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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Shear Measurement with Poorly Resolved Images

Weak lensing studies typically require excellent seeing conditions for the purpose of maximizing the number density of well-resolved galaxy images. It is interesting to ask to what extent the seeing size limits the usefulness of the astronomical images in weak lensing. In this work, we study this issue with the data of the DECam Legacy Survey, which is a part of the target selection program for the Dark Energy Spectroscopic Instrument. Using the Fourier_Quad shear measurement pipeline, we demonstrate that images with relatively poor seeing conditions (~1$^"_.$5) can still yield accurate shear estimators. We do not find any correlation between systematic shear error and the image resolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Automated TEM Reveals Intercrystalline Correlations of Conjugated Polymers

Transmission electron microscopy (TEM) continues to transform polymer science by revealing key aspects of chain packing, phase separation and nanoscale structure. The development of instrumentation and data analyses tools is driving the field forward and enabling new experiments. Here, we use automated high-resolution TEM (HRTEM) and image processing to identify the structure of a conjugated polymer used in organic electronics. Analysis of more than 600 HRTEM images reveals lattice parameters and orientation correlations between crystals, including the preferred alignment of neighboring crystals along the same crystallographic direction that is likely the result of liquid crystalline order.

Fair, Ryan A. [Pennsylvania State University, Univ↗
DOE OSTI2025

3D quantum ghost imaging microscope

Quantum ghost imaging uses quantum-entangled photons to generate a two-dimensional image with only a bucket detector at the sample. Here we expand on this approach to generate a three-dimensional image without scanning. A quantum-entangled light source directly links information between a pair of 2D sensors, one of which captures a standard image from one perspective and a second sensor which captures a ghost image from a perpendicular perspective. By correlating the spatial information from the two detectors for each photon pair, we obtain three dimensions of spatial information (x, y, and z) for each scattered photon. We demonstrate that this system can study microscopic environments by imaging scattering from metallic nanoparticle clusters. This approach has the potential to greatly reduce the flux of light required to obtain a 3D image of a biological sample and thereby extend the number of images that can be obtained before photodamaging the sample.

Eshun, Audrey [Lawrence Livermore National Laborat↗
DOE OSTI2024

Correlating activities and defects in (photo)electrocatalysts using in-situ multi-modal microscopic imaging

Photo(electro)catalysts use sunlight to drive chemical reactions such as water splitting. A major factor limiting photocatalyst development is physicochemical heterogeneity which leads to spatially dependent reactivity. To link structure and function in such systems, simultaneous probing of the electrochemical environment at microscopic length scales and a broad range of timescales (ns to s) is required. Here, we address this challenge by developing and applying in- situ (optical) microscopies to map and correlate local electrochemical activity, with hole lifetimes, oxygen vacancy concentrations and photoelectrode crystal structure. Using this multi-modal approach, we study prototypical hematite (α-Fe 2 O 3 ) photoelectrodes. We demonstrate that regions of α-Fe 2 O 3 , adjacent to microstructural cracks have a better photoelectrochemical response and reduced back electron recombination due to an optimal oxygen vacancy concentration, with the film thickness and extended light exposure also influencing local activity. Our work highlights the importance of microscopic mapping to understand activity, in even seemingly homogeneous photoelectrodes.

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

Quantitative assessment of particle dispersion in polymeric composites and its effect on mechanical properties

In this work, an automated image analysis tool is developed to establish quantitative correlations between the particle/cluster size distribution and the mechanical properties of particle reinforced polymer composites (PRPC).This automated image analysis tool is developed within python programming software to process and analyze the microstructural images of the polymerbased composite materials. The spent coffee bean powder (SCBP) reinforced poly-propylene carbonate (PPC) polymer composite with differing wt.% of the filler is selected for the analysis. Detailed statistical analysis of the microstructural images reveals that ‘clustering of clusters’ is also presented in addition to the most commonly reported ‘clustering of particles’, and the distribution of particle/clusters is bimodal. Based on these findings, an effective volume fraction for the filler material is proposed to mainly capture the agglomeration effect. With this effective volume fraction, the standard rule-of-mixture model correctly captures the experimentally measured tensile strength and modulus as a function of filler wt.%. Further, the applicability of this effective volume fraction for other theoretical models is also analyzed. The detailed statistical analysis of the microstructure and the proposed effective volume fraction helps to develop a deeper quantitative understanding of the PRPC than the conventional qualitative correlation of microstructural features with the properties and failure processes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Characterization of damage processes in Montney siltstone under triaxial compression using acoustic emission and diagnostic imaging

SUMMARY Crack nucleation and rock failure processes in a fine-grained siltstone (Montney Formation) under triaxial compression are investigated using combined diagnostic techniques, including ultrasonic-wave measurement, acoustic-emission (AE) monitoring, computed tomography (CT) scanning, and thin-section imaging. The sample displays a weak-to-moderate inherent seismic anisotropy and noticeable stress-induced anisotropy prior to failure. No AE event was detected until the applied axial stress reached 95 per cent of the peak value. The signal-to-noise ratio is relatively low, however, and detectable AE events are more diffuse than those observed in highly brittle rocks. The AE locations correlate with a shear fracture zone imaged by CT scanning. AE moment-tensor analysis reveals that events with larger relative magnitudes are characterized by high volumetric (tensile or compressive) components, and the initiation of the failure zone is dominated by combined shear-tensile (ST) failure. Stress inversion of the AE events with high tensile components is in good agreement with the known applied stress. Microscopic imaging of thin sections from the failed sample shows that the failure zone is an en echelon structure consisting of a major fracture with branching micro and minor cracks. This failure mechanism is consistent with a ST source mechanism and is interpreted to be associated with the fine granular structure and mineral composition of Montney siltstone.

Jia, Suzie Q. (ORCID:0000000326496190)↗
DOE OSTI2023

UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation

Unpaired image-to-image translation has broad applications in art, design, and scientific simulations. One early breakthrough was CycleGAN that emphasizes one-to-one mappings between two unpaired image domains via generative-adversarial networks (GAN) coupled with the cycle-consistency constraint, while more recent works promote one-to-many mapping to boost diversity of the translated images. Motivated by scientific simulation and one-to-one needs, this work revisits the classic CycleGAN framework and boosts its performance to outperform more contemporary models without relaxing the cycle-consistency constraint. To achieve this, we equip the generator with a Vision Transformer (ViT) and employ necessary training and regularization techniques. Compared to previous best-performing models, our model performs better and retains a strong correlation between the original and translated image. An accompanying ablation study shows that both the gradient penalty and self-supervised pre-training are crucial to the improvement. To promote reproducibility and open science, the source code, hyperparameter configurations, and pre-trained model are available at https: //github.com/LS4GAN/uvcgan.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Spatiotemporal distribution of chemical signatures exhibited by Myxococcus xanthus in response to metabolic conditions

Myxococcus xanthus is a common soil bacterium with a complex life cycle, which is known for production of secondary metabolites. However, little is known about the effects of nutrient availability on M. xanthus metabolite production. In this study, we utilize confocal Raman microscopy (CRM) to examine the spatiotemporal distribution of chemical signatures secreted by M. xanthus and their response to varied nutrient availability. Here, ten distinct spectral features are observed by CRM from M. xanthus grown on nutrient-rich medium. However, when M. xanthus is constrained to grow under nutrient-limited conditions, by starving it of casitone, it develops fruiting bodies, and the accompanying Raman microspectra are dramatically altered. The reduced metabolic state engendered by the absence of casitone in the medium is associated with reduced, or completely eliminated, features at 1140 cm –1 , 1560 cm –1 , and 1648 cm –1 . In their place, a feature at 1537 cm –1 is observed, this feature being tentatively assigned to a transitional phase important for cellular adaptation to varying environmental conditions. In addition, correlating principal component analysis heat maps with optical images illustrates how fruiting bodies in the center co-exist with motile cells at the colony edge. While the metabolites responsible for these Raman features are not completely identified, three M. xanthus peaks at 1004, 1151, and 1510 cm –1 are consistent with the production of lycopene. Thus, a combination of CRM imaging and PCA enables the spatial mapping of spectral signatures of secreted factors from M. xanthus and their correlation with metabolic conditions.

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

Constraints on the origin of the radio synchrotron background via angular correlations

ABSTRACT The origin of the radio synchrotron background (RSB) is currently unknown. Its understanding might have profound implications in fundamental physics or might reveal a new class of radio emitters. In this work, we consider the scenario in which the RSB is due to extragalactic radio sources and measure the angular cross-correlation of Low-Frequency Array (LOFAR) images of the diffuse radio sky with matter tracers at different redshifts, provided by galaxy catalogues and cosmic microwave background lensing. We compare these measured cross-correlations to those expected for models of RSB sources. We find that low-redshift populations of discrete sources are excluded by the data, while higher redshift explanations are compatible with available observations. We also conclude that at least 20 per cent of the RSB surface brightness level must originate from populations tracing the large-scale distribution of matter in the Universe, indicating that at least this fraction of the RSB is of extragalactic origin. Future measurements of the correlation between the RSB and tracers of high-redshift sources will be crucial to constraining the source population of the RSB.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Terahertz near-field imaging of sidewall-induced losses in superconducting qubits

Correlating superconducting qubit performance with advanced materials analysis is a key strategy for improving coherence. Existing diagnostics for key properties, such as dielectric loss, structural discontinuity, and interface heterogeneity, often rely on destructive electron microscopy or low-throughput millikelvin measurements. Here, in this study, we demonstrate noninvasive terahertz (THz) nano-imaging/spectroscopy of encapsulated niobium transmon qubits as a high-throughput proxy for performance evaluation. We identify large variations in sidewall near-field signals, implicating sidewall loss and discontinuity as major coherence limiters, and also use THz hyperspectral line scans to probe dielectric responses and field participation at Al junction interfaces.

Kim, Richard H. J. [Ames Laboratory (AMES), Ames, ↗
DOE OSTI2025

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models

Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images from diverse sources—such as varying physical groundings or data acquisition systems—and to learn spatio-temporal correlations using transformer architectures. However, tokenizing and aggregating images can be compute-intensive, a challenge not fully addressed by current distributed methods. In this work, we introduce the Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) approach designed for datasets with a large number of channels across image modalities. Our method is compatible with any model-parallel strategy and any type of vision transformer architecture, significantly improving computational efficiency. We evaluated D-CHAG on hyperspectral imaging and weather forecasting tasks. When integrated with tensor parallelism and model sharding, our approach achieved up to a 75% reduction in memory usage and more than doubled sustained throughput on up to 1,024 AMD GPUs on the Frontier Supercomputer.

Tsaris, Aristeidis (aris) [ORNL] (ORCID:0000000277↗
DOE OSTI2020

Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images

The maturation or activation status of dendritic cells (DCs) directly correlates with their behavior and immunofunction. A common means to determine the maturity of dendritic cells is from high-resolution images acquired via scanning electron microscopy (SEM) or atomic force microscopy (AFM). While direct and visual, the determination has been made by directly looking at the images by researchers. Here we report a machine learning approach using pattern recognition in conjunction with cellular biophysical knowledge of dendritic cells to determine the maturation status of dendritic cells automatically. The determination from AFM images reaches 100% accuracy. The results from SEM images reaches 94.9%. The results demonstrate the accuracy of using machine learning for accelerating data analysis, extracting information, and drawing conclusions from high-resolution cellular images, paving the way for future applications requiring high-throughput and automation, such as cellular sorting and selection based on morphology, quantification of cellular structure, and DC-based immunotherapy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Correlating armature and needle dynamics with voltage waveforms of solenoid-actuated GDI injector

The injector voltage hump that appears near the needle closing has been used for the real-time monitoring and feedback control of fuel injection duration in modern engines. This voltage hump has been thought to result from the abrupt change in electromagnetic induction by the stoppage of needle motion but detailed electromagnetic processes and associated armature and needle dynamics during the needle closing have not been thoroughly investigated in a wide range of injection conditions, which knowledge is crucial for the delicate control of fuel injection based on the voltage hump. Here, the current study analyzes the transient armature and needle dynamics of a solenoid-actuated gasoline direct injection injector using an X-ray phase-contrast imaging technique. Then, the results are correlated with voltage waveforms during the needle closing transient under various injection pressures, injection pulse durations, and dwell times of split injections. The time derivatives of voltage waveforms showed lower and upper peaks in order in the regime of the voltage hump. Inconsistent with conventional understandings, the lower peak timing of the voltage derivative did not match with the timing of needle closing (end of injection) but rather matched with the abrupt descent timing of the armature and needle. The inflection timing and upper peak timing of the voltage derivative matched with the timings of actual needle closing and armature closing respectively. The amplitude of the voltage hump was near linearly dependent on the needle closing speed. The needle closing speed decreased upon the decrease of injection pulse duration and injection pressure which made it difficult to detect the voltage humps in ballistic injection regimes and low injection pressures. In split injection conditions, the voltage hump of the first injection was not detectable if the dwell time was shorter than the needle closing delay, the time from the current cut-off to the actual needle closing.

42 ENGINEERING↗
DOE OSTI2020

Windowed Fourier transform and cross-correlation algorithms for molecular tagging velocimetry

In this study, simulated and experimental molecular tagging velocimetry (MTV) images have been analyzed with a technique commonly used to process grid images on surfaces, the windowed Fourier transform with local spectrum analysis (WFT-LSA). A systematic synthetic image study of the modulation transfer function (MTF) and error tendencies of the WFT-LSA was performed and compared with a PIV-style cross-correlation algorithm to see if advanced strategies such as iterative image deformation can improve analysis of gridded images with high noise levels. Testing of single-pass algorithms showed that in typical MTV images, the WFT-LSA yields significantly lower bias errors than cross-correlation (CC) at displacements greater than 1 pixel but slightly higher random error at all displacements and image conditions. Analysis of the MTF shows that CC provided better resolution of spatial fluctuations than the WFT-LSA in many combinations of grid size and interrogation window. Tests of image deformation algorithms showed that the gap in performance between CC and WFT-LSA is maintained even as both methods improve. Additionally, WFT-LSA and CC methods are applied to real MTV experiments in high-speed gas jet flows. A preliminary analysis of phosphorescence lifetime provided by acetone vapor excited at 266 nm is used for assessing the required gas speeds for making MTV application feasible. The application of WFT-LSA to real MTV images demonstrates the ability of the algorithm to handle further real-world effects that could not be considered in the synthetic image analysis, like reduced signal-to-noise ratio and non-uniform intensity of the tagging grid across the image introduced by the actual laser beam energy distribution. With experimental images, CC is more accurate with shear flows but less robust to high noise levels than WFT-LSA, as predicted by the synthetic image analysis.

42 ENGINEERING↗
DOE OSTI2026

Deep generative learning of magnetic frustration in artificial spin ice from magnetic force microscopy images

Increasingly large datasets of microscopic images with nanoscale resolution facilitate the development of machine learning methods to identify and analyze subtle physical phenomena embedded within the images. In this work, microscopic images of honeycomb lattice spin-ice samples serve as datasets from which we automate the calculation of net magnetic moments and directional orientations of spin-ice configurations. In the first stage of our workflow, machine learning models are trained to accurately predict magnetic moments and directions within spin-ice structures. Variational Autoencoders (VAEs), an emergent unsupervised deep learning technique, are employed to generate high-quality synthetic magnetic force microscopy (MFM) images and extract latent feature representations, thereby reducing experimental and segmentation errors. The second stage of proposed methodology enables precise identification and prediction of frustrated vertices and nanomagnetic segments, effectively correlating structural and functional aspects of microscopic images. This facilitates the design of optimized spin-ice configurations with controlled frustration patterns, enabling potential on-demand synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Imaging moiré excited states with photocurrent tunnelling microscopy

Moiré superlattices provide a highly tuneable and versatile platform to explore novel quantum phases and exotic excited states ranging from correlated insulators to moiré excitons. Scanning tunnelling microscopy has played a key role in probing microscopic behaviours of the moiré correlated ground states at the atomic scale. However, imaging of quantum excited states in moiré heterostructures remains an outstanding challenge. In this report we develop a photocurrent tunnelling microscopy technique that combines laser excitation and scanning tunnelling spectroscopy to directly visualize the electron and hole distribution within the photoexcited moiré exciton in twisted bilayer WS 2 . The tunnelling photocurrent alternates between positive and negative polarities at different locations within a single moiré unit cell. This alternating photocurrent originates from the in-plane charge transfer moiré exciton in twisted bilayer WS 2 , predicted by our GW-Bethe–Salpeter equation calculations, that emerges from the competition between the electron–hole Coulomb interaction and the moiré potential landscape. Our technique enables the exploration of photoexcited non-equilibrium moiré phenomena at the atomic scale.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards Three-Dimensional Neutron Imaging with Light-field Technology

A broad range of applications in nuclear safeguards and security can benefit from compact, fast neutron imagers with large angular acceptance. However, accurate 3D multi-vertex reconstruction in a monolithic detector remains a significant barrier to realization. Current approaches, such as the centroiding approach and the use of shadow masks, have not yet resulted in a successful demonstration in a light-starved environment. This project explored the use of cutting-edge technology—a light field camera—which inherently preserves both the spatial and directional information of incident light. Such technology can, in principle, resolve multi-vertex events and offer accurate position reconstruction in 3D with relatively simple readout electronics. Since the project's inception in May 2023, we have built an experimental setup to calibrate and characterize a commercial light-field camera (Lytro Illum). We assessed its 3D event reconstruction capability in a relatively low-light intensity environment by analyzing the cross-correlation of a series of 2D images of a characterized tunable light source taken at various distances. A sub-cm resolution, lower than typical neutron interaction separations in a compact scintillator volume, was observed with the LED light source, suggesting the promising capability of its nominal optics design to provide the adequate resolution required for a compact monolithic neutron directional detector. However, using a light-field-based readout for a neutron camera requires incorporating an ultra-low-noise light sensor, which is beyond the scope of this project. We also initiated the development of a 3D light-field-based reconstruction algorithm tailored to sparse scenes, as expected from particle interactions in a scintillator medium. Additionally, we demonstrated the capability of the algorithm to replicate the ground truth. Finally, we began the development of a neutron directional detector simulation to determine the performance criteria for light-field-based reconstruction that allows for good neutron directional reconstruction. This Feasibility Study has led to a successful follow-up project under the DNN R&D innovation portfolio starting May 2024.

42 ENGINEERING↗