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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Imaging the dynamics of initial laser-driven shocks and blowoff plasmas in polystyrene under laser-direct-drive fusion conditions

The dynamics of laser-driven shock propagation in solid ablator material, along with the concomitant processes of shinethrough and blowoff plasma plume expansion, at conditions relevant to laser-direct-drive fusion are measured using a tabletop system capable of supporting rapid dataset development with significantly higher resolution compared to existing methods. The experimental results are directly compared with current radiation-hydrodynamic simulations using the two-dimensional code . Discrepancies between experiments and simulations are evidenced and attributed to limitation of simulation to account for important processes involved in the solid-to-plasma transition. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗
DOE OSTI2025

Mechanically flexible mid-wave infrared imagers using black phosphorus ink films

The mid-wave infrared (MWIR) spectral range (λ = 3–8 μm) enables important sensing and imaging applications, including non-invasive bioimaging, night vision, and autonomous navigation. Commercial MWIR photodetectors are limited to rigid imagers based on heteroepitaxial materials. There is an emerging need for mechanically flexible MWIR imagers to broaden their functionality and practicality. Recently, photodetectors using van der Waals (vdW) black phosphorus (BP) flakes have demonstrated highly sensitive room-temperature photodetection. Additionally, vdW materials are solution-processable, facilitating scalable processing and flexible device fabrication. In this work, we present flexible MWIR imagers consisting of photodiodes fabricated on thin plastic substrates using BP ink films. We demonstrate mechanically robust responsivity up to 2.5-mm bending radii and after 5000 bending cycles. Leveraging this flexibility, we achieve full-azimuthal imaging, detecting directional light sources with precision. These results establish a scalable approach for large-area, conformable MWIR imaging and pave the way for integration with flexible electronics.

Wijaya, Theodorus Jonathan↗
DOE OSTI2025

Improving microstructures segmentation via pretraining with synthetic data

Image analysis of material microstructures through microscopy is an integral capability in the field of materials science. The topological and chemical information obtained through microscopy allow us to draw vital connections between material microstructures, properties, and processing. While scanning electron microscopy (SEM) is able to yield a considerable wealth of information interpretable by the intuition of experts, there has been considerable interest in using machine learning, convolutional neural networks (CNNs) in particular, for such image analysis task. Training CNNs for an image analysis task requires a large annotated dataset. However, in many materials science applications, obtaining a large annotated dataset is cost and labor intensive. In this work, we study the use of synthetic data to enlarge the available annotated experimental data of uranium oxide. We utilize a modified Potts model to simulate uranium oxide particles with morphologies similar to those observed experimentally. We then leverage an image-to-image translation model to synthesize the simulated particles as if they are acquired with SEM. Through this process, we obtain pairs of particle images and their corresponding SEM representations, which corresponds to pairs of annotations and images. Unlike previous works, we leverage synthetic data for pretraining a CNN model prior, and finetune that model further with experimental data. We experimentally demonstrate that using synthetic data as incremental learning process benefits the overall performance compared to training a model on combined synthetic and experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Aerial and Processed Model Data Representing As-built Conditions in Coastal Port Arthur, Texas in May 2025

This dataset was collected by the Co-Design Team of the Southeast Texas Urban Integrated Field Lab, a research initiative led by the University of Texas at Austin and funded by the U.S. Department of Energy. The broader project focuses on developing climate-resilient design solutions for the Beaumont–Port Arthur region, with more information available at www.setx-uifl.org. Our team conducted aerial surveys of the Port Arthur coastal neighborhood in May 2025, before the start of construction scheduled for Summer 2026. These pre-construction datasets are designed to facilitate comparative analyses, including pre- and post-construction assessments and simulated inundation scenario evaluations. Aerial images were captured using DroneDeploy autonomous flight systems, with imagery processed through the DroneDeploy engine. All original aerial photographs are provided in JPG format and organized in zipped folders by area. The processed data package includes: 3D surface models Orthomosaics Geospatial and topographic mappings Point clouds For guidance on file contents, structure, and recommended usage, please refer to the included README file.

2D mapping↗
DOE OSTI2024

Massive all-atom analysis of 2D materials with quantum properties (Final report)

Improvements in microscopy have enabled the acquisition of data at a scale that is difficult to process manually, making automated machine learning approaches to analyzing experimental images essential. In this project, we developed and applied machine learning (ML) workflows for atomic resolution scanning transmission electron microscopy (STEM) images. This development included improving both methodology as well as generating user-friendly codes. We developed machine learning architectures which, after training, automatically identify the location and types of defects throughout a material. We used these data to produce class-averaged images of 2D atomic coordinates with up to 0.3 pm precision, uncovering the structure and oscillations of long-range strain fields around point defects in WSe 2-2x Te 2x . We also resolved a long-standing problem in this field in the training of ML models, a lack of labeled experimental data, by developing a cycle-GAN that transformed simulated-generated labeled data into labeled data indistinguishable from experiment and therefore suitable for training. This removed the remaining parts of the ML data processing workflow where human intervention was still critical and therefore a bottleneck to working at scale. Codes have been developed and released for this full machine learning workflow. ML approaches to partially automate STEM acquisition were also developed. Finally we applied ML and other advanced data processing methods to several materials science problems in two-dimensional materials, including studying the evolution of hyperuniformity with defect concentration in WSe2, understanding phase transformations in transition metal dichalcogenides during in-situ heating in the STEM, and exploring how 2D interfaces transform from twisted into aligned structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Infrared triggered dwell and active cooling thermal control effects on microstructural uniformity in DED

Directed Energy Deposition (DED) offers rapid large scale fabrication, but difficulty in delivering consistent microstructures and properties hinders the use of DED fabricated components in safety or performance critical applications. Variability stems from the complex thermal cycles generated by the toolpath used to print the required geometry. Several practical methods have become established in DED to regulate overheating, such as active cooling of the baseplate structure or the use of an infrared camera to inject interlayer pauses to ensure the top layer of the component cools to a set temperature, which have been shown to affect microstructure. However, no critical assessment has been performed as to how effective these controls are in promoting microstructural uniformity in the context of complex layer timing commonly generated by non-prismatic geometries. Here we show how controls influence the thermal field, phase transformations, and dynamic annealing of a low-temperature transformation steel using infrared imaging and operando neutron diffraction. Counterintuitively, common thermal homogenization process controls can reduce microstructural uniformity because these approaches stabilize peak temperature while overlooking temperatures near the solid-state phase transformation fronts. Instead, the cyclic reheating induces spatially-variant dynamically annealed regions which can be modulated via control parameters. We show that these controls have spatially linked effects centimeters away from the active weld, which implies that microstructure control must co-optimize thermal input across many subsequent layers. In conclusion, our results demonstrate the pressing need for higher order controls that integrate predictive elements of simulation data to stabilize printed properties for future qualification of DED components.

Directed energy deposition↗
DOE OSTI2024

Pore2Chip

Pore2Chip is a Python module designed to streamline the process of analyzing X-ray computed tomography (XCT) images of soil and creating 2D micromodel designs based on that analysis.

Mudunuru, Maruti↗
DOE OSTI2024

Reconfigurable unitary transformations of optical beam arrays

Spatial transformations of light are ubiquitous in optics, with examples ranging from simple imaging with a lens to quantum and classical information processing in waveguide meshes. Multi-plane light converter (MPLC) systems have emerged as a platform that promises completely general spatial transformations, i.e., a universal unitary. However, until now, MPLC systems have demonstrated transformations that are far from general, e.g., converting from a Gaussian to Laguerre-Gauss mode. Here, we demonstrate the promise of an MLPC, the ability to impose an arbitrary unitary transformation that can be reconfigured dynamically. Specifically, we consider transformations on superpositions of parallel free-space beams arranged in an array, which is a common information encoding in photonics. We experimentally test the full gamut of unitary transformations for a system of two parallel beams and make a map of their fidelity. We obtain an average transformation fidelity of 0.85 ± 0.03. This high-fidelity suggests that MPLCs are a useful tool for implementing the unitary transformations that comprise quantum and classical information processing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Imaging systems and related methods including radar imaging with moving arrays or moving targets

Imaging systems, including radio frequency, microwave and millimeter-wave arrangements, and related methods are described. According to one aspect, an imaging system includes an antenna array, a position capture system configured to generate position information indicative of locations of one of the antenna array and the target at the first and second moments in time, and wherein the one of the antenna array and the target move between the first and second moments in time, a transceiver configured to control the antenna array to emit electromagnetic energy towards the target and to generate an output that is indicative of the received electromagnetic energy, a data acquisition system configured to generate radar data, processing circuitry configured to process the position information and the radar data to generate image data regarding the target, and an interface configured to use the image data to generate visual images regarding the target.

Sheen, David M.↗
DOE OSTI2024

A three-point velocity estimation method for two-dimensional coarse-grained imaging data

Time delay and velocity estimation methods have been widely studied subjects in the context of signal processing, with applications in many different fields of physics. The velocity of waves or coherent fluctuation structures is commonly estimated as the distance between two measurement points divided by the time lag that maximizes the cross correlation function between the measured signals, but this is demonstrated to result in erroneous estimates for two spatial dimensions. We present an improved method to accurately estimate both components of the velocity vector, relying on three non-aligned measurement points. We introduce a stochastic process describing the fluctuations as a superposition of uncorrelated pulses moving in two dimensions. Using this model, we show that the three-point velocity estimation method, using time delays calculated through cross correlations, yields the exact velocity components when all pulses have the same velocity. The two- and three-point methods are tested on synthetic data generated from realizations of such processes for which the underlying velocity components are known. The results reveal the superiority of the three-point technique. Finally, we demonstrate the applicability of the velocity estimation on gas puff imaging data of strongly intermittent plasma fluctuations due to the radial motion of coherent, blob-like structures at the boundary of the Alcator C-Mod tokamak.

Materials Science↗
DOE OSTI2025

Non-linear enhancement of ultrafast X-ray diffraction through transient resonances

Diffraction-before-destruction imaging with ultrashort X-ray pulses can visualize non-equilibrium processes, such as chemical reactions, with sub-femtosecond precision in the native environment. Here, a nanospecimen diffracts a single X-ray flash before it disintegrates. The sample structure can be reconstructed from the coherent diffraction image (CDI). State-of-the-art X-ray snapshots lack high spatial resolution because of weak diffraction signal. Bleaching effects from photo-ionization significantly restrain image brightness scaling. We find that non-linear transient ion resonances can overcome this barrier if X-ray laser pulses are shorter than in most experiments. We compared snapshots from individual ≈ 100 nm Xe nanoparticles as a function of pulse duration and incoming X-ray fluence. Our experimental results and Monte Carlo simulations suggest that transient resonances can increase ionic scattering cross sections significantly beyond literature values. This provides a novel avenue towards substantial improvement of the spatial resolution in CDI in combination with sub-femtosecond temporal precision at the nanoscale.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Quantum Imaging with X-rays

Quantum imaging encompasses a broad range of methods that exploit the quantum properties of light to capture information about an object. One such approach involves using a two-photon quantum state, where only one photon interacts with the object being imaged while its entangled partner carries spatial or temporal information. To implement this technique, it is necessary to generate specific quantum states of light and detect photons at the single-photon level. While this method has been successfully demonstrated in the visible electromagnetic spectrum, extending it to X-rays has faced significant challenges due to the difficulties in producing a sufficient rate of X-ray photon pairs and detecting them with adequate resolution. Here, we demonstrate record high rates of correlated X-ray photon pairs produced via a spontaneous parametric down-conversion process and we employ these photons to perform quantum correlation imaging of several objects, including a biological sample (E. cardamomum seedpod). Notably, we report an unprecedented detection rate of about 6,300 pairs per hour and the observation of energy anti-correlation for the X-ray photon pairs. We also present a detailed analysis of the properties of the down-converted X-ray photons, as well as a comprehensive study of the correlation imaging formation, including a study of distortions and corrections. These results mark a substantial advancement in X-ray quantum imaging, expanding the possibilities of X-ray quantum optical technologies, and illustrating the pathway towards enhancing biological imaging with reduced radiation doses.

Gofron, Kaz [ORNL] (ORCID:0000000314415736)↗
DOE OSTI2024

Surface Self-Diffusion Induced Sintering of Nanoparticles

Despite the critical role of sintering phenomena in constraining the long-term durability of nanosized particles, a clear understanding of nanoparticle sintering has remained elusive due to the challenges in atomically tracking the neck initiation and discerning different mechanisms. Through the integration of in situ transmission electron microscopy and atomistic modeling, this study uncovers the atomic dynamics governing the neck initiation of Pt–Fe nanoparticles via a surface self-diffusion process, allowing for coalescence without significant particle movement. Real-time imaging reveals that thermally activated surface morphology changes in individual nanoparticles induce significant surface self-diffusion. Further, the kinetic entrapment of self-diffusing atoms in the gaps between closely spaced nanoparticles leads to the nucleation and growth of atomic layers for neck formation. This surface self-diffusion-driven sintering process is activated at a relatively lower temperature compared to the classic Ostwald ripening and particle migration and coalescence processes. The fundamental insights have practical implications for manipulating the morphology, size distribution, and stability of nanostructures by leveraging surface self-diffusion processes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An integrated atom array-nanophotonic chip platform with background-free imaging

Arrays of neutral atoms trapped in optical tweezers have emerged as a leading platform for quantum information processing and quantum simulation due to their scalability, reconfigurable connectivity, and high-fidelity operations. Individual atoms are promising candidates for quantum networking due to their capability to emit indistinguishable photons that are entangled with their internal atomic states. Integrating atom arrays with photonic interfaces would enable distributed architectures in which nodes hosting many processing qubits could be efficiently linked together via the distribution of remote entanglement. However, many atom array techniques cease to work in close proximity to photonic interfaces, with atom detection via standard fluorescence imaging presenting a major challenge due to scattering from nearby photonic devices. Here, we demonstrate an architecture that combines atom arrays with up to 64 optical tweezers and a millimeter-scale photonic chip hosting more than 100 nanophotonic cavities. We achieve high-fidelity ( ~ 99.2%), background-free imaging in close proximity to nanofabricated cavities using a multichromatic excitation and detection scheme. The atoms can be imaged while trapped a few hundred nanometers above the dielectric surface, which we verify using Stark shift measurements of the modified trapping potential. Finally, we rearrange atoms into defect-free arrays and load them simultaneously onto the same or multiple devices.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol–cloud interactions

Abstract. Process-oriented observational constraints for the anthropogenic effective radiative forcing due to aerosol–cloud interactions (ERFaci) are highly desirable because the uncertainty associated with ERFaci poses a significant challenge to climate prediction. The contoured frequency by optical depth diagram (CFODD) analysis supports the evaluation of model representation of cloud liquid-to-rain conversion processes because the slope of a CFODD, generated from joint MODerate Resolution Imaging Spectroradiometer (MODIS)-CloudSat cloud retrievals, provides an estimate of cloud droplet collection efficiency in single-layer warm liquid clouds. Here, we present an updated CFODD analysis as an observational constraint on the ERFaci due to warm rain processes and apply it to the U.S. Department of Energy's Energy Exascale Earth System Model version 2 (E3SMv2). A series of sensitivity experiments shows that E3SMv2 droplet collection efficiencies and ERFaci are highly sensitive to autoconversion, i.e., the rate of mass transfer from cloud liquid to rain, yielding a strong correlation between the CFODD slope and the shortwave component of ERFaci (ERFaciSW; Pearson's R=-0.91). E3SMv2's CFODD slope (0.20 ± 0.04) is in agreement with observations (0.20 ± 0.03). The strong sensitivity of ERFaciSW to the CFODD slope provides a useful constraint on highly uncertain warm rain processes, whereby ERFaciSW, constrained by MODIS-CloudSat, is estimated by calculating the intercept of the linear association between the ERFaciSW and the CFODD slopes, using the MODIS-CloudSat CFODD slope as a reference.

Beall, Charlotte M. (ORCID:0000000311370835)↗
DOE OSTI2024

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

36 MATERIALS SCIENCE↗