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Presentation to be presented during the Site-Directed Research and Development (SDRD) program FY 2021 review meeting (Webex), September 22–23, 2021.
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Explore the source record for details and available documents.
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.
Checkpoint immunotherapy has made great strides in the treatment of solid tumors, but many patients do not respond to immune checkpoint inhibitors. Identification of tumor‐infiltrating cytotoxic T cells (CTLs) has the potential to stratify patients and monitor immunotherapy responses. In this study, the design of cluster of differentiation (CD8 + ) T cell‐targeted nanoprobes that emit shortwave infrared (SWIR) light in the second tissue‐transparent window for noninvasive, real‐time imaging of CTLs in murine models of breast cancer is presented. SWIR‐emitting rare‐earth nanoparticles encapsulated in human serum albumin are conjugated with anti‐CD8α to target CTLs with high specificity. CTL targeting is validated in vitro through binding of nanoprobes to primary mouse CTLs. The potential for the use of SWIR fluorescence intensity to determine CTL presence is validated in two syngeneic mammary fat pad tumor models, EMT6 and 4T1, which differ in immune infiltration. SWIR imaging using CD8‐targeted nanoprobes successfully identifies the presence of CTLs in the more immunogenic EMT6 model, while imaging confirms the lack of substantial immune infiltration in the nonimmunogenic 4T1 model. In this work, the opportunity for SWIR imaging using CD8‐targeted nanoprobes to assess CTL infiltration in tumors for the stratification and monitoring of responders to checkpoint immunotherapy is highlighted.
Optical technologies enable real-time, noninvasive analysis of complex systems but are limited to discrete regions of the optical spectrum. While wavelengths in the short-wave infrared (SWIR) window (typically, 1700-3000 nm) should enable deep subsurface penetration and reduced photodamage, there are few luminescent probes that can be excited in this region. Here, we report the discovery of lanthanide-based upconverting nanoparticles (UCNPs) that efficiently convert 1740 or 1950 nm excitation to wavelengths compatible with conventional silicon detectors. Screening of Ln3+ ion combinations by differential rate equation modeling identifies Ho3+/Tm3+ or Tm3+ dopants with strong visible or NIR-I emission following SWIR excitation. Experimental upconverted photoluminescence excitation (U-PLE) spectra find that 10% Tm3+-doped NaYF4 core/shell UCNPs have the strongest 800 nm emission from SWIR wavelengths, while UCNPs with an added 2% or 10% Ho3+ show the strongest red emission when excited at 1740 or 1950 nm. Mechanistic modeling shows that addition of a low percentage of Ho3+ to Tm3+-doped UCNPs shifts their emission from 800 to 652 nm by acting as a hub of efficient SWIR energy acceptance and redistribution up to visible emission manifolds. Parallel experimental and computational analysis shows rate equation models are able to predict compositions for specific wavelengths of both excitation and emission. These SWIR-responsive probes open a new IR bioimaging window, and are responsive at wavelengths important for vision technologies.
Abstract The mantle source beneath the Southwest Indian Ridge (SWIR) reflects a complex history of contamination. Magmatic volatile contents are vital tracers of these kinds of heterogeneities, which may fractionate otherwise constant volatile/non‐volatile elemental ratios, such as the H 2 O/Ce ratio. Although several studies have recently used trace element and isotopic data to address mantle source heterogeneity and magmatic processes at SWIR 48°–51°E region, volatile element constraints provide a valuable test of models for the origins of mantle heterogeneities in this region. Here, we present new data for nine rare basaltic glass samples from the 48°–51°E region, which enable careful assessment of the effects of primary versus secondary processes on the glass volatile contents. These samples are strongly affected by variable extents of carbon degassing, and shallow assimilation of Cl‐rich fluid, but also reveal consistently high H 2 O/Ce ratios (458.8 ± 14.9), among the highest in MORBs, that cannot be explained by late‐stage secondary processes, crustal assimilation, or simple melting of peridotite mantle at variable depths. Instead, the high H 2 O/Ce ratios are features of the mantle source composition. The 48°–51°E region is notably more depleted in highly incompatible trace elements relative to other regions of the SWIR, although this depletion is not apparent in H 2 O, which is similarly abundant throughout the SWIR. We link the high H 2 O/Ce ratios in these glasses with other trace element characteristics diagnostic of subduction and fluid addition, suggesting that the mantle source reflects signatures of a refractory mantle residue that previously melted within a subduction zone.
We report that local thermal history can significantly vary in parts during metal Additive Manufacturing (AM), leading to local defects. However, the sequential layer-by-layer nature of AM facilitates in-situ part voxelmetric observations that can be used to detect and correct these defects for part qualification and quality control. The challenge is to relate this local radiometric data with local defect information to estimate process error likelihood in future builds. This paper uses a Short-Wave Infrared (SWIR) camera to record the temperature history for parts manufactured with Laser Powder Bed Fusion (LPBF) processes. The porosity from a cylindrical specimen is measured by ex-situ micro-computed tomography (μCT). Specimen data from the SWIR camera, combined with the μCT data, are used to generate thermal feature-based porosity probability maps. The porosity predictions made by various SWIR thermal feature-porosity probability maps of a specimen with a complex geometry are scored against the true porosity obtained via μCT. The receiver operating characteristic curves constructed from the predictions for the complex sample demonstrate the porosity probability mapping methodology’s potential for in-situ based porosity detection.
We report the extent and duration of snow cover, a critical component of the hydrologic cycle and the global climate system, is expected to shift dramatically under climate change. Therefore, developing high-resolution assessments of snow cover change is crucial for estimating the impact of changing snow cover on watershed and ecosystems processes in cold regions. Remote sensing tools provide a powerful method for mapping snow-covered area (SCA) across a landscape. The most common method for estimating SCA utilizes the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI can effectively estimate catchment- to regional-scale SCA, but it cannot be used to assess fine-scale SCA because of current limitations on the spatial resolution of satellite-derived SWIR measurements. Here, we map SCA using a threshold of blue wavelengths and high-resolution satellite imagery. The thresholding method, which we call the Blue Snow Threshold algorithm (BST), has previously been used with digital camera imagery. We refine and automate the algorithm for use with cloud-free high-resolution satellite imagery and find that the BST can be used to assess fine-scale SCA. For validation, we compared BST-derived estimates of SCA to a) airborne lidar surveys, b) Landsat fractional SCA, and c) snow disappearance dates from Snow Telemetry (SNOTEL) stations. When compared to airborne lidar surveys of SCA, the BST predicted SCA had a range of F-scores between 0.81 and 0.94 in four study areas in California and Colorado. We also found general agreement between SCA and snow disappearance at multiple SNOTEL sites across the western United States. Given the relatively recent availability of high-resolution satellite imagery with spectral measurements in the visible wavelengths but lacking in SWIR, the BST offers a reliable and easy-to-apply tool for examining fine-scale snow-related processes.
We experimentally demonstrate a low-cost transfer process of GeSn ribbons to insulating substrates for short-wave infrared (SWIR) sensing/imaging applications. By releasing the original compressive GeSn layer to nearly fully relaxed state GeSn ribbons, the room-temperature spectral response of the photodetector is further extended to 3.2 μm, which can cover the entire SWIR range. Compared with the as-grown GeSn reference photodetectors, the fabricated GeSn ribbon photodetectors have a fivefold improvement in the light-to-dark current ratio, which can improve the detectivity for high-performance photodetection. The transient performance of a GeSn ribbon photodetector is investigated with a rise time of about 40 μs, which exceeds the response time of most GeSn (Ge)-related devices. In addition, this transfer process can be applied on various substrates, making it a versatile technology that can be used for various applications ranging from optoelectronics to large-area electronics. These results provide insightful guidance for the development of low-cost and high-speed SWIR photodetectors based on Sn-containing group IV low-dimensional structures.
Abstract Photodetectors operating across the short‐, mid‐, and long‐wave infrared (SWIR–LWIR, λ = 1–14 µm) underpin modern science, technology, and society in profound ways. Narrow bandgap semiconductors that form the basis for these devices require complex manufacturing, high costs, cooling, and lack compatibility with silicon electronics, attributes that remain prohibitive for their widespread usage and the development of emerging technologies. Here, a photoconductive detector, fabricated using a solution‐processed narrow bandgap conjugated polymer is demonstrated that enables charge carrier generation in the infrared and ultrasensitive SWIR–LWIR photodetection at room temperature. Devices demonstrate an ultralow electronic noise that enables outstanding performance from a simple, monolithic device enabling a high detectivity ( D *, the figure of merit for detector sensitivity) >2.44 × 10 9 Jones (cm Hz 1/2 W −1 ) using the ultralow flux of a blackbody that mirrors the background emission of objects. These attributes, ease of fabrication, low dark current characteristics, and highly sensitive operation overcome major limitations inherent within modern narrow–bandgap semiconductors, demonstrate practical utility, and suggest that uncooled detectivities superior to many inorganic devices can be achieved at high operating temperatures.
Near infrared (NIR, 700–1000 nm) and short-wave infrared (SWIR, 1000–2000 nm) dye molecules exhibit significant nonradiative decay rates from the first singlet excited state to the ground state. While these trends can be empirically explained by a simple energy gap law, detailed mechanisms of nearly universal behavior have remained unsettled for many cases. Theoretical and experimental results for two representative NIR/SWIR dye molecules reported here clarify the key mechanism for the observed energy gap law behavior. It is shown that the first derivative nonadiabatic coupling terms serve as major coupling pathways for nonadiabatic decay processes from the first excited singlet state to the ground state for these NIR and SWIR dye molecules and that vibrational modes other than the highest frequency modes also make significant contributions to the rate. Finally, this assessment is corroborated by further theoretical comparison with possible alternative mechanisms of intersystem crossing to triplet states and also by comparison with experimental data for deuterated molecules.
Corrosion in engine connecting rods is a critical issue in the automotive industry, potentially leading to catastrophic engine failure, monetary losses, and safety hazards. The labor shortage in the industry further emphasizes the need for fast, accurate, and automated corrosion detection methods to ensure appropriate surface treatments can be applied to restore component integrity. We present an unsupervised image-based framework for classifying corrosion severity in automobile engine connecting rods using short-wave infrared (SWIR) and telecentric grayscale imaging. We employ the structural similarity index measure (SSIM) as a dissimilarity metric and the k-medians clustering algorithm for classification. Our algorithm achieves an overall accuracy of 80.64% for SWIR images, with 100% accuracy in classifying highly corroded samples. For grayscale images, the method attains an overall accuracy of 77.42%, with 90.91% accuracy for highly corroded samples. The method’s ability to work with different imaging modalities and its high accuracy in identifying severe corrosion cases make it a promising tool for automated corrosion assessment in the automotive industry, potentially improving efficiency and safety in engine component maintenance.
We demonstrate a mid-infrared optical parametric chirped pulse amplifier (OPCPA), delivering 2.1 µm center wavelength pulses with 20 fs duration and 4.9 mJ energy at 10 kHz repetition rate. This self-seeded system is based on a kW-class Yb:YAG thin-disk amplifier driving a CEP stable short-wavelength-infrared (SWIR) generation and three consecutive OPCPA stages. Our SWIR source achieves an average power of 49 W, while still maintaining excellent phase and average power stability with sub-100 mrad carrier-envelope-phase-noise and 0.8% average power fluctuations. These parameters enable the OPCPA setup to drive attosecond pump probe spectroscopy experiments with photon energies in the water window.
A custom short-wave infrared (SWIR) galvanometric streak camera was developed to provide nanosecond- scale, time-resolved optical diagnostics. The system was designed as a compact, mechanically driven alternative to traditional tube-based streak cameras, using a galvanometer mirror and multiple-reflection optical path to achieve high temporal resolution while maintaining electronic synchronization for trigger sequencing. Temporal resolution achieved 139.5 ns/px, allowing direct conversion of image-slope gradients to physical wave velocities. The instrument was validated using hydrogen-oxygen-argon detonations within a modular detonation tube, capturing transient emission structures and post-detonation decay behavior with microsecond precision. Although signal levels in filtered configurations were limited by lens transmission and detector sensitivity, the system successfully resolved intensity rise and decay characteristics across 10-60 % argon mixtures. The observed trends demonstrate the feasibility of compact galvanometer-based streak systems for high-speed SWIR imaging in reactive environments!
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.
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).