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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
NASA NTRS1994

Ultrasonic Evaluation of Thermal Degradation in Adhesive Bonds

The critical role played by adhesive bonds in lap joints is well known. A good knowledge of the mechanical properties of adhesive bonds in lap joints is a prerequisite to the design and reliable prediction of the performance of these bonded structures. Furthermore, the lap joint may be subject to high-temperature environments in service. Early detection of the degree of thermal degradation in adhesive bonds is required under these circumstances. A variety of ultrasonic nondestructive evaluation (NDE) techniques can be used to determine the thickness and the elastic moduli of adhesively bonded joints. In this paper we apply a previously developed technique based on the leaky Lamb wave (LLW) experiment to investigate the possibility of characterizing the thermal degradation of adhesive bonds in lap joints. The degradation of the adhesive bonds is determined through comparison between experimental data and theoretical calculations.

degradation lap joints mechanical properties bonde↗
NASA NTRS1998

Material Properties and Defects Characterization Using LLW Data

Leaky Lamb Wave (LLW) propagation in composite materials has been studied extensively since it was first observed in 1982. The wave is induced using a pitch-catch arrangement and the plate wave modes are detected by identifying minima in the reflected spectra to obtain the dispersion data.

Composites↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
DOE OSTI2026

Electron emissions and hot spots in dual-phase LXe TPCs

Persistent photon and single-electron emissions - in the form of “electron trains” and localized “hot spots” - have been observed in multiple dual-phase liquid xenon (LXe) time projection chambers (TPCs), often persisting long after ionizing events. Here, we show that these phenomena are naturally explained by photon-triggered single-electron emission from resistive mixed-oxide films on stainless-steel (SS304 or s.s.) wires, which behave as leaky capacitors with long RC time constants at LXe temperature. Positive ions landing on these oxides can further enhance local fields and drive Malter-like electron emission. We outline the materials physics (Cr 2 O 3 /Cr 2 O 3-x /Cr(OH) 3 mosaics), quantify expected time scales (∼1 s under illumination), and demonstrate how small damaged regions with enhanced QE can produce persistent hot spots.

Va'vra, J. [SLAC National Accelerator Laboratory (↗
DOE OSTI2026

Harnessing the Second-Order Metal−Insulator Transition for Neuromorphic Computing

Vanadium oxides are widely studied phase change materials for brain-inspired computing architectures. Systems like VO 2 and V 2 O 3 exhibit first-order metal−insulator transitions (MITs) with hysteresis and percolative switching, increasing stochasticity and device variability. Here, we focus on the less explored Magnéli phase V 4 O 7 , which undergoes a continuous, non-hysteretic, second-order MIT. This surprisingly enables highly reproducible volatile resistive switching in spiking-neuron-type devices. We synthesize V 4 O 7 films, characterize their structural and transport properties, and demonstrate voltage and current-driven threshold switching with electrothermal feedback. In a Pearson–Anson oscillator, V 4 O 7 devices produce stable, tunable spiking across 20–200 kHz, with consistent operation among multiple devices. We introduce a numerical analog leaky-integrate-and-fire (aLIF) model that captures waveform shapes and their dependence on load resistance, temperature, and voltage. Furthermore, these findings suggest that second-order MIT materials like V 4 O 7 are promising for deterministic, scalable spiking neuron arrays for neuromorphic computing.

V4O7↗
DOE OSTI2024

Sputter-Coated TiO 2 Films as Passivation and Hole Transfer Layers for Improved Energy Conversion with Solar Fuel WO 3 /CuWO 4 Photoanodes

Atomic layer deposited (ALD) “leaky” TiO 2 have gained interest as charge-selective protection layers for semiconductor solar fuel electrodes. Here we demonstrate the use of sputter-deposited TiO 2 layers as hole selective contacts for WO 3 /CuWO 4 type 2 heterojunction water oxidation photoanodes for the first time. TiO 2 protection layers with varying thicknesses (2 to 128 nm) were deposited using the RF magnetron sputtering technique. The resulting TiO 2 films are amorphous based on Raman spectroscopy and powder XRD. Photoelectrochemical scans and Vibrating Kelvin probe photovoltage spectroscopy show that 2-8 nm TiO 2 layers nearly double the photocurrent to 0.97 mA cm -2 under AM 1.5 illumination (19% AQE at 350 nm), increase the surface photovoltage signal by 25%, and increase the WO 3 /CuWO 4 bandgap. These effects can be attributed to the selectivity of TiO 2 for photoholes. Additionally, SPV data suggest that TiO 2 overlayers suppress copper-based surface recombination defects. Reduced photocurrent and the photovoltage are seen in thicker TiO 2 films (16 to 128 nm) as a result of an increasing hole transfer resistance and because of light shading effects according to photoaction spectra. The TiO 2 films also improve the stability of the WO 3 /CuWO 4 photoelectrodes, allowing nearly constant O 2 evolution over 3 hours after an initial 20-35% loss. Overall, this work establishes RF magnetron sputtering as a useful method to install amorphous TiO 2 passivation layers for improved WO 3 /CuWO 4 solar fuel photoelectrodes. Furthermore, we show how the combination of PEC with SPV measurements provides insight into the function of the TiO 2 coatings.

CuWO4↗
DOE OSTI2024

Catalytic Resonance Theory: Turnover Efficiency and the Resonance Frequency

Programmable catalysts exhibiting forced oscillation in the free energy of reacting surface species were simulated to understand the general mechanisms leading to efficient use of the input energy. Catalytic ratchets with either positive or negative adsorbate scaling exhibited oscillation conditions of both high and low turnover efficiency, yielding catalytic turnover frequencies either close to or significantly lower than the applied catalyst oscillation frequency, respectively. The “effective rate”, defined as the product of the catalytic turnover frequency and the turnover efficiency (ηTOE), was limited via two catalytic mechanisms: a leaky catalytic ratchet existed when molecules repeatedly traversed backward through the catalytic transition state upon catalyst oscillation, while a catalytic ratchet with low surface participation exhibited reduced formation of a gas-phase final product due to low surface product coverage. Furthermore, a single applied frequency yielding a maximum effective catalytic rate defined as the “resonance frequency” provided maximum combined benefit for catalytic rate and efficiency.

25 ENERGY STORAGE↗
DOE OSTI2025

Steerable terahertz beams using surface waves on an active metasurface

The development of dynamic components for controlling wave fronts in the sub-terahertz region of the electromagnetic spectrum has emerged as a frontier research topic for many applications in sensing and communications. One approach which has attracted much attention involves the use of active metasurfaces, tiled arrays of sub-wavelength elements with properties that can be reconfigured via external actuation. In nearly all cases, these metasurfaces are employed as either transmissive or reflective elements, taking advantage of their strong and tunable interaction with free-space electromagnetic waves. These interactions can be significantly enhanced through the use of surface waves propagating parallel to the metasurface array, although very few studies have exploited this option. Here, we integrate a metasurface into the interior of a parallel-plate waveguide in a configuration explicitly designed to exploit this surface-wave geometry. We show that varying the electrical properties of the active metasurface changes the wave vector of the guided mode, and thereby alters the emission angle of radiation out-coupled through a leaky-wave slot aperture. These results, which are consistent with numerical simulations, represent a new approach to broadband beam steering suitable for the sub-terahertz spectral range.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Adapting C 4 photosynthesis to atmospheric change and increasing productivity by elevating Rubisco content in sorghum and sugarcane

Meta-analyses and theory show that with rising atmospheric [CO 2 ], Rubisco has become the greatest limitation to light-saturated leaf CO 2 assimilation rates (A sat ) in C 4 crops. So would transgenically increasing Rubisco increase A sat and result in increased productivity in the field? Here, we successfully overexpressed the Rubisco small subunit (RbcS) with Rubisco accumulation factor 1 (Raf1) in both sorghum and sugarcane, resulting in significant increases in Rubisco content of 13 to 25% and up to 90% respectively. A sat increased 12 to 15% and Rubisco enzyme activity ~40% in three independent transgenic events of both species. Sorghum plants also showed increased speeds of photosynthetic induction and decreased bundle sheath leakiness. These improvements translated into average increases of 15.5% in biomass in field-grown sorghum and a 37 to 81% increase in greenhouse-grown sugarcane. This suggests a potential opportunity to achieve substantial increases in productivity of this key economically important clade of C 4 crops, future proofing their value under global atmospheric change.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Multilayered regulation by RNA thermometers enables precise control of Cas9 expression in E. coli

Cas9-based genome editing technologies can rapidly generate mutations to probe a diverse array of mutant genotypes. However, aberrant Cas9 nuclease translation and activity can occur despite the use of inducible promoters to control expression, leading to extensive cell death. This background killing caused by promoter leakiness severely limits the application of Cas9 for generating mutant libraries because of the potential for population skew. We demonstrate the utility of temperature sensitive RNA elements as a layer of post-transcriptional regulation to reduce the impact of promoter leak. We observe significant temperature-dependent increases in cell survival when certain RNA thermometers (RNATs) are placed upstream of the cas9 coding sequence. We also show that the most highly repressing RNAT, hsp17rep, significantly reduces population skew with a library of characterized guide RNAs in Escherichia coli. This strategy should be applicable to all bacterial Cas9-based methods and technologies.

Kammerdiener, Elise K. [Oak Ridge National Laborat↗
DOE OSTI2025

Gauge-fixing quantum density operators at scale

We provide a theory, algorithms, and simulations of nonequilibrium quantum systems using a one-dimensional (1D) completely positive (CP), matrix-product (MP) density-operator (𝜌) representation. By generalizing the matrix product state's orthogonality center, to additionally store positive classical mixture correlations, the MP⁢𝜌 factorization naturally emerges. In this setting, we analytically and numerically examine the virtual gauge freedoms associated with the representation of quantum density operators. Based on this perspective, we simplify algorithms in certain limits to speed up the integration of the canonical-form master-equation dynamics. This enables us to quickly evolve under the dynamics of two-body quantum channels without resorting to optimization-based methods. In addition to this technical advance, we also scale up numerical examples and discuss implications for accurately modeling hardware architectures and predicting their performance in the near term. This includes an example of the quantum to classical transition of informationally leaky, i.e., decohering, qubits. In this setting, because of loss from environmental interactions, nonlocal complex coherence correlations are converted into global incoherent classical statistical mixture correlations. Lastly, the representation of both global and local correlations is discussed. We expect this work to have applications in additional nonequilibrium settings, beyond qubit engineering.

Gangapuram, Amit Jamadagni [Oak Ridge National Lab↗
DOE OSTI2025

Direct Evidence of Multispecies Hydrodynamics in Ignition-Scale Hohlraums

A targeted experiment at the National Ignition Facility (NIF) confirms the presence of multispecies hydrodynamics in inertial confinement fusion hohlraums relevant to ignition. Here, the effects are identified by filling the gold hohlraum with a deuterium-tritium (DT) gas mixture instead of helium. As the hohlraum is heated by the NIF lasers, it implodes inward, compressing and heating the DT, which leads to fusion. The resulting DT-fusion neutrons are measured in space, time, yield, angle, and energy. A distinct, peaked, triangular shape in the radial neutron emission profile provides evidence of a “leaky piston” effect caused by the interpenetration of DT into the expanding gold. This process reduces the reversibility of DT compression and decreases neutron generation on the temporal trailing edge compared to the leading edge. These results are well described by multispecies hydrodynamics simulations, which reproduce the observed spatial and temporal features, as well as the total neutron yield and angularly resolved energy spectra. In contrast, conventional simulations that use only single-species physics fail to match the experimental data. They overpredict the neutron yield, produce a flatter-than-observed spatial profile, and show excessive emission at late times compared to the experimental measurements.

Higginson, Drew P. [Lawrence Livermore National La↗
DOE OSTI2026

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning

Spiking Neural Networks (SNNs) are computational models inspired by the event-driven communication and connectivity patterns of biological neural circuits. They enable high energy efficiency and natural support for diverse architectures ranging from layered networks to small-world and graphstructured topologies. In this work, we introduce NeuroCoreX, an open-source, FPGA-based spiking neural network emulator that provides real-time, on-chip learning and flexible network organization. NeuroCoreX supports both feedforward sensory inputs streamed directly from sensors or PCs via UART and recurrent on-chip connectivity, enabling simultaneous processing and learning from external stimuli and internal network dynamics-capabilities rarely available in existing FPGA SNN platforms. The system implements a Leaky Integrate-and-Fire (LIF) neuron model with current-based synapses and supports pair-based STDP learning on both feedforward and recurrent synapses. A lightweight Python interface enables interactive configuration, live monitoring, weight read-back, and experiment control. Importantly, NeuroCoreX is tightly integrated with the SuperNeuroMAT simulator, allowing SNN models to be transferred seamlessly from software to hardware for hardware-in-the-loop development. By combining real-time plasticity, flexible connectivity, and an open-source VHDL implementation, NeuroCoreX provides an extensible and accessible platform for neuromorphic research, algorithm-hardware co-design, and energy-efficient edge intelligence.

Gautam, Ashish [ORNL]↗
DOE OSTI2026

A Unidirectional Two-Compartment Neuron Circuit with On-chip STDP learning

Most neuromorphic chips implement the single-compartment point neuron model where synapse circuits connect directly to a leaky integrate and fire (LIF) soma circuit. However, when using a biologically plausible soma circuit (e.g., Hodgkin-Huxley neuron model), an interface circuitry, such as a current conveyor circuit, is needed to transmit synaptic current to the soma circuit. This is especially true for ultra-low power neuron circuits, where membrane capacitance is on the order of 20 fF. This need for an interface circuit arises because the parasitic capacitance and leakage current caused by fabrication mismatch and second-order effects of the output transistors in the synapse circuits can disturb the spiking dynamics of the soma circuit if connected without an interface. Using an interface circuit to isolate the soma’s membrane capacitor from synapses resolves this issue. We propose to use a unidirectional resistor (a transconductance circuit) to connect the synapse and soma circuits instead of conventional current conveyor circuits. Using a biologically plausible spike pattern detection model, we show that the on-chip spike-timing-dependent plasticity (STDP) learning performance of the proposed unidirectional two-compartment neuron circuit is similar to a single-compartment circuit (with a current conveyor as an interface) and additionally, it is more power-efficient and biologically plausible. The chip is fabricated in the Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm technology node and comprises a single neuron circuit.

Gautam, Ashish [ORNL]↗
DOE OSTI2024

Zero-crosstalk silicon photonic refractive index sensor with subwavelength gratings

Abstract Silicon photonic index sensors have received significant attention for label-free bio and gas-sensing applications, offering cost-effective and scalable solutions. Here, we introduce an ultra-compact silicon photonic refractive index sensor that leverages zero-crosstalk singularity responses enabled by subwavelength gratings. The subwavelength gratings are precisely engineered to achieve an anisotropic perturbation-led zero-crosstalk, resulting in a single transmission dip singularity in the spectrum that is independent of device length. The sensor is optimized for the transverse magnetic mode operation, where the subwavelength gratings are arranged perpendicular to the propagation direction to support a leaky-like mode and maximize the evanescent field interaction with the analyte space. Experimental results demonstrate a high wavelength sensitivity of − 410 nm/RIU and an intensity sensitivity of 395 dB/RIU, with a compact device footprint of approximately 82.8 μm 2 . Distinct from other resonant and interferometric sensors, our approach provides an FSR-free single-dip spectral response on a small device footprint, overcoming common challenges faced by traditional sensors, such as signal/phase ambiguity, sensitivity fading, limited detection range, and the necessity for large device footprints. This makes our sensor ideal for simplified intensity interrogation. The proposed sensor holds promise for a range of on-chip refractive index sensing applications, from gas to biochemical detection, representing a significant step towards efficient and miniaturized photonic sensing solutions. Graphical Abstract

Materials Science↗
DOE OSTI2025

Emerging applications: Neuromorphic computing and reservoir computing

The emergence of doped hafnium oxide (HfO 2 )-based ferroelectric films has enabled highly scalable and silicon-compatible ferroelectric devices, opening new frontiers in neuromorphic and reservoir computing. Among these, ferroelectric field-effect transistors (FeFETs) are particularly promising due to their analog memory characteristics and unique polarization dynamics. These properties make FeFETs ideal candidates for artificial synapses in neuromorphic architectures, supporting deep neural networks and spiking neural networks based on leaky-integrate-and-fire (LIF) mechanisms. Beyond neuromorphic computing, FeFETs also play a crucial role in physical reservoir computing, leveraging their intrinsic nonlinear and history-dependent behavior for efficient real-time learning. This approach offers significant advantages for time-series processing and edge artificial intelligence (AI) applications, addressing the growing need for energy-efficient computing. As a result, this article explores the principles, key demonstrations, and future potential of FeFET-based neuromorphic and reservoir computing, highlighting their impact on next-generation AI hardware.

36 MATERIALS SCIENCE↗