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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Contrasting pressure evolution of f -electron hybridized states in CeRhIn 5 and YbNi 3 Ga 9 : An optical conductivity study

Optical conductivity [σ(ω)⁡] of CeRhIn 5 and YbNi 3 ⁢Ga 9 have been measured at external pressures to 10 GPa and at low temperatures to 6 K. Regarding CeRhIn 5 , at ambient pressure the main feature in σ(ω) is a Drude peak due to free carriers. With increasing pressure, however, a characteristic midinfrared (mIR) peak rapidly develops in σ(ω), and its peak energy and width increase with pressure. These features are consistent with an increased conduction (c)-f electron hybridization at high pressure and show that pressure has tuned the electronic state of CeRhIn 5 from very weakly to strongly hybridized ones. As for YbNi 3 ⁢Ga 9 , in contrast, a marked mIR peak is observed already at ambient pressure, indicating a strong c-f hybridization. At high pressures, however, the mIR peak shifts to lower energy and becomes diminished and seems to merge with the Drude component at 10 GPa. Namely, CeRhIn 5 and YbNi 3 ⁢Ga 9 exhibit some opposite tendencies in the pressure evolution of σ(ω) and electronic structure. Further, these results are discussed in terms of the pressure evolution of c-f hybridized electronic states in Ce and Yb compounds, in particular in terms of the electron-hole symmetry often considered between Ce and Yb.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Determination of rutile transition metal oxide (110) surface terminations by scanning tunneling microscopy contrast reversal

The surfaces of rutile transition-metal oxides (TMO 2 ) are widely investigated for catalysis, photoelectrochemical solar cells, memristors, and supercapacitors, but their structures have remained controversial. Here we employ density functional theory to predict that a universal behavior of metallic TMO 2 surfaces, i.e., the stoichiometric TMO 2 surfaces, exhibit a contrast reversal in simulated scanning tunneling microscopy (STM) images at different scanning biases. The predictions are verified by experimental STM imaging of RuO 2 (110) surfaces and this feature is shown to enable accurate determinations of the TMO 2 (110) surface structures under various conditions. This research provides different insights into the electronic properties of TMO 2 (110) surfaces and offers an effective method to directly map the surface structure and point defects using bias-dependent STM.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2011

Distribution of unresolvable anisotropic microstructures revealed in visibility-contrast images using x-ray Talbot interferometry

X-ray Talbot interferometry has been widely used as a technique for x-ray phase imaging and tomography. We propose a method using this interferometry for mapping distribution of parameters characterizing anisotropic microstructures, which are typically of the order of {mu}m in size and cannot be resolved by the imaging system, in a sample. The method uses reduction in fringe visibility, which is caused by such unresolvable microstructures, in moire images obtained using an interferometer. We applied the method to a chloroprene rubber sponge sample, which exhibited uniaxial anisotropy of reduced visibility. We measured the dependencies of reduced visibility on both the Talbot order and the orientation of the sample and obtained maps of three parameters and their anisotropies that characterize the unresolvable anisotropic microstructures in the sample. The maps indicated that the anisotropy of the sample's visibility contrast mainly originated from the anisotropy of the microstructure elements' average size. Our method directly provides structural information on unresolvable microstructures in real space, which is only accessible through the ultra-small-angle x-ray scattering measurements in reciprocal space, and is expected to be broadly applied to material, biological, and medical sciences.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Contrasting behavior of covalent and molecular carbon allotropes exposed to extreme ultraviolet and soft x-ray free-electron laser radiation

All carbon materials, e.g., amorphous carbon (a-C) coatings and C 60 fullerene thin films, play an important role in short-wavelength free-electron laser (FEL) research motivated by FEL optics development and prospective nanotechnology applications. We investigate responses of a-C and C 60 layers to the extreme ultraviolet (SPring-8 Compact SASE Source in Japan) and soft x-ray (free-electron laser in Hamburg) free-electron laser radiation by Raman spectroscopy, differential interference contrast, and atomic force microscopy. A remarkable difference in the behavior of covalent (a-C) and molecular ( C 60 ) carbonaceous solids is demonstrated under these irradiation conditions. Low thresholds for ablation of a fullerene crystal (estimated to be around 0.15 eV/atom for C 60 vs 0.9 eV/atom for a-C in terms of the absorbed dose) are caused by a low cohesive energy of fullerene crystals. An efficient mechanism of the removal of intact C 60 molecules from the irradiated crystal due to Coulomb repulsion of fullerene-cage cation radicals formed by the ionizing radiation is revealed by a detailed modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhancements in laser-generated hot-electron production via focusing cone targets at short pulse and high contrast

We report on the increase in the accelerated electron number and energy using compound parabolic concentrator (CPC) targets from a short-pulse (~150 fs), high-intensity (>10 18 W/cm 2 ), and high-contrast (~10 8 ) laser-solid interaction. We report on experimental measurements using CPC targets where the hot-electron temperature is enhanced up to ~ 9 times when compared to planar targets. The temperature measured from the CPC target is (T e ) = 4.4 ± 1.3 MeV. Using hydrodynamic and particle in cell simulations, we identify the primary source of this temperature enhancement is the intensity increase caused by the CPC geometry that focuses the laser, reducing the focal spot and therefore increasing the intensity of the laser-solid interaction, which is also consistent with analytic expectations for the geometrical focusing.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Heat transfer and transport property contrast effects on the compressible Rayleigh-Taylor instability

In extreme environments, the Rayleigh-Taylor instability (RTI) may occur under large variations in density and temperature and with fluid transport properties strongly dependent on temperature. Direct numerical simulations of the 3D fully compressible RTI are conducted, examining the idealized configuration of a hotter, less dense fluid pushing against a colder, denser fluid. Various temperature ratios and transport property configurations are explored to examine how heat conduction, large variations in transport properties, and sudden changes in transport properties can affect the evolution of the mixing layer. Nonuniform fluid expansion and contraction induced by heat transfer can significantly affect local density differences and overall instability growth, causing profile asymmetries about the initial interface for flow and mixing statistics. The departures from classical self-similar development of the instability along with misalignment between regions of mixing and regions of most intense turbulent activity caused by both heat transfer and transport property contrasts are examined. After sudden changes in fluid transport properties, which may occur as a result of rapid heating (e.g., in inertial confinement fusion), the flow quickly responds and begins to relax towards quasi-self-similar late-time evolution. For many dynamical quantities such as vorticity and dissipation, this late-time evolution resembles that of the configuration that already started with the final transport property magnitudes, suggesting that these quantities depend only on the transport properties and not on past flow history, provided that the density field distributions for the flows remain similar. On the other hand, the mixing evolution after the transport property change is unique, implying that both property magnitudes and previous history are impactful on the mixing. In conclusion, these simulations demonstrate how various temperature-related effects are extremely important to consider in compressible RTI flows with large temperature variations.

42 ENGINEERING↗
DOE OSTI2017

Collimated Propagation of Fast Electron Beams Accelerated by High-Contrast Laser Pulses in Highly Resistive Shocked Carbon

In this study, collimated transport of ultrahigh intensity electron current was observed in cold and in laser-shocked vitreous carbon, in agreement with simulation predictions. The fast electron beams were created by coupling high-intensity and high-contrast laser pulses onto copper-coated cones drilled into the carbon samples. The guiding mechanism—observed only for times before the shock breakout at the inner cone tip—is due to self-generated resistive magnetic fields of ~ 0.5–1 kT arising from the intense currents of fast electrons in vitreous carbon, by virtue of its specific high resistivity over the range of explored background temperatures. The spatial distribution of the electron beams, injected through the samples at different stages of compression, was characterized by side-on imaging of hard x-ray fluorescence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

How relative defect migration energies drive contrasting temperature-dependent microstructural evolution in irradiated ceramics

Ceramic materials have become widely used in various fields of material science, and ceramic oxides such as cubic zirconia (c- ZrO 2 ) and magnesia (MgO) are candidate materials for nuclear energy applications. For the corresponding in-service conditions of these materials, there is a crucial need in studies at moderate or high temperatures of the physical phenomena underlying the damage buildup under irradiation. Here in the present work, we show, using x-ray diffraction, that these two materials exhibit a similar damage accumulation process under ion irradiation at fixed temperature. However, they display an unexpected opposite damaging rate to changes in the irradiation temperature. In fact, as the temperature is increased, the final damage state is reached earlier in c- ZrO 2 while it is delayed in MgO. Rate equation cluster dynamics simulations show that defect clustering is favored over defect recombination in c- ZrO 2 , but the situation is reversed for MgO, explaining the observed opposite response to temperature of the two materials. This contrasting behavior can be rationalized in terms of nonequivalent interstitial versus vacancy defect migration energies in MgO. We finally demonstrate that these results allow for a qualitative prediction of the evolution of the experimental irradiation-induced disorder with temperature, henceforth potentially reducing the cost in selecting and developing ad hoc materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Competing mechanisms at vibrated interfaces of density-contrast fluids

Fluid-fluid interfacial instability and subsequent fluid mixing are ubiquitous in nature and engineering. The hydrodynamic instability of fluid interfaces has long centered on the pressure gradient-driven long-wavelength Rayleigh-Taylor instability and the resonance-induced short-wavelength Faraday instability. However, neither instability alone can explain the dynamics when both mechanisms are present. We identify a previously unseen multi-modal instability emerging from their coexistence. When the denser fluid is polydimethylsiloxane, the mixed region at a high density contrast (Atwood number = 0.9) spans a vibration amplitude range approximately twice the gravitational acceleration. Using Floquet stability analysis, we show how vibrations govern transitions between the RT and Faraday instabilities, leading to contention between these instabilities rather than resonant enhancement. Here, the initial transient growth is represented by the exponential modal growth of the most unstable Floquet exponent, along with its accompanying periodic behavior. Direct numerical simulations validate these findings and track interface breakup into the multiscale and nonlinear regimes. Specifically, we show that growing RT modes nonlinearly suppresses Faraday responses even when the initial growth rate of the Faraday instability is 3.63 times that of RT, so a bidirectional competition hinders their sustained coexistence.

Direct numerical simulations↗
DOE OSTI2020

Phase-contrast 3D tomography of HeLa cells grown in PLLA polymer electrospun scaffolds using synchrotron X-rays

Advanced imaging is useful for understanding the three-dimensional (3D) growth of cells. X-ray tomography serves as a powerful noninvasive, nondestructive technique that can fulfill these purposes by providing information about cell growth within 3D platforms. There are a limited number of studies taking advantage of synchrotron X-rays, which provides a large field of view and suitable resolution to image cells within specific biomaterials. In this study, X-ray synchrotron radiation microtomography at Diamond Light Source and advanced image processing were used to investigate cellular infiltration of HeLa cells within poly L-lactide (PLLA) scaffolds. This study demonstrates that synchrotron X-rays using phase contrast is a useful method to understand the 3D growth of cells in PLLA electrospun scaffolds. Two different fiber diameter (2 and 4 µm) scaffolds with different pore sizes, grown over 2, 5 and 8 days in vitro , were examined for infiltration and cell connectivity. After performing visualization by segmentation of the cells from the fibers, the results clearly show deeper cell growth and higher cellular interconnectivity in the 4 µm fiber diameter scaffold. Finally, this indicates the potential for using such 3D technology to study cell–scaffold interactions for future medical use.

3D culture↗
DOE OSTI2020

Electron image contrast analysis of mosaicity in rutile nanocrystals using direct electron detection

Direct electron detection provides high detective quantum efficiency, significantly improved point spread function and fast read-out which have revolutionized the field of cryogenic electron microscopy. However, these benefits for high-resolution electron microscopy (HREM) are much less exploited, especially for in situ study where major impacts on crystallographic structural studies could be made. By using direct detection in electron counting mode, rutile nanocrystals have been imaged at high temperature inside an environmental transmission electron microscope. The improvements in image contrast are quantified by comparison with a charge-coupled device (CCD) camera and by image matching with simulations using an automated approach based on template matching. Together, these approaches enable a direct measurement of 3D shape and mosaicity (~1°) of a vacuum-reduced TiO 2 nanocrystal about 50 nm in size. Thus, this work demonstrates the possibility of quantitative HREM image analysis based on direct electron detection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Contrasting advantages of learning with random weights and backpropagation in non-volatile memory neural networks

Recently, a Cambrian explosion of novel, non-volatile memory (NVM) devices known as memristive devices have inspired effort in building hardware neural networks that learn like the brain. Early experimental prototypes built simple perceptrons from nanosynapses, and recently, fully-connected multi-layer perceptron (MLP) learning systems have been realized. However, while backpropagating learning systems pair well with high-precision computer memories and achieve state-of-the-art performances, this typically comes with a massive energy budget. For future Internet of Things/peripheral use cases, system energy footprint will be a major constraint, and emerging NVM devices may fill the gap by sacrificing high bit precision for lower energy. In this work, we contrast the well known MLP approach with the Extreme Learning Machine (ELM) or NoProp approach, which uses a large layer of random weights to improve the separability of high-dimensional tasks, and is usually considered inferior in a software context. However, we find that when taking device non-linearity into account, NoProp manages to equal hardware MLP system in terms of accuracy. While also using a sign-based adaptation of the delta rule for energy-savings, we find that NoProp can learn effectively with four to six ’bits’ of device analog capacity, while MLP requires eight bit capacity with the same rule. This may allow the requirements for memristive devices to be relaxed in the context of online learning. By comparing the energy footprint of these systems for several candidate nanosynapses, as well as realistic peripherals, we confirm that memristive NoProp systems save energy compared to MLP systems. Lastly, we show that ELM/NoProp systems can achieve better generalization abilities than nanosynaptic MLP systems when paired with pre-processing layers (which do not require backpropagated error). Collectively, these advantages make such systems worthy of consideration in future accelerators or embedded hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

DENSECL: Haze Mitigation Using Dense Blocks and Contrastive Loss Regularization

Haze, which occurs as a result of the scattering of light in the atmosphere by small particles, diminishes the visibility of scene objects, inflicting important image applications such as object detection. To address the problem, this paper introduces a new physics-based end-to-end deep learning approach to haze mitigation in outdoor scenes, including those in airborne images. The proposed model named DenseCL is designed with dense blocks and adopts a contrastive loss function as an additional regularization. The model also maintains the cycle consistency by remapping the dehazed outputs into a hazy image using the physics-based light scattering function. DenseCL has been trained with publicly available outdoor images and demonstrates outstanding performance on outdoor, indoor, and remotely sensed nonhomogeneous haze satellite images.

Mitra, Somosmita↗
DOE OSTI2025

Accelerated Over-The-Air Neural Receiver Training Using Self-Contrastive Learning

Self-contrastive learning (SCL), a self-supervised learning method, has been shown to improve image and signal classifier accuracies and reduce the training time for neural communications receivers. In particular, prior work has shown that SCL applied as a pre-training step can improve simulated performance of OFDM in 3GPP TDL channel models by reducing the training time of the downstream classification task (demodulation and demapping). In this work a practical implementation demonstrating SCL pre-training using software defined radios (SDRs) is proposed.

Cooke, Corey [ORNL] (ORCID:0000000234263672)↗
DOE OSTI2016

High-spectral-contrast symmetric modes in photonic crystal dual nanobeam resonators

Here, we demonstrate accurate control of mode symmetry in suspended dual-nanobeam resonators on a silicon-on-insulator chip. Each nanobeam consists of a Fabry-Perot nanocavity bounded by tapered 1-D photonic crystals. Even and odd cavity-modes are formed due to lateral evanescent coupling between the two nanobeams. The odd cavity-mode can be excited by mode-symmetry-transforming Mach-Zehnder couplers. Modal contrasts over 27 dB are measured in fabricated structures. The influence of the optical field in the middle air slot on the background transmission and quality factors is discussed. The observed peak wavelength separations of the modes at various nanobeam spacings are in good agreement with simulation results. These nanobeam resonators are potentially useful in applications, such as ultrafast all-optical modulation, filtering, and switching.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer’s Diagnosis

Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics. Recent studies demonstrate enhanced diagnostic sensitivity for Alzheimer’s disease (AD) through synergistic integration of neuroimaging data (e.g., sMRI, fMRI) with tabular data (e.g., behavioral and cognitive tests). However, the intrinsic heterogeneity across modalities (e.g., 4D spatiotemporal fMRI dynamics vs. 3D anatomical sMRI structure) presents critical challenges for discriminative feature fusion, often leading to information loss or biased fusion. To bridge this gap, we propose M2M-AlignNet: a multimodal co-attention network with latent alignment for early AD diagnosis using sMRI and fMRI. At the core of our approach is a multi-patch-to-multi-patch (M2M) contrastive loss function that quantifies and reduces representational discrepancies via weighted patch correspondence, explicitly aligning fMRI components across brain regions with their sMRI structural substrates without one-to-one constraints. Additionally, we propose a latent-as-query co-attention module to autonomously discover fusion patterns, circumventing modality prioritization biases while minimizing feature redundancy. We conduct extensive experiments to confirm the effectiveness of our method and highlight the correspondence between fMRI and sMRI as AD biomarkers.

Wei, Yuxiang [Georgia Institute of Technology]↗
DOE OSTI2026

Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.

Accumulated Local Effects↗
DOE OSTI2026

Transgenic Mixed‐Linkage‐Glucan Enhancement Affects Root Characteristics and Decomposition in Soils of Contrasting Vegetation History

ABSTRACT Development of transgenic bioenergy sorghum [ Sorghum bicolor (L.) Moench] with increased contents of mixed‐linkage (1,3;1,4)‐β‐glucan (MLG) is an important step towards enhancing quality of bioenergy feedstocks. Since MLG‐enhancement leads to greater biomass digestibility, our overarching hypothesis is that root residues of MLG‐enhanced plants may be more readily decomposed in the soil, potentially creating new opportunities for optimizing soil carbon (C) sequestration, nutrient cycling, and overall agricultural sustainability. The study examined morphological, chemical, and enzymatic characteristics of fine and coarse roots of four bioenergy sorghum genotypes. Then, we incubated the roots within soils with contrasting vegetation histories while measuring C mineralization, microbial biomass C (MBC), and activity of hydrolytic enzymes and calculating vector length and vector angle enzymatic stoichiometry parameters. The results indicated that MLG‐enhancing transformations increased root total nitrogen (N) contents, decreased C/N ratios, and were associated with higher MLG concentrations in fine than in coarse roots. Incubations with transgenic roots led to 16%–38% higher MBC and 19%–41% lower microbial metabolic quotient (qCO 2 ). While enzyme activity differed markedly among the studied genotypes, it did not directly respond to MLG levels in root tissues. The increase in MBC without concurrent increases in C mineralization or hydrolytic enzyme activities in transgenic genotypes suggests that MLG enhancement promoted microbial anabolic retention of root‐derived C rather than stimulating catabolic decomposition. Enzymatic vector results indicated that these parameters reflect a variety of drivers behind microbial enzyme production, including availability of specific substrates, such as MLG here, and/or deficiency in specific nutrients, such as phosphorus (P). The study confirms the positive impacts from the roots of engineered MLG‐enhanced bioenergy plants on soil microbial activity and highlights the interactive influences on the MLG‐enhancement effects from root size and inherent soil properties.

Mahmoodabadi, Majid [Department of Plant, Soil, an↗