.
Stevan M Sperno↗
Application of Powder Diffraction Methods to the Analysis of the Atomic Structure of Nanocrystals: The Concept of the Apparent Lattice Parameter (ALP)
The applicability of standard methods of elaboration of powder diffraction data for determination of the structure of nano-size crystallites is analysed. Based on our theoretical calculations of powder diffraction data we show, that the assumption of the infinite crystal lattice for nanocrystals smaller than 20 nm in size is not justified. Application of conventional tools developed for elaboration of powder diffraction data, like the Rietveld method, may lead to erroneous interpretation of the experimental results. An alternate evaluation of diffraction data of nanoparticles, based on the so-called 'apparent lattice parameter' (alp) is introduced. We assume a model of nanocrystal having a grain core with well-defined crystal structure, surrounded by a surface shell with the atomic structure similar to that of the core but being under a strain (compressive or tensile). The two structural components, the core and the shell, form essentially a composite crystal with interfering, inseparable diffraction properties. Because the structure of such a nanocrystal is not uniform, it defies the basic definitions of an unambiguous crystallographic phase. Consequently, a set of lattice parameters used for characterization of simple crystal phases is insufficient for a proper description of the complex structure of nanocrystals. We developed a method of evaluation of powder diffraction data of nanocrystals, which refers to a core-shell model and is based on the 'apparent lattice parameter' methodology. For a given diffraction pattem, the alp values are calculated for every individual Bragg reflection. For nanocrystals the alp values depend on the diffraction vector Q. By modeling different a0tomic structures of nanocrystals and calculating theoretically corresponding diffraction patterns using the Debye functions we showed, that alp-Q plots show characteristic shapes which can be used for evaluation of the atomic structure of the core-shell system. We show, that using a simple model of a nanocrystal with spherical shape and centro-symmetric strain at the surface shell we obtain theoretical alp-Q values which match very well the alp-Q plots determined experimentally for Sic, GaN, and diamond nanopowders. The theoretical models are defined by the lattice parameter of the grain core, thickness of the surface shell, and the magnitude and distribution of the strain field in the surface shell. According to our calculations, the part of the diffraction pattern measured at relatively low diffraction vectors Q (below 10/angstrom) provides information on the surface strain, whle determination of the lattice parameters in the grain core requires measurements at large Q-values (above 15 - 20/angstrom).
Palosz, B.↗
Time‐Lapse Image Classification Using a Diffractive Neural Network
Diffractive deep neural networks (D 2 NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all‐optical object classification and perform universal linear transformations. Herein, a “time‐lapse” image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super‐resolution applications; inspired by their success, a time‐lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time‐lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR‐10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR‐10 dataset. Time‐lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all‐optical processors.
36 MATERIALS SCIENCE↗
Artifact identification in X-ray diffraction data using machine learning methods
In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices ( e.g. battery materials) or in complex sample environments ( e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions ( e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye–Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 µm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
The dyadic diffraction coefficient for a curved edge
A compact dyadic diffraction coefficient for electromagnetic waves obliquely incident on a curved edge formed by perfectly conducting curved or plane surfaces is obtained. This diffraction coefficent remains valid in the transition regions adjacent to shadow and reflection boundaries, where the diffraction coefficients of Keller's original theory fail. The method is on Keller's method of the canonical problem, which in this case is the perfectly conducting wedge illuminated by plane, cylindrical, conical, and spherical waves. When the proper ray fixed coordinate system is introduced, the dyadic diffraction coefficient for the wedge is found to be the sum of only two dyads, and it is shown that this is also true for the dyadic diffraction coefficients of higher order edges. One dyad contains the acoustic soft diffraction coefficient; the other dyad contains the acoustic hard diffraction coefficient. The expressions for the acoustic wedge diffraction coefficients contain Fresnel integrals, which ensure that the total field is continuous at shadow and reflection boundaries. The diffraction coefficients have the same form for the different types of edge illumination; only the arguments of the Fresnel integrals are different. Since diffraction is a local phenomenon, and locally the curved edge structure is wedge shaped, this result is readily extended to the curved edge.
Kouyoumjian, R. G.↗
Diffraction-based optical correlator
Method and system for wavelength-based processing of a light beam. A light beam, produced at a chemical or physical reaction site and having at least first and second wavelengths, ?1 and ?2, is received and diffracted at a first diffraction grating to provide first and second diffracted beams, which are received and analyzed in terms of wavelength and/or time at two spaced apart light detectors. In a second embodiment, light from first and second sources is diffracted and compared in terms of wavelength and/or time to determine if the two beams arise from the same source. In a third embodiment, a light beam is split and diffracted and passed through first and second environments to study differential effects. In a fourth embodiment, diffracted light beam components, having first and second wavelengths, are received sequentially at a reaction site to determine whether a specified reaction is promoted, based on order of receipt of the beams. In a fifth embodiment, a cylindrically shaped diffraction grating (uniform or chirped) is rotated and translated to provide a sequence of diffracted beams with different wavelengths. In a sixth embodiment, incident light, representing one or more symbols, is successively diffracted from first and second diffraction gratings and is received at different light detectors, depending upon the wavelengths present in the incident light.
Spremo, Stevan M.↗
Computing diffraction patterns of microstructures from phase-field simulations
The diffraction pattern of a material contains information not only on the crystal structures of its constituting phases, but also on its mesoscale spatial distributions of phases, grains, and ferroelastic, ferroelectric, and ferromagnetic domains. While diffraction patterns from experiments such as X-ray diffraction are presented in the reciprocal or Fourier space, mesoscale microstructure models such as the phase-field method naturally produce real-space images of spatial distribution of chemical composition, structural, and ferroic domains. Although one could rather readily compute the Fourier amplitudes of chemical and structural domain distributions generated by mesoscale simulations, they only contain information about the length scale and alignment of the real-space chemical and structure domains. Therefore, a direct comparison between diffraction experiments and mesoscale microstructure simulations is not possible. Here, we develop a theoretical approach to directly compute the crystal diffraction patterns of microstructures predicted by phase-field simulations. In particular, we consider five representative examples of microstructure patterns involving purely compositional domains, a single pair of tetragonal twin structures, multiple twin variants in a hexagonal system, ferroelectric polar vortices, and polycrystalline grains. The results are compared with previous experimental observations as well as X-ray diffraction experiments performed in the present study. Furthermore, the theoretical framework allows one to directly connect material microstructures and diffraction patterns predicted from phase-field simulations and the corresponding diffraction patterns from experiments, and thus providing guidance to experimental diffraction characterization and interpretation of microstructures.
36 MATERIALS SCIENCE↗
Beamline K11 DIAD: a new instrument for dual imaging and diffraction at Diamond Light Source
The Dual Imaging and Diffraction (DIAD) beamline at Diamond Light Source is a new dual-beam instrument for full-field imaging/tomography and powder diffraction. This instrument provides the user community with the capability to dynamically image 2D and 3D complex structures and perform phase identification and/or strain mapping using micro-diffraction. The aim is to enable in situ and in operando experiments that require spatially correlated results from both techniques, by providing measurements from the same specimen location quasi-simultaneously. Using an unusual optical layout, DIAD has two independent beams originating from one source that operate in the medium energy range (7–38 keV) and are combined at one sample position. Here, either radiography or tomography can be performed using monochromatic or pink beam, with a 1.4 mm × 1.2 mm field of view and a feature resolution of 1.2 µm. Micro-diffraction is possible with a variable beam size between 13 µm × 4 µm and 50 µm × 50 µm. One key functionality of the beamline is image-guided diffraction, a setup in which the micro-diffraction beam can be scanned over the complete area of the imaging field-of-view. This moving beam setup enables the collection of location-specific information about the phase composition and/or strains at any given position within the image/tomography field of view. The dual beam design allows fast switching between imaging and diffraction mode without the need of complicated and time-consuming mode switches. Real-time selection of areas of interest for diffraction measurements as well as the simultaneous collection of both imaging and diffraction data of (irreversible) in situ and in operando experiments are possible.
36 MATERIALS SCIENCE↗