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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine learning assisted quantum super-resolution microscopy

One of the main characteristics of optical imaging systems is spatial resolution, which is restricted by the diffraction limit to approximately half the wavelength of the incident light. Along with the recently developed classical super-resolution techniques, which aim at breaking the diffraction limit in classical systems, there is a class of quantum super-resolution techniques which leverage the non-classical nature of the optical signals radiated by quantum emitters, the so-called antibunching super-resolution microscopy. This approach can ensure a factor of $\sqrt{n}$ improvement in the spatial resolution by measuring the n -th order autocorrelation function. The main bottleneck of the antibunching super-resolution microscopy is the time-consuming acquisition of multi-photon event histograms. We present a machine learning-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed framework paves the way to the practical realization of scalable quantum super-resolution imaging devices that can be compatible with various types of quantum emitters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Methods and systems of proteome analysis and imaging

Provided herein are methods and systems for proteome analysis that are at least partially automated and/or performed robotically. In some aspects, the methods and systems described herein can rapidly and efficiently provide protein identification of each of the proteins from a proteome, or a complement of proteins, obtained from extremely small amounts of biological samples. The identified proteins can be imaged quantitatively over a spatial region. Automation and robotics facilitates the throughput of the methods and systems, which enables protein imaging and/or rapid proteome analysis.

Piehowski, Paul D.↗
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 OSTI2023

Listeria monocytogenes loss of cultivability on carrot is associated with the formation of mesosome-like structures

Raw carrot is known to have antimicrobial activity against Listeria monocytogenes, but the mechanism of action has not been fully elucidated. In this study, we examined carrot antilisterial activity against several strains of Listeria species (including L. grayi, L. innocua, L. seeligeri, and L. welshimeri) and L. monocytogenes. A representative strain of L. monocytogenes was subsequently used for further characterizing carrot antilisterial activity. Exposure to fresh-cut carrot for 15 min resulted in a similar loss of cultivability, ranging from 2.5 to 4.7 log units, across all Listeria strains evaluated. L. monocytogenes recovered from the fresh-cut surface of different raw carrots was 1.6 to 4.1 log lower than levels obtained from paired boiled carrot samples with abolished antilisterial activity. L. monocytogenes levels recovered from fresh-cut carrot were 2.8 to 3.1 log lower when enumerated by culture-dependent methods than by the culture-independent method of PMAxx-qPCR, a qPCR assay that is performed using DNA pre-treated to selectively sequester DNA from cells with injured membranes. These results suggested that L. monocytogenes loss of cultivability on fresh-cut carrot was not associated with a loss of L. monocytogenes cell membrane integrity and putative cell viability. Transmission electron microscopy imaging revealed that L. monocytogenes rapidly formed mesosome-like structures upon exposure to carrot fresh-cut surface but not upon exposure to boiled carrot surface, suggesting there may be an association between the formation of these mesosome-like structures and a loss of cultivability in L. monocytogenes. As a result, further research is necessary to conclude the causality of this association.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Advances in the photon avalanche luminescence of inorganic lanthanide-doped nanomaterials

Photon avalanche (PA)—where the absorption of a single photon initiates a ‘chain reaction’ of additional absorption and energy transfer events within a material—is a highly nonlinear optical process that results in upconverted light emission with an exceptionally steep dependence on the illumination intensity. Over 40 years following the first demonstration of photon avalanche emission in lanthanide-doped bulk crystals, PA emission has been achieved in nanometer-scale colloidal particles. The scaling of PA to nanomaterials has resulted in significant and rapid advances, such as luminescence imaging beyond the diffraction limit of light, optical thermometry and force sensing with (sub)micron spatial resolution, and all-optical data storage and processing. In this review, we discuss the fundamental principles underpinning PA and survey the studies leading to the development of nanoscale PA. Finally, we offer a perspective on how this knowledge can be used for the development of next-generation PA nanomaterials optimized for a broad range of applications, including mid-IR imaging, luminescence thermometry, (bio)sensing, optical data processing and nanophotonics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Small animal PET: a review of what we have done and where we are going

Abstract Small animal research is an essential tool in studying both pharmaceutical biodistribution and disease progression over time. Furthermore, through the rapid development of in vivo imaging technology over the last few decades, small animal imaging (also referred to as preclinical imaging) has become a mainstay for all fields of biologic research and a center point for most preclinical cancer research. Preclinical imaging modalities include optical, MRI and MRS, microCT, small animal PET, ultrasound, and photoacoustic, each with their individual strengths. The strong points of small animal PET are its translatability to the clinic; its quantitative imaging capabilities; its whole-body imaging ability to dynamically trace functional/biochemical processes; its ability to provide useful images with only nano- to pico- molar concentrations of administered compounds; and its ability to study animals serially over time. This review paper gives an overview of the development and evolution of small animal PET imaging. It provides an overview of detector designs; system configurations; multimodality PET imaging systems; image reconstruction and analysis tools; and an overview of research and commercially available small animal PET systems. It concludes with a look toward developing technologies/methodologies that will further enhance the impact of small animal PET imaging on medical research in the future.

Engineering↗
DOE OSTI2021

Real-time discovery of AT2020xnd: a fast, luminous ultraviolet transient with minimal radioactive ejecta

ABSTRACT The many unusual properties of the enigmatic AT2018cow suggested that at least some subset of the empirical class of fast blue optical transients (FBOTs) represents a genuinely new astrophysical phenomenon. Unfortunately, the intrinsic rarity and fleeting nature of these events have made it difficult to identify additional examples early enough to acquire the observations necessary to constrain theoretical models. We present here the Zwicky Transient Facility discovery of AT2020xnd (ZTF20acigmel, the ‘Camel’) at z = 0.243, the first unambiguous AT2018cow analogue to be found and confirmed in real time. AT2018cow and AT2020xnd share all key observational properties: a fast optical rise, sustained high photospheric temperature, absence of a second peak attributable to ejection of a radioactively heated stellar envelope, extremely luminous radio, millimetre, and X-ray emission, and a dwarf-galaxy host. This supports the argument that AT2018cow-like events represent a distinct phenomenon from slower-evolving radio-quiet supernovae, likely requiring a different progenitor or a different central engine. The sample properties of the four known members of this class to date disfavour tidal disruption models but are consistent with the alternative model of an accretion powered jet following the direct collapse of a massive star to a black hole. Contextual filtering of alert streams combined with rapid photometric verification using multiband imaging provides an efficient way to identify future members of this class, even at high redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Combustion Feature Characterization using Computer Vision Diagnostics within Rotating Detonation Combustors

Rotating detonation engines (RDEs) theoretically achieve pressure gain by continuously propagating detonation waves in a cylindrical annulus. Research aims to implement them in gas turbines for improved propulsion and power generation efficiency. Current diagnostic methods, such as point measurements and optical diagnostics, face limitations due to high data acquisition rates required to analyze rapid detonation interactions. In contrast, image classification and time series classification achieve real-time capabilities with rates of 4 and 5 Hz, respectively. Object detection offers the highest time-step resolution at 20 μsec, while real-time methods require longer resolutions of 80 msec, highlighting the advancements in diagnostic capabilities for RDEs.

May, Kristyn Johnson↗
DOE OSTI2025

Pulsed Infrared Thermography Nondestructive Imaging of SiC-SiC f Composite Cladding Architectures; Understanding the Performance of SiC-SiC f Composite Cladding Architectures with Cr Coating in Normal Operating and Accident Conditions in LWRs and Advanced Reactors

SiC-SiC f composites, consisting of silicon carbide fibers embedded in a silicon carbide matrix, are advanced materials with high thermal conductivity, temperature stability, and resistance to radiation damage. Traditional methods for quality control of fabricated SiC-SiC f composites involve nondestructive evaluation (NDE) with X-ray computed tomography (XCT). However, XCT imaging of typical SiC-SiC f structures for cladding applications can involve several hours. In this project, we investigate an alternative approach to NDE of SiC-SiC f composites that involves rapid (on the order of seconds) imaging with Pulsed infrared thermography (PIT). PIT images of a planar SiC-SiC f specimen show the structure of the surface monolith layer and internal SiC f structures. The capability of PIT imaging in visualizing SiC f structures is qualitatively confirmed by observing similarity in the PIT and X-ray transmission images of the same specimen. Computer vision analysis of defects in the PIT image of the monolith was performed with thresholding followed by topological structural analysis that computed geometric descriptors, including major/minor axes of fitted ellipses, area, perimeter, Feret diameter, circularity, roundness, and solidity.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

New Design Yields Robust Large-Area Framing Camera

Manuscript to be submitted to Review of Scientific Instruments to determine interest in possible publication. Website: https://aip.scitation.org/rsi/authors/manuscript High-speed imaging is a core diagnostic capability for the Nevada National Security Site (NNSS). Data from experiments that measure the behavior of materials under extreme conditions are essential for the stockpile mission. For example, during subcritical experiments, materials that are shocked and compressed under high-explosive drive change behavior rapidly over timescales measured in millionths or even billionths of a second. Characterizing and understanding this behavior requires radiographic and/or optical imaging to track motion on these timescales. Framing cameras provide a discrete series of images over a short period of time in a manner that mimics a high-speed movie camera, but with individual shutter times that must be extremely short in order to capture freeze-frame images of rapidly evolving phenomena such as high-explosive shock-driven ejecta, dynamic compression of metals, and high-velocity fluid flow.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Imaging of multiple fluorescent proteins in canopies enables synthetic biology in plants

Reverse genetics approaches have revolutionized plant biology and agriculture. Phenomics has the prospect of bridging plant phenotypes with genes, including transgenes, to transform agricultural fields. Genetically encoded fluorescent proteins (FPs) have revolutionized plant biology paradigms in gene expression, protein trafficking and plant physiology. While the first instance of plant canopy imaging of green fluorescent protein (GFP) was performed over 25 years ago, modern phenomics has largely ignored fluorescence as a transgene expression device despite the burgeoning FP colour palette available to plant biologists. Here, we show a new platform for stand-off imaging of plant canopies expressing a wide variety of FP genes. The platform-the fluorescence-inducing laser projector (FILP)-uses an ultra-low-noise camera to image a scene illuminated by compact diode lasers of various colours, coupled with emission filters to resolve individual FPs, to phenotype transgenic plants expressing FP genes. Each of the 20 FPs screened in plants were imaged at >3 m using FILP in a laboratory-based laser range. We also show that pairs of co-expressed fluorescence proteins can be imaged in canopies. The FILP system enabled a rapid synthetic promoter screen: starting from 2000 synthetic promoters transfected into protoplasts to FILP-imaged agroinfiltrated Nicotiana benthamiana plants in a matter of weeks, which was useful to characterize a water stress-inducible synthetic promoter. FILP canopy imaging was also accomplished for stably transformed GFP potato and in a split-GFP assay, which illustrates the flexibility of the instrument for analysing fluorescence signals in plant canopies.

abiotic stress↗
DOE OSTI2021

Image Analysis: Comparison Metrics

Despite an increased reliance on computational modeling in engineering and physics, graphically comparing computational and experimental results has often been qualitative, via a so-called ‘viewgraph norm’. Image recognition, computer vision and artificial intelligence (AI) are fields of study in themselves and rapidly progressing, but relying on AI to grade image similarity evokes a notion of asking for an expert judgement, which could be seen as an artificial version of the viewgraph norm. It is, therefore, desirable to use simpler metrics which are more tractable and unambiguous, even though they may not be as ‘intelligent.’ In this document, a few metrics are studied by systematically altering an image and their implications are analyzed

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image Quality

X-ray microcomputed tomography (micro-CT) is a common tool for the study of porous media structures and properties. High-quality micro-CT data are required to accurately capture pore structures. Acquiring high-quality micro-CT data, however, is not always possible, owing to application limitations and experimental constraints. Therefore, we propose a geometrical-based generative adversarial network (GAN) to rapidly restore noisy micro-CT images to their clean counterparts. The training data and related ground-truth (GT) data are scanned for 7 min and 9.5 h, respectively. To evaluate the performance of the geometrical-based GAN, a 6003 voxel image that has never been used for training is reconstructed and compared with the corresponding GT image. Histogram matching and linear normalization are implemented to adjust the histogram of the reconstructed image to that of the GT image. A watershed-based segmentation method is then applied to delineate pore and solid phases. Lastly, we measure the Minkowski functionals and petrophysical properties, including absolute permeability, pore size distribution, drainage capillary pressure-saturation curve, and imbibition relative permeability, to estimate the physical accuracy of the denoised image. The results show that the proposed geometrical-based GAN can accurately restore noisy micro-CT data. By reducing the scanning time from 9.5 h to 7 min, the expenditure of collecting micro-CT can be decreased significantly. This is particularly important for applications where time-lapse images of a dynamic process are required, high-throughput imaging is necessary for real-time data analysis, or where the quantification of large sample volumes is required.

58 GEOSCIENCES↗
DOE OSTI2024

Analytical noise bias correction for precise weak lensing shear inference

Noise bias is a significant source of systematic error in weak gravitational lensing measurements that must be corrected to satisfy the stringent standards of modern imaging surveys in the era of precision cosmology. This paper reviews the analytical noise bias correction method and provides analytical derivations demonstrating that we can recover shear to its second order using the ‘renoising’ noise bias correction approach introduced by METACALIBRATION. We implement this analytical noise bias correction within the AnaCal shear estimation framework and propose several enhancements to the noise bias correction algorithm. We evaluate the improved AnaCal using simulations designed to replicate Rubin Legacy Survey of Space and Time (LSST) imaging data. These simulations feature semi-realistic galaxies and stars, complete with representative distributions of magnitudes and Galactic spatial density. We conduct tests under various observational challenges, including cosmic rays, defective CCD columns, bright star saturation, bleed trails, and spatially variable point spread functions. Our results indicate a multiplicative bias in weak lensing shear recovery of less than a few tenths of a per cent, meeting LSST Dark Energy Science Collaboration requirements without requiring calibration from external image simulations. Additionally, our algorithm achieves rapid processing, handling one galaxy in less than a millisecond.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2023

Filming enhanced ionization in an ultrafast triatomic slingshot

Abstract Filming atomic motion within molecules is an active pursuit of molecular physics and quantum chemistry. A promising method is laser-induced Coulomb Explosion Imaging (CEI) where a laser pulse rapidly ionizes many electrons from a molecule, causing the remaining ions to undergo Coulomb repulsion. The ion momenta are used to reconstruct the molecular geometry which is tracked over time (i.e., filmed) by ionizing at an adjustable delay with respect to the start of interatomic motion. Results are distorted, however, by ultrafast motion during the ionizing pulse. We studied this effect in water and filmed the rapid “slingshot” motion that enhances ionization and distorts CEI results. Our investigation uncovered both the geometry and mechanism of the enhancement which may inform CEI experiments in many other polyatomic molecules.

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

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

54 ENVIRONMENTAL SCIENCES↗