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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.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

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↗
DOE OSTI2020

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↗
DOE OSTI2023

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↗
DOE OSTI2017

Elucidating structural order and disorder phenomena in mullite-type Al{sub 4}B{sub 2}O{sub 9} by automated electron diffraction tomography

The crystal structure and disorder phenomena of Al{sub 4}B{sub 2}O{sub 9}, an aluminum borate from the mullite-type family, were studied using automated diffraction tomography (ADT), a recently established method for collection and analysis of electron diffraction data. Al{sub 4}B{sub 2}O{sub 9}, prepared by sol-gel approach, crystallizes in the monoclinic space group C2/m. The ab initio structure determination based on three-dimensional electron diffraction data from single ordered crystals reveals that edge-connected AlO{sub 6} octahedra expanding along the b axis constitute the backbone. The ordered structure (A) was confirmed by TEM and HAADF-STEM images. Furthermore, disordered crystals with diffuse scattering along the b axis are observed. Analysis of the modulation pattern implies a mean superstructure (AAB) with a threefold b axis, where B corresponds to an A layer shifted by ½a and ½c. Diffraction patterns simulated for the AAB sequence including additional stacking disorder are in good agreement with experimental electron diffraction patterns. - Graphical abstract: Crystal structure and disorder phenomena of B-rich Al{sub 4}B{sub 2}O{sub 9} studied by automated electron diffraction tomography (ADT) and described by diffraction simulation using DISCUS. - Highlights: • Ab-initio structure solution by electron diffraction from single nanocrystals. • Detected modulation corresponding mainly to three-fold superstructure. • Diffuse diffraction streaks caused by stacking faults in disordered crystals. • Observed streaks explained by simulated electron diffraction patterns.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

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↗
DOE OSTI2019

Overcoming the diffraction limit by multi-photon interference: a tutorial

The nature of light, extending from the optical to the x-ray regime, is reviewed from a diffraction point of view by comparing field-based statistical optics and photon-based quantum optics approaches. The topic is introduced by comparing historical diffraction concepts based on wave interference, Dirac’s notion of photon self-interference, Feynman’s interference of space–time photon probability amplitudes, and Glauber’s formulation of coherence functions based on photon detection. The concepts are elucidated by a review of how the semiclassical combination of the disparate photon and wave concepts have been used to describe light creation, diffraction, and detection. The origin of the fundamental diffraction limit is then discussed in both wave and photon pictures. By use of Feynman’s concept of probability amplitudes associated with independent photons, we show that quantum electrodynamics, the complete theory of light, reduces in lowest order to the conventional wave formalism of diffraction. As an introduction to multi-photon effects, we then review fundamental one- and two-photon experiments and detection schemes, in particular the seminal Hanbury Brown–Twiss experiment. The formal discourse of the paper starts with a treatment of first-order coherence theory. In first order, the statistical optics and quantum optics formulations of coherence are shown to be equivalent. This is elucidated by a discussion of Zernike’s powerful theorem of partial coherence propagation, a cornerstone of statistical optics, followed by its quantum derivation based on the interference of single-photon probability amplitudes. The treatment is then extended to second-order coherence theory, where the equivalence of wave and particle descriptions is shown to break down. This is illustrated by considering two photons whose space–time probability amplitudes are correlated through nonlinear birth processes, resulting in entanglement or cloning. In both cases, the two-photon diffraction patterns are shown to exhibit resolution below the conventional diffraction limit, defined by the one-photon diffraction patterns. The origin of the reduction is shown to arise from the interference of two-photon probability amplitudes. By comparing first- and second-order diffraction, it is shown that the conventional first-order concept of partial coherence with its limits of chaoticity and first-order coherence has the second-order analogue of partial entanglement, with its limits corresponding to two entangled photons (“entangled biphotons”) and two cloned photons (“cloned biphotons”), the latter being second-order coherent. The concept of cloned biphotons is extended to the case of $n$ cloned photons, resulting in a $1/n$ reduction of the diffraction limit. In the limit of $n$th-order collective state are shown to propagate on particle like trajectories, reproducing the 0th-order ray-optics picture. These results are discussed in terms of the linearity of quantum mechanics and Heisenberg’s space–momentum uncertainty principle. Finally, a general concept of coherence based on photon density is developed that in first order is equivalent to the conventional wave-based picture.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

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↗
DOE OSTI2018

A proper assessment of TEM diffraction patterns originating from CrN nitrides in a ferritic matrix

Highlights: • A proper assessment of TEM diffraction pattern along the [001]{sub α-Fe} of a nitrided Fe-Cr alloy is proposed • Diffraction pattern results from the diffraction of the ferritic matrix and the three nitrides variants • Thin foil’s thickness plays a key role in the obtained diffraction pattern • Relrods located in the first layer of the reciprocal lattice can provide extra reflections in the diffraction patterns - Abstract: Upon nitriding of a Fe-3%Cr alloy at 550 °C during 48 h at a nitriding potential K{sub N} = 5 atm{sup −1/2}, finely dispersed CrN grains precipitate. Considering that the nitrides developing within the nitrided layer adopt a cubic, rock-salt structure, Transmission Electron Microscopy (TEM) investigations revealed the Bain orientation relationships (also called Baker-Nutting) existing between the matrix and the precipitates. Given the latter and the structures of both ferrite (bcc) and nitrides (fcc), we have shown that group theory predicts the existence of three nitrides variants, orthogonal to each other, developing in ferrite along the <001>{sub α-Fe} directions, which is in agreement with our experimental observations. The nitrides adopt a very thin platelet-like morphology, energetically corresponding to an absolute extremum. Diffraction patterns obtained along the [001]{sub α-Fe} result from the simultaneous diffraction of the ferritic matrix and the three nitrides variants. The platelet-like nitride precipitates give birth to diffuse intensity lines, originating from the two families developing along the (100) and (010) ferrite planes, as well as possible appearance of additional reflections, located at the forbidden positions {100}{sub α-Fe} and {110}{sub CrN} of the remaining variant, driving the interpretation of such diffraction patterns complicated. Foil thickness has proven to play a key role in the occurrence of this phenomenon. When the geometric diffraction conditions are favorable (i.e. very thin foil along the zone axis and very fine precipitates platelets), TEM analysis permitted to attribute these extra reflections to the nodes located in the first layer of the third variant's reciprocal space.

36 MATERIALS SCIENCE↗