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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

3D nanolithography with metalens arrays and spatially adaptive illumination

The growing demand for advanced materials, miniaturized devices and integrated microsystems calls for the reliable fabrication of complex, multiscale, three-dimensional (3D) architectures, a need increasingly addressed through light-based and laser-based processes. However, owing to the field-of-view (FOV) limitations of conventional imaging optics, existing 3D laser nanofabrication techniques face fundamental challenges in throughput, proximity error and stitching defects on the path to scaling. Here, in this study, we present a scalable 3D nanofabrication platform that uses a metalens-generated focal spot array to parallelize two-photon lithography (TPL) beyond centimetre-scale write field areas. Metalenses are ideally suited for producing submicron-scale focal spots for high-throughput nanolithography, as they uniquely feature large numerical apertures (NAs), immersion media compatibility and large-scale manufacturability. We experimentally demonstrate a printing system that uses a 12-cm 2 metalens array to produce more than 120,000 cooperative focal spots, corresponding to a throughput exceeding 10 8 voxels s −1 . By programmatically patterning the focal spot array using a spatial light modulator (SLM), an adaptive parallel printing strategy is developed for precise greyscale linewidth modulation and choreographed printing of semiperiodic and fully aperiodic 3D geometries. We demonstrate parallel printing of replicated microstructures (>50 M microparticles per day), centimetre-scale 3D architectures with feature sizes down to 113 nm, and photonic and mechanical metamaterials. This work demonstrates the potential of 3D nanolithography towards wafer-scale production, showing how TPL could be used at scale for applications in microelectronics, biomedicine, quantum technology and high-energy laser targets.

Materials science↗
DOE OSTI2025

Review: Recent advances of ToF-SIMS for environmental analysis and imaging

Background: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface analysis technique, initially developed and applied in inorganic materials and semiconductors. In past decades, ToF-SIMS has attracted more attention in its analysis capabilities of organic materials, with increased applications in biology, medical, and health development. It has also become a versatile and effective tool in environmental analysis due to its high mass resolution, mass accuracy, and depth profiling. Results: In this review, we first give an overview of the principle of ToF-SIMS and follow with recent ToF-SIMS applications in exemplary environmental study cases, including atmospheric aerosol, soil, water, plant, and organic solvent analysis. Moreover, sample preparation techniques are summarized in relation to corresponding environmental applications. Specifically, we call attention to ToF-SIMS investigations showcasing studies in surface chemical compositions, images, and depth profile analysis. These findings emphasize the important role of interfacial chemistry in environmental processes and provide valuable insights into dynamic processes, such as chemical transformation, particle formation, plant biology, and microbial inspired biotechnology development. The mass spectral imaging results acquired by ToF-SIMS offer a deeper understanding of intermediate stages and transient phases for environmental specimens. Significance: In situ and operando imaging offer new possibilities in studying phenomena in real time with high spatial resolution. Furthermore, it is anticipated that more research groups will use ToF-SIMS in environmental research given recent advances in measurement capabilities and surging needs in chemical mapping of complex analytes and systems.

Aerosol↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2025

Sparse measurement medical CT reconstruction using multi-fused block matching denoising priors

A major challenge for medical X-ray CT imaging is reducing the number of X-ray projections to lower radiation dosage and reduce scan times without compromising image quality. However these under-determined inverse imaging problems rely on the formulation of an expressive prior model to constrain the solution space while remaining computationally tractable. Traditional analytical reconstruction methods like Filtered Back Projection (FBP) often fail with sparse measurements, producing artifacts due to their reliance on the Shannon-Nyquist Sampling Theorem. Consensus Equilibrium, which is a generalization of Plug and Play, is a recent advancement in Model-Based Iterative Reconstruction (MBIR), has facilitated the use of multiple denoisers are prior models in an optimization free framework to capture complex, non-linear prior information. However, 3D prior modelling in a Plug and Play approach for volumetric image reconstruction requires long processing time due to high computing requirement. Instead of directly using a 3D prior, this work proposes a BM3D Multi Slice Fusion (BM3D-MSF) prior that uses multiple 2D image denoisers fused to act as a fully 3D prior model in Plug and Play reconstruction approach. Our approach does not require training and are thus able to circumvent ethical issues related with patient training data and are readily deployable in varying noise and measurement sparsity levels. In addition, reconstruction with the BM3D-MSF prior achieves similar reconstruction image quality as fully 3D image priors, but with significantly reduced computational complexity. We test our method on clinical CT data and demonstrate that our approach improves reconstructed image quality.

Hossain, Maliha [ORNL]↗
DOE OSTI2025

Advancing X-ray quantum imaging through Monte-Carlo simulations

Imaging with X-rays poses fundamental limits due to radiation damage of the highly energetic photons. This becomes problematic for sensitive biological systems such as subcellular structures. Lowering the radiation dose, without sacrificing the signal-to-noise ratio, would be desirable for any kind of imaging modalities involving X-rays. To achieve this goal, quantum imaging with entangled X-ray photons constitutes a promising route. Production of biphotons have been demonstrated in the X-ray regime by the process of Spontaneous Parametric Down-Conversion (SPDC). However, compared to SPDC in the regime of visible light, the production rate for X-ray biphotons is extremely low. With the introduction of new high average brightness X-ray sources, such as 4th generation synchrotrons and high repetition rate Free-Electron X-ray Lasers (XFEL), quantum imaging may become practical. We introduce a ray tracing approach using Monte-Carlo sampling, specifically designed for quantum imaging with entangled X-ray photons generated by SPDC. By simulation, the superior image quality of quantum over classical imaging methods is demonstrated using realistic experimental conditions available at high repetition rate XFELs. With these simulations, we can efficiently assist the design of future experiments at beam lines, which can substantially accelerate the advancement of X-ray quantum imaging and reduce costs.

Entangled Photons↗
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)↗
DOE OSTI2025

Characterization of ablator dynamics initiated by picket-pulse conditions

Direct-drive laser-fusion targets may include low-cost plastic ablator material. Here, this work describes a tabletop experimental system designed to enable the investigation of the interaction of such ablator material as a function of the laser wavelength and the associated excitation mechanism. This experimental system offers time-resolved imaging of the material response with subpicosecond temporal resolution and ≈1-μm spatial resolution. The system involves synchronized femtosecond and picosecond lasers to initiate and characterize the dynamics of polystyrene planar targets irradiated by conditions similar to a “picket” prepulse from a direct-drive pulse shape. Ultrafast imaging of the initial excitation process followed by the resulting critical plasma formation, shock-wave propagation, and blowoff plasma expansion is used to compare the impact of excitation wavelength, 355 nm or 266 nm, on the ensuing dynamics. This work aims to investigate fundamental laser material interactions to help advance physics models that can inform the development of laser driver and target designs.

42 ENGINEERING↗
DOE OSTI2024

Synthesis and Characterization of Enhanced Conductivity Copper Composites [Abstract]

In this project, PNNL will extrude and characterize copper alloy wires embedded with graphene additives using the billets provided by NAECO via the shear assisted processing and extrusion (ShAPE TM ) technology. PNNL will measure the electrical properties of the extruded wires and also perform multimodal imaging to determine the microstructural features as required. These activities will be performed as part of a voucher service provided by PNNL for the CABLE Manufacturing Prize stewarded by DOE Advanced Manufacturing Office. This 6-month effort will be executed at PNNL with a budget of $100,000.00 (USD).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Innovative Method for Reliable Measurement of PEM Water Electrolyzer Component Resistances

Understanding the sheet resistance of porous electrodes is essential for improving the performance of polymer electrolyte membrane (PEM) water electrolyzers and related technologies. Despite its importance, existing methods often fail to provide reliable and comprehensive data, especially for porous materials with complex morphologies and non‐uniform thicknesses. This study introduces a robust and straightforward method for determining the sheet resistance of porous electrodes using a novel probe concept based on industrial printed circuit board (PCB) technology. This probe measures resistance across ten distances, ranging from 250 µm to 2500 µm, enabling local mapping of resistance. The study focuses on the sheet resistance of key components in PEM water electrolyzers, including the gas diffusion layer (GDL), porous transport layer (PTL), and catalyst layers deposited on a membrane. Additionally, an image‐processing‐based method is presented to obtain the thickness distribution of the studied catalyst layers, facilitating a detailed analysis of the electrical in‐plane resistivity with thickness variations. Overall, this methodology has the potential to expedite material integration and bridge the gap between electrode engineering and single‐cell testing, thereby advancing the development of PEM water electrolyzers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Key Intermediate Nanostructures in the Self-Assembly of Amphiphilic Polypeptoids Revealed by Cryo-TEM

Amphiphilic copolypeptoids are known to form a variety of nanostructures (fibers, tubes, sheets, etc.), but the assembly mechanisms and key intermediates remain underexplored. This study investigates the intermediate structures formed during the early stages of self-assembly in diblock copolypeptoids using cryo-transmission electron microscopy (cryo-TEM). Here we focused on two diblock copolypeptoids, one with a free N-terminus and the other with a capped N-terminus, which ultimately form less-ordered nanofibers and well-ordered nanosheets, respectively. Through cryo-TEM imaging of vitrified solutions at various time points during the self-assembly process, the study identified micelles and vesicles as key intermediate structures. Notably, the formation of vesicles as intermediates is unusual in crystallization-driven self-assembly and suggests a unique pathway in polypeptoid self-assembly. The study provides direct imaging evidence of key intermediates in polypeptoid self-assembly, advancing the understanding of their self-assembly mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Methods development towards automated, physics-informed, quantitative quality control of TRISO-SiC

Tristructural-isotropic (TRISO) fuel particles have been developed as a high-performance fuel for use in high-temperature gas-cooled reactor (HTGR) systems due to their high efficiency and stability under both normal and off-normal conditions. Broader deployment of this technology in advanced nuclear applications may benefit from quantitative quality assurance and quality control (QA/QC) methods that directly link TRISO properties to downstream performance. A key TRISO property is the SiC layer microstructure, which influences fission product retention during irradiation. However, existing QA/QC for the TRISO-SiC microstructure comprises only a qualitative visual inspection; therefore, there is a clear opportunity for the development of quantitative methods for TRISO QA/QC. Here, to this end, previous work has demonstrated an image processing approach to grain boundary (GB) identification and subsequent extraction of microstructural metrics; however, extensive twinning within the SiC layer complicates such analyses because twin GBs significantly influence microstructural metrics but are not expected to contribute to fission product transport. This study presents the initial development, training, and testing of an ML-based image segmentation algorithm designed to identify and remove twin GBs from standard backscattered electron micrographs, providing an industrially applicable, quantitative, and physically meaningful QA/QC approach for the TRISO-SiC microstructure. Although pixel-wise performance metrics for the twin predictions are low, the change in grain area and the number of GB pixels after twin removal predicted by the ML workflow are within 1% of the true values calculated using crystallographic data. This suggests that the model is well capable of predicting overall twin boundary structures and grain morphology, and continued advancement of this approach could enable automated, scalable, and physics-informed QA/QC for TRISO-SiC microstructures, supporting the reliable qualification of coated particle fuels for next-generation reactor systems.

Computer vision↗
DOE OSTI2026

Emergence of complex-regular eutectic patterns in Al–Ge: Observations from correlative nano-imaging

Eutectic solidification exemplifies nonequilibrium pattern formation, making it a well-studied moving boundary problem. Yet the mechanisms behind the formation of complex-regular microstructures – particularly in highly anisotropic systems with a significant volume fraction of a faceted phase – remain poorly understood. Our understanding of such systems is made complicated by the nonlinear interface kinetics and unique growth dynamics characteristic of faceted phases. To address these challenges, we investigate a model Al–Ge eutectic system, where the faceted Ge phase constitutes a substantial volume fraction (~0.35) and where the two solid phases arrange into so-called “fishbone” or “feather” complex-regular patterns. Using synchrotron-based x-ray nano-imaging and nanotomography with high spatial resolution (22 nm per pixel), we capture in real-time the evolution of the solid–liquid interfaces and the resulting three-dimensional microstructures in this faceted/non-faceted eutectic system. By integrating these observations with electron backscattered diffraction, we elucidate the crystallographic biases on the solidification process and the mechanisms driving the formation of such complex-regular microstructures. These findings inform a new growth model for irregular eutectics in (near-)symmetrical phase diagrams, offering insight on advanced microstructural design and processing strategies. More broadly, we demonstrate how interfacial curvature is generated in irregular eutectic alloys and how it depends on the volume fraction of the faceted phase.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Step-by-Step Protocol from METASPACE to Biological Interpretation

Mass spectrometry imaging (MSI) represents an exceptional tool for exploring complex biological systems spatially at the molecular level. However, due to its multidimensional nature and large-scale data output, it presents considerable challenges when it comes to extracting meaningful biological insights. Recent advancements, such as the METASPACE platform, have enabled researchers to efficiently process, annotate, and interpret MSI datasets by leveraging machine learning and cloud-based infrastructure. In this tutorial, we present a detailed and user-friendly R-pipeline designed to help METASPACE users navigate untargeted metabolomic annotations and transform them into practical insights about their biological systems. By combining METASPACE annotations with rapid R-based screening, this workflow not only streamlined the analytical process but also enhanced the understanding of spatial molecular distribution, especially for complex systems. Here, this easy-to-follow approach has the potential for applications in diagnostics, drug discovery, environmental and ecological processes, and more. We envision this pipeline to be particularly useful for newcomers to the field of MSI and

Moreno Pedraza, Abigail↗
DOE OSTI2025

Structure of molten ytterbium aluminum garnet

Rare earth aluminum garnets are important materials in optical, dielectric, and thermal barrier applications. To advance the understanding of their melt processing and glass forming ability, we report the atomic structure of molten Yb 3 Al 5 O 12 over 1770–2630 K, which spans the equilibrium and supercooled liquid regimes. The melt density at T m = 2283 K is 5.50 g cm –3 , measured via silhouette imaging of electrostatically levitated drops over 1010–2420 K. Four separate structure measurements were made with aerodynamically levitated melts using x-ray and neutron diffraction with isotope substitution of Yb ( 172 Yb, 174 Yb, or nat Yb). Empirical potential structure refinement models were developed, which are in excellent agreement with the experiments. Coordination environments for Al–O are predominantly 4- and 5-coordinate, with a mean coordination of n AlO = 4.43(8), while Yb–O environments mostly range from 5- to 8-coordinate, with n YbO = 6.26(8). The cation–oxygen polyhedra are connected primarily by corner-sharing, with edge-sharing constituting up to ~1/3 of the connectivity among polyhedra with Yb or higher-coordinated Al–O. Structurally, the –Al–O– network in molten Yb 3 Al 5 O 12 appears conducive to glass formation: n OAl = 1.85(3), there are 1.86 AlO x –AlO x connections per Al atom (e.g., a mixture of Q 3 and Q 4 units), and the modal ring size is six cations. These characterize a network that is somewhat less constrained compared to SiO 2 glass, yet Yb 3 Al 5 O 12 cannot be quenched into crystal-free glass. Here, aluminum garnet compositions with larger rare earth cations do form glass, so these characterizations help reveal the structural characteristics corresponding to the limit of glass forming ability in rare earth aluminates.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data-driven picosecond X-ray imaging for quantitative plasma-induced shock characterization

Imaging dynamic events, especially shockwave behavior, is key to advancing high-energy-density (HED) research. Recent advances in fourth- and fifth-generation X-ray light sources allow for high-resolution imaging of fast phenomena, but limited beam time necessitates maximizing data acquisition. We present a benchtop-scale pulsed plasma device submerged in liquid heptane, capable of generating dynamic events at rates exceeding 10 Hz, supporting the field’s data-driven goals by producing large, high-quality imaging datasets. Using X-ray phase contrast imaging (XPCI) at the Advanced Photon Source, we imaged weak shockwaves (Mach ~ 1.2) in heptane interacting with plasma-induced cavitation bubbles, causing deviation from Rankine-Hugoniot behavior; to our knowledge, this represents the first direct imaging of such interaction. Our quantitative analysis offers insight into weak shock phenomena and energy-focusing applications in pulsed plasmas. These results highlight the potential for large datasets to advance dynamic HED research at current light source facilities, and have implications for fields such as inertial confinement fusion, plasma-enhanced chemical processing, and biomedical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High‐speed 4‐dimensional scanning transmission electron microscopy using compressive sensing techniques

Abstract Here we show that compressive sensing allows 4‐dimensional (4‐D) STEM data to be obtained and accurately reconstructed with both high‐speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4‐D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal‐to‐noise ratio in the recovered phase using 0.3% of the total data. Lay abstract : Four‐dimensional scanning transmission electron microscopy (4‐D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2‐dimensional signal is acquired at each 2‐D probe location, equating to a 4‐D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4‐D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100‐1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4‐D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10‐100 times. The acquired data is then processed to fill‐in or inpaint the missing data, taking advantage of the inherently low‐complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4‐D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4‐D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100‐300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.

Robinson, Alex W.↗
DOE OSTI2024

Thermomechanical Degradation of Sintered Copper Under High-Temperature Thermal Shock

The need for reliable bonded interface materials is critical to realize the performance benefits of wide-bandgap devices in power electronics modules, especially in operating temperatures greater than 150 degrees C. In this paper, we investigate the thermomechanical performance of sintered copper (Cu) as a large-area attachment, bonded between Cu baseplates and active-metal-bonded substrates, under accelerated thermal shock (-40 degrees C to 200 degrees C) conditions. In the fabrication phase of the samples, we used different stencil patterns and found that the grid and stripe patterns resulted in a better outgassing of the residual organics during the sintering process, thereby ensuring a substantially improved bond quality compared to a full-area print. The paste consisted of Cu microflakes, and we performed sintering using a Budatec SP300 sintering press at 275 degrees C with 15 MPa of bonding pressure for 5 minutes in a nitrogen atmosphere. Under accelerated experiments, we monitored the degradation of the sintered Cu bond in the samples through C-mode scanning acoustic microscope (C-SAM) images. To quantify the defect percentage in C-SAM images, we investigated image denoising techniques to exclude the pattern prints. Finally, we cross-sectioned a sample and obtained scanning electron microscope images, which revealed adhesive fracture as the dominant failure mechanism.

ADVANCED PROPULSION SYSTEMS,INORGANIC, ORGANIC, PH↗
DOE OSTI2025

Hyperspectral segmentation of plants in fabricated ecosystems

Hyperspectral imaging provides a powerful tool for analyzing above-ground plant characteristics in fabricated ecosystems, offering rich spectral information across diverse wavelengths. This study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed scale convolution neural networks. The segmentation process leverages the diversity of ensembles to achieve high accuracy with minimal labeled data, reducing labor-intensive annotation efforts. To further enhance robustness, we incorporate image alignment techniques to address spatial variability in the dataset. Downstream analysis focuses on using the segmented data for processing spectral data, enabling monitoring of plant health. This approach provides a scalable solution for spectral segmentation, and facilitates actionable insights into plant conditions in complex, controlled environments. Our results demonstrate the utility of combining advanced machine learning techniques with hyperspectral analytics for high-throughput plant monitoring.

Zwart, Petrus H.↗