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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Intercomparison of flood inundation models across land use types and hydrological flood stages

Flood Inundation Mapping (FIM) model selection is a key operational decision because accurate, rapid mapping underpins early warning and resource allocation. FIM performance is context-dependent and can vary with hydrograph phase, land-use/land-cover (LULC), and the evaluation benchmark. Intercomparison studies typically assess a single near-peak snapshot against one reference dataset. Here, we provide a context-stratified intercomparison across (i) multiple hydrograph phases, (ii) LULC classes, and (iii) benchmark types, for five FIM approaches spanning a wide range of physical complexity and operational cost (TRITON, LISFLOOD-FP, HEC-RAS 2D, ARC-Curve2Flood, and OWP HAND-FIM). We use the Hurricane Matthew flood (2016) in the Neuse River Basin, North Carolina, USA, as a case study. Using high-resolution remote sensing-derived flood inundation maps, hand-labeled points, and building footprints, we assess model skill across two rising and two falling hydrograph limbs and across major LULC types. Results show that model rankings shift systematically across contexts: LISFLOOD-FP ranks highest in three of four flood phases, while TRITON leads during one rising limb phase; LISFLOOD-FP performs best in vegetated areas, whereas HEC-RAS improves relative performance in agricultural and urban areas; and benchmark choice influences conclusions, with LISFLOOD-FP performing best for flooded-building detection in the late falling limb, while TRITON ranks highest against hand-labeled points. We also report representative wall-clock runtimes for each workflow to provide use-case context for operational feasibility. Together, these results offer transferable guidance for model selection and for designing large-scale, benchmark-aware FIM intercomparison studies.

Nikrou, Parvaneh [University of Alabama]↗
DOE OSTI2023

A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomics

Effective phosphoproteome of nanoscale sample analysis remains a daunting task, primarily due to significant sample loss associated with non-specific surface adsorption during enrichment of low stoichiometric phosphopeptide. We develop a tandem tip phosphoproteomics sample preparation method that is capable of sample cleanup and enrichment without additional sample transfer, and its integration with our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches provides a streamlined workflow enabling sensitive, high-throughput nanoscale phosphoproteome measurements. This approach significantly reduces both sample loss and processing time, allowing the identification of >3000 (>9500) phosphopeptides from 1 (10) µg of cell lysate using the label-free method without a spectral library. It also enables precise quantification of ~600 phosphopeptides from 100 sorted cells (single-cell level input for the enriched phosphopeptides) and ~700 phosphopeptides from human spleen tissue voxels with a spatial resolution of 200 µm (equivalent to ~100 cells) in a high-throughput manner. The new workflow opens avenues for phosphoproteome profiling of mass-limited samples at the low nanogram level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Roots selectively decompose litter to mine nitrogen and build new soil carbon

Abstract Plant–microbe interactions in the rhizosphere shape carbon and nitrogen cycling in soil organic matter (SOM). However, there is conflicting evidence on whether these interactions lead to a net loss or increase of SOM. In part, this conflict is driven by uncertainty in how living roots and microbes alter SOM formation or loss in the field. To address these uncertainties, we traced the fate of isotopically labelled litter into SOM using root and fungal ingrowth cores incubated in a Miscanthus x giganteus field. Roots stimulated litter decomposition, but balanced this loss by transferring carbon into aggregate associated SOM. Further, roots selectively mobilized nitrogen from litter without additional carbon release. Overall, our findings suggest that roots mine litter nitrogen and protect soil carbon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Volumetric imaging of the 3D orientation of cellular structures with a polarized fluorescence light-sheet microscope

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations in biological samples, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the diffraction-limited three-dimensional distribution of the orientations and positions of ensembles of fluorescent dipoles that label biological structures. We share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model the distributions based on the polarization-dependent efficiency of excitation and detection of emitted fluorescence, using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labeled giant unilamellar vesicles, fast-scarlet-labeled cellulose in xylem cells, and phalloidin-labeled actin in U2OS cells. Additionally, we observe phalloidin-labeled actin in mouse fibroblasts grown on grids of labeled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.

Science & Technology - Other Topics↗
DOE OSTI2024

A Tip-based Workflow for Sensitive IMAC-based Low Nanogram Level Phosphoproteomics

Analyzing the phosphoproteome at nanoscale poses a significant challenge, mainly due to the substantial sample loss from non-specific surface adsorption during the enrichment of low stoichiometric phosphopeptides. Here, we describe a tandem tip-based phosphoproteomics sample preparation method capable of sequential sample cleanup and enrichment without the need for additional sample transfer, thereby minimizing sample loss. Integration of this method to our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches creates a streamlined workflow, enabling sensitive, high-throughput nanoscale phosphoproteomics measurements.

Phosphoproteome, Immobilized metal ion affinity ch↗
DOE OSTI2024

Upcycling of polyethylene to gasoline through a self-supplied hydrogen strategy in a layered self-pillared zeolite

Conversion of plastic wastes to valuable carbon resources without using noble metal catalysts or external hydrogen remains a challenging task. Here we report a layered self-pillared zeolite that enables the conversion of polyethylene to gasoline with a remarkable selectivity of 99% and yields of >80% in 4 h at 240 °C. The liquid product is primarily composed of branched alkanes (selectivity of 72%), affording a high research octane number of 88.0 that is comparable to commercial gasoline (86.6). In situ inelastic neutron scattering, small-angle neutron scattering, solid-state nuclear magnetic resonance, X-ray absorption spectroscopy and isotope-labelling experiments reveal that the activation of polyethylene is promoted by the open framework tri-coordinated Al sites of the zeolite, followed by β-scission and isomerization on Brönsted acids sites, accompanied by hydride transfer over open framework tri-coordinated Al sites through a self-supplied hydrogen pathway to yield selectivity to branched alkanes. This study shows the potential of layered zeolite materials in enabling the upcycling of plastic wastes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Improving Building Footprint Extraction Using NAIP and 3DEP Lidar Derived Features with Deep Learning

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

Liu, Jung Kuan [United States Geological Survey (U↗
DOE OSTI2021

Immobilization of Cyanines in DNA Produces Systematic Increases in Fluorescence Intensity

Cyanines are useful fluorophores for a myriad of biological labeling applications, but their interactions with biomolecules are unpredictable. Cyanine fluorescence intensity can be highly variable due to complex photoisomerization kinetics, which are exceedingly sensitive to the surrounding environment. This introduces large errors in Förster resonance energy transfer (FRET)-based experiments where fluorescence intensity is the output parameter. However, this environmental sensitivity is a strength from a biological sensing point of view if specific relationships between biomolecular structure and cyanine photophysics can be identified. We describe a set of DNA structures that modulate cyanine fluorescence intensity through the insertion of adenine or thymine bases. These structures simultaneously provide photophysical predictability and tunability. We characterize these structures using steady-state fluorescence measurements, fluorescence correlation spectroscopy (FCS), and time-resolved photoluminescence (TRPL). Here, we find that the photoisomerization rate decreases over an order of magnitude across the adenine series, which is consistent with increasing immobilization of the cyanine moiety by the surrounding DNA structure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Quantitative stable isotope probing (qSIP) and cross-domain networks reveal bacterial-fungal interactions in the hyphosphere

Interactions between fungi and bacteria have the potential to substantially influence soil carbon dynamics in soil, but we have yet to fully identify these interactions and partners in their natural environment. In this study, we stacked two powerful methods, 13 C quantitative stable isotope probing (qSIP) and cross-domain co-occurrence network, to identify interacting fungi and bacteria in a California grassland soil. We used in-field whole plant 13 CO 2 labeling along with sand-filled ingrowth bags (that trap fungi and hyphae-associated bacteria) to amplify the signal of fungal-bacterial interactions, separate from the bulk soil background. We found a total of 54 bacterial ASVs and 9 fungal OTUs that were significantly 13 C-enriched. These were saprotrophic and biotrophic fungi, and motile, sometimes predatory bacteria. Among these, 70% of all 13 C-enriched bacteria identified were motile. Notably, we detected fungal-bacterial network links between a fungal OTU of the genus Alternaria and several bacterial ASVs of the genera Bacteriovorax, Mucilaginibacter, and Flavobacterium, providing empirical evidence of their direct interactions through C exchange. We observed a strong positive co-occurrence pattern between predatory bacteria of the phylum Bdellovibrionota and fungal OTUs, suggesting the transfer of C across the soil food web. To date, our ability to associate microbial co-occurrence network patterns with biological interactions is limited, but the incorporation of qSIP allowed us to more precisely detect interacting partners by narrowing in on the taxa that were actively incorporating plant-fixed, fungal-transported labeled substrates. Together, these approaches can help build a mechanistic understanding of the complex nature of fungal-bacterial interactions in soil.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Manganese effects on plant residue decomposition and carbon distribution in soil fractions depend on soil nitrogen availability

Recent studies have highlighted the critical role of manganese (Mn) in plant litter decomposition and soil organic carbon (C) cycling in forest ecosystems. Long term nitrogen (N) deposition and N fertilization can increase soil acidity and mobilize bioavailable Mn (Mn 2+ ) in soil. However, no studies have examined the interactive effect of N and Mn fertilization on litter decomposition and carbon distribution in agricultural soils, despite agroecosystems being subject to both N and Mn management. We hypothesized that increased soil N and Mn availability would accelerate plant residue decomposition and transfer of its C to mineral-associated organic matter (MAOM), and that the combined effect of Mn and N enrichment would be greater than the individual effect. Here, we conducted a laboratory incubation experiment by adding 13 C-labeled residue of perennial grass Glyceria striata (Lam.) to agricultural soils that had received 225 kg N ha –1 yr –1 for 27 years (N 1 ) and comparable soils that received no N (N 0 ). Before the experiment, these soils also received three levels of dissolved Mn 2+ , designated M 0 (no additional Mn), M 1 (50 mg kg –1 ), or M 2 (250 mg kg –1 ). We measured total CO 2 production as well as distribution of 13 C from the residue into CO 2 , particulate organic matter (POM), MAOM, and dissolved organic carbon (DOC) over a 1-year period. Manganese amendments significantly increased CO 2 production from residue decomposition in the N 1 soil, but no such effect was observed in the N 0 soil. Manganese also accelerated the loss of residue-derived C from POM and DOC, but increased its recovery in MAOM. However, the positive effect of added Mn in decomposition and recovery in MAOM in the presence of N fertilization occurred only during the initial 30-day decomposition period, where M 2 showed a 12% increase in cumulative CO 2 production from residue, 8% increase in POM loss, and 43% increase in recovery of residue C in MAOM compared to M 0 . For M 1 , only CO 2 emission from residue was significantly higher than Mo during this period. At 365 days M 2 showed 8% increase in CO 2 production, 1% increase in POM loss, and 16% increase in recovery of residue C in MAOM compared to M 0 , but none of these were statistically significant (p < 0.05). This study adds to the growing evidence that increasing Mn availability enhances plant litter decomposition. However, the occurrence and magnitude of Mn-induced stimulation of decomposition is context specific. Further investigation with greater temporal resolution, involving a multitude of litter and soil types and including microbial compositional and functional characterization, is recommended to fully elucidate the interactive role of Mn and N on C cycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Complex pH-Dependent Interactions between Weak Polyelectrolyte Block Copolymer Micelles and Molecular Fluorophores

Amphiphilic block copolymers with weak polyelectrolyte blocks can assemble stimulus-responsive nanostructures and interfaces. Applications of these materials in drug delivery, biomimetics, and sensing largely rely on the well-understood swelling of polyelectrolyte chains upon deprotonation, often induced by changes in pH or ionic strength. This deprotonation can also tune interfacial interactions between the polyelectrolyte blocks and surrounding solution, an effect which is less studied than morphological swelling of polyelectrolytes but can be just as critical for intended function. Here, we investigate whether the pH-driven morphological response of polyelectrolyte-bearing nanostructures also affects the interactions of these nanostructures with molecules in solution, using micelles of a short-chain polybutadiene-block-poly(acrylic acid) (pBd–pAA) as a model system. Here we introduce a Förster resonance energy transfer (FRET) approach to probe interactions between micelles and fluorescent molecular solutes as a function of solution pH. As expected, the pAA corona of these pBd–pAA micelles increases in thickness monotonically as a function of pH. However, FRET efficiency, which provides a metric of the spatial proximity of fluorescently labeled micelles and freely diffusing fluorophores, exhibits complex nonmonotonic behavior as a function of pH, indicating that the average separation of micelles and acceptor fluorophores is not strictly correlated with micelle swelling. Dialysis experiments quantify the affinity of fluorophores for micelles as a function of pH, confirming that changes in FRET are driven almost entirely by the pH-dependent affinity of the pAA block for the investigated molecular fluorophores, not simply by a shape change of the pAA corona. This study provides key insights into the interfacial interactions between weak-polyelectrolyte-bearing nanostructures and molecular solutes, of importance for the development of their stimulus-responsive applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Superbolts: The Most Powerful Optical Signals Generated by Lightning [Slides]

The most powerful optical lightning events are termed “superbolts." Superbolts can arise from physical lightning phenomena or radiative transfer effects. The most intense superbolts are caused by long-horizontal “megaflashes” in stratiform clouds that are particularly effective optical emitters. The scale, intensity, and rarity of superbolts and megaflashes make them difficult to observe. RF-powerful events recently labeled “superbolts” are a different phenomenon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine Learning for Prediction of Thermodynamic Descriptors

Our objective is to apply machine learning (ML) algorithms for the prediction of molecular catalysis descriptors from geometric properties derived from experimental crystallographic databases. Catalysis is often considered a “low-data” discipline that is poorly suited for ML methods. An exception is the extensive structural information that is available for molecular catalysts through the Cambridge Structural Database (CSD), which contains atomically precise molecular structures from X-ray diffraction analysis for >600K metal complexes. As a proof-of-principle, we targeted the prediction of hydricity, a thermodynamic property that provides understanding and control of catalytic hydride transfer. We built a training set composed of ~100 molecular complexes with a known hydricity and structural information from the CSD. This data set was converted into a machine-readable format using the smooth overlap of atomic positions (SOAP) representation and further labeled with simple electronic descriptors for the metal centers. Multiple different neural networks were trained on this data set, and the accuracy of the hydricity predictions ranged from < 2 kcal/mol to 20 kcal/mol. The accuracy of each model was highly sensitive to which compounds were in the train versus test set, underscoring the challenges associated with small and chemically diverse data sets. Finally, to further augment the data set, we attempted to experimentally measure several new hydricity values, however these experiments were unsuccessful due to undesired chemical reactivity of the selected complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population

This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. We used a DenseNet121 model pre-trained MIMIC-CXR dataset for deep learning-based multi-label classification using ground truth labels from radiology reports extracted using the CheXpert and CheXbert Labeler. We compared the performance of the 14 chest X-ray labels on the MIMIC-CXR and Veterans Healthcare Administration chest X-ray dataset (VA-CXR). The validation of ground truth and the assessment of multi-label classification performance across various NLP extraction tools revealed that the VA-CXR dataset exhibited lower disagreement rates than the MIMIC-CXR datasets. Additionally, there were notable differences in AUC scores between models utilizing CheXpert and CheXbert. When evaluating multi-label classification performance across different datasets, minimal domain shift was observed in the unseen VA dataset, except for the label “Enlarged Cardiomediastinum.” The subgroup with the most significant variations in multi-label classification performance was study year. These findings underscore the importance of considering domain shift in chest X-ray classification tasks, paying particular attention to the temporality of the exam. Our study reveals the significant impact of domain shift and demographic factors on chest X-ray classification, emphasizing the need for improved transfer learning and robust model development. Addressing these challenges is crucial for advancing medical imaging research and improving patient care.

chest X-ray image classification↗
DOE OSTI2022

Hierarchical deep reinforcement learning reveals a modular mechanism of cell movement

Time-lapse images of cells and tissues contain rich information about dynamic cell behaviours, which reflect the underlying processes of proliferation, differentiation and morphogenesis. However, we lack computational tools for effective inference. Here we exploit deep reinforcement learning (DRL) to infer cell–cell interactions and collective cell behaviours in tissue morphogenesis from three-dimensional (3D) time-lapse images. We use hierarchical DRL (HDRL), known for multiscale learning and data efficiency, to examine cell migrations based on images with a ubiquitous nuclear label and simple rules formulated from empirical statistics of the images. When applied to Caenorhabditis elegans embryogenesis, HDRL reveals a multiphase, modular organization of cell movement. Imaging with additional cellular markers confirms the modular organization as a novel migration mechanism, which we term sequential rosettes. Furthermore, HDRL forms a transferable model that successfully differentiates sequential rosettes-based migration from others. Our study demonstrates a powerful approach to infer the underlying biology from time-lapse imaging without prior knowledge.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Robust surfactant-assisted one-pot sample preparation for label-free single-cell and nanoscale proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk cells. However, such bulk measurement obscures cell to cell heterogeneity, precluding proteome profiling of single cells and small numbers of cells of interest. To address this issue, in recent 5 years there are a surge of small sample preparation methods developed for robust effective collection and processing of single cells and small numbers of cells for in-depth MS-based proteome profiling. Based on their broad accessibility, they can be categorized into two types: specific device- and standard PCR tube- or multi-well plate-based methods. Herein we describe the detailed protocol of our recently developed, easily adoptable, Surfactant-assisted One-Pot (SOP) sample preparation coupled with MS method termed SOP-MS for label-free single-cell and nanoscale proteomics. SOP-MS capitalizes on the combination of a MS-compatible surfactant, DDM (n-Dodecyl-ß-D-maltoside), and standard low-bind PCR tube or multi-well plate for ‘all-in-one’ one-pot sample preparation without sample transfer. With its robust and convenient features, SOP-MS can be readily implemented in any MS laboratory for single-cell and nanoscale proteomics. With further improvements in MS detection sensitivity and sample throughput, we believe that SOP-MS could open an avenue for single-cell proteomics with broad applicability in the biological and biomedical research.

Single-cell proteomics, nanoscale proteomics, SOP-↗
DOE OSTI2025

P–P Coupling with and without Terminal Metal–Phosphorus Intermediates

Terminal metal–phosphorus (M–P) complexes are of significant contemporary interest as potential platforms for P-atom transfer (PAT) chemistry. Decarbonylation of metal–phosphaethynolate (M–PCO) complexes has emerged as a general synthetic approach to terminal M–P complexes. M–P complexes that are stabilized by strong M–P multiple bonds are kinetically persistent and isolable. In the absence of strong M–P stabilization, the formation of diphosphorus-bridged complexes (i.e., M–P–P–M species) is often interpreted as evidence for the intermediacy of reactive, unobserved M–P species. Here, we demonstrate that while diphosphorus complexes can arise from reactive M–P species, P–P coupling can also proceed directly from M–PCO species without the intermediacy of M–P complexes. Photochemical decarbonylations of a pincer-supported Ni (II)–PCO complex at 77 K afford a spectroscopically observed terminal Ni–P complex, which is best described as a triplet, Ni(II)-metallophosphinidene with two unpaired electrons localized on the atomic phosphorus ligand. Thermal annealing of this transient Ni–P complex results in rapid dimerization to afford the corresponding P 2 2– -bridged dinickel complex. Unexpectedly, the same P 2 2– -bridged dinickel complex can also be accessed via a thermally promoted process in the absence of light. The analysis of reaction kinetics, isotope-labeling studies, and computational results indicate that the thermal P–P coupling process proceeds via a noncanonical mechanism that avoids terminal M–P intermediates. Together, these results represent the first observation of P–P coupling from characterized terminal M–P species and demonstrate that terminal M–P intermediates are not required to obtain P–P coupling products. These observations provide critical mechanistic understanding of the activation modes relevant to P-atom transfer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗