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15 records

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DOE OSTI2025

Leveraging hyperspectral phenotyping for accurate, non-destructive prediction of metabolite profiles in poplar under drought stress

Accurately predicting drought tolerance in woody perennial bioenergy crops is critical for sustainable biomass production under fluctuating precipitation. Hyperspectral imaging (HSI) in the visible-near-infrared (VNIR) and shortwave-infrared (SWIR) ranges offers a promising approach for predicting plant biochemical traits, yet its application in metabolite profiling remains underexplored. We integrated VNIR+SWIR HSI with untargeted metabolomics to investigate drought-induced metabolic shifts in Populus leaves from eight Populus genotypes. Metabolite profiling identified 127 compounds, with 73 showing significant drought responses spanning amino acids (AA), carbohydrates (CHO), phenolic glycosides (PG), organic acids (OA), fatty acids and alcohols (FA), terpenes (T), phenolic metabolites (P), and unclassified metabolites. Spectral analysis revealed consistently higher reflectance across VNIR and SWIR wavelengths in drought-stressed plants, corresponding with increased accumulation of AA and reduced CHO and PG levels. Least absolute shrinkage and selection operator (LASSO) regression modeling identified robust spectral predictors of metabolite concentrations, associating VNIR wavelengths (500–700 nm) predominantly with AA and P, whereas SWIR wavelengths (1680–1700 nm) reliably predicted CHO, OA, and T. Several stable spectral-metabolite associations persisted across the two watering regimes (drought vs. well-watered), highlighting their potential as spectral biomarkers for non-destructive stress monitoring. Minimal genotype-specific variation suggests that observed spectral and metabolic responses were driven primarily by environmental factors, likely reflecting limited genetic diversity among the commercial Populus genotypes examined. This work establishes VNIR+SWIR hyperspectral imaging as a powerful, non-destructive phenotyping tool for precision monitoring and targeted improvement of drought resilience in bioenergy crops.

Biochemical trait prediction↗

DOE OSTI2023

Sub-1 Volt and high-bandwidth visible to near-infrared electro-optic modulators

Abstract Integrated electro-optic (EO) modulators are fundamental photonics components with utility in domains ranging from digital communications to quantum information processing. At telecommunication wavelengths, thin-film lithium niobate modulators exhibit state-of-the-art performance in voltage-length product ( V π L ), optical loss, and EO bandwidth. However, applications in optical imaging, optogenetics, and quantum science generally require devices operating in the visible-to-near-infrared (VNIR) wavelength range. Here, we realize VNIR amplitude and phase modulators featuring V π L ’s of sub-1 V ⋅ cm, low optical loss, and high bandwidth EO response. Our Mach-Zehnder modulators exhibit a V π L as low as 0.55 V ⋅ cm at 738 nm, on-chip optical loss of ~0.7 dB/cm, and EO bandwidths in excess of 35 GHz. Furthermore, we highlight the opportunities these high-performance modulators offer by demonstrating integrated EO frequency combs operating at VNIR wavelengths, with over 50 lines and tunable spacing, and frequency shifting of pulsed light beyond its intrinsic bandwidth (up to 7x Fourier limit) by an EO shearing method.

42 ENGINEERING↗

DOE OSTI2022

High-bandwidth CMOS-voltage-level electro-optic modulation of 780 nm light in thin-film lithium niobate

Integrated photonics operating at visible-near-infrared (VNIR) wavelengths offer scalable platforms for advancing optical systems for addressing atomic clocks, sensors, and quantum computers. The complexity of free-space control optics causes limited addressability of atoms and ions, and this remains an impediment on scalability and cost. Networks of Mach-Zehnder interferometers can overcome challenges in addressing atoms by providing high-bandwidth electro-optic control of multiple output beams. Here, we demonstrate a VNIR Mach-Zehnder interferometer on lithium niobate on sapphire with a CMOS voltage-level compatible full-swing voltage of 4.2 V and an electro-optic bandwidth of 2.7 GHz occupying only 0.35 mm 2 . Our waveguides exhibit 1.6 dB/cm propagation loss and our microring resonators have intrinsic quality factors of 4.4 × 10 5 . This specialized platform for VNIR integrated photonics can open new avenues for addressing large arrays of qubits with high precision and negligible cross-talk.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Spectroscopy of the Hamburg Meteorite, Michigan H4

Spectroscopic studies of the Hamburg (Michigan H4) meteorite using visible–near-infrared (VNIR), mid-infrared (MIR), Raman, and Mössbauer data reproduce the results of more conventional laboratory measurements of petrology and geochemistry. This combination provides general information on the mineral modes of silicates, although spectroscopy was performed on different splits of this meteorite, and varying results may be explained by heterogeneity that is typical of ordinary chondrites. Raman also detects small features assigned to first-order D and G carbon bands, while Mössbauer data show the presence of Fe oxides and carbides. The electron microprobe (EMPA) composition of olivine in this meteorite is accurately measured to be Fo 81.3 , while MIR and Raman closely agree with Fo 80 and Fo 82 , respectively, and Mössbauer, at Fo 60 − Fo 70 , is in the ballpark. Similarly, P. R. Heck et al. report pyroxenes with compositions of Fs 16 Wo 1 , while Raman suggests a somewhat similar composition of Fs 28 Wo 0 . Both VNIR and MIR detect the presence of small amounts of feldspar, while EMPA identifies its composition as An 14 Ab 8 Or 5 . VNIR data were matched to spectral libraries of meteorite and asteroid data and shown to closely match other L and H meteorites. The best spectral matches to the Michigan H4 meteorite are to asteroid classes L/H/LL/URE, EH/EL/AUB, and CO/CV from M. D. Dyar et al.; objects in this group are intermediate in semimajor axis lengths for their orbits. The results highlight the strengths and weaknesses of each technique and show their collective strength when applied together to a single meteorite sample.

58 GEOSCIENCES↗

DOE OSTI2023

Lightning radiometry in visible and infrared bands

Calibrated measurements of lightning optical emissions are critical for both quantifying the impacts of lightning in our atmosphere and devising detection instruments with sufficient dynamic range capable of yielding close to 100% detection efficiency. However, to date, there is only a limited number of investigations that have attempted to take such calibrated measurements. Here, in this work, we report the power radiated by lightning in both visible and infrared bands, assuming isotropic emission, and accounting for atmospheric absorption. More precisely, we report peak radiated power and total radiated energy in the combined visible plus near-infrared range (VNIR, 0.34–1.1 μm), around the H α line (652–667 nm), and for the 2–2.5 μm infrared band. The estimated peak power and total energy radiated by negative cloud-to-ground return strokes in the VNIR range is 130 MW and 20 kJ, respectively. Additionally, we detected peak radiated powers of 12 and 0.19 MW in the H α and infrared bands, respectively. We cross-reference the optical data set with peak current reported by a lightning detection network. The resulting trend is that optical power emitted around the H α line scales with peak return stroke current according to a power law with exponent equal to 1.25. This trend, which should be approximately true across the entire visible spectrum, can be attributed to the plasma negative differential resistance of the lightning return stroke channel. We conclude by discussing the challenges in performing calibrated measurements of lightning optical power in different bands and comparing the results with previously-collected data with different experimental setups, observation conditions, and calibration methods.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Fe(III) (oxyhydr)oxide reduction by the thermophilic iron-reducing bacterium Desulfovulcanus ferrireducens

Some thermophilic bacteria from deep-sea hydrothermal vents grow by dissimilatory iron reduction, but our understanding of their biogenic mineral transformations is nascent. Mineral transformations catalyzed by the thermophilic iron-reducing bacterium Desulfovulcanus ferrireducens during growth at 55°C were examined using synthetic nanophase ferrihydrite, akaganeite, and lepidocrocite separately as terminal electron acceptors. Spectral analyses using visible-near infrared (VNIR), Fourier-transform infrared attenuated total reflectance (FTIR-ATR), and Mössbauer spectroscopies were complemented with x-ray diffraction (XRD) and transmission electron microscopy (TEM) using selected area electron diffraction (SAED) and energy dispersive X-ray (EDX) analyses. The most extensive biogenic mineral transformation occurred with ferrihydrite, which produced a magnetic, visibly dark mineral with spectral features matching cation-deficient magnetite. Desulfovulcanus ferrireducens also grew on akaganeite and lepidocrocite and produced non-magnetic, visibly dark minerals that were poorly soluble in the oxalate solution. Bioreduced mineral products from akaganeite and lepidocrocite reduction were almost entirely absorbed in the VNIR spectroscopy in contrast to both parent minerals and the abiotic controls. However, FTIR-ATR and Mössbauer spectra and XRD analyses of both biogenic minerals were almost identical to the parent and control minerals. The TEM of these biogenic minerals showed the presence of poorly crystalline iron nanospheres (50–200 nm in diameter) of unknown mineralogy that were likely coating the larger parent minerals and were absent from the controls. The study demonstrated that thermophilic bacteria transform different types of Fe(III) (oxyhydr)oxide minerals for growth with varying mineral products. These mineral products are likely formed through dissolution-reprecipitation reactions but are not easily predictable through chemical equilibrium reactions alone.

12 reflectance spectroscopy↗

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 OSTI2023

A Spectroscopic Study of Mars-analog Materials with Amorphous Sulfate and Chloride Phases: Implications for Detecting Amorphous Materials on the Martian Surface

The Chemistry and Mineralogy X-ray diffraction (XRD) instrument aboard the Curiosity rover consistently identifies amorphous material at Gale Crater, which is compositionally variable, but often includes elevated sulfur and iron, suggesting that amorphous ferric sulfate (AFS) may be present. Understanding how desiccating ferric sulfate brines affect the spectra of Martian material analogs is necessary for interpreting complex/realistic reaction assemblages. Visible and near-infrared reflectance (VNIR), mid-infrared attenuated total reflectance (MIR, FTIR-ATR), and Raman spectra, along with XRD data are presented for basaltic glass, hematite, gypsum, nontronite, and magnesite, each at three grain sizes (<25, 25–63, and 63–180 μm), mixed with ferric sulfate (+/–NaCl), deliquesced, then rapidly desiccated in 11% relative humidity or via vacuum. All desiccated products are partially or completely XRD amorphous; crystalline phases include starting materials and trace precipitates, leaving the bulk of the ferric sulfate in the amorphous fraction. Due to considerable spectral masking, AFS detectability is highly dependent on spectroscopic technique and minerals present. This has strong implications for remote and in situ observations of Martian samples that include an amorphous component. AFS is only identifiable in VNIR spectra for magnesite, nontronite, and gypsum samples; hematite and basaltic glass samples appear similar to pure materials. Sulfate features dominate Raman spectra for nontronite and basaltic glass samples; the analog material dominates Raman spectra of hematite and gypsum samples. MIR data are least affected by masking, but basaltic glass is almost undetectable in MIR spectra of those mixtures. NaCl produces similar FTIR-ATR and Raman features, regardless of analog material.

58 GEOSCIENCES↗

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 OSTI2022

Ultraviolet Photooxidation of Smectite-Bound Fe(II) and Implications for the Origin of Martian Nontronites

Clay minerals detected with orbital and in situ instruments in ancient Martian terrains constrain Mars' climate and aqueous alteration history. Early in its history, Mars experienced an atmospheric redox change and iron-bearing clay minerals may preserve the effects of that transition. Ferrous smectites, the thermodynamically predicted product of chemical weathering of basalts under anoxic conditions, may have undergone oxidation by exposure to chemical oxidants in the atmosphere or regolith, or by direct photooxidation at the surface. To assess these potential oxidation pathways, ferrous trioctahedral smectites of varying initial iron content were synthesized and subjected to oxidation by ultraviolet (UV) irradiation. Experimental UV irradiation under an anoxic atmosphere equivalent to approximately 7 years of flux on the Martian surface caused partial oxidation of smectite-bound Fe (Fe 3+ /ΣFe = 16–18%) and octahedral sheet contraction. Metal-OH vibrational bands in visible/near infrared (VNIR) reflectance spectra of oxidized smectites changed in band depth and asymmetry with higher iron content. X-ray diffraction patterns of UV irradiated samples indicate the formation of a mixed di- and trioctahedral smectite or a secondary nontronite phase, possibly on the surfaces of higher iron content smectites. Furthermore, these experiments suggest that UV irradiation is able to oxidize structurally bound iron in smectites without the presence of other chemical oxidants. Photooxidation may have influenced the mineralogy, both syndepositionally and postdepositionally, of Martian alteration assemblages formed near the surface and this process needs not be limited to one part of their formation history.

58 GEOSCIENCES↗

DOE OSTI2021

A Survey of Thermal Sensing Application in Additive Manufacturing

Industry 4.0 represents a significant change in manufacturing with the inclusion of such technologies as additive manufacturing and data analytics. In support of these changes, infrared thermal imaging (IR) and very-near infrared (VNIR) have proven invaluable in numerous applications related to additive manufacturing. In this review, these applications will be presented as they relate to polymer and metal-based additive manufacturing processes. Applications including in-situ characterization, post-build analysis, experimental setups, modeling, and machine learning will be covered. With a general overview complete, the review will conclude with a discussion of challenges identified throughout the literature and thoughts on future trends.

Borish, Michael↗

DOE OSTI2023

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

59 BASIC BIOLOGICAL SCIENCES↗

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

Landscape-scale Characterization of Arctic Tundra Vegetation Composition, Structure, and Function with a Multi-sensor Unoccupied Aerial System: Supporting Data.

Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform, the Osprey, operated by the Terrestrial Ecosystem Science and Technology (TEST) group (https://www.bnl.gov/testgroup). This package includes data from three flights flown over the NGEE-Arctic Council, Kougarok and Teller sites in July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface 'skin' temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range 350 - 1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model (CHM). In addition, a VNIR surface reflectance file is provided for the trigger locations collected during each flight campaign. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Unprocessed and processed data products are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *.kml, *hdr, *.enp), tabular (*.dat, *.csv, *.waypoint, ENVI format (no extension)), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort 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

Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.

15 GEOTHERMAL ENERGY↗