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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Commissioning the H i Observing Mode of the Beam Former for the Cryogenically Cooled Focal L-band Array for the GBT (FLAG)

We present the results of commissioning observations for a new digital beam-forming back end for the Focal plane L-band Array for the Robert C. Byrd Green Bank Telescope (FLAG), a cryogenically cooled Phased Array Feed (PAF) with the lowest measured T {sub sys}/η of any PAF outfitted on a radio telescope to date. We describe the custom software used to apply beam-forming weights to the raw element covariances to create research-quality spectral-line images for the new fine-channel mode, study the stability of the beam weights over time, characterize FLAG’s sensitivity over a frequency range of 150 MHz, and compare the measured noise properties and observed distribution of neutral hydrogen emission from several extragalactic and Galactic sources with data obtained with the current single-pixel L-band receiver. These commissioning runs establish FLAG as the preeminent PAF receiver currently available for spectral-line observations on the world’s major radio telescopes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

An assessment of controlled source EM for monitoring subsurface CO 2 injection at the wyoming carbonSAFE geologic carbon storage site

Here we evaluate if electromagnetic (EM) geophysical methods for monitoring geologic carbon storage (GCS) efforts at the Wyoming CarbonSAFE project adjacent to the Dry Fork Station power plant near Gillette, Wyoming. This first involved acquiring both electric and magnetic fields at eleven different locations ranging in distance from immediately adjacent to 4 km from the plant. Passive EM measurements were made to provide spectral EM noise measurements generated by electricity production at the plant and to determine if useful magnetotelluric (MT) data can be successfully collected in the region. The processed data indicate that useful MT data can be collected as long as the site is located more than 2km away from the power plant as well as active roads and rail lines. Controlled source EM data were collected using three different source configurations, two of which connected to steel casings used to complete the injection wells. Comparing the EM noise measurements to the CSEM data show measurable electric and magnetic field signals at all sites. Next a series of three-dimensional (3D) numerical models were built that simulate resistivity changes caused by the proposed CO2 injection at depths ranging from 2.4 to 3.0km. These models were used to simulate various EM measurement configurations. The modeling shows that casing-source CSEM monitoring can provide sensitivity to the injected CO 2 if source electrodes are connected to the bottom of one or both of the injection wells.

58 GEOSCIENCES↗
DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Interlaboratory Study of Digital Volume Correlation Error Due to X-Ray Computed Tomography Equipment and Scan Parameters: an Update from the DVC Challenge

Background: The quality of Digital Volume Correlation (DVC) full-field displacement measurements depends directly on the characteristics of the X-ray Computed Tomography (XCT) equipment, and scan procedures used to acquire the tomographic images. Objective: In this work, we seek to experimentally study the effects of XCT equipment and tomographic scan procedures on the quality of these images for DVC analysis, and to survey the level of DVC error that may be achieved using standard XCT operating procedures. Methods: Six participants in an interlaboratory study acquired high-quality XCT scans of a syntactic foam before and after rigid body motion. The resulting images were correlated using commercial DVC software to quantify error sources due to random image noise, reconstruction artifacts, as well as systematic spatial or temporal distortion. Results: In the absence of rigid body motion, the standard deviation of the displacement measurements ranged from 0.012 to 0.043 voxels using a moderate subvolume size, indicating that subvoxel measurement resolution could readily be achieved with a variety of XCT equipment and scan recipes. Comparison of consecutive scans without rigid body motion showed transient dilatational displacement gradients due to self-heating of the X-ray source and/or thermal expansion of the foam. Evaluation of the scans after rigid body motion showed significant, machine-specific spatial distortion in the displacement fields of up to 0.5 voxels; new approaches to remove this error need to be developed. Conclusions: Analysis of the scan protocols used in the interlaboratory study, as well as a complementary parametric sensitivity study, showed that the DVC error was strongly influenced by the XCT equipment, but could be mitigated by adjusting the total scan duration.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Legacy Survey of Space and Time Data Preview 1: source dataset type

The Legacy Survey of Space and Time Data Preview 1 (DP1) is the first release of data from the NSF-DOE Vera C. Rubin Observatory. It consists of raw and calibrated single-epoch images, co-adds, difference images, detection catalogs, and other derived data products. DP1 is based on 1792 science-grade optical/near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera, LSSTComCam, on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile during the first on-sky commissioning campaign in late 2024. DP1 covers a total of approximately 15 sq. deg. over seven roughly equally-sized non-contiguous fields, each independently observed in six broad photometric bands, ugrizy, spanning a range of stellar densities and latitudes and overlapping with external reference datasets. This dataset is a subset of the full data release consisting of the source dataset type. These are measurements for detected sources in processed visit images. This release contains 1,786 datasets of this type.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Characterization of a collimated neutron imager for low-rate fast neutron imaging

Spatial localization of special nuclear materials (SNM) via their neutron signatures amidst background requires knowledge of the background neutron environment or a means of separating a source from background based on low amounts of information. This requirement has created the need for characterizing the spatial distribution of the cosmogenic neutron background. Neutron scatter cameras have been developed and optimized for rapid detection of high activity sources, but have low imaging efficiency, making it difficult to use them to characterize low rate diffuse sources, such as the neutron background. The Low Intensity Neutron Imaging System (LINIS) is a collimated neutron imager that has been designed and optimized for imaging diffuse cosmogenic neutron background in the energy range of 0.5–15 MeV. LINIS operates using 16 liquid scintillation detectors shielded by ultra-high molecular weight polyethylene cylindrical collimators in a staggered orientation and rotates to 7 discrete positions, giving it roughly 2π sensitivity. Finally, LINIS has been characterized using (α, n) and fission neutron sources using two imaging techniques for neutron source localization, simple backprojection and Maximum Likelihood Expectation Maximization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A High-Speed, High-Resolution Transition Edge Sensor Spectrometer for Soft X-Rays at the Advanced Photon Source

This project explores the design and development of a transition edge sensor (TES) spectrometer for resonant soft X-ray scattering (RSXS) measurements developed in collaboration between Argonne National Laboratory (ANL) and the National Institute of Standards and Technology (NIST). Soft X-ray scattering is a powerful technique for studying the electronic and magnetic properties of materials on a microscopic level. However, the lack of high-performance soft X-ray spectrometers has limited the potential of this technique. TES spectrometers have the potential to overcome these limitations due to their high energy resolution, high efficiency, and broad energy range. This project aims to optimize the design of a TES spectrometer for RSXS measurements and more generally soft X-ray spectroscopy at the Advanced Photon Source (APS) 29-ID, leading to improved understanding of advanced materials. We will present a detailed description of the instrument design and implementation. The spectrometer consists of a large array of approximately 250 high-speed and high-resolution pixels. The pixels have saturation energies of approximately 1 keV, sub-ms pulse duration and energy resolution of approximately 1 eV. The array is read out using microwave multiplexing chips with MHz bandwidth per channel, enabling efficient data throughput. To facilitate measurement of samples in situ under ultra-high vacuum conditions at the beamline, the spectrometer is integrated with an approximately 1 m long snout.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Equal abundance of summertime natural and wintertime anthropogenic Arctic organic aerosols

Aerosols play an important yet uncertain role in modulating the radiation balance of the sensitive Arctic atmosphere. Organic aerosol is one of the most abundant, yet least understood, fractions of the Arctic aerosol mass. Here we use data from eight observatories that represent the entire Arctic to reveal the annual cycles in anthropogenic and biogenic sources of organic aerosol. We show that during winter, the organic aerosol in the Arctic is dominated by anthropogenic emissions, mainly from Eurasia, which consist of both direct combustion emissions and long-range transported, aged pollution. In summer, the decreasing anthropogenic pollution is replaced by natural emissions. These include marine secondary, biogenic secondary and primary biological emissions, which have the potential to be important to Arctic climate by modifying the cloud condensation nuclei properties and acting as ice-nucleating particles. Their source strength or atmospheric processing is sensitive to nutrient availability, solar radiation, temperature and snow cover. Our results provide a comprehensive understanding of the current pan-Arctic organic aerosol, which can be used to support modelling efforts that aim to quantify the climate impacts of emissions in this sensitive region.

58 GEOSCIENCES↗
DOE OSTI2026

Legacy Survey of Space and Time Data Preview 2: source dataset type

We present Rubin Data Preview 2 (DP2), the second data preview from the NDF-DOE Vera C. Rubin Observatory. Data Preview 2 (DP2) comprises coadds, detection catalogs, and ancillary data products; and when fully released will also include single-epoch images and difference images. DP2 is derived from observations acquired by the LSST Science Camera (LSSTCam) on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile, primarily during the on-sky commissioning campaign between 2025-04-16 and 2025-09-21, supplemented by observations taken between 2025-10-25 and 2026-01-06 that overlap the commissioning footprint. The DP2 footprint comprises the Science Validation wide-area survey, five Deep Drilling Fields, and a number of targeted small-field regions, including Trifid and Lagoon, Prawn, M49, and New Horizons, all observed as part of the Rubin First Look campaign. Each field was imaged in up to six broad photometric bands, ugrizy, and coadded to produce deep imaging covering an estimated 3,000 deg2. The addition of single-visit-only areas expands the total DP2 footprint to an estimated 15,000 deg2, with coverage in at least one filter. The median per-visit PSF FWHM across the wide-area survey ranges from 1.17 arcsec in the z band to 1.26 arcsec in g and r bands. The deepest field, reaches estimated coadded 5σ depths of u=26 mag, g=26.8 mag, r=26.3 mag, i=26.1 mag, z=25.3 mag, y=23.9 mag. Based on a roughly five-month primary observing baseline and covering only part of the eventual LSST footprint, DP2's area, depth, and multiband coverage nonetheless support a broad range of early science investigations ahead of LSST Data Release This dataset is a subset of the full data release consisting of the source dataset type. These are measurements for detected sources in processed visit images. This release contains 28,589 datasets of this type.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Measurements of long-range two-particle correlation over a wide pseudorapidity range in p–Pb collisions at $ \sqrt{s_{\textrm{NN}}}$ = 5.02 TeV

Correlations in azimuthal angle extending over a long range in pseudorapidity between particles, usually called the “ridge” phenomenon, were discovered in heavy-ion collisions, and later found in pp and p–Pb collisions. In large systems, they are thought to arise from the expansion (collective flow) of the produced particles. Extending these measurements over a wider range in pseudorapidity and final-state particle multiplicity is important to understand better the origin of these long-range correlations in small collision systems. In this Letter, measurements of the long-range correlations in p–Pb collisions at $\sqrt{^SNN}$ = 5.02 TeV are extended to a pseudorapidity gap of Δη ~ 8 between particles using the ALICE forward multiplicity detectors. After suppressing non-flow correlations, e.g., from jet and resonance decays, the ridge structure is observed to persist up to a very large gap of Δη ~ 8 for the first time in p–Pb collisions. This shows that the collective flow-like correlations extend over an extensive pseudorapidity range also in small collision systems such as p–Pb collisions. The pseudorapidity dependence of the second-order anisotropic flow coefficient, v 2 (η), is extracted from the long-range correlations. The v 2 (η) results are presented for a wide pseudorapidity range of –3.1 < η < 4.8 in various centrality classes in p–Pb collisions. To gain a comprehensive understanding of the source of anisotropic flow in small collision systems, the v 2 (η) measurements are compared with hydrodynamic and transport model calculations. The comparison suggests that the final-state interactions play a dominant role in developing the anisotropic flow in small collision systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Towards an Integrated Process for CO 2 Capture and Utilization: Cultivation of Scenedesmus acutus Using Gaseous CO 2 and NH 3

Integrating CO 2 scrubbing from flue gas with its utilization in algae cultivation represents a potential means of lowering the cost of CO 2 capture. Towards this goal, this study sought to assess the feasibility of using gaseous a CO 2 /NH 3 stream, derived from CO 2 capture using aqueous ammonia, as a C- and N-source for algae cultivation. Scenedesmus acutus was cultured in 800 mL photobioreactors using gaseous CO 2 /NH 3 in mole ratios varying from 7 to 18. Excellent growth of Scenedesmus acutus was observed, the average growth rate for CO 2 /NH 3 = 10 of 0.171 ± 0.015 g/L·day exceeding that obtained using 1% CO 2 /N 2 and urea as the N-source (0.099 ± 0.28 g/L·day). Under optimal growth conditions (CO 2 /NH 3 mole ratio of 10), CO 2 utilization ranged from 57% to 72%, while the NH 3 utilization was >90%. The CO 2 /NH 3 feed rate was also found to exert a significant effect on algae productivity, excessive feed rates leading to accumulation of NH 3 in the culture at concentrations that were toxic to the algae. Consequently, to avoid the toxic effects of high NH 3 concentrations (>2.0 mM), it proved necessary to balance the NH 3 supply with the algae growth rate so that excessive NH 3 accumulation was prevented. This indicates that for practical applications, a CO 2 /NH 3 feed control strategy would be required that takes into account the ammonium ion concentration in solution and the pH so as to avoid significant concentrations of free NH 3 . Furthermore, analysis of the harvested biomass revealed a high protein (≥ 47 wt%) and a low ash content (< 3.6 wt%), suggesting it would be well suited for use as animal feed or as a feedstock for the production of bioplastics.

03 NATURAL GAS↗
DOE OSTI2025

deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.

Citizen science↗
DOE OSTI2025

Techno-Economic Analysis and Life Cycle Assessment of Alternative Fuels for Locomotives in the U.S. Freight Rail Sector

Freight rail is more energy-efficient than truck transport over long-haul distances, offering a low-energy and emissions-intensive option for transporting freight. This study evaluates techno-economic analysis and life cycle assessment of seven alternative unblended fuels for freight locomotive engines─biodiesel, renewable diesel (RD), bio-oils, methanol, dimethyl ether (DME), ethanol, and ammonia─across 16 fuel pathways utilizing soybean, corn, woody biomass, renewable hydrogen, and waste sources, e.g., sludge, manure, and industrial CO 2 , and compares these to conventional diesel. The minimum fuel selling price (MFSP) ranged from $\$2.05$ to $\$8.27$ per diesel gallon equivalent (2020 US dollars), with biocrude and RDs produced from hydrothermal liquefaction (HTL) of sludge having the lowest MFSPs due to coproduct credits and avoided waste treatment cost. Life cycle GHG emissions ranged from −41 to 53 g of CO 2 e/MJ. RD from waste via HTL achieves negative emissions by diverting sludge/manure from GHG-intensive conventional management. Few pathways such as biocrude, methanol, and DME require additional control for SO X emissions in the refinery, while ethanol, FT-diesel, and bio-oil require additional control for particulate matter emissions. Bio-oil and RD from sludge have lower marginal abatement cost or MAC (–$\$38$/tonne CO 2 lowest) while methanol and ammonia with renewable hydrogen have higher MAC ($\$490$/tonne CO 2 maximum).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Phosphine Ligand Binding and Catalytic Activity of Group 10–14 Heterobimetallic Complexes

We report heterobimetallic complexes have attracted much interest due to their broad range of structures and reactivities as well as unique catalytic abilities. Additionally, these complexes can be utilized as single-source precursors for the synthesis of binary intermetallic compounds. An example is the family of bis(pyridine-2-thiolato)dichloro-germanium and tin complexes of group 10 metals (Pd and Pt). The reactivity of these heterobimetallic complexes is highly tunable through substitution of the group 14 element and the neutral ligand bound to the transition metal. Here, we study the binding energies of three different phosphorous-based ligands, PR 3 (R = Bu, Ph, and OPh) by density functional theory and restricted Hartree–Fock methods. The PR 3 ligand-binding energies follow the trend of PBu 3 > PPh 3 > P(OPh) 3 , in agreement with their sigma-bonding ability. These results are confirmed by ligand exchange experiments monitored with 31 P NMR spectroscopy, in which a weaker binding PR 3 ligand is replaced with a stronger one. Furthermore, we demonstrate that the heterobimetallic complexes are active catalysts in the Negishi coupling reaction, where stronger binding PR 3 ligands inhibit access to an active site at the metal center. Similar strategies could be applied to other complexes to better understand their ligand-binding energetics and predict their reactivity as both precursors and catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An artificial intelligence accelerated virtual screening platform for drug discovery

Abstract Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel Na V 1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to Na V 1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Science & Technology - Other Topics↗
DOE OSTI2020

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10 4 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Attosecond inner-shell lasing at ångström wavelengths

Since the invention of the laser, nonlinear effects such as filamentation, Rabi cycling and collective emission have been explored in the optical regime, leading to a wide range of scientific and industrial applications. X-ray free-electron lasers (XFELs) have extended many optical techniques to X-rays for their advantages of ångström-scale spatial resolution and elemental specificity. An example is XFEL-driven inner-shell Kα 1 (2p 3/2 → 1s 1/2 ) X-ray lasing in elements ranging from neon to copper, which has been used for nonlinear spectroscopy and development of new X-ray laser sources. Here, in this study, we show that strong lasing effects similar to those in the optical regime can occur at 1.5–2.1 Å wavelengths during high-intensity (>10 19 W cm −2 ) XFEL-driven Kα 1 lasing of copper and manganese. Depending on the temporal XFEL pump pulse substructure, the resulting X-ray pulses (about 10 6 −10 8 photons) can exhibit strong spatial inhomogeneities and spectral splitting, inhomogeneities and broadening. Three-dimensional Maxwell–Bloch calculations show that the observed spatial inhomogeneities result from X-ray filamentation and that the broad spectral features are driven by sub-femtosecond Rabi cycling. Our simulations indicate that these X-ray pulses can have pulse lengths of less than 100 attoseconds and coherence properties that provide opportunities for quantum X-ray optics applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Optical and x-ray characterization of the Daedalus ultrafast x-ray imager

The Daedalus ultrafast x-ray imager is the latest generation in Sandia’s hybrid CMOS detector family. With three frames along an identical line of sight, 1 ns minimum integration time, a higher full well than Icarus, and added features, Daedalus brings exciting new capabilities to diagnostic applications in inertial confinement fusion and high energy density science. In this work, we present measurements of time response, dynamic range, spatial uniformity, pixel cross-talk, and absolute x-ray sensitivity using pulsed optical and x-ray sources. Here, we report a measured 1.5 Me — full well, pixel sensitivity at 9.58 × 10 —7 V/e — , and an estimate of spatial uniformity at ~5% across the sensor array.

47 OTHER INSTRUMENTATION↗