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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Scanning Electrochemical Microscopy: An Evolving Toolbox for Revealing the Chemistry within Electrochemical Processes

The parallel development of ultramicroelectrodes (UMEs) and groundbreaking scanning probe microscopy techniques in the late 1980s led to the development of the scanning electrochemical microscope. Scanning electrochemical microscopy (SECM) was born from the idea of using a tiny electrode to measure the local electrochemical behavior at operating electrodes. From its foundations, the technique displayed an inherent versatility in measuring sample properties beyond topography. It allowed experimenters to measure and map chemical reactions occurring at diverse interfaces, from inspecting the reversibility of redox mediators at metal electrodes, to detecting the hallmarks of cellular respiration on living plant leaves. Related but distinct electrochemical scanning probe techniques, such as electrochemical atomic force microscopy (EC-AFM), scanning ion conductance microscopy (SICM), and scanning electrochemical cell microscopy (SECCM) have developed in parallel. These techniques have demonstrated exquisite spatial resolution down to the nanoscale regime. However, it is the proposition of this review that SECM remains unmatched at revealing the chemical aspects of electrochemistry. Furthermore, it is our intention to review and demonstrate that the versatile architecture of SECM continues to evolve and address fundamental and emerging challenges in the fields of energy storage and conversion, chemical biology, materials science, and environmental chemistry, among others.

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

Applying Multimodal Mass Spectrometry to Image Tumors Undergoing Ferroptosis Following In Vivo Treatment with a Ferroptosis Inducer

Epithelial ovarian cancer (EOC) is the most common form of ovarian cancer. The poor prognosis generally associated with this disease has led to the search for improved therapies such as ferroptosis-inducing agents. Ferroptosis is a form of regulated cell death that is dependent on iron and is characterized by lipid peroxidation. Precise mapping of lipids and iron within tumors exposed to ferroptosis-inducing agents may provide insight into processes of ferroptosis in vivo and ultimately assist in the optimal deployment of ferroptosis inducers in cancer therapy. In this work, we present a method for combining matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) with secondary ion mass spectrometry (SIMS) to analyze changes in spatial lipidomics and metal composition, respectively, in ovarian tumors following exposure to a ferroptosis inducer. Tumors were obtained by injecting human ovarian cancer tumor-initiating cells into mice, followed by treatment with the ferroptosis inducer erastin. SIMS imaging detected iron accumulation in the tumor tissue, and sequential MALDI-MS imaging of the same tissue section displayed two chemically distinct regions of lipids. Additionally, one region was associated with the iron-rich area detected with SIMS, and the other region encompassed the remainder of the tissue section. Bulk lipidomics confirmed the lipid assignments putatively assigned from the MALDI-MS data. Overall, we demonstrate the ability of multimodal MSI to identify the spatial locations of iron and lipids in the same tissue section and associate these regions with clinical pathology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Good and proper: self-similarity of N -body simulations with proper force softening

ABSTRACT Analysis of self-similarity in scale-free N-body simulations reveals the spatial and temporal scales for which statistics measured in cosmological simulations are converged to the physical continuum limit. We examine how the range of scales in which the two-point correlation function is converged depends on the force softening length and whether it is held constant in comoving or proper coordinates. We find that a proper softening that reaches roughly 1/30th of the inter-particle spacing by the end of the simulation resolves the same spatial and temporal scales as a comoving softening of the same length while using a third fewer time-steps, for a range of scale factors typical to Lambda cold dark matter (ΛCDM) simulations. We additionally infer an inherent resolution limit, set by the particle mass and scaling as a−1/2, beyond which reducing the softening does not improve the resolution. We postulate a mapping of these results with spectral index n = −2 to ΛCDM simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I—Harnessing theory

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question—How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? In conclusion, the analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning

The deep operator network (DeepONet) has shown remarkable potential in solving partial differential equations (PDEs) by mapping between infinite-dimensional function spaces using labeled datasets. However, in scenarios lacking labeled data, the physics-informed DeepONet (PI-DeepONet) approach, which utilizes the residual loss of the governing PDE to optimize the network parameters, faces significant computational challenges, particularly due to the curse of dimensionality. This limitation has hindered its application to high-dimensional problems, making even standard 3D spatial with 1D temporal problems computationally prohibitive. Additionally, the computational requirement increases exponentially with the discretization density of the domain. Here, to address these challenges and enhance scalability for high-dimensional PDEs, we introduce the Separable physics-informed DeepONet (Sep-PI-DeepONet). This framework employs a factorization technique, utilizing sub-networks for individual one-dimensional coordinates, thereby reducing the number of forward passes and the size of the Jacobian matrix required for gradient computations. By incorporating forward-mode automatic differentiation (AD), we further optimize computational efficiency, achieving linear scaling of computational cost with discretization density and dimensionality, making our approach highly suitable for high-dimensional PDEs. We demonstrate the effectiveness of Sep-PI-DeepONet through three benchmark PDE models: the viscous Burgers’ equation, Biot’s consolidation theory, and a parameterized heat equation. Our framework maintains accuracy comparable to the conventional PI-DeepONet while reducing training time by two orders of magnitude. Notably, for the heat equation solved as a 4D problem, the conventional PI-DeepONet was computationally infeasible (estimated 289.35 h), while the Sep-PI-DeepONet completed training in just 2.5 h. These results underscore the potential of Sep-PI-DeepONet in efficiently solving complex, high-dimensional PDEs, marking a significant advancement in physics-informed machine learning.

Neural operator↗
DOE OSTI2023

ZeoNet: 3D convolutional neural networks for predicting adsorption in nanoporous zeolites

Zeolites are one of the most widely used materials in the chemical industry due to their nanometer-sized pores that can adsorb and react upon molecules selectively. With hundreds of known framework topologies and hundreds of thousands of computationally predicted structures, the ability to rapidly predict zeolite performance allows researchers to prioritize their efforts on the most promising structures for a given application. Although the accuracy of forcefield-based atomistic simulations has advanced significantly in the past two decades, these simulations can be computationally expensive, especially for long-chain, complex molecules. Here, we present ZeoNet, a representation learning framework using convolutional neural networks (ConvNets) and 3D volumetric representations for predicting adsorption in zeolites. ZeoNet was trained on the task of predicting Henry's constants for adsorption, k H , of n-octadecane in more than 330 000 known and predicted zeolite materials. Employing a 3D grid based on the distances to solvent-accessible surfaces, a volumetric representation that can be generated efficiently, the best-performing ZeoNet achieved a correlation coefficient r 2 = 0.977 and a mean-squared error MSE = 3.8 in ln k H , which corresponds to an error of 9.3 kJ mol -1 in adsorption free energy. In comparison, a model based on hand-designed geometric features has values of r 2 = 0.783 and MSE = 35.7. ZeoNet is also relatively efficient and can process ≈8 structures per second on an Nvidia RTX 2080TI GPU, orders of magnitude faster than forcefield-based simulations. A systematic analysis was conducted to investigate how the choice of ConvNet architectures, the linear dimension (L) and spatial resolution (Δd) of the distance grids, batch size, optimizer, and learning rate impact the model performance. We found that ConvNets based on the ResNet architecture offer the best tradeoff between expressiveness and efficiency. The performance for all models reaches a plateau at L = 30–45 Å and depends less sensitively on grid resolution, with a small benefit around Δd = 0.30–0.45 Å. Finally, saliency maps were visualized to identify which regions of the materials contributed the most to model predictions. It was found, interestingly, that the predictions are driven primarily by the accessible pore volume rather than the region occupied by the framework atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Floquet Insulators and Lattice Fermions

Floquet insulators are periodically driven quantum systems that can host novel topological phases as a function of the drive parameters. These new phases exhibit features reminiscent of fermion doubling in discrete-time lattice fermion theories. We make this suggestion concrete by mapping the spectrum of a noninteracting (1+1)D Floquet insulator for certain drive parameters onto that of a discrete-time lattice fermion theory with a time-independent Hamiltonian. The resulting Hamiltonian is distinct from the Floquet Hamiltonian that generates stroboscopic dynamics. Finally, it can take the form of a discrete-time Su-Schrieffer-Heeger model with half the number of spatial sites of the original model, or of a (1+1)D Wilson-Dirac theory with one quarter of the spatial sites.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Cis-regulatory evolution shapes facial diversity in birds and mammals

Birds and mammals exhibit extraordinary facial diversity, reflecting adaptations to distinct ecological niches and feeding strategies. While core face-building developmental programs are conserved and orchestrated by interactions between ectodermal organizers and the underlying mesenchyme, mechanisms driving facial shape variation remain poorly understood. Here, we integrate single-cell transcriptomic and chromatin accessibility profiling of mouse and chicken developing face to construct a comparative regulatory map. Although both ectodermal and mesenchymal populations display distinct regulatory features in each species, the mesenchyme exhibits markedly greater divergence, pointing to its central role in shaping facial morphology. We further reveal unexpected molecular complexity in the main face-shaping organizer, including a mouse-specific Shh/Wnt5a expression domain. At key morphogen loci (Bmp4, Fgf8, and Wnt5a), conserved and lineage-specific enhancers exhibit spatially restricted activity patterns that mirror divergent signaling domains. These findings demonstrate how cis-regulatory evolution modulates conserved developmental programs to generate morphological novelty, providing a valuable resource for studying vertebrate facial evolution.

Kyomen, Stella [Max Planck Institute for Evolution↗
DOE OSTI2020

Classified channel masks of the East River, Colorado, U.S.A and areas of floodplain erosion and accretion ranging from 1955 to 2015

This dataset provides four sets of geotiffs used for the mapping and analysis an alluvial floodplain reach of the East River, downstream of Gothic, CO, U.S.A near Crested Butte. The files include binary masks of the river channel at five in intervals from 1955 to 2015. Another set of rasters provide a map of the channel centerline pixels of the river for each date. Also included in the dataset are rasters of the areas of channel change due to migration over 8 time intervals. The masks were generated from aerial and satellite imagery collected on seven dates over a sixty-year timespan: 1955, 1973, 1983, 1990, 2001, 2011, and 2015. The masks were analyzed using the Spatially Continuous Riverbank Erosion and Accretion Measurements (SCREAM) software detailed in Rowland et al. 2016 to create the channel centerlines and the change area rasters. The change masks were generated for the following time periods: 1955-1973; 1955-2015; 1973-1983; 1983-1990; 1990-2001; 2001-2011; 2001-2015; and 2011-2015.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hierarchical Fractional Advection-Dispersion Equation (FADE) to Quantify Anomalous Transport in River Corridor over a Broad Spectrum of Scales: Theory and Applications

Fractional calculus-based differential equations were found by previous studies to be promising tools in simulating local-scale anomalous diffusion for pollutants transport in natural geological media (geomedia), but efficient models are still needed for simulating anomalous transport over a broad spectrum of scales. This study proposed a hierarchical framework of fractional advection-dispersion equations (FADEs) for modeling pollutants moving in the river corridor at a full spectrum of scales. Applications showed that the fixed-index FADE could model bed sediment and manganese transport in streams at the geomorphologic unit scale, whereas the variable-index FADE well fitted bedload snapshots at the reach scale with spatially varying indices. Further analyses revealed that the selection of the FADEs depended on the scale, type of the geomedium (i.e., riverbed, aquifer, or soil), and the type of available observation dataset (i.e., the tracer snapshot or breakthrough curve (BTC)). When the pollutant BTC was used, a single-index FADE with scale-dependent parameters could fit the data by upscaling anomalous transport without mapping the sub-grid, intermediate multi-index anomalous diffusion. Pollutant transport in geomedia, therefore, may exhibit complex anomalous scaling in space (and/or time), and the identification of the FADE’s index for the reach-scale anomalous transport, which links the geomorphologic unit and watershed scales, is the core for reliable applications of fractional calculus in hydrology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A kinematic excess in the annular gap and gas-depleted cavity in the disc around HD 169142

ABSTRACT We present ALMA band 6 images of the 12CO, 13CO, and C18O J = 2-1 line emissions for the circumstellar disc around HD 169142, at ∼8 au spatial resolution. We resolve a central gas-depleted cavity, along with two independent near-symmetric ring-like structures in line emission: a well-defined inner gas ring [∼25 au] and a second relatively fainter and diffuse outer gas ring [∼65 au]. We identify a localized super-Keplerian feature or vertical flow with a magnitude of ∼75 ms−1 in the 12CO map. This feature has the shape of an arc that spans azimuthally across a position angle range of −60° to 45° and radially in between the B1[26au] and B2[59au] dust rings. Through reconstruction of the gas surface density profile, we find that the magnitude of the background perturbations by the pressure support and self-gravity terms are not significant enough to account for the kinematic excess. If of planetary origin, the relative depletion in the gas-density profile would suggest a 1 MJ planet. In contrast, the central cavity displays relatively smooth kinematics, suggesting either a low-mass companion and/or a binary orbit with a minimal vertical velocity component.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Relating Hydro–Mechanical and Elastodynamic Properties of Dynamically Stressed Tensile–Fractured Rock in Relation to Applied Normal Stress, Fracture Aperture, and Contact Area

We exploit nonlinear elastodynamic properties of fractured rock to probe the micro-scale mechanics of fractures and understand the relation between fluid transport and fracture aperture under dynamic stressing. Experiments were conducted on rough, tensile-fractured Westerly granite subject to triaxial stresses. We measure fracture permeability for steady-state fluid flow with deionized water. Pore pressure oscillations are applied at amplitudes ranging from 0.2 to 1 MPa at 1 Hz frequency. During dynamic stressing we transmit ultrasonic signals through the fracture using an array of piezoelectric transducers (PZTs) to monitor evolution of interface properties. We examine the influence of fracture aperture and contact area by conducting measurements at effective normal stresses of 10–20 MPa. Additionally, the evolution of contact area with stress is characterized using pressure sensitive film. These experiments are conducted separately with the same fracture and map contact area at stresses from 9 to 21 MPa. The measurements are a proxy for “true” contact area for the fracture surface and we relate them to elastic properties using the calculated PZT sensor footprints via numerical modeling of Fresnel zones. We compare the elastodynamic response of the fracture using the stress-induced changes in ultrasonic wave velocities for transmitter-receiver pairs to image spatial variations in contact properties. We show that nonlinear elasticity and permeability enhancement decrease with increasing normal stress. Additionally, post-oscillation wave velocity and permeability exhibit quick recoveries toward pre-oscillation values. Estimates of fracture contact area (global and local) demonstrate that the elastodynamic and permeability responses are dominated by fracture topology.

58 GEOSCIENCES↗
DOE OSTI2025

Magnon spectroscopy in the electron microscope

Abstract The miniaturization of transistors is approaching its limits owing to challenges in heat management and information transfer speed 1 . To overcome these obstacles, emerging technologies such as spintronics 2 are being developed, which make use of the electron’s spin as well as its charge. Local phenomena at interfaces or structural defects will greatly influence the efficiency of spin-based devices, making the ability to study spin-wave propagation at the nanoscale and atomic scale a key challenge 3,4 . The development of high-spatial-resolution tools to investigate spin waves, also called magnons, at relevant length scales is thus essential to understand how their properties are affected by local features. Here we detect bulk THz magnons at the nanoscale using scanning transmission electron microscopy (STEM). By using high-resolution electron energy-loss spectroscopy with hybrid-pixel electron detectors, we overcome the challenges posed by weak signals to map THz magnon excitations in a thin NiO nanocrystal. Advanced inelastic electron scattering simulations corroborate our findings. These results open new avenues for detecting magnons and exploring their dispersions and their modifications arising from nanoscale structural or chemical defects. This marks a milestone in magnonics and presents exciting opportunities for the development of spintronic devices.

Science & Technology - Other Topics↗
DOE OSTI2022

MULTI-TECHNIQUE CHARACTERIZATION OF SUPERCONDUCTING MATERIALS FOR PARTICLE ACCELERATOR APPLICATIONS

We investigated the performance limitations of superconducting radio-frequency (SRF) cavities and materials using multiple experimental techniques. In particular, this study focuses on understanding the surface properties of nitrogen-doped Nb cavities and super?conducting thin films with higher Tc such as Nb3Sn. The main goal of this work is to use different techniques to better understand each aspect of the complex loss mechanism in superconductors to further improve the already highly efficient SRF cavities. Nitrogen doping applied to a Nb SRF cavity significantly improves the quality factor Q0 compared to a conventional Nb cavity, at an expense of reduced maximum accelerating gradient. The early quench mechanism was analyzed by using temperature maps before and during the quenching. The temperature maps revealed insignificant heating before the quench, and we concluded that nitrogen doping reduces critical magnetic fields in local regions, leading to premature quenching. To understand the origin of the increasing Q0 with the rf field, the density of states (DOS) of cold spots from nitrogen-doped and standard cavities were measured and analyzed using scanning tunneling microscopy. The results suggested that nitrogen doping reduces the spatial inhomogeneity of superconducting properties and shrinks the metallic suboxide layers, which tunes the DOS in such a way as to produce the field-induced reduction in the surface resistance. To characterize SRF thin films, an experimental setup for measuring a coplanar waveguide (CPW) resonator was developed and tested. A surface impedance measurement of the Nb film showed good agreement with the BCS calculation. The preliminary results from measurements of Nb3Sn and NbTiN films are also presented here. The nonlinear Meissner effect was investigated in Nb3Sn film CPW resonators by mea?suring the resonant frequency as a function of a parallel magnetic field. Contrary to a conventional quadratic dependence of the penetration depth ?(B) on the applied magnetic field B, as expected in s-wave superconductors, nearly a linear increase of ?(B) with B was observed. It was concluded that this behavior of ?(B) is due to weakly linked grain bound?aries on the polycrystalline Nb3Sn films, which can mimic the NLME expected in a clean d-wave superconductor.

Makita, Junki↗
DOE OSTI2026

The Q 10 of in situ microbial soil respiration varies with mean annual temperature, precipitation, pH, and plant cover: a meta-analysis and spatial prediction of Q 10

The temperature sensitivity of soil microbial respiration, commonly quantified using the Q 10 coefficient, is a key parameter in carbon cycle models. Uncovering how environmental factors affect in situ Q 10 values can therefore provide critical insight into potential shifts in global carbon stocks under climate change. We collected data from previously published field experiments that measured soil microbial respiration across a range of temperatures. We hypothesized that the Q 10 coefficient of in situ soil microbial respiration would vary based on environmental factors including mean annual temperature (MAT), mean annual precipitation (MAP), plant cover type, pH, soil C:N, and latitude. Linear regression revealed that Q 10 correlates negatively with MAT and MAP and positively with pH and absolute latitude. Additionally, average Q 10 varied significantly across different plant cover types; it was highest in mountain grasslands and lowest in tropical moist forests. Variation in microbial Q 10 across environmental factors may arise from underlying mechanisms such as enzyme kinetics, substrate availability and complexity, and microbial adaptation. To capture patterns in Q 10 more comprehensively, we developed a multiple linear regression model of Q 10 based on the most individually significant environmental drivers and applied it to public datasets to generate a global map of predicted Q 10 . Q 10 was higher in high-latitude and high-altitude regions, where large permafrost carbon stores are vulnerable to thawing and decomposition. We also compared fits between the Q 10 equation and a model produced from macromolecular rate theory (MMRT). We found that the MMRT model had the superior fit and may be better suited to model temperature sensitivity of complex biological reactions. Overall, our results emphasize that relationships between microbial Q 10 and environmental variables should be accounted for in climate models. Incorporating these variations in the Q 10 parameter, rather than using a fixed value, will help predict whether CO 2 emissions will be buffered or exacerbated by soil microbial respiration under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Spectral Fidelity of Earth's Terrestrial and Aquatic Ecosystems

The Surface Biology and Geology (SBG) investigation will create global maps of spectral surface reflectance and emissivity at a cadence of 16 days or better, with coverage to address global questions about Earth's geology, cryosphere and ecosystems. The revolutionary potential poses a commensurate challenge: creating contiguous maps free from regional biases induced by atmosphere, observation geometry, or inversion error. This will require an accurate calibration with precise knowledge of each channel's spectral response. In this work, we quantify the impact of spectral calibration on SBG's aquatic and terrestrial ecosystem objectives. We find that contemporary algorithms for ecosystem trait retrieval demand more accurate spectral calibration than historical missions. Errors due to drift or spatial nonuniformity in the wavelength calibration that have previously been considered acceptable can cause systematic errors larger than the instrument noise and of the same order as the variability SBG aims to measure. Moreover, their impact on atmospheric correction can induce climate-dependent systematic errors that thwart comparisons between ecosystems. These results underscore the importance of spectral response accuracy in SBG mission design.

54 ENVIRONMENTAL SCIENCES↗