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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Excited state lifetime modulation in semiconductor nanocrystals for super-resolution imaging

Abstract We report on proof of principle measurements of a concept for a super-resolution imaging method that is based on excitation field density-dependent lifetime modulation of semiconductor nanocrystals. The prerequisite of the technique is access to semiconductor nanocrystals with emission lifetimes that depend on the excitation intensity. Experimentally, the method requires a confocal microscope with fluorescence-lifetime measurement capability that makes it easily accessible to a broad optical imaging community. We demonstrate with single particle imaging that the method allows one to achieve a spatial resolution of the order of several tens of nanometers at moderate fluorescence excitation intensity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

X-ray Imaging-Based Void Fraction Measurement in Saturated Flow Boiling Experiments with Seawater Coolant

Recent studies have shown that the presence of dissolved salts in water can exhibit peculiar flow boiling and two-phase flow regimes. Two-phase flow and convective flow boiling are typically characterized with the help of void fraction measurements. To quantitatively improve our understanding of two-phase flow and boiling phenomenon with seawater coolant, void fraction data are needed, which can not be obtained from optical imaging. In this paper, we present experimental void fraction measurements of saturated flow boiling of tap water and seawater using X-ray radiography. X-rays with a maximum energy level of 40 KeV were used for imaging the exit region of the heated test section. At lower heat flux levels, the two phase flow in seawater was bubbly and homogeneous in nature, resulting in higher void fractions as compared to tap water. With an increase in heat flux, the flow regime was similar to slug flow, and void fraction measurements approached similarity with tap water. The predicted pressure drop using the measured void faction shows good agreement with the measured total pressure drop across the test section, demonstrating the validity of the measurement process.

Eckels, Seth↗
DOE OSTI2023

Single-cell multimodal imaging uncovers energy conversion pathways in biohybrids

Microbe–semiconductor biohybrids, which integrate microbial enzymatic synthesis with the light-harvesting capabilities of inorganic semiconductors, have emerged as promising solar-to-chemical conversion systems. Improving the electron transport at the nano–bio interface and inside cells is important for boosting conversion efficiencies, yet the underlying mechanism is challenging to study by bulk measurements owing to the heterogeneities of both constituents. Here we develop a generalizable, quantitative multimodal microscopy platform that combines multi-channel optical imaging and photocurrent mapping to probe such biohybrids down to single- to sub-cell/particle levels. We uncover and differentiate the critical roles of different hydrogenases in the lithoautotrophic bacterium Ralstonia eutropha for bioplastic formation, discover this bacterium’s surprisingly large nanoampere-level electron-uptake capability, and dissect the cross-membrane electron-transport pathways. Furthermore, this imaging platform, and the associated analytical framework, can uncover electron-transport mechanisms in various types of biohybrid, and potentially offers a means to use and engineer R. eutropha for efficient chemical production coupled with photocatalytic materials.

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

Detection and characterization of detached tidal dwarf galaxies

Tidal interactions between galaxies often give rise to tidal tails, which can harbor concentrations of stars and interstellar gas resembling dwarf galaxies. Some of these tidal dwarf galaxies (TDGs) have the potential to detach from their parent galaxies and become independent entities, but their long-term survival is uncertain. In this study, we conducted a search for detached TDGs associated with a sample of 39 interacting galaxy pairs in the local Universe using infrared, ultraviolet, and optical images. We employed IR colors and UV/optical/IR spectral energy distributions to identify potential interlopers, such as foreground stars or background quasars. Through spectroscopic observations using the Boller and Chivens spectrograph at San Pedro Mártir Observatory, we confirmed that six candidate TDGs are at the same redshift as their putative parent galaxy pairs. We identified and measured emission lines in the optical spectra and calculated nebular oxygen abundances, which range from log(O/H) = 8.10 ± 0.01 to 8.51 ± 0.02. We have serendipitously discovered an additional detached TDG candidate in Arp72 using available spectra from SDSS. Utilizing the photometric data and the CIGALE code for stellar population and dust emission fitting, we derived the stellar masses, stellar population ages, and stellar metallicities for these detached TDGs. Compared to standard mass-metallicity relations for dwarf galaxies, five of the seven candidates have higher than expected metallicities, confirming their tidal origins. One of the seven candidates remains unclear due to large uncertainties in metallicity, and another has stellar and nebular metallicities compatible with those of a preexisting dwarf galaxy. The latter object is relatively compact in the optical relative to its stellar mass, in contrast to the other candidate TDGs, which have large diameters for their stellar masses compared to most dwarf galaxies. The derived stellar population ages range from 100 Myr to 900 Myr, while the inferred stellar masses are between 2 × 10 6 M ⊙ and 8 × 10 7 M ⊙ . Four of the six TDGs are associated with the gas-rich M51-like pair Arp 72, one TDG is associated with a second M51-like pair Arp 86, and another is associated with Arp 65, an approximately equal mass pair. In spite of the relatively low stellar masses of these TDGs, they have survived for at least 100–900 Myrs, suggesting that they are stable and in dynamical equilibrium. We conclude that encounters with a relatively low-mass companion (1/10th–1/4th of the mass of the primary) can also produce long-lasting TDGs.

Astronomy & Astrophysics↗
DOE OSTI2024

Exposed Phosphatidylserine as a Biomarker for Clear Identification of Breast Cancer Brain Metastases in Mouse Models

Brain metastasis is the most common intracranial malignancy in adults. The prognosis is extremely poor, partly because most patients have more than one brain lesion, and the currently available therapies are nonspecific or inaccessible to those occult metastases due to an impermeable blood–tumor barrier (BTB). Phosphatidylserine (PS) is externalized on the surface of viable endothelial cells (ECs) in tumor blood vessels. In this study, we have applied a PS-targeting antibody to assess brain metastases in mouse models. Fluorescence microscopic imaging revealed that extensive PS exposure was found exclusively on vascular ECs of brain metastases. The highly sensitive and specific binding of the PS antibody enables individual metastases, even micrometastases containing an intact BTB, to be clearly delineated. Furthermore, the conjugation of the PS antibody with a fluorescence dye, IRDye 800CW, or a radioisotope, 125I, allowed the clear visualization of individual brain metastases by optical imaging and autoradiography, respectively. In conclusion, we demonstrated a novel strategy for targeting brain metastases based on our finding that abundant PS exposure occurs on blood vessels of brain metastases but not on normal brain, which may be useful for the development of imaging and targeted therapeutics for brain metastases.

Oncology↗
DOE OSTI2023

The influence of hydrothermal synthesis temperature on the electronic structure and luminescence property of Cr-doped ZnGa 2 O 4 nanoparticles

Cr-doped ZnGa 2 O 4 (CZGO) is a near-infrared-emitting material with long-lasting persistent luminescence. This unique property makes it a great candidate in optical imaging and sensing for various biomedical applications. Nanosized CZGO can be synthesized via a hydrothermal approach, but the reaction temperatures reported in existing literature vary from 120°C to 220°C, approaching the highest temperature allowed for a standard autoclave vessel. The reason for choosing a particular hydrothermal synthesis temperature has rarely been discussed, and it is unclear whether the resulting CZGO possess the same optical properties. This work compares CZGO nanoparticles synthesized at temperatures from the lowest reported 120°C and to as high as 220°C. We find that although all the synthesized CZGO are light emitting, they exhibit different luminescence intensities and respond differently to the change of excitation energy. X-ray absorption fine structure (XAFS) analysis is employed to investigate the local chemical environment around Zn, Ga and Cr, respectively. We found synthesis temperature strongly influences the Ga species formed in these particles. The energy transfer mechanism is further elucidated using X-ray excited optical luminescence (XEOL) in combination with element-specific XAFS. Two energy transfer paths are identified, which explains the different excitation energy dependencies of the observed photoluminescence.

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

Accelerated Stress Testing of Perovskite Photovoltaic Modules: Differentiating Degradation Modes with Electroluminescence Imaging

Herein, electroluminescence (EL) and thermal imaging are used to examine p–i–n metal halide perovskite (MHP) photovoltaic (PV) mini-modules (MA0.6FA0.4PbI3, 20 cells, 78 cm2) before and after indoor-accelerated stress testing or outdoor deployment. Distinct spatial patterns in the EL images emerge, which depend on the external stress conditions experienced by the mini-module. Imaging results highlight a distribution of dark speckle features that dominate after UV stress, attributed to widespread interfacial contact degradation. Lateral intensity gradients across cells dominate after thermal cycling (TC) stress, attributed to current crowding near scribe defects. While current–voltage analysis alone does not give full insight on the degradation process, this study shows that distinct degradation modes can be further defined by multimodal electro-optical imaging (i.e., EL combined with photoluminescence and dark lock-in thermography). Neither UV exposure nor TC-accelerated stress testing alone replicates the same degradation signatures observed after outdoor deployment, suggesting that multiple degradation modes occur under concurrent stressors outdoors. Finally, spatial characterization of degradation modes in MHP PV mini-modules before and after accelerated stress testing lays the groundwork for developing targeted accelerated stress testing procedures through comparison with outdoor aging.

14 SOLAR ENERGY↗
DOE OSTI2024

High-quality Extragalactic Legacy-field Monitoring (HELM) with DECam

High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term observing program that photometrically monitors several well-studied extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the CTIO 4m Blanco telescope. Since Feb 2019, HELM has been monitoring regions within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 82 with few-day cadences in the $(u)gri(z)$ bands, over a collective sky area of $\sim 38$ deg${\rm ^2}$. The main science goal of HELM is to provide high-quality optical light curves for a large sample of active galactic nuclei (AGNs), and to build decades-long time baselines when combining past and future optical light curves in these legacy fields. These optical images and light curves will facilitate the measurements of AGN reverberation mapping lags, as well as studies of AGN variability and its dependences on accretion properties. In addition, the time-resolved and coadded DECam photometry will enable a broad range of science applications from galaxy evolution to time-domain science. We describe the design and implementation of the program and present the first data release that includes source catalogs and the first $\sim 3.5$ years of light curves during 2019A--2022A.

79 ASTRONOMY AND ASTROPHYSICS↗
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 OSTI2023

Star cluster classification using deep transfer learning with PHANGS- HST

Currently available star cluster catalogues from the Hubble Space Telescope (HST) imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. Here, the time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models that accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D ≲ 20 Mpc). Here, we use HST ultraviolet (UV)–optical imaging of over 20 000 sources in 23 galaxies from the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: (i) distance-dependent models, based on cluster candidates binned by galaxy distance (9–12, 14–18, and 18–24 Mpc), and (ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (~60–80 per cent) and shows improvement by a factor of 2 in classifying asymmetric and multipeaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and spectral energy distribution (SED)-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N ~ 200 000) for public release.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Automatic Detection and Classification of Radio Galaxy Images by Deep Learning

Abstract Surveys conducted by radio astronomy observatories, such as SKA, MeerKAT, Very Large Array, and ASKAP, have generated massive astronomical images containing radio galaxies (RGs). This generation of massive RG images has imposed strict requirements on the detection and classification of RGs and makes manual classification and detection increasingly difficult, even impossible. Rapid classification and detection of images of different types of RGs help astronomers make full use of the observed astronomical image data for further processing and analysis. The classification of FRI and FRII is relatively easy, and there are more studies and literature on them at present, but FR0 and FRI are similar, so it is difficult to distinguish them. It poses a greater challenge to image processing. At present, deep learning has made breakthrough progress in the field of image analysis and processing and has preliminary applications in astronomical data processing. Compared with classification algorithms that can only classify galaxies, object detection algorithms that can locate and classify RGs simultaneously are preferred. In target detection algorithms, YOLOv5 has outstanding advantages in the classification and positioning of small targets. Therefore, we propose a deep-learning method based on an improved YOLOv5 object detection model that makes full use of multisource data, combining FIRST radio with SDSS optical image data, and realizes the automatic detection of FR0, FRI, and FRII RGs. The innovation of our work is that on the basis of the original YOLOv5 object detection model, we introduce the SE Net attention mechanism, increase the number of preset anchors, adjust the network structure of the feature pyramid, and modify the network structure, thereby allowing our model to demonstrate galaxy classification and position detection effects. Our improved model produces satisfactory results, as evidenced by experiments. Overall, the mean average precision (mAP@0.5) of our improved model on the test set reaches 89.4%, which can determine the position (R.A. and decl.) and automatically detect and classify FR0s, FRIs, and FRIIs. Our work contributes to astronomy because it allows astronomers to locate FR0, FRI, and FRII galaxies in a relatively short time and can be further combined with other astronomically generated data to study the properties of these galaxies. The target detection model can also help astronomers find FR0s, FRIs, and FRIIs in future surveys and build a large-scale star RG catalog. Moreover, our work is also useful for the detection of other types of galaxies.

Astronomy & Astrophysics↗
DOE OSTI2021

Unprecedented Fluorophore Photostability Enabled by Low‐Loss Organic Hyperbolic Materials

Abstract The dynamics of photons in fluorescent molecules plays a key role in fluorescence imaging, optical sensing, organic photovoltaics, and displays. Photobleaching is an irreversible photodegradation process of fluorophores, representing a fundamental limitation in relevant optical applications. Chemical reagents are used to suppress the photobleaching rate but with exceptionally high specificity for each type of fluorophore. Here, using organic hyperbolic materials (OHMs), an optical platform to achieve unprecedented fluorophore photostability without any chemical specificity is demonstrated. A more than 500‐fold lengthening of the photobleaching lifetime and a 230‐fold increase in the total emitted photon counts are observed simultaneously. These exceptional improvements solely come from the low‐loss hyperbolic dispersion of OHM films and the large resultant Purcell effect in the visible spectral range. The demonstrated OHM platform may open up a new paradigm in nanophotonics and organic plasmonics for super‐resolution imaging and the engineering of light–matter interactions at the nanoscale.

Lee, Yeon Ui↗
DOE OSTI2020

Optical Investigation of Catalyst Heating Operation in a Light-Duty Compression-Ignition Engine

It is well known that cold start engine operation contributes a majority of tail-pipe emissions during a vehicle drive cycle. Aftertreatment catalysts have poor conversion efficiency during this period due to low conversion efficiency at temperatures less than their light-off temperature. A late post injection strategy, with a delayed injection to increase exhaust enthalpy, can help decrease the time required to heat-up the catalyst. However, late injections into conditions with decreasing temperature and density can result in high emissions and combustion instability. In this study a three-injection strategy with a late-post injection was investigated in an optically-accessible engine. For the first two injections (pilot and main) timing and duration were held constant as the post injection timing was swept and duration varied to hold load constant, at a relevant light-load catalyst heating operating condition. Simultaneous double-pass schlieren and OH chemiluminescence data were acquired in an optically-accessible 0.4 L single-cylinder engine to understand spray characteristics and combustion behavior for #2 diesel fuel. Optical images analysis revealed higher liquid penetration and lower vaporization of the liquid fuel jet as post injection is retarded. It is also observed that retarding post injection timing to +25 CAD resulted in lower observed soot concentration in the piston bowl.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Tomographic imaging of atmospheric pressure plasma on complex surfaces

Many plasma types and behaviors such as streamer, arcs, cathode spots, anode spots, ionization waves, and magnetic field interactions create non-symmetric, fully 3D plasma structures. The plasma distribution in 3D space is heavily influenced by complex surfaces and the coupling interactions between plasma properties and the interfacing material properties. For example, ionization waves propagate in directions where ionization rates are highest, leading to complex configurations that are not fully understood or well characterized. Recent advances in laser diagnostics and models have been able to investigate well-controlled idealized plasmas in 2D fashion, but the complex structure in actual plasmas requires a technique than can provide a more complete 3D picture. However, 3D plasma diagnostics do not currently exist. To address this limitation, this activity will leverage available equipment to build a new tomographic optical imaging capability and advance the state-of-the-art in plasma diagnostics to investigate 3D phenomena on complex surfaces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

Abstract Physics‐driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics‐based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure–property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk‐ and edge plasmon discovery in MnPS 3 , a lesser‐known beam‐sensitive layered 2D material. This approach is universal, can be directly used as‐is with any specimen, and is expected to be applicable to any probe‐based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging.

42 ENGINEERING↗
DOE OSTI2023

From Images to Dark Matter: End-to-end Inference of Substructure from Hundreds of Strong Gravitational Lenses

Abstract Constraining the distribution of small-scale structure in our universe allows us to probe alternatives to the cold dark matter paradigm. Strong gravitational lensing offers a unique window into small dark matter halos (<10 10 M ⊙ ) because these halos impart a gravitational lensing signal even if they do not host luminous galaxies. We create large data sets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST’s COSMOS field. Using a simulation-based inference pipeline, we train a neural posterior estimator of the subhalo mass function (SHMF) and place constraints on populations of lenses generated using a separate set of galaxy sources. We find that by combining our network with a hierarchical inference framework, we can both reliably infer the SHMF across a variety of configurations and scale efficiently to populations with hundreds of lenses. By conducting precise inference on large and complex simulated data sets, our method lays a foundation for extracting dark matter constraints from the next generation of wide-field optical imaging surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data

Scattering-type scanning near-field optical microscope (s-SNOM) is a modern technique for subdiffractional optical imaging and spectroscopy. Over the past two decades, tremendous efforts have been devoted to modeling complex tip-sample interactions in s-SNOM, aimed at understanding the electrodynamics of materials at the nanoscale. However, due to complexities in analytical methods and the limited computation power for fully numerical simulations, compromises must be made to facilitate the modeling of tip-sample interaction, such as using quasistatic approximation or unrealistic tip geometries. Here, we apply a variety of widely utilized machine-learning methods, including k nearest neighbor and feedforward neural network etc. to study the phase-resolved spectroscopic near-field response. With only a small set of training data, which is simulated using the finite-dipole model, we demonstrate that the relation between the experimental near-field signal and sample optical constant can be one to one mapped without the need for tip modeling: for a given material with a moderate dielectric function, its complex near-field spectrum can be accurately determined within the mid-IR spectral range, and vice versa. Our preliminary study sets the stage for future exploration using real experimental data. Our method is beneficial for processing the increasing amount of data accumulated across many research groups and especially useful for user facilities such as synchrotron-based national laboratories where a large amount of data is generated on a daily basis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks

Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact-detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor-quality image subtractions, false detections from low signal-to-noise images, and detections of preexisting variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92% on our data set. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗