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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Detection of Foreign Materials on Broiler Breast Meat Using a Fusion of Visible Near-Infrared and Short-Wave Infrared Hyperspectral Imaging

Foreign material (FM) found on a poultry product lowers the quality and safety of the product. We developed a fusion method combining two hyperspectral imaging (HSI) modalities in the visible-near infrared (VNIR) range of 400–1000 nm and the short-wave infrared (SWIR) range of 1000–2500 nm for the detection of FMs on the surface of fresh raw broiler breast fillets. Thirty different types of FMs that could be commonly found in poultry processing plants were used as samples and prepared in two different sizes (5 × 5 mm 2 and 2 × 2 mm 2 ). The accuracies of the developed Fusion model for detecting 2 × 2 mm 2 pieces of polymer, wood, and metal were 95%, 95%, and 81%, respectively, while the detection accuracies of the Fusion model for detecting 5 × 5 mm 2 pieces of polymer, wood, and metal were all 100%. The performance of the Fusion model was higher than the VNIR- and SWIR-based detection models by 18% and 5%, respectively, when F1 scores were compared, and by 38% and 5%, when average detection rates were compared. The study results suggested that the fusion of two HSI modalities could detect FMs more effectively than a single HSI modality.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths: Supporting Data

The extent and duration of snow cover is predicted to be altered as the climate changes. Developing high-resolution estimates of snow cover change is crucial for estimating changes in snow cover and the effects of these changes on watershed and ecosystems processes. Remote sensing tools have been a common method for rapidly mapping snow covered area (SCA) across a landscape. The most common remote sensing method for estimating SCA uses satellite-based calculations of the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI is effective at catchment- to regional-scale estimates of SCA, but due to spatial resolution limitations of SWIR measurements, NDSI cannot be used to assess fine-scale SCA. In this work, we develop a new algorithm, called the Blue Snow Threshold (BST) algorithm, that maps high-resolution SCA by calculating a threshold on the blue wavelengths from high-resolution satellite imagery. This data package includes Orthorectified IKONOS-2 imagery (IkonosTestImage.tif) from August 14, 2004 at 1.00 meters Ground Sample Distance for Cook Inlet, Alaska (59.966414 , -152.982975). The Blue Snow Threshold algorithm (BST.py) was then used to produce a snow cover estimate (IkonosTestImage_BST.tif) for this study area. Additional imagery metadata is included in the ImageInfo.txt file. See Thaler et al., 2023 (https://doi.org/10.1016/j.rse.2022.113403) for more information about the BST algorithm.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Desert Peak Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to the Desert Peak Geothermal Field. It includes all input and output files used in the project. The files include data categories of raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs including Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files for the Desert Peak Geothermal Site are used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Desert Peak Geothermal Field.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files are used with the Geothermal Exploration Artificial Intelligence for the Salton Sea Geothermal Site to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Salton Sea Geothermal Site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

APPL Hyperspectral_Imaging_Dataset_for_Heritability_Analysis_in_Populus_trichocarpa

This dataset contains hyperspectral imaging data collected at the Advanced Plant Phenotyping Laboratory (APPL) at Oak Ridge National Laboratory. Natural variants of Populus trichocarpa were imaged using a high-throughput hyperspectral phenotyping pipeline to quantify spectral reflectance traits for downstream quantitative genetics analyses. The dataset includes hyperspectral image files and derived reflectance data products suitable for extracting spectral features across the measured wavelength range (e.g., VNIR and/or SWIR, depending on instrument configuration), along with associated sample metadata (e.g., genotype identifiers, experimental design factors, and imaging run identifiers). These data were generated to support analyses of broad-sense heritability of hyperspectral traits and their relationships with biochemical phenotypes (including lignin traits from Py-MBMS).

APPL↗
DOE OSTI2026

How Does Metal Spin State Affect Electronic Communication in Mixed-Valence Dimers? Insights from Ultrafast Near-Infrared and Soft X-ray Transient Absorption Spectroscopy

Recent advancements in photocatalysis, photovoltaics, and quantum information science take advantage of electron spin, and determining how spin multiplicity affects electron transfer is key to understanding these phenomena. Here, in this study, we examine how metal spin state affects electronic communication in an organometallic mixed-valence dimer, ferrocenyl cobaltocenium ([Fe II Cp 2 Co III Cp 2 ] + ). This complex can be photoexcited from its low-spin singlet Fe II ground state to form intermediate-spin triplet Fe II and high-spin quintet Fe II excited states. Using femtosecond optical transient absorption (OTA) spectroscopy with visible (vis), near-infrared (NIR), and short-wave IR (SWIR) probes, supported by time-dependent density functional theory (TD-DFT) calculations, we measure Fe II Co III →Fe III Co II intervalence charge transfer (IVCT) bands in each of the Fe II spin states. Mulliken–Hush analysis of the excited-state IVCT bands was used to compute the electronic coupling between the metal centers in all three spin states, which increased as quintet < triplet < singlet. Meanwhile, the peak energy of the bands, and thus the ΔG of the IVCT transition, increased as triplet < quintet < singlet. Then, to directly probe the electronic structure at each metal center, we employed picosecond soft X-ray transient absorption (XTA) spectroscopy at the Fe and Co L 3 edges. Our results show that the low-spin and high-spin states of [Fe II Cp 2 Co III Cp 2 ] + are valence-localized, while the intermediate-spin state is partially delocalized. The differences in charge delocalization are attributed to differences in orbital occupation and geometry that affect the free energy and superexchange coupling.

Burke, John H. [Univ. of Illinois at Urbana-Champa↗
DOE OSTI2025

Enantioselective Se lattices for stable chiroptoelectronic processing media

Chiroptoelectronic devices are crucial for applications in quantum computing, spin optical communications, and magnetic recording. However, the limited efficiency and low stability of conventional circularly polarized light (CPL)-sensing materials have restricted their broader use. Here, we introduce atomic chiral Se nanorod (NRs) films as broadband CPL detectors, leveraging the intrinsic chirality and stability of Se nanocrystals. We also perform incident circular polarization (ICP)-Raman optical activity (ROA) to explore the chiroptical activity of the large-area films. The Se NRs thin films detected CPL across a broad range from ultraviolet (UV) to short-wavelength infrared (SWIR), with a responsivity dissymmetry factor of up to 0.4, maintaining high stability under ambient conditions for longer than 13 months. CPL-sensitive Se NRs with intrinsic chirality have potential applications in chiral photonic synapses, chiral spin devices, and CPL-sensitive photocatalysts. ICP-ROA mapping also advances the analysis of 2D chiral materials.

Nanophotonics and plasmonics↗
DOE OSTI2024

Chemical and spectroscopic characterization of plutonium tetrafluoride

Anhydrous plutonium tetrafluoride is an important intermediate in the production of metallic Pu. This historically important compound is also known to exist in at least two distinct, yet understudied hydrate forms, PuF 4 ·xH 2 O(s) (0.5 ≤ x ≤ 2) and PuF 4 ·2.5H 2 O(s). X-ray diffraction (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) are the most common tools used to characterize these materials, often in a context for studying structural and morphological changes that arise from aging or calcination. However, fundamental electronic and vibrational spectroscopic information is rather scarce. Here, in this study, we measured the visible and shortwave infrared (SWIR) diffuse reflectance, Fourier transform infrared (FTIR), fluorescence and Raman spectra of PuF 4 (s) and PuF 4 ·xH 2 O(s) to obtain a better electronic and vibrational fingerprint. Our work provides clear indication of the polymeric structure of anhydrous PuF 4 , consistent with the Raman spectrum of UF 4 (s) and its hydrates. This is supplemented with XRD, TGA and SEM analysis. Findings in this study indicate that the spectra are modified by particle size, which in turn is influenced by synthetic technique.

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

Performance analysis of photodetectors based on 2D materials and heterostructures

The unprecedented demand for sophisticated, self-powered, compact, ultrafast, cost-effective, and broadband light sensors for a myriad of applications has spurred a lot of research, precipitating in a slew of studies over the last decade. Apart from the photosensing ability of an active element in the light sensor, the device architecture is crucial in terms of photoinduced charge carrier generation and separation. Since the inception of graphene and the subsequent research growth in the atomically thin 2D materials, researchers have developed and adapted different families of 2D materials and device architectures, including single element 2D, 0D/2D, 2D/2D, 1D/2D stacked structures, and so on. Here this review discusses the recent reports on the light-sensing properties of various 2D materials, their heterostructures, and characteristics applicable to the ultraviolet-near infrared (UV-NIR), short-wave IR (SWIR), mid-wave IR (MWIR), long-wave IR (LWIR), and terahertz (THz) spectral ranges. It highlights the novelty of the burgeoning field, the heightened activity at the boundaries of engineering and materials science, particularly in the generation of charge carriers, their separation, and extraction, and the increased understanding of the underpinning science through modern experimental approaches. Devices based on the simultaneous effects of the pyro-phototronic effect (PPE) and the localized surface plasmon resonance (LSPR) effect, the photothermoelectric effect (PTE)-assisted photodetectors (PDs), waveguide-integrated silicon-2D PDs, metal-2D-metal PDs, and organic material PDs are examined rigorously. Theoretical treatment utilizing various computational approaches to investigate 2D materials and heterostructures for photodetection applications is also briefly discussed. At the end, current challenges and solutions to enhance the figures of merit of photodetectors are proposed.

2D transition metal chalcogenides↗
DOE OSTI2021

Miniaturized integrated spectrometer using a silicon ring-grating design

We introduce and experimentally demonstrate a miniaturized integrated spectrometer operating over a broad bandwidth in the short-wavelength infrared (SWIR) spectrum that combines an add-drop ring resonator narrow band filter with a distributed Bragg reflector (DBR) based broadband filter realized in a silicon photonic platform. The contra-directional coupling DBR filter in this design consists of a pair of waveguide sidewall gratings that act as a broadband filter (i.e., 3.9 nm). The re-directed beam is then fed into the ring resonator which functions as a narrowband filter (i.e., 0.121 nm). In this scheme the free spectral range (FSR) limitation of the ring resonator is overcome by using the DBR as a filter to isolate a single ring resonance line. The overall design of the spectrometer is further simplified by simultaneously tuning both components through the thermo-optic effect. Moreover, several ring-grating spectrometer cells with different central wavelengths can be stacked in cascade in order to cover a broader spectrum bandwidth. This can be done by centering each unit cell on a different center wavelength such that the maximum range of one-unit cell corresponds to the minimum range of the next unit cell. This configuration enables high spectral resolution over a large spectral bandwidth and high extinction ratio (ER), making it suitable for a wide variety of applications.

42 ENGINEERING↗
DOE OSTI2022

Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)

Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.

58 GEOSCIENCES↗
DOE OSTI2023

Development of quantum dot materials for infrared cameras (Final CRADA Report)

The aim of this project was to develop scalable methods to produce infrared (IR) mercury telluride (HgTe) colloidal quantum dot (CQD) thin films and demonstrate their utility in a proof-of-concept monolithic SWIR focal plane array (FPA). These objectives were accomplished by scaling up the HgTe CQD synthesis, characterizing physical and electrical properties of HgTe CQDs, evaluating solution-processed coating methods for quality and efficiency, and developing a process flow to integrate HgTe CQDs with commercial-off-the-shelf silicon CMOS readout circuits by solution-processed coating to produce monolithic FPAs. The FPA is the image sensor in an infrared imaging system responsible for detecting and processing reflected or emitted light into an infrared image of the scene under observation. The quality of the image is determined by the sensitivity and resolution of the image sensor in the system. Higher resolution IR FPAs enable higher throughput in manufacturing quality assurance, wider field of view for autonomous navigation, and longer range surveillance for defense.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Development of Spectroscopic Tools for Nuclear Forensics Signatures Development

Diversion of nuclear materialsfrom legitimate commercial processes for use by nefarious actors is a frightening and surreptitious problem. Smuggled nuclear material can be used to manufacture a simple radiological dispersion device (RDD) and potentially incorporated into the development of a nuclear weapon. With some frequency, smuggled nuclear material is interdicted in foreign countries as actors cross border checkpoints. Nuclear material interdicted in this way can be analyzed using a variety of tools. Plutonium-239 is commonly used as the fissile material in nuclear weapon production as an alternative to uranium-235. Plutonium in a production, refining, or finishing facility will exist in many forms including oxide precursors (PuF4, PuF3, Pu oxalate, etc.), oxide, and metal. The ability to identify plutonium in each of these forms and determine properties, characteristics, or history of these materials is crucial for determining if the processes occurring in a nuclear facility are legitimate or not. This presentation will focus on the development and application ofspectroscopic tools developed at SRNL using doubled-walled cells (DWC) to characterize Pu-bearing compounds. Advances in the use of vibrational spectroscopy techniques (Raman and IR), UV-VIS-SWIR diffuse reflectance spectroscopy, gamma emission spectroscopy, luminescence spectroscopy, laser-induced breakdown spectroscopy in conjunction with XRD and SEM in the characterization of Pu-bearing compounds will be presented with emphasis in material processing history.

Villa-Aleman, Eliel↗
DOE OSTI2025

Zero-Power Wireless Infrared Digitizing Sensors for Large Scale Energy-Smart Farm

This project, funded by ARPA-E and led by Northeastern University, developed zeropower infrared digitizing sensors to optimize irrigation and enhance crop yields. Traditional water stress detection methods are costly and require frequent maintenance, limiting their effectiveness. Our research identified shortwave infrared (SWIR) transmittance as the most reliable indicator of plant water stress and developed plasmonically enhanced micromechanical photoswitches (PMPs) that operate with minimal power. The sensors offer low-cost, large-scale deployment, auto-calibration across different crops, and a 10-year battery life, significantly reducing maintenance costs. The system achieved 4x greater accuracy than conventional soil moisture sensors while ensuring economic feasibility. By enabling precision irrigation, this technology conserves water, enhances crop productivity, and lowers operational costs, making it a scalable solution for sustainable agriculture and global food security.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗
DOE OSTI2021

Experiment Based Superposition Thermal Modeling of Laser Powder Bed Fusion

This paper evaluates experiment-based superposition thermal modeling for Laser Powder Bed Fusion (LPBF) with a pulsed laser. An analytical pulsed laser thermal model establishes the modeling procedure. The framework inverts a powder bed’s single pulse temperature response from experimental spatiotemporal Short-Wave Infrared (SWIR) camera data. Superimposing this response along a scan path simulates multi-pulse LPBF. Results show the experimentally informed superposition model rapidly and accurately predicts a layer’s temperature history. The model has applications in correction of thermally driven LPBF errors and in-situ part qualification.

Lough, Cody↗