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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3x) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. In conclusion, new methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.

13 HYDRO ENERGY↗
DOE OSTI2025

Spectral kernel machines with electrically tunable photodetectors

Spectral machine vision collects spectral and spatial information as three-dimensional hypercubes and digitally processes them, which causes a data bottleneck, limiting power efficiency, frame rate, and spectral-spatial resolution. This work introduces spectral kernel machines (SKMs) to overcome these bottlenecks. SKM directly compresses spectral analysis through the output photocurrent and learns from example objects to identify and classify new samples in a "sniff-and-seek" mode. We experimentally demonstrated SKMs with electrically tunable bipolar black phosphorus-molybdenum disulfide (bP-MoS2) photodiodes in the near- and mid-infrared band and silicon photoconductors in the visible band, performing versatile intelligent tasks from chemometrics to semiconductor metrology. This architecture consumed substantially less power and was more than an order of magnitude faster than existing solutions for hyperspectral image analysis, defining an intelligent imaging and sensing paradigm with intriguing possibilities.

Zhang, Dehui↗
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

Applications Of Machine Learning to Gas Plume Analysis In Longwave Infrared Hyperspectral Images

Longwave infrared hyperspectral images can be used for gas plume analysis, as many gases exhibit distinct absorption features in this portion of the electromagnetic spectrum. In practice, accurately identifying weak gas signatures is difficult because the observed radiance is dominated by background radiance, which varies with material, temperature, and viewing conditions. Many gas plume analysis pipelines operate on single images, limiting the ability to leverage spatial and multi-view information that could enhance the analysis. The goal of this dissertation is to explore how machine learning and deep learning methods can complement classical approaches to improve gas plume identification in longwave infrared hyperspectral imagery, and to investigate the use of neural radiance fields for hyperspectral scene reconstruction.

3D Scene Reconstruction↗
DOE OSTI2024

HyperKube: A Kubernetes Based System for the Automation of Processing and Analysis of Hyperspectral Data Obtained from Multiple Hyperspectral Imaging Systems

Hyperspectral imagery is an emerging field of technology that has enormous potential for remote and proximal sensing in numerous areas of research. The plant phenotyping community is applying this technology to advance the throughput and accuracy of plant phenotypes based on airborne and lab-based hyperspectral imaging technology. Here we report an automated processing and analysis pipeline for four different hyperspectral imaging platforms, discuss the data issues involved, and present a strategy for computing and data architecture to handle hyperspectral data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Hyperspectral X-ray Imaging: Progress Towards Chemical Analysis in the SEM

The Hyperspectral X-ray Imaging (HXI) project will enable non-destructive chemical-state determination of nano-scale samples in the electron microscope for nuclear safeguards applications. To efficiently measure chemical state through electron-excited X-ray emission requires a combination of wide spectral bandwidth, high resolution, and high count rate capability. We are building a next-generation X-ray detector based on an array of transition edge sensors (TESs) to make these measurements possible and routine in the scanning electron microscope (SEM). Leveraging the large pixel densities afforded by microwave multiplexing readout and continuous, uninterrupted operation of a cryogen-free dilution refrigerator, this instrument will have efficiency to allow chemical species identification of nano-scale samples in hours instead of days to weeks. We describe prototype pixel designs for this HXI instrument, comprising three pixel types that will make up the hybrid TES array. Engineering design of the integrated HXI cryostat and SEM system is in progress, with full detector commissioning expected in Spring 2021 followed closely by full-scale integration with the SEM. Furthermore, we also report on the commissioning of a complementary TES-based X-ray emission spectroscopy platform for bulk samples to build a spectral library for HXI sample identification.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators

Atmospheric correction of airborne hyperspectral imaging spectroscopy (AHIS) to obtain high-quality surface reflectance is the prerequisite for remote sensing applications. Over the last decades, different atmospheric correction methods have been developed based on radiative transfer models (RTMs), however, the relative performances of different algorithms are unclear. Automated operational atmospheric correction methods to process large-volume AHIS data in a high-accurate and high-throughput manner are still lacking. Therefore, this study proposed an operational atmospheric correction pipeline for deriving surface reflectance from AHIS data. To ensure the accuracy and efficiency of the pipeline, we focused on three specific aspects: (1) selecting a suitable RTM for the development of atmospheric lookup tables (LUTs) by comparing the commercial MODerate resolution atmospheric TRANsmission (MODTRAN) and open-sourced Library for Radiative TRANsfer (LibRadTRAN) models, where the widely-used software, Atmospheric/Topographic Correction for Airborne Imagery (ATCOR), was used as benchmarks; (2) identifying key atmospheric correction parameters and determining suitable sources for parameter retrievals including AHIS, Moderate Resolution Imaging Spectroradiometer (MODIS), and AErosol RObotic NETwork (AERONET); and (3) testing the performance of using machine learning emulators to speed up the RTM-based atmospheric correction. Results indicate that (1) atmospheric correction based on MODTRAN LUTs can produce surface reflectance accurately with mean absolute errors < 0.05 and cosine similarities > 0.98 compared to field measurements, which is comparable to the software ATCOR and slightly outperforms the LibRadTRAN LUTs; (2) sobol global sensitivity analysis demonstrates that in the atmospheric correction, visibility and water vapor are two key parameters that can be accurately derived from AHIS in contrast to MODIS or AERONET data; and (3) Random Forest emulators can produce accurate estimations of surface reflectance with mean absolute errors < 0.03 and cosine similarities > 0.98 for higher processing efficiency and determine a suitable set of wavelengths for retrieving atmospheric visibility and water vapor. In conclusion, the proposed atmospheric correction pipeline also improved the four-stream radiative transfer theory for airborne applications by considering adjacent effects from airborne surrounding pixels and can also be applied for atmospheric correction of hyperspectral data from spaceborne missions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

Plant Sciences↗
DOE OSTI2025

Combining hyperspectral imaging and micro-X-ray fluorescence imaging methods for ultra-high-resolution biogeochemical analysis

Current non-destructive methods for high-resolution geochemical imaging open new avenues for high-resolution paleoenvironmental research. We discuss how two of these methods, hyperspectral imaging and micro-X-ray fluorescence imaging, can be combined for organic and inorganic geochemical analyses at a micrometer scale.

Zander, Paul [Lawrence Livermore National Laborato↗
DOE OSTI2024

Rapid subsurface analysis of frequency-domain thermoreflectance images with K-means clustering

K-means clustering analysis is applied to frequency-domain thermoreflectance (FDTR) hyperspectral image data to rapidly screen the spatial distribution of thermophysical properties at material interfaces. Performing FDTR while raster scanning a sample consisting of 8.6 μm of doped-silicon (Si) bonded to a doped-Si substrate identifies spatial variation in the subsurface bond quality. Routine thermal analysis at select pixels quantifies this variation in bond quality and allows assignment of bonded, partially bonded, and unbonded regions. Performing this same routine thermal analysis across the entire map, however, becomes too computationally demanding for rapid screening of bond quality. To address this, K-means clustering was used to reduce the dimensionality of the dataset from more than 20 000 pixel spectra to just K = 3 component spectra. The three component spectra were then used to express every pixel in the image through a least-squares minimized linear combination providing continuous interpolation between the components across spatially varying features, e.g., bonded to unbonded transition regions. Fitting the component spectra to the thermal model, thermal properties for each K cluster are extracted and then distributed according to the weighting established by the regressed linear combination. Thermophysical property maps are then constructed and capture significant variation in bond quality over 25 μm length scales. The use of K-means clustering to achieve these thermal property maps results in a 74-fold speed improvement over explicit fitting of every pixel.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Application of automated iterative target detection for standoff hyperspectral imaging

The utility of hyperspectral imaging (HSI) has been well established for a wide array of applications but has generated a need for automated screening of high volumes of large HSI cubes. We report two important automated algorithms for more efficient standoff processing: atmospheric correction and target detection. The atmospheric correction method is based on a fast asymmetric least squares approach that is applied on a pixel-by-pixel basis. Here, the correction can be applied to entire images without manually identifying regions of interest and utilizes only in-scene information, no ancillary modeling of the atmosphere is required. An iterative target detection approach is also introduced which demonstrates faster speeds relative to moving window approaches. The target detection algorithm classifies each pixel as true target detections, near target detections, clutter, and no-calls. The algorithms were tested on forty images of twenty-two solid mineral targets placed at a 14-meter standoff distance allowing general observations on expected detection performance for a variety of minerals. In addition to identifying anomalous pixels, the inclusion of “no-calls” reduced the number of false detections significantly.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Wildfires Temperature Estimation by Complementary Use of Hyperspectral PRISMA and Thermal (ECOSTRESS & L8)

This paper deals with detection and temperature analysis and of wildfires using PRISMA imagery. Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 400–2,500 nm and an average spectral resolution less than 12 nm and a spatial resolution of 30 m/pixel. This study focuses on the wildfire temperature estimation over the Bootleg Fire, US 2021. The analysis starts by considering the Hyperspectral Fire Detection Index (HFDI) which is used to analyze the informative content of the images, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. This first analysis is used as input to perform a temperature estimation of the areas with active wildfire. Surface temperature is retrieved using PRISMA radiance and a linear mixing model based on two background components (vegetation and burn scar) and two active fire components. PRISMA temperatures are compared with LST (Land Surface Temperature) products from NASA's ECOSTRESS and Landsat 8 which imaged the Bootleg Fire before and after PRISMA. A critical discussion of the results obtained with PRISMA is presented, followed by the advantages and limitation of the proposed approach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Gaussian process analysis of electron energy loss spectroscopy data: multivariate reconstruction and kernel control

Abstract Advances in hyperspectral imaging including electron energy loss spectroscopy bring forth the challenges of exploratory and physics-based analysis of multidimensional data sets. The multivariate linear unmixing methods generally explore similarities in the energy dimension, but ignore correlations in the spatial domain. At the same time, Gaussian process (GP) explicitly incorporate spatial correlations in the form of kernel functions but is computationally intensive. Here, we implement a GP method operating on the full spatial domain and reduced representations in the energy domain. In this multivariate GP, the information between the components is shared via a common spatial kernel structure, while allowing for variability in the relative noise magnitude or image morphology. We explore the role of kernel constraints on the quality of the reconstruction, and suggest an approach for estimating them from the experimental data. We further show that spatial information contained in higher-order components can be reconstructed and spatially localized.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multidimensional Coherent Spectroscopy of van der Waals materials and heterostructures (Final Technical Report)

This project advanced multidimensional coherent spectroscopy (MDCS) and related spectroscopic-imaging methods as quantitative probes of many-body optical excitations in two-dimensional (2D) van der Waals (vdW) semiconductors and their heterostructures. Monolayer transition-metal dichalcogenides (TMDs) such as MoSe 2 and WSe 2 provide a uniquely strong platform for excitonic physics due to reduced dielectric screening, which enhances Coulomb interactions and makes higher-order correlated states (e.g., biexcitons and exciton–trion correlations) more prominent than in conventional bulk semiconductors. At the same time, the same sensitivity that enables strong interactions also increases susceptibility to spatial inhomogeneity—e.g., strain gradients, wrinkles, nanoscale disorder potentials, and charge puddling—which can broaden resonances, obscure interaction signatures, and complicate device-to-device reproducibility. The work reported here addressed this dual opportunity and challenge by: (i) developing and deploying coherent multidimensional methods (including double-quantum MDCS) capable of isolating interaction signals with reduced background, (ii) integrating electrostatic control to tune carrier populations and interaction strengths in situ, and (iii) introducing complementary spatially resolved photoluminescence (PL) techniques—hyperspectral PL imaging and polarization-/field-dependent PL lineshape analysis—to directly quantify disorder, strain, and localization that govern the coherent response.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine Learning Based Software Package for Reproducible Data Analysis for Electron Microscopy

Advances in hardware and computation technologies are enabling large hyperspectral imaging during Electron Microscopy (EM), resulting in datasets being produced at rapidly increasing spatial and temporal resolutions. This presents exciting opportunities for microstructure analysis, but also poses great challenges in data analysis. Under this STTR program, QuesTek Innovations LLC, a leader in the field of integrated computational materials engineering (ICME), collaborated with Lawrence Livermore National Laboratory and Argonne National Laboratory. The team developed an open-source machine learning (ML) powered tool for EM data analysis of multiple systems and data types, and collected EM data for ML model development using the cutting-edge PicoProbe equipment at Argonne National Laboratory.

Lin, Yu↗
DOE OSTI2025

Hyperspectral x-ray imaging mapping capabilities for nuclear forensics

Nuclear forensics relies on the integration of complementary signatures to constrain the origins and history of materials. Outcomes benefit from the timeliness and precision of the disparate methods that form typical analysis chains. Sample forms are often either minute in quantity or contain signatures like morphology or composition heterogeneity encoded on a microscale, so many analysis techniques focus on resolving signatures on ever-smaller length scales. The new hyperspectral x-ray imaging (HXI) instrument developed at Los Alamos National Laboratory seeks to improve the information available from scanning electron microscopy (SEM) x-ray spectrum analysis through superior spectral energy resolution vs. typical energy dispersive spectroscopy (EDS) systems in common use in nuclear forensics and other microanalysis fields. Based on arrays of transition-edge sensor (TES) microcalorimeter detectors, this instrument achieves a typical energy resolution of 7 eV full-width at half-maximum (FWHM) at 2 keV, opening new possibilities in trace element detection/analysis and chemical state determination through spectral shape shifts. We present here some of the first applications of the HXI instrument to actinide samples and discuss potential maturation of this nascent technology for future analysis pipelines.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. In conclusion, the results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

42 ENGINEERING↗