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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Performance of a Modular Ton-Scale Pixel-Readout Liquid Argon Time Projection Chamber

The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day data set consisting of 25 million cosmic ray events collected in the spring of 2021. We use this sample to demonstrate the imaging performance of the charge and light readout systems as well as the signal correlations between the two. We also report argon purity and detector uniformity measurements and provide comparisons to detector simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Coherent mode and turbulence measurements with a fast camera

This study employs a fast camera with frame rates up to 900,000 fps to measure the transfer of energy across spatial scales in helicon source plasmas and during flux rope mergers and the measurement of azimuthal mode structures in helicon plasmas. By extracting pixel-scale dispersion relations and power spectral density (PSD) measurements, we measure the details of turbulent wave modes and energy distribution across a broad range of spatial scales within the plasma. We confirm the presence of drift waves in helicon plasmas, as well as the existence of strong dissipation regions in the PSD at electron skin depth scales for both helicon and flux rope merger experiments. This approach overcomes many limitations of conventional probes, providing high spatial and temporal resolution, without perturbing the plasma.

Instruments & Instrumentation↗
DOE OSTI2025

Fast and Accurate Pixel Calibration of Tof Neutron Diffractometers with Machine Learning

At a spallation neutron source, neutron pulses of varying energies are generated, and the detection of neutrons by instrument detectors is recorded as time-of-flight from the emission of the neutron pulse to its arrival at specific detector pixels with high time resolution. The flight path of neutrons from the moderator to the sample and then to the detector must be precisely calibrated at the detector-pixel level using standard powders, so the neutron events from all pixels can be time-focused to produce high-resolution diffraction patterns. Modern time-of-flight neutron diffractometers at spallation neutron sources are equipped with two-dimensional detectors with millimeter-scale pixelations. The number of pixels in a diffraction instrument can reach millions, which makes a single-pixel-level calibration process time-consuming or even impossible with conventional refinement or fitting approaches. Here we present a machine-learning-aided calibration process using a train-and-predict approach, in which machine learning models are trained on the relationship between an individual pixel time-of-flight diffraction pattern and its diffraction constant. These models use a portion of the available pixels for training, and a good model then predicts the diffraction constants precisely and rapidly for large sets of pixel diffraction patterns.

detector pixel calibration↗
DOE OSTI2026

Extremized nonlinear and linearized responses in soft metamaterials enabled by gradient-based design and grayscale digital light processing

In this study, we develop a gradient-based design approach that exploits grayscale digital light processing (DLP) 3D printing for extremizing the nonlinear and linearized response of soft metamaterials — materials that harness engineered geometric instabilities to undergo large and programmable changes in configuration. Grayscale DLP approaches modulate local mechanical properties at the pixel scale by tuning the light intensity within a single grayscale image, unlocking an exceptionally large design space. To effectively navigate this space, we develop smooth mappings between local light intensity values and global quantities of interest that characterize the behavior of soft metamaterials. Enabling these smooth mappings are robust and differentiable nonlinear finite element simulations powered by a trust region solver. A PDE-constrained optimization problem is then solved to invert these mappings and produce light intensity distributions that endow the printed part with varying stiffness and flexibility in distinctive regions. It is shown that optimizing the distribution of soft and stiff phases throughout a metamaterial structure results in markedly different buckling and self-contact configurations to drive extremized nonlinear compression and linearized vibration responses. Optimized light intensity distributions are translated to grayscale images and directly used to print soft metamaterial samples, showing remarkable agreement between the buckling and self-contact response in simulated and measured deformed configurations.

Additive manufacturing↗
DOE OSTI2026

Low power on-chip data transmission for wafer-scale monolithic active pixel sensors

Here, this paper details the implementation of the digital pulse shaping subsystem within the Backbone Transmission Line Encoding (BTLE) driver, a low-power, long-distance on-chip data transmission solution designed in a 65 nm CMOS process. Digital pulse shaping is critical for minimizing inter-symbol interference (ISI) caused by bandwidth limitations of on-chip interconnects, especially in wafer-scale monolithic active pixel sensors (MAPS). A duobinary encoder coupled with a parallelized polyphase finite impulse response (FIR) filter is used for efficient shaping of the transmitted signal spectrum. This reconfigurable architecture achieves reliable 160 Mb/s data transfer over a 10 cm on-chip link, as validated by simulations demonstrating low power consumption (FoM 37.3 fJ/bit/mm of transmission line length) and effective ISI mitigation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Results for pixel and strip centimeter-scale AC-LGAD sensors with a 120 GeV proton beam

Here, we present the results of an extensive evaluation of strip and pixel AC-LGAD sensors tested with a 120 GeV proton beam, focusing on the influence of design parameters on the sensor temporal and spatial resolutions. Results show that reducing the thickness of pixel sensors significantly enhances their time resolution, with 20-μm-thick sensors achieving around 20 ps. Uniform performance is attainable with optimized n + sheet resistance, making these sensors ideal for future timing detectors. Conversely, 20-μm-thick strip sensors exhibit higher jitter than similar pixel sensors, negatively impacting time resolution, despite reduced Landau fluctuations with respect to the 50-μm-thick versions. Additionally, it is observed that a low resistivity in strip sensors limits signal size and time resolution, whereas higher resistivity improves performance. This study highlights the importance of tuning the n+ sheet resistance and suggests that further improvements should target specific applications like the Electron–Ion Collider or other future collider experiments. In addition, the detailed performance of four AC-LGADs sensor designs is reported as examples of possible candidates for specific detector applications. These advancements position AC-LGADs as promising candidates for future 4D tracking systems, pending the development of specialized readout electronics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Gaia: segmented germanium detector for high-energy X-ray fluorescence and spectroscopic imaging

We present Gaia, a monolithic array of 96 high-purity germanium pixel detectors integrated with a custom low-noise application-specific integrated circuit (ASIC) and a field-programmable gate array (FPGA)-based data acquisition system. The sensor operates at ∼100 K using a commercial closed-cycle cryocooler, with the in-vacuum electronics thermally isolated from the cold finger to ensure thermal stability. The system demonstrates an average energy resolution of 711 eV at 122 keV, measured using a 57 Co source, and 253 eV at 5.89 keV, measured with 55 Fe across all channels. The readout architecture incorporates a high-performance FPGA paired with a dual-core ARM processor, forming a complete embedded Linux-based computing platform. Communication between the processor and FPGA is handled via memory-mapped I/O, and data are streamed over high-speed gigabit Ethernet. A full-scale 384-pixel Gaia detector, based on this 96-element module, is currently under fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗
DOE OSTI2024

2x2 Demonstrator High-Coverage Light Readout System in a Novel LArTPC

DUNE is a long-baseline Liquid Argon Time Projection Chamber (LArTPC) neutrino oscillation experiment. Its near detectors, located at Fermi National Laboratory, will constrain systematic errors on far detector measurements. The 2x2 Demonstrator, a novel proof of concept prototype for the ND LArTPC, has a modular TPC design that mitigates event pileup in a high-occupancy nearline beam environment, further aided by a native 3D pixelated charge readout and a 30% coverage, low profile light readout. This will be the first large-scale LArTPC to utilize pixels in place of wire planes

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

High-count-rate effects in event processing for the XRISM/Resolve X-ray microcalorimeter. II. Energy scale and resolution in orbit

The Resolve instrument on the X-ray Imaging and Spectroscopy Mission (XRISM) uses a 36 pixel microcalorimeter designed to deliver high-resolution, non-dispersive X-ray spectroscopy. Although it is optimized for extended sources with low count rates, Resolve observations of bright point sources are still able to provide unique insights into the physics of these objects, as long as high-count-rate effects are addressed in the analysis. These effects include the loss of exposure time for each pixel, changes in the energy scale, and changes in the energy resolution. To investigate these effects under realistic observational conditions, we observed the bright X-ray source, the Crab Nebula, with XRISM at several offset positions with respect to the Resolve field of view and with continuous illumination from 55 Fe sources on the filter wheel. For the spectral analysis, we excluded data where exposure-time loss was too significant to ensure reliable spectral statistics. The energy scale at 6 keV shows a slight negative shift in the high-count-rate regime. The energy resolution at 6 keV worsens as the count rate in electrically neighboring pixels increases, but can be restored by applying a nearest-neighbor coincidence cut (“cross-talk cut”). We examined how these effects influence the observation of bright point sources, using GX 13+1 as a test case, and identified an eV-scale energy offset at 6 keV between the inner (brighter) and outer (fainter) pixels. Users who seek to analyze velocity structures on the order of tens of km s–1 should account for such high-count-rate effects. These findings will aid in the interpretation of Resolve data from bright sources and provide valuable considerations for designing and planning for future microcalorimeter missions.

X-rays: general↗
DOE OSTI2024

TinyTPC: A Test Stand for LAr Doping

LArTPCs are widely used in neutrino experiments at the GeV scale. They measure the charge and light produced by interactions between charged particles and argon atoms. While charge is collected efficiently, light is not. TinyTPC is a small scale LArTPC with a pixelated readout for R&D with photosensitive dopants, which convert light into charge. TinyTPC studies the effects of adding photosensitive dopants and xenon to liquid argon to enhance charge signals of events in the MeV scale.

Rodriguez Thorne, Paloma↗
DOE OSTI2025

Solar Neutrino Detection with a Pixelated Liquid-Argon Time Projection Chamber

This thesis presents a study of low-energy solar neutrino detection using large-scale LArTPCs, focussing on novel pixelated readout technologies. Solar neutrinos offer a unique probe of fundamental neutrino properties and solar physics, but their detection in the MeV range is challenged by backgrounds. We investigate two complementary technologies: SoLAr, which integrates LArPix-based pixelated charge collection with Silicon Photomultipliers (SiPMs) in a hybrid anode design for simultaneous charge and light detection; and Q-Pix, a triggerless pixelated readout architecture based on charge integrate-reset circuits with local clocks, where Reset Time Differences encode ionisation waveforms via time-to-charge conversion. Two SoLAr prototypes were developed and operated, demonstrating VUV-sensitive SiPM performance in liquid argon and accurate charge-light signal matching with a charge detection threshold of $\sim 100 \mathrm{keV}$. We also implement a complete simulation and reconstruction framework, incorporating realistic detector geometry, electron transport, readout response, and detailed signal and background models, including intrinsic argon and radon progeny, as well as site-specific $\gamma$-ray and neutron fluxes. For Q-Pix, we demonstrate that with a pixel size of $4\times 4$ mm$^2$ and a reset threshold of 1 fC ($\sim 0.1475$ MeV), full-scale operation produces data volumes below 1 PB per 10 ktonne-year. For SoLAr, assuming a shielded DUNE-like detector and 100 ktonne-year exposure, we project uncertainties of $0.90\times 10^{-5}$ eV$^2$ on $\Delta m^2_{21}$ and 0.033 on $\sin^2\theta_{12}$, improving to $0.46\times 10^{-5}$ eV$^2$ and 0.025 with 400 kilotonne-year. At this higher exposure, we also obtain a day–night flux asymmetry at the level of $( -5.6 \pm 3.6 ) \%$. Combining Monte Carlo modelling, hardware validation, and advanced reconstruction techniques, this work establishes a path toward next-generation ktonne-scale LArTPCs as observatories for precision solar neutrino physics.

Ruiz Ferreira, Guilherme [Manchester U.] (ORCID:00↗
DOE OSTI2026

Comparison of automated chemical-guided segmentation and human annotation of soil organic matter in X-ray microcomputed tomography imaging in contrasted soil types

Soil organic matter (OM) formation and persistence is strongly influenced by the spatial distribution of organic substrates and microscale soil heterogeneity by dictating OM accessibility to microorganisms. However, traditional size and/or density fractionation techniques disrupt aggregate architecture, eliminating spatial information needed to fully understand intra-aggregate OM distribution. To quantify three-dimensional OM spatial distribution and automate segmentation in X-ray microcomputed tomography (µCT) imaging without human annotation bias, we developed an iodine gas vapor (I2) based staining workflow that eliminates labor-intensive manual annotation while maintaining segmentation accuracy, using aggregates from four taxonomically diverse soils (Xerofluvent, Haploxeroll Sphagnofibrist, Palehumult) with an 8-fold range of soil organic carbon. Human annotation of 10 µCT slices by the experienced and inexperienced annotators resulted in variations up to 3% in the Dice similarity coefficient (DSC), reflecting a degree of inherent subjectivity of manual labeling. Such inconsistencies are expected to compound as the number of manually annotated slices increases. Dual-energy µCT imaging at 33.1 keV (below the iodine (I) K-edge) and 33.2 keV (above the I K-edge) was used to resolve aggregate microstructure following I2 staining. The automated image subtraction pipeline identified OM regions by the I Kedge induced brightness increases, achieving DSC values of 0.58–0.83 relative to an experienced annotator. Sensitivity analyses revealed that the reconstruction alpha value—optimized via the open-source tool TomocuPy—and the 3D registration slice count were the primary determinants of accuracy, providing a novel benchmark for dual-energy soil imaging. The pipeline without GPU acceleration achieved 9.6 to 43.2 times faster than manual annotation. Using GPU-accelerated image post-processing and affine transformation matrices, the pipeline successfully segmented OM elements for large-scale datasets (3232×3232 pixel, 2048 slices) within ~5200 s from raw file acquisition to segmented output. The high-throughput approach enables the quantification of OM spatial distribution across diverse and heterogeneous soil.

Soil microbial biomass↗
DOE OSTI2025

Estimation of cutting tool wear using an elastomeric tactile sensor

Machining performance of cutting tools and part quality are affected by the geometric condition of the cutting edge, which is influenced by thermomechanical loads experienced during the process. Tool condition monitoring (TCM) systems provide insight for timely replacement of cutting tools. However, existing TCM systems are expensive and require specialized equipment or sensors, hindering widespread adoption. A novel TCM system is developed herein using an elastomeric tactile sensor. Sensor images of the cutting edge are used to quantify wear using two distinct algorithms. In the first algorithm, the unworn and worn edges are identified based on Canny edge detect. In the second, the unworn edge is identified using edge detection while the region of wear is identified using a relative intensity method. In both cases, the maximum wear width is calculated based on an experimentally determined pixel to-physical distance scale. The TCM system is first used to estimate flank wear on a solid carbide helical end mill before evaluating its robustness by employing it to estimate insert wear of an indexable helical end mill. Measurements are also performed manually using an optical microscope and a high-resolution focus variation microscope for verification. The novel technique estimates flank wear in the solid carbide tool with a high accuracy of 98%. Larger discrepancies are observed for the inserts, however, with overlapping uncertainties. In conclusion, the technique shows promise in adaptability, automation, and closed loop control of machine tools.

Machining↗
DOE OSTI2026

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit

Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models based on architectures like vision transformers. To address this challenge, we introduce a multiscale hierarchical Turbulence Transformer that reduces sequence length from billions to a few millions and a novel RingX sequence parallelism approach that enables scalable long-context learning. We perform scaling and science runs on the Frontier supercomputer. Our approach demonstrates excellent performance up to 1.1 EFLOPS on 32,768 AMD GPUs, with a scaling efficiency of 94\%. To our knowledge, this is the first AI model for turbulence that can capture small-scale eddies down to the dissipative range in three-dimensional turbulence at high Reynolds numbers.

Yin, Junqi [ORNL] (ORCID:0000000338435520)↗
DOE OSTI2024

Smart Pixel Sensors for the HL-LHC

Large-scale particle physics experiments produce tens of terabytes of data every second. Innovative methods to manage the data rate at the HL-LHC, which expects to operate at 10x the luminosity of what the LHC was initially designed for, are needed. AI-on the chip provides a way to intelligently filter out low momentum clusters in the pixel detector. This will open up an opportunity to use the pixel detector for the first time in the CMS Level-1 trigger, and lead to increased sensitivity to new physics measurements and searches. We have taped out our first chip, which incorporates a $p_T$ filtering algorithm on an ASIC chip. Our initial $p_T$ filtering algorithm considers clusters that are tracked by CMS. We will report on ongoing studies seeking to enhance the performance of our filter by utilizing unsupervised learning on untracked clusters, thus increasing background rejection.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Z-Target Radiography Postprocessing With A Deep Convolution Neural Network

Analyzing X-ray radiographs is crucial for understanding target behavior in Inertial Confinement Fusion (ICF) and High Energy Density (HED) platforms. However, the density of Magneto Raleigh Taylor (MRT) bands and limitations of target materials often obscure relevant spike growth and density information. To address this issue, machine learning postprocessing techniques can be applied to remove darkened regions in radiography images. In this study, a novel method is presented for removing MRT darkened regions from z-target radiographs using a convolutional neural network (CNN). The CNN, consisting of six layers, treats the darkened regions as noise and employs a mixed loss function and end-to-end frameworks to suppress them while preserving sharpness. The six-layer architecture is designed to effectively learn features when provided with a larger volume of learning space. Each layer is optimized using a mixed loss function that combines a standard loss pixel approach with a multi-scaled structural similarity index loss, which considers luminance, contrast, and structure in local neighborhoods. This approach is particularly beneficial for capturing the stochastic structure of MRT limbs. Due to the limited availability of experimental data, training is conducted using synthetic target radiography from 3D Alegra simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

28nm front end ASIC and 12” LGADs for 3D integration

The 3DIntSenS Collaboration—a joint effort between SLAC, Fermilab, and LLNL—is developing enabling technologies for next-generation radiation imaging detectors that combine ultra-fine spatial resolution (≈10 μm) with precision timing (<20 ps), while maintaining low power <1 W/cm2 and high data throughput. The approach leverages 3D integration between advanced CMOS readout ASICs and finely pixelated LGAD sensors to achieve the performance and scalability required for large-area, high-rate applications. High-granularity, precision-timing detectors are essential for scientific advances in HEP, NP, BES, and FES, but widespread adoption is limited by the cost and complexity of 3D integration. To close this gap, the collaboration is developing LGAD sensors compatible with 12-inch commercial CMOS processes, enabling cost-effective integration with high-performance ASICs under development. We present the design and results from a 28 nm CMOS ASIC prototype, including a low-jitter front end, and in-pixel TDC demonstrating sub-10 ps timing resolution. We also report on the co-design and characterization of reticle-scale LGAD sensors with 50 μm and 100 μm pixels and introduce the next 10k-pixel ASIC designed for full 3D integration. These advances represent a critical step toward scalable, high-resolution radiation imaging systems for future scientific instrumentation.

England, Troy [Fermilab] (ORCID:0000000154405255)↗