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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multimodal Chemical Characterization of Brown Carbon in Atmosphere and Snowpack during SAIL Field Campaign Report

The main goal of this field project was to characterize the chemical and physical properties of light-absorbing particles (LAP) present in the atmosphere and snowpack. The specific field efforts included: 1) online measurement and sampling of light-absorbing particles using a Magee Scientific model AE33 aethalometer (AE33) and a time-resolved aerosol collector (TRAC); and 2) sampling of snowpack during observed events of LAP deposition on snow, confirmed by onsite U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility measurements and meteorological data. The ongoing research objectives of our follow-up sample and data analysis include: 1) Characterization of aerosol regimes and sources, identifying particle-type populations, mixing states, and atmospheric transformations based on correlative analysis of our detailed chemical imaging and chemical characterization measurements and real-time records from the second ARM Mobile Facility (AMF2) and other instruments available from the Surface Atmosphere Integrated Field Laboratory (SAIL) experiment; 2) Assessment of the optical and chemical properties of snowpack deposits to investigate how aerosol deposition influences snowpack lifetime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Gate-Tunable Short-Wave Infrared Polycrystalline GeSn Phototransistors on Noncrystalline Substrates

GeSn is a group-IV alloy with immense potential to advance microelectronics technology due to its intrinsic compatibility with existing Si CMOS processes. With a sufficiently high Sn composition, GeSn is classified as a direct bandgap semiconductor. Polycrystalline GeSn holds several additional advantages, including its significantly lower synthesis cost compared to its epitaxial counterpart, as well as the versatility to grow these films on a variety of substrates. Here, in this work, we present a polycrystalline thin-film GeSn phototransistor on a fused silica substrate with a Sn composition of ~10%, showing a photoresponse in the short-wave infrared wavelength range, critical for emerging sensing applications. This device shows a gate-tunable response, with responsivities approaching up to 1.7 mA/W with only a 30 nm-thick GeSn layer. Furthermore, phototransistors offer additional adaptability through gating, which allows for the reduction of dark current. This not only enhances the signal-to-noise ratio but also offers more flexible integration with various image sensor readout implementations using different substrates. The specific detectivity of this phototransistor is within an order of magnitude of those of previously reported GeSn photodetectors grown by molecular beam epitaxy and chemical vapor deposition, even though the absorber is 3 to 20× thinner while the electrode spacing for photocarrier transport is approximately 15× longer than the carrier diffusion length in this work, showing great potential benefits of extending similar device structures to epitaxial GeSn layers. As these GeSn phototransistors utilize a noncrystalline substrate, our work establishes a fundamentally more versatile path toward monolithically integrated GeSn-based photodetectors for next-generation multimodal sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Heterogeneous microstructure of yttrium hydride and its relation to mechanical properties

Here, the goal of this study is to investigate the properties of yttrium hydride materials in relation to the microstructure, especially its homogeneity. High-throughput nanoindentation mapping was used to evaluate hardness distribution. Raman spectral imaging demonstrated its sensitivity to the presence of YH2 and impurities. Raman peak position maps were correlated with residual stress in the specimens. Electron backscatter diffraction mapping provided phase distributions with correlation to high-energy X-ray diffraction analysis. The experimental mapping data were combined and analyzed using unsupervised machine learning cluster procedures. The machine learning analysis revealed that yttrium hydride specimens contained a major δ-YH2 – x phase component and minor α-Y and δ-YH2 – x components with significant residual stress. The minor phase fraction decreased with increasing nominal H/Y ratio, which affected the nanoindentation and Vickers hardness. The multimodal mapping procedures described herein affect developing important microstructure–property relationships, as well as correlations in heterogeneity and mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

High-energy synchrotron X-ray multimodal computed tomography: enabling multiscale materials characterization at NSLS-II

We report the commissioning of a multimodal computed tomography experimental setup at the 28-ID-2 (XPD) beamline of the National Synchrotron Light Source II. This high-energy (>60 keV) resource features a tunable X-ray beam size ranging from several millimetres to a few micrometres and enables comprehensive characterization of high-Z materials—an essential capability for nuclear and advanced materials research. It provides four complementary computed tomography modalities: X-ray absorption, X-ray fluorescence, X-ray diffraction, and pair distribution function tomography. A case study using a custom-made heterogeneous sample demonstrates these abilities to simultaneously capture atomic, elemental, and morphological information. This unique combination of imaging, structural, and chemical sensitive methods provides a holistic approach to study complex materials with amorphous and crystalline systems across multiple length scales.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1997

NASA Robotic Neurosurgery Testbed

The detection of tissue interface (e.g., normal tissue, cancer, tumor) has been limited clinically to tactile feedback, temperature monitoring, and the use of a miniature ultrasound probe for tissue differentiation during surgical operations. In neurosurgery, the needle used in the standard stereotactic CT (Computational Tomography) or MRI (Magnetic Resonance Imaging) guided brain biopsy provides no information about the tissue being sampled. The tissue sampled depends entirely upon the accuracy with which the localization provided by the preoperative CT or MRI scan is translated to the intracranial biopsy site. In addition, no information about the tissue being traversed by the needle (e.g., a blood vessel) is provided. Hemorrhage due to the biopsy needle tearing a blood vessel within the brain is the most devastating complication of stereotactic CT/MRI guided brain biopsy. A robotic neurosurgery testbed has been developed at NASA Ames Research Center as a spin-off of technologies from space, aeronautics and medical programs. The invention entitled 'Robotic Neurosurgery Leading to Multimodality Devices for Tissue Identification' is nearing a state ready for commercialization. The devices will: 1) improve diagnostic accuracy and precision of general surgery, with near term emphasis on stereotactic brain biopsy, 2) automate tissue identification, with near term emphasis on stereotactic brain biopsy, to permit remote control of the procedure, and 3) reduce morbidity for stereotactic brain biopsy. The commercial impact from this work is the potential development of a whole new generation of smart surgical tools to increase the safety, accuracy and efficiency of surgical procedures. Other potential markets include smart surgical tools for tumor ablation in neurosurgery, general exploratory surgery, prostate cancer surgery, and breast cancer surgery.

Mah, Robert↗
DOE OSTI2021

Transverse mode analysis for free-space laser beams using Bayesian analysis

Reliable and rapid assessment of the transverse mode quality of a free-space laser beam has a wide range of applications in laser development, research, and utilization. It has become even more important with recent advances in developing orbital angular momentum photon beams across a broad spectral region. In this work, a general modal analysis method for a free-space multimode laser beam has been developed based on Bayesian analysis. After transforming mode decomposition into a linear system problem, a Gaussian probabilistic model is used to find a closed-form solution. The method is found to be robust with the presence of Gaussian noise. Prior knowledge about the mode content can be incorporated into the method to improve the solution for situations when coherent disturbances or contamination are present in the laser beam. This method can be used to analyze the mode content for laser beams in different bases, such as Hermite–Gaussian (HG) modes and Laguerre–Gaussian (LG) modes. Three applications of this method are presented: a detailed modal analysis of the beam image from the incoherent intensity addition of HG modes and two examples of mode decomposition using the complex wavefront from the coherent superposition of HG and LG modes. The feasibility of this method is demonstrated using various simulation results. Based on digital images of a laser beam recorded without complex wavelength-limiting optics, in principle, this method can be used in a wide wavelength range from infrared to ultraviolet, and possibly x ray.

Liu, Peifan (ORCID:0000000273816153)↗
DOE OSTI2024

A Deep Multimodal Representation Learning Framework for Accurate Molecular Properties Prediction

Drug discovery is a complex and challenging process, requiring the optimization of candidate compounds to identify those with the potential to become safe and effective drugs. Predicting molecular properties is an indispensable step in the drug discovery pipeline. Traditionally, this process is costly and time-intensive, involving multiple rounds of experiments and clinical trials, rendering it impractical for every candidate compound. Deep learning techniques have emerged as a promising approach to drug discovery to reduce the cost and time required to identify novel drugs. However, prevalent research in deep learning models focused on predicting molecular properties has primarily fixated on single-modal models, which utilize a single modality of data, neglecting the potential benefits of combining different data modalities. To overcome this limitation, we introduce MRL-Mol: a deep \textbf{M}ultimodal \textbf{R}epresentation \textbf{L}earning framework for accurate \textbf{Mol}ecular properties prediction. MRL-Mol harnesses three data modalities: sequence, graph, and image, augmenting the depth of comprehension. Leveraging a large-scale unlabeled dataset~($\sim$1M unique molecules), we pretrain MRL-Mol to extract inter- and intra-modal information. Our study demonstrates the superior performance of MRL-Mol in predicting molecular properties across six benchmark datasets, including both classification and regression tasks. Notably, MRL-Mol outperforms other state-of-the-art molecular properties prediction models. These findings suggest that by combining information from multiple data modalities, MRL-Mol can comprehend molecules better than single-modal deep learning models and identify molecular properties with better accuracy.

Yang, Yuxin↗
DOE OSTI2022

Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum

Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data (thermal, RGB, and multispectral) derived from an unmanned aerial vehicle (UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination (R 2 ) ranging from 0.40 to 0.53 under water-stressed environment and 0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yield-based drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data. Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance.

09 BIOMASS FUELS↗
DOE OSTI2021

Multiscale operando X-ray investigations provide insights into electro-chemo-mechanical behavior of lithium intercalation cathodes

The electrochemical performance and cycle life of lithium-ion batteries (LIBs) depend on the electrochemical, chemical, and mechanical behavior of electrodes and electrolytes. Despite extensive studies conducted previously, challenges exist to decouple these behaviors, capture the evolution of electro-chemo-mechanical behavior in realistic conditions, and correlate atomic-scale stress evolution to micro-scale bulk mechanical degradation. Here, we report multiscale operando techniques to investigate polydisperse battery electrodes by integrating volume-averaged quantitative synchrotron X-ray scattering with high-resolution transmission X-ray microscopy (TXM). The former provides us information spanning a wide spatial range, from Angstrom-level atomic structures to micrometer-level particle scales, while the latter provides time-resolved 2D images of the particles during cycling. The complementarity of the two operando techniques is demonstrated by an over-lithiation test of LiCoO 2 electrodes, where particles crack and eventually pulverize. Additionally, the techniques are applied to study LiCoO 2 cycling stability from 3.0 V to 4.5 V. Operando X-ray scattering result shows nanometer-scale features keep forming in LiCoO 2 electrodes during cycling, resulting in an increased projected area observed by the TXM experiment. The formation of such features is inhibited by a polymer coating on the electrode, leading to vastly improved cycling stability. The polymer coating alleviates LiCoO 2 surface deterioration, reduces side product generation, and inhibits LiCoO 2 particles volume expansion during the cycling test. These operando multimodal X-ray techniques presented herein thus offer a novel, multiscale diagnostic modality for studying existing and emerging battery materials, aiding the development of next-generation LIBs.

25 ENERGY STORAGE↗
DOE OSTI2021

Ambient Tip-Enhanced Photoluminescence with 5 nm Spatial Resolution

We achieve record spatial resolution in ambient tip-enhanced photoluminescence (TEPL) from CdSe/ZnS semiconductor quantum dots (QDs). Our approach only takes advantage of surface roughness at the apex of a sputtered plasmonic gold probe. When irradiated with visible light (633 and 532 nm lasers), nano-corrugated regions of the apex of an Au tip support 5-nm spatial resolution in TEPL imaging of QD aggregates on silicon. The reproducibility of our observations is illustrated using different plasmonic probes and samples. The origin of the observed near-field contrast, underlying the demonstrated high spatial resolution in TEPL, is discussed in the context of field enhancement at the metal tip-fluorophore interface.

Chemical Dynamics Initiative↗
DOE OSTI2020

Photofield electron emission from an optical fiber nanotip

In this work, we demonstrate a nanotip electron source based on a graded index multimode silica optical fiber, tapered at one end to a radius of curvature r ~50 nm and coated with a thin film of gold. We report observation of laser-induced electron photoemission at tip bias potentials below the onset of dark field emission. Single-photon photofield emission is identified as the emission mechanism that exhibits fast switching times with an upper limit on the order of 1 μ s. The explored fiber optic nanotips are flexible back-illuminated emitters, which can be operated in continuous wave and pulsed modes using lasers with photon energies in the visible range or higher. The mechanical flexibility of the source can facilitate externally controlled positioning. Multiple, individually addressable, nanotips may be assembled into a bundle for applications such as computational electron ghost imaging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Flow structures of the cross-flow over a five-layer helically coiled steam generator geometry

The helically-coiled heat exchanger (HCSG) offers advantages over straight tubes such as compactness in geometry, increased heat transfer coefficients, and capability to absorb thermal expansion. In this study, the time-resolved velocity field data of shell-side crossflow over a five-layer helically coiled steam generator geometry was obtained using Particle Image Velocimetry (PIV) between adjacent rods in four regions with up to 5000 frames per second (5 kHz) at approximately Re u = 3600. The slant rod bundle created different flow patterns in different regions. Proper Orthogonal Decomposition (POD) revealed high-energy-mode flow structures. Due to the confined flow channel geometry, the large flow structures were not advected resulting in no strong POD mode paring. Continuous Wavelet Transform (CWT) visualized the characteristics of flow fluctuation in time and frequency domain simultaneously, which ranged in Strouhal number, S u , between 0.03 and 1.17. In the wake regions of the rods, flows changed in both pattern and magnitude over time. Once a pattern formed, it continued for relatively shorter than straight tube arrays, before changing the pattern again. The histograms of the POD time coefficients presented this multimodal flow characteristics. The current study focuses more on whole flow field analyses in the selected regions rather than local point-wise analyses. Finally, this study revealed that various Strouhal numbers can exist in the helically coiled heat exchanger geometry, which was not observed in straight tube arrays. In particular, Strouhal numbers smaller than 0.1 do not seem to be caused by vortex shedding but seem to be the result of the combination of complex geometry and possible multimodal trends. Based on the current observation, further works would involve intensive instantaneous point-wise analyses on the interactions between important locations such as separation points, stagnation points, shear layers, etc.

42 ENGINEERING↗
NASA NTRS2020

NASA Next-Generation Sensing Technologies for Earth & Planetary Science

Dr. Ved Chirayath's seminar will highlight new sensing technologies and airborne platforms he is developing for Earth & Space Science as director of the NASA Silicon Valley Laboratory for Advanced Sensing. His talk will feature recent work on Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, MiDAR, a next-generation active hyperspectral remote sensing and optical communications instrument, airborne gas sensing of multipollutant combustion sources, and a plasma actuated unmanned aerial vehicle (UAV) that utilized high-voltage dielectric discharge devices to achieve the first plasma controlled flight in history.Fluid Lensing and the NASA FluidCam instrument have been used extensively to provide distortion-free 3D multispectral imagery from UAVs of shallow marine systems around the world. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments as an analog for future Ocean Worlds robotic exploration missions. Finally, Chirayath will present preliminary results from NeMO-Net, a supercomputer-based neural network that uses a citizen science video game for global multimodal coral reef benthic habitat mapping, fusing remote sensing data from Fluid Lensing, MiDAR, NASA's Earth Observing System, and commercial satellites, to better understand the present and past dynamics of shallow marine systems. Together, these maturing technologies present promising new ways in which to explore terrestrial, marine, and aerial systems on Earth, and, ultimately, aid in the search for extraterrestrial life within our solar system and beyond.

Chirayath, Ved↗
NASA NTRS2019

Alternative Vegetation States in Tropical Forests and Savannas: The Search for Consistent Signals in Diverse Remote Sensing Data

Globally, the spatial distribution of vegetation is governed primarily by climatological factors (rainfall and temperature, seasonality, and inter-annual variability). The local distribution of vegetation, however, depends on local edaphic conditions (soils and topography) and disturbances (fire, herbivory, and anthropogenic activities). Abrupt spatial or temporal changes in vegetation distribution can occur if there are positive (i.e., amplifying) feedbacks favoring certain vegetation states under otherwise similar climatic and edaphic conditions. Previous studies in the tropical savannas of Africa and other continents using the MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF) satellite data product have focused on discontinuities in the distribution of tree cover at different rainfall levels, with bimodal distributions (e.g., concentrations of high and low tree cover) interpreted as alternative vegetation states. Such observed bimodalities over large spatial extents may not be evidence for alternate states, as they may include regions that have different edaphic conditions and disturbance histories. In this study, we conduct a systematic multi-scale analysis of diverse MODIS data streams to quantify the presence and spatial consistency of alternative vegetation states in Sub-Saharan Africa. The analysis is based on the premise that major discontinuities in vegetation structure should also manifest as consistent spatial patterns in a range of remote sensing data streams, including, for example, albedo and land surface temperature (LST). Our results confirm previous observations of bimodal and multimodal distributions of estimated tree cover in the MODIS VCF. However, strong disagreements in the location of multimodality between VCF and other data streams were observed at 1 km scale. Results suggest that the observed distribution of VCF over vast spatial extents are multimodal, not because of local-scale feedbacks and emergent bifurcations (the definition of alternative states), but likely because of other factors including regional scale differences in woody dynamics associated with edaphic, disturbance, and/or anthropogenic processes. These results suggest the need for more in-depth consideration of bifurcation mechanisms and thus the likely spatial and temporal scales at which alternative states driven by different positive feedback processes should manifest.

Savanna↗
DOE OSTI2022

MENUS—Materials engineering by neutron scattering

Materials engineering by neutron scattering (MENUS) at the second target station will be a transformational high-flux, versatile, multiscale materials engineering diffraction beamline with unprecedented new capabilities for the study of complex materials and structures. It will support both fundamental and applied materials research in a broad range of fields. MENUS will combine unprecedented long-wavelength neutron flux and unique detector coverage to enable real-time studies of complex structural and functional materials under external stimuli. The incorporated small angle neutron scattering and transmission/imaging capabilities will extend its sensitivity to larger length scales and higher spatial resolution. Multimodal MENUS will provide crystallographic and microstructure data to the materials science and engineering community to understand lattice strain/phase transition/microstructure/texture evolution in three orthogonal directions in complex material systems under combined extreme applied conditions. Finally, the capabilities of MENUS will open new scientific opportunities and meet the research needs for science challenges to enable studies of a range of phenomena and answer the key questions in material design/exploration, advanced material processing, transformative manufacturing, and material operations of national impacts in our daily life.

47 OTHER INSTRUMENTATION↗
NASA NTRS2005

Multiangle Imaging Spectroradiometer (MISR) Global Aerosol Optical Depth Validation Based on 2 Years of Coincident Aerosol Robotic Network (AERONET) Observations

Performance of the Multiangle Imaging Spectroradiometer (MISR) early postlaunch aerosol optical thickness (AOT) retrieval algorithm is assessed quantitatively over land and ocean by comparison with a 2-year measurement record of globally distributed AERONET Sun photometers. There are sufficient coincident observations to stratify the data set by season and expected aerosol type. In addition to reporting uncertainty envelopes, we identify trends and outliers, and investigate their likely causes, with the aim of refining algorithm performance. Overall, about 2/3 of the MISR-retrieved AOT values fall within [0.05 or 20% x AOT] of Aerosol Robotic Network (AERONET). More than a third are within [0.03 or 10% x AOT]. Correlation coefficients are highest for maritime stations (approx.0.9), and lowest for dusty sites (more than approx.0.7). Retrieved spectral slopes closely match Sun photometer values for Biomass burning and continental aerosol types. Detailed comparisons suggest that adding to the algorithm climatology more absorbing spherical particles, more realistic dust analogs, and a richer selection of multimodal aerosol mixtures would reduce the remaining discrepancies for MISR retrievals over land; in addition, refining instrument low-light-level calibration could reduce or eliminate a small but systematic offset in maritime AOT values. On the basis of cases for which current particle models are representative, a second-generation MISR aerosol retrieval algorithm incorporating these improvements could provide AOT accuracy unprecedented for a spaceborne technique.

global meaurement↗
DOE OSTI2022

Temporal evolution of scalar modes in Richtmyer–Meshkov instability of inclined interface using high-speed PIV and PLIF measurements at 60 kHz

Abstract The current work presents simultaneous, high-speed measurements at 60,000 fields per second of velocity and mole fraction using particle image velocimetry (PIV) and planar laser induced acetone-fluorescence in a Richtmyer–Meshkov instability of an inclined interface (Atwood number, At = 0.22). Specifically, around 2 ms of temporal evolution of the vortex structures and their associated scalar modes immediately following the interface-reshock interaction is presented. Two initial interface conditions are discussed—(a) a sharp, inclined ‘single mode’ interface and (b) a ‘multi-mode’ interface where small perturbations are imposed on the single mode case. A 2D wavelet decomposition of the scalar flow field shows a highly intermittent distribution of small-scale variance throughout the interface even at late times. These are correlated strongly with the vortex structures and local turbulence intensity, where each small-scale scalar mode is sandwiched between two co-rotating vortex structures. This indicates that the interstitial regions between the vortices are significant hotspots of entrainment, which is then dispersed by the induced, counter-flow velocity fields. The multimode case demonstrates similar organization at large scales, while the scalar field is much more homogeneous at smaller scales. These observations highlight the importance of capturing the early time vortex evolution to accurately estimate any late time intermittency, especially where deposition of intense vorticity on sharp interfaces is present.

Engineering↗
DOE OSTI2023

Resolution-enhanced X-ray fluorescence microscopy via deep residual networks

Abstract Multimodal hard X-ray scanning probe microscopy has been extensively used to study functional materials providing multiple contrast mechanisms. For instance, combining ptychography with X-ray fluorescence (XRF) microscopy reveals structural and chemical properties simultaneously. While ptychography can achieve diffraction-limited spatial resolution, the resolution of XRF is limited by the X-ray probe size. Here, we develop a machine learning (ML) model to overcome this problem by decoupling the impact of the X-ray probe from the XRF signal. The enhanced spatial resolution was observed for both simulated and experimental XRF data, showing superior performance over the state-of-the-art scanning XRF method with different nano-sized X-ray probes. Enhanced spatial resolutions were also observed for the accompanying XRF tomography reconstructions. Using this probe profile deconvolution with the proposed ML solution to enhance the spatial resolution of XRF microscopy will be broadly applicable across both functional materials and biological imaging with XRF and other related application areas.

36 MATERIALS SCIENCE↗