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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Resonant inelastic X-ray scattering using a miniature dispersive Rowland refocusing spectrometer

X-ray absorption spectroscopy (XAS) beamlines worldwide are steadily increasing their emphasis on full photon-in/photon-out spectroscopies, such as resonant inelastic X-ray scattering (RIXS), resonant X-ray emission spectroscopy (RXES) and high energy resolution fluorescence detection XAS (HERFD-XAS). In such cases, each beamline must match the choice of emission spectrometer to the scientific mission of its users. Previous work has recently reported a miniature tender X-ray spectrometer using a dispersive Rowland refocusing (DRR) geometry that functions with high energy resolution even with a large X-ray spot size on the sample. This instrument has been used in the laboratory in multiple studies of non-resonant X-ray emission spectroscopy using a conventional X-ray tube, though only for preliminary measurements at a low-intensity microfocus synchrotron beamline. This paper reports an extensive study of the performance of a miniature DRR spectrometer at an unfocused wiggler beamline, where the incident monochromatic flux allows for resonant studies which are impossible in the laboratory. Here, the results support the broader use of the present design and also suggest that the DRR method with an unfocused beam could have important applications for materials with low radiation damage thresholds and that would not survive analysis on focused beamlines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Mjölnir: a miniature triaxial rock deformation apparatus for 4D synchrotron X-ray microtomography

An X-ray transparent experimental triaxial rock deformation apparatus, here named 'Mjölnir', enables investigations of brittle-style rock deformation and failure, as well as coupled thermal, chemical and mechanical processes relevant to a range of Earth subsurface environments. Designed to operate with cylindrical samples up to 3.2 mm outside-diameter and up to 10 mm length, Mjölnir can attain up to 50 MPa confining pressure and in excess of 600 MPa axial load. The addition of heaters extends the experimental range to temperatures up to 140°C. Deployment of Mjolnir on synchrotron beamlines indicates that full 3D datasets may be acquired in a few seconds to a few minutes, meaning full 4D investigations of deformation processes can be undertaken. Mjölnir is constructed from readily available materials and components and complete technical drawings are included in the supporting information.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

High-energy micrometre-scale pixel direct conversion X-ray detector

The objective of this work was to fabricate and characterize a new X-ray imaging detector with micrometre-scale pixel dimensions (7.8 µm) and high detection efficiency for hard X-ray energies above 20 keV. A key technology component consists of a monolithic hybrid detector built by direct deposition of an amorphous selenium film on a custom designed CMOS readout integrated circuit. Characterization was carried out at the synchrotron beamline 1-BM-B at the Advanced Photon Source of Argonne National Laboratory. The direct conversion detector demonstrated micrometre-scale spatial resolution with a 63 keV modulation transfer function of 10% at Nyquist frequency. In addition, spatial resolving power down to 8 µm was determined by imaging a transmission bar target at 21 keV. X-ray signal linearity, responsivity and lag were also characterized in the same energy range. Finally, phase contrast edge enhancement was observed in a phase object placed in the beam path. This amorphous selenium/CMOS detector technology can address gaps in commercially available X-ray detectors which limit their usefulness for existing synchrotron applications at energies greater than 50 keV; for example, phase contrast tomography and high-resolution imaging of nanoscale lattice distortions in bulk crystalline materials using Bragg coherent diffraction imaging. Here, the technology will also facilitate the creation of novel synchrotron imaging applications for X-ray energies at or above 20 keV.

36 MATERIALS SCIENCE↗
DOE OSTI2023

hpMCA: A Python-based Graphical Program for Energy Dispersive X-Ray Diffraction Data Collection and Analysis

Energy-dispersive X-ray diffraction (EDXD) at synchrotron beamlines is commonly used for the study of material properties under high pressure and/or high temperature. Experimenters typically rely on the availability of robust data collection and analysis at a beamline, but this has become increasingly difficult, especially with the introduction of multi-element detectors that generate complex, multi-dimensional data sets. These data sets have energy resolution, and they can also be resolved in relation to sample position, diffraction angle, or different external stimuli. We report a new Python-based graphical program, hpMCA, for EDXD data collection and analysis that streamlines the experimental process for the beamline users. The program features a user-friendly interface, capability for online viewing and analyzing data from multi-element energy-dispersive detectors, and includes features useful for working with samples under high pressure and/or high temperature, such as crystal phase identification, real-time unit cell lattice refinement, and pressure determination based on an equation of state.

data analysis software↗
DOE OSTI2024

Oil-Pressure Based Apparatus for In-Situ High-Energy Synchrotron X-Ray Diffraction Studies During Biaxial Deformation

Background: Understanding biaxial loading response at the microstructural level is crucial in helping better design sheet manufacturing processes and calibrate/validate material deformation models. Objective: The objective of this work was to develop a low-cost testing apparatus to probe, with sufficient spatial resolution, the micro-mechanical response of a sheet material in-situ under biaxial loading conditions. Methods: The testing apparatus fabricated as a part of this study operates in a similar fashion to a standard bulge test and uses oil pressure to generate biaxial loading conditions. This biaxial testing apparatus was operated within a synchrotron beamline to characterize the mechanical response of a flash-processed steel sheet using in-situ high-energy X-ray diffraction (XRD) measurements. Further, the GSAS-II package was utilized to develop a workflow for the analysis of the large volume of diffraction data acquired. The workflow was then used to extract the peak position, width, and integrated intensity of the XRD peaks corresponding to the major body-centered cubic phase. Results: The equi-biaxial nature of the loading in the measured area was independently corroborated using experimental (XRD) and simulation (finite element analysis) methods. Furthermore, we discuss the evolution of elastic strain in the major body-centered cubic phase as a function of applied oil pressure and location on the steel sheet. Conclusions: A key advantage of the biaxial apparatus fabricated in this synchrotron study is demonstrated using the results obtained for the flash-processed steel sheet – i.e., mapping the lattice plane-dependent response to biaxial loading for a relatively large sample area in a spatially resolved manner.

36 MATERIALS SCIENCE↗
DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

30 years of semiconductor nanowire research: A Personal Journey

This project is about flux line pinning in high T c superconductors using columnar defects as the pinning centers. The basic idea is that linear structural defects can pin the flux lines within HTSCs so that their critical current density (the maximal current density that these HTSCs can carry with zero resistance) can be enhanced. The question is how to create such linear structural defects within crystalline cuprate thin films. Scientists have already successfully demonstrated the concept by irradiating HTSC crystals with high-energy ions at synchrotron beamline facilities. However, such heavy ion irradiation is practically not viable for large-scale processes. Finally, we reasoned that maybe we could introduce some sort of linear nanoscopic objects into HTSC to form a composite, and then these linear nanoscopic objects would serve as the magnetic flux line pinning centers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Inline small-angle X-ray scattering-coupled chromatography under extreme hydrostatic pressure

As continuing discoveries highlight the surprising abundance and resilience of deep ocean and subsurface microbial life, the effects of extreme hydrostatic pressure on biological structure and function have attracted renewed interest. Biological small-angle X-ray scattering (BioSAXS) is a widely used method of obtaining structural information from biomolecules in solution under a wide range of solution conditions. Due to its ability to reduce radiation damage, remove aggregates, and separate monodisperse components from complex mixtures, size-exclusion chromatography-coupled SAXS (SEC-SAXS) is now the dominant form of BioSAXS at many synchrotron beamlines. While BioSAXS can currently be performed with some difficulty under pressure with non-flowing samples, it has not been clear how, or even if, continuously flowing SEC-SAXS, with its fragile media-packed columns, might work in an extreme high-pressure environment. Here we show, for the first time, that reproducible chromatographic separations coupled directly to high-pressure BioSAXS can be achieved at pressures up to at least 100 MPa and that pressure-induced changes in folding and oligomeric state and other properties can be observed. Further, the apparatus described here functions at a range of temperatures (0°C–50°C), expanding opportunities for understanding biomolecular rules of life in deep ocean and subsurface environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hyperspectral X-ray Imaging with TES Detectors for Nanoscale Chemical Speciation Mapping

We are developing an imaging capability (“Hyperspectral X-ray Imaging”) for mapping chemical information (molecular formula, phase, oxidation state, hydration) that is based on ultra-high-resolution X-ray emission spectroscopy with large transition-edge sensor microcalorimeter arrays in the scanning electron microscope. By combining microcalorimeter arrays with hundreds of pixels, high-bandwidth microwave frequency-division multiplexing, and fast digital electronics for near real-time data processing, our goal is to enable measurements using laboratory-scale instrumentation rather than synchrotron beamlines. Our application focus here is on mapping the chemical form of uranium compounds on the nanoscale. Furthermore, we will present our approach to developing the Hyperspectral X-ray Imaging capability, progress toward a 128-pixel microwave multiplexed X-ray fluorescence instrument at LANL, and the path to high-throughput nanoscale chemical mapping.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning

X-ray diffraction patterns contain information about the atomistic structure and microstructure (defect population) of materials, extracting detailed information from diffraction patterns is complex, demanding and relies on prior knowledge. Here, we hypothesize that deep-learning techniques can help to perform an effective and accurate analysis with high throughput rates. To demonstrate this concept, we applied a novel deep learning framework to determine the evolution of the β-phase volume fraction in a Ti–6Al–4V alloy during heat-treatment from video sequences of 2D diffraction patterns recorded in transmission and with highly monochromatic radiation in a synchrotron beamline. In particular, we studied the impact of network design on prediction reliability and computational performance. Networks of different architectures were trained using 3008 experimental 2D patterns. A well-tuned model was found to reproduce the phase fractions of another experimental data set, consisting of 1100 diffraction patterns, with a mean-square error as small as 2.6 x 10 -4 . The average prediction error of β-phase volume fraction was within 1.6 x 10 -2 (in each diffraction pattern) of the values obtained by conventional methods. Our work demonstrates that convolutional neural networks can evaluate high energy X-ray diffraction patterns with a remarkable level of reliability. Furthermore, it demonstrates the significance of network design on the reliability of predictions and computational performance. The most complex models do not necessarily result in highest accuracy and may even fail to learn from the data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-pitch nano-accuracy surface profiler for strongly curved X-ray mirror metrology

We present our recent research and development effort on the Multi-Pitch Nano-accuracy Surface Profiler (MPNSP). This metrology instrument is developed to characterize strongly curved X-ray mirrors proposed to achieve diffraction-limited soft X-ray focusing for scientific applications at the synchrotron beamlines. The measurement process consists of forward-and-backward scans on the test mirror surface along its tangential direction at multiple pitch angles. Our research and development in this work aim to take the challenge of measuring strongly curved X-ray mirrors with a typical total slope range ≳10 mrad, while maintaining the Root Mean Square (RMS) value of measurement repeatability and self-consistency at 50 nrad RMS level. We first introduce the mechanical design, followed by a brief review of the mathematical model and the optimization algorithm for the MPNSP technique. By detecting the rotation axis of the mirror pitch with a machine vision approach, we can determine and then reduce the vertical distance between the pitch rotation axis and the test mirror surface to a sub-mm level. In addition, we describe a practical data acquisition procedure for strongly curved X-ray mirrors with a total slope range larger than the slope measuring range of the autocollimator used in the instrument. As a result, the proposed MPNSP measurements with different mirror orientations achieve remarkable self-consistency and reproducibility of < 50 nrad RMS in slope and < 0.5 nm RMS in height.

42 ENGINEERING↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2020

On-the-fly closed-loop materials discovery via Bayesian active learning

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Direct observations of transient weakening during phase transformations in quartz and olivine

Phase transformations are widely invoked as a source of rheological weakening during subduction, continental collision, mantle convection and various other geodynamic phenomena. However, despite more than half a century of research, the likelihood and magnitude of such weakening in nature remain poorly constrained. Here we use experiments performed on a synchrotron beamline to reveal transient weakening of up to three orders of magnitude during the polymorphic quartz to coesite (SiO 2 ) and olivine to ringwoodite (Fe 2 SiO 4 ) phase transitions. Weakening becomes increasingly prominent as the transformation outpaces deformation. We suggest that this behaviour is broadly applicable among silicate minerals undergoing first-order phase transitions and examine the likelihood of weakening due to the olivine-spinel, (Mg,Fe) 2 SiO 4 , transformation during subduction. Modelling suggests that cold, wet slabs are most susceptible to transformational weakening, consistent with geophysical observations of slab stagnation in the mantle transition zone beneath the western Pacific. In conclusion, our study highlights the importance of incorporating transformational weakening into geodynamic simulations and provides a quantitative basis for doing so.

Cross, Andrew J. [Woods Hole Oceanographic Institu↗
DOE OSTI2020

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments

Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels

Abstract A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in the efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously-steered experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A new generation of direct X-ray detectors for medical and synchrotron imaging applications

Abstract Large-area X-ray imaging is one of the most widely used imaging modalities that spans several scientific and technological fields. Currently, the direct X-ray conversion materials that are being commercially used for large-area (> 8 cm × 4 cm without tiling) flat panel applications, such as amorphous selenium (a-Se), have usable sensitivities of up to only 30 keV. Although there have been many promising candidates (such as polycrystalline HgI 2 and CdTe), none of the semiconductors were able to assuage the requirement for high energy (> 40 keV) large-area X-ray imaging applications due to inadequate cost, manufacturability, and long-term performance metrics. In this study, we successfully demonstrate the potential of the hybrid Methylammonium lead iodide (MAPbI 3 ) perovskite-based semiconductor detectors in satisfying all the requirements for its successful commercialization in synchrotron and medical imaging. This new generation of hybrid detectors demonstrates low dark current under electric fields needed for high sensitivity X-ray imaging applications. The detectors have a linear response to X-ray energy and applied bias, no polarization effects at a moderate bias, and signal stability over long usage durations. Also, these detectors have demonstrated a stable detection response under BNL’s National Synchrotron Light Source II (NSLS-II) 70 keV monochromatic synchrotron beamline.

47 OTHER INSTRUMENTATION↗