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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms

The quantum approximate optimization algorithm (QAOA) has enjoyed increasing attention in noisy, intermediate-scale quantum computing with its application to combinatorial optimization problems. QAOA has the potential to demonstrate a quantum advantage for NP-hard combinatorial optimization problems. As a hybrid quantum-classical algorithm, the classical component of QAOA resembles a simulation optimization problem in which the simulation outcomes are attainable only through a quantum computer. The simulation that derives from QAOA exhibits two unique features that can have a substantial impact on the optimization process: (i) the variance of the stochastic objective values typically decreases in proportion to the optimality gap, and (ii) querying samples from a quantum computer introduces an additional latency overhead. In this paper, we introduce a novel stochastic trust-region method derived from a derivative-free, adaptive sampling trust-region optimization method intended to efficiently solve the classical optimization problem in QAOA by explicitly taking into account the two mentioned characteristics. The key idea behind the proposed algorithm involves constructing two separate local models in each iteration: a model of the objective function and a model of the variance of the objective function. Exploiting the variance model allows us to restrict the number of communications with the quantum computer and also helps navigate the nonconvex objective landscapes typical in QAOA optimization problems. In conclusion, we numerically demonstrate the superiority of our proposed algorithm using the SimOpt library and Qiskit when we consider a metric of computational burden that explicitly accounts for communication costs.

Derivative-free Optimization↗
DOE OSTI2024

Ab Initio Theory of Unconventional Superconductivity [Poster]

This is an overview of the Quasiparticle Self-Consistent GW Approximation (QSGW) with the following: (1) Origins of Superconductivity in FeSe, (2) Properties of YFe 2 Ge 2 , (3) Dynamical Mean Field Theory: for strong local correlations, and (4) is TiSe 2 an excitonic insulator?

42 ENGINEERING↗
DOE OSTI2025

Learning from knockout reactions using a dispersive optical model

We present the empirical dispersive optical model (DOM) as applied to direct nuclear reactions. The DOM links both scattering and bound-state experimental data through a dispersion relation, which allows for fully consistent, data-informed predictions for nuclei where such data exist. In particular, we review investigations of the electron-induced proton knockout reaction from both 40 Ca and 48 Ca in a distorted-wave impulse approximation (DWIA) utilizing the DOM for a fully consistent description. Viewing these reactions through the lens of the DOM allows us to connect the documented quenching of spectroscopic factors with the increased high-momentum proton content in neutron-rich nuclei. A similar DOM-DWIA description of the proton-induced knockout from 40 Ca, however, does not currently fit in the consistent story of its electron-induced counterpart. With the main difference in the proton-induced case being the use of an effective proton–proton interaction, we suggest that a more sophisticated in-medium interaction would produce consistent results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Single-qubit multi-party transmission using universal symmetric quantum cloning

This study considers the hypothetical quantum network case where Alice wishes to transmit one qubit of information (specifically a pure quantum state) to M parties, where M is some large number. The remote receivers locally perform single-qubit quantum state tomography on the transmitted qubits in order to compute the quantum state within some error rate (dependent on the tomography technique and the number of transmitted qubits). We show that with the use of an intermediate optimal symmetric universal quantum cloning machine (between Alice and the remote receivers) as a repeater-type node in a hypothetical quantum network, Alice can send significantly fewer qubits compared to direct transmission of the message qubits to each of the M remote receivers. This is possible due to two properties of quantum cloning. The first is that single qubit quantum clones retain the same Bloch angle as the initial quantum state. This means that if the mixed state of the quantum clone can be computed to high enough accuracy, the original pure quantum state can be inferred by extrapolating that vector to the surface of the Bloch sphere. The second property is that the state overlap of approximate quantum clones, with respect to the original pure quantum state, quickly converges (specifically for 1 → M , the limit of the fidelity as M goes to infinity is $\frac{2}{3}$). This means that Alice can prepare a constant number of qubits (which are then passed through the quantum cloning machine) in order to achieve a desired error rate if M is large enough. Combined, these two properties mean that for a large M , Alice can prepare many orders of magnitude fewer qubits in order to achieve the same single qubit transmission accuracy compared to the naive direct qubit transmission approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography

The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.

3D cell mapping↗
DOE OSTI2026

The Extended Embedded Self-Shielding Method in SCALE 6.3/Polaris

The SCALE transport lattice code, Polaris, has been previously developed to generate few-group homogenized cross sections for whole-core nodal diffusion simulators in which the embedded self-shielding method (ESSM) is used for resonance self-shielding calculations to process cross sections. Although the ESSM capability has been very successful in light-water reactor analysis, it may require enhancements in computational efficiency; treatment of spatially dependent resonance self-shielding effects; and handling of interrelated resonance effects among fuel, cladding, and control rod materials. Therefore, this study focuses on improving computational efficiency by using a Dancoff-based Wigner–Seitz approximation combined with a material-based resonance categorization, through which a spatially dependent ESSM capability is developed to accurately estimate self-shielded cross sections inside the fuel. Benchmark results show that the new capability significantly enhances computational efficiency and accuracy for spatially dependent local zones within the fuel and through depletion.

ESSM↗
DOE OSTI2024

A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems: Preprint

Accurate and quick identification of high-impedance faults (HIFs) is critical for the reliable operation of distribution systems. Unlike other faults in power grids, HIFs are very difficult to detect by conventional overcurrent relays due to the low fault current. Although HIFs can be affected by various factors, the voltage-current characteristics can substantially imply how the system responds to the disturbance and thus provides opportunities to effectively localize HIFs. In this work, we propose a data-driven approach for the identification of HIF events. To tackle the nonlinearity of the voltage-current trajectory, first, we formulate optimization problems to approximate the trajectory with piecewise functions. Then we collect the function features of all segments as inputs and use the support vector machine approach to efficiently identify HIFs at different locations. Numerical studies on the IEEE 123-node test feeder demonstrate the validity and accuracy of the proposed approach for real-time HIF identification.

explainable artificial intelligence↗
DOE OSTI2024

Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement

In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

electro-chemo-mechanical coupling↗
DOE OSTI2024

Tailoring the Electronic Structures and Spectral Properties of ZnO with Irradiation Defects Generated Under Intense Electronic Excitation: A Combined Experimental and DFT Approach

Here, the relationship between the composition of the internal defect states, spectral properties, and correlated electronic structures of wurtzite zinc oxide (ZnO) crystals under 645 MeV Xe 35+ irradiation is systematically investigated, employing experimental characterizations combined with first-principle calculations. Based on the ion irradiation-induced thermal expansion and relaxation processes, the high concentration of vacancy/interstitial defects produced from the transient disordered phase in molten track states trigger photoelectric changes, as follows: i) the generation of internal defect states effectively reduces the intrinsic bandgap (3.25 eV → 2.66 eV); ii) a large number of defective active sites inhibits the recombination between electron–hole pairs, causing dark conductance and photoconductance to increase with increasing damage levels until optimal fluence is achieved. Based on the density functional theory (DFT) with the GGA + U (GGA = generalized gradient approximation) method, the defective models associated with the different electronic structures, density of states, formation energy, and the nature of the chemical bonding are established. The narrowing of the bandgap observed experimentally and the enhancement of carrier concentration originating from the internal electron defect states are qualitatively verified, therefore laying the foundation for designing future nanoscale photoelectronic devices and microelectronics applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Detection and Signal Processing for Near-Field Nanoscale Fourier Transform Infrared Spectroscopy

Researchers from a broad spectrum of scientific and engineering disciplines are increasingly using scattering-type near-field infrared spectroscopic techniques to characterize materials non-destructively with nanoscale spatial resolution. However, a sub-optimal understanding of a technique's implementation can complicate data interpretation and act as a barrier to entering the field. Here the key detection and processing steps involved in producing scattering-type near-field nanoscale Fourier transform infrared spectra (nano-FTIR) are outlined. The self-contained mathematical and experimental work derives and explains: i) how normalized complex-valued nano-FTIR spectra are generated, ii) why the real and imaginary components of spectra qualitatively relate to dispersion and absorption respectively, iii) a new and generally valid equation for spectra which can be used as a springboard for additional modeling of the scattering processes, and iv) an algebraic expression that can be used to extract an approximation to the sample's local extinction coefficient from nano-FTIR. The algebraic model for weak oscillators is validated with nano-FTIR and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra on samples of polystyrene and Kapton and further provides a pedagogical pathway to cementing some of the technique's key qualitative attributes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Concurrent Amorphization and Nanocatalyst Formation in Cu‐Substituted Perovskite Oxide Surface: Effects on Oxygen Reduction Reaction at Elevated Temperatures

Abstract The activity and durability of chemical/electrochemical catalysts are significantly influenced by their surface environments, highlighting the importance of thoroughly examining the catalyst surface. Here, Cu‐substituted La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3‐δ is selected, a state‐of‐the‐art material for oxygen reduction reaction (ORR), to explore the real‐time evolution of surface morphology and chemistry under a reducing atmosphere at elevated temperatures. Remarkably, in a pioneering observation, it is discovered that the perovskite surface starts to amorphize at an unusually low temperature of approximately 100 °C and multicomponent metal nanocatalysts additionally form on the amorphous surface as the temperature raises to 400 °C. Moreover, this investigation into the stability of the resulting amorphous layer under oxidizing conditions reveals that the amorphous structure can withstand a high‐temperature oxidizing atmosphere (≥650 °C) only when it has undergone sufficient reduction for an extended period. Therefore, the coexistence of the active nanocatalysts and defective amorphous surface leads to a nearly 100% enhancement in the electrode resistance for the ORR over 200 h without significant degradation. These observations provide a new catalytic design strategy for using redox‐dynamic perovskite oxide host materials.

Jeon, SungHyun↗
DOE OSTI2026

Active Learning‐Driven Inkless Additive Nanomanufacturing for Printed Electronics

Inkless additive nanomanufacturing for printed electronics promises broad material and substrate versatility, yet the high-dimensional print parameter space makes tuning print parameters time-intensive. We present a Bayesian optimization study that constructs a digital twin from printed-silver data to benchmark surrogate models, acquisition functions, and batch sizes head-to-head to achieve user-specified target resistance. Tested surrogate models included Gaussian process, random forest, and Bayesian neural network surrogates with expected improvement and confidence bound acquisition functions. In total, we evaluate 48 unique model configurations alongside a random sampling baseline for comparison. For printed silver, the Bayesian neural network with a batch size of one achieved the lowest average cumulative regret, approximately four times more efficient on average than random sampling. To balance performance and substrate space, a random forest model with expected improvement and a batch size of four was chosen as the model for validation testing. Applying this chosen configuration to copper with an additional print parameter, the model achieved a resistance within 0.15 Ω of a 1 Ω target in fewer than 30 printed lines across five validation sets. Altogether, the workflow yields a tuned and validated model that efficiently guides experiments toward the target while simultaneously learning the parameter space.

Bevel, Colton [Auburn University, AL (United State↗
DOE OSTI2025

High‐Speed Slot‐Die Coating with Donor‐Priority Rapid Aggregation Kinetics for Improved Morphology and Efficiency in Ecofriendly Organic Solar Cells

Abstract Solution‐processable organic solar cells (OSCs) represent a promising renewable photovoltaic technology with significant potential for eco‐compatible production. While high power conversion efficiencies (PCEs) have been achieved in OSCs, scaling this technology for high‐throughput manufacturing remains challenging. Key reason lies in the lack of efficient control strategies for the complex and long‐duration morphology evolution during high‐speed coating process with ecofriendly solvents. Here, a donor‐priority rapid aggregation process (DP‐RAP) scheme is proposed to solve this issue by adjusting the aggregation kinetics of donor and acceptor components. DP‐RAP enables blends with a nanoscale fiber network structure and favorable crystallinity, which contributes to balanced carrier transport and reduced recombination losses. As a result, the PCE is improved from 14.3% (reference) to 17.4% (DP‐RAP) for ultra‐high speed coated PM6:BTP‐eC9 devices in atmosphere, which is one of the highest values for non‐halogenated solvent‐processed solar cells at coating speeds of 500 mm s −1 . Moreover, the DP‐RAP based devices remain a stable PCE of approximately 17.4% across a broad range of coating speeds (20–500 mm s −1 ), illustrating its tolerance to the varied manufacturing conditions. This work highlights a promising avenue for the high‐speed, ecofriendly production of efficient OSCs, pushing the boundaries of practical manufacturing in renewable energy technologies.

Chemistry↗
DOE OSTI2026

Wireless Bioelectronic Modulation of Membrane Potential in Glioblastoma Using Carbon Nanotube Porins

Disruption of membrane potential (V mem ) can activate pathways associated with cancer proliferation. Manipulating ion channels may therefore present an effective strategy for treating cancers that fail to respond to conventional therapies. One approach to target these channels is to manipulate the membrane charge, which involves the use of wireless bipolar electrodes such as carbon nanotube porins (CNTPs) inserted into cell membranes to modulate membrane charge and ionic flux. By utilizing membrane dyes, we observed alterations in V mem induced by CNTPs and externally applied voltages. Analyses of cellular behaviors and processes indicated that V mem is more receptive to stimuli in invasive cancers, while it leads to increased metabolism in less invasive cancers, with notable changes in the cell cycle occurring at approximately 48 h post-treatment in Glioblastoma (GB) cell lines. This work shows that CNTPs, in combination and with externally applied voltages, can modulate V mem and alter cancer cell processes, supporting their potential as a therapeutic.

bioelectricity↗
DOE OSTI2024

Electronic Properties of Ultra‐Wide Bandgap B x Al 1− x N Computed from First‐Principles Simulations

Abstract Ultra‐wide bandgap (UWBG) materials such as AlN and BN hold great promise for future power electronics due to their exceptional properties. They exhibit large bandgaps, high breakdown fields, high thermal conductivity, and high mechanical strengths. AlN and BN have been extensively researched, however, their alloys, B x Al 1− x N, are much less studied despite their ability to offer tunable properties by adjusting x . In this article, the electronic properties of 17 recently predicted ground states of B x Al 1− x N in the x = 0 − 1 range are predicted using first‐principles density functional theory and many‐body perturbation theory within GW approximation. All the B x Al 1− x N structures are found to be UWBG materials and have bandgaps that vary linearly from that of wurtzite‐phase ( w ) AlN (6.19 eV) to that of w ‐BN (7.47 eV). The bandstructures of B x Al 1− x N show that a direct‐to‐indirect bandgap crossover occurs near x = 0.25. Furthermore, it is found that B x Al 1− x N alloys have much larger dielectric constants than the constituent bulk materials (AlN = 9.3 ɛ 0 or BN = 7.3 ɛ 0 ), with values reaching as high as 12.1 ɛ 0 . These alloys are found to exhibit large dielectric breakdown fields in the range 9–35 MV cm −1 with a linear dependence on x . This work provides the much needed advancement in the understanding of the properties of B x Al 1− x N to aid their application in next‐generation devices.

Milne, Cody L.↗
DOE OSTI2025

Realization of Non‐Equilibrium Wurtzite Structure in Heterovalent Ternary MgSiN 2 Film Grown by Reactive Sputtering

The piezoelectric and ferroelectric applications of heterovalent ternary materials are not well explored. Epitaxial MgSiN 2 films are grown at 600 °C on (111)Pt//(001)Al 2 O 3 substrates by the reactive sputtering method using metallic Mg and Si under the N 2 atmosphere. Detailed X-ray diffraction measurements and transmission electron microscopy observations revealed that the epitaxially grown films on the substrates have a hexagonal wurtzite structure with c-axis out-of-plane orientation. The random occupation of this structure by Mg and Si differs from that of the previously reported structure in which these two cations periodically occupy the cationic sites. However, the lattice spacings closely approximate those that are previously reported, irrespective of the ordering, and they are almost comparable with those of (Al 0.8 Sc 0.2 )N. The wide bandgap of >5.0 eV in deposited MgSiN 2 is compatible with that of AlN and suggests durability against the application of strong external electric fields, possibly to induce polarization switching. In addition, MgSiN 2 is shown to have piezoelectric properties with an effective d 33 value of 2.3 pm V −1 for the first time. This work demonstrates the compositional expansion of hexagonal wurtzite to heterovalent ternary nitrides for novel piezoelectric materials, whose ferroelectricity is expected.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Electrochemically recovered struvite matches conventional fertilizers in soil nutrient supply

Phosphorus (P) is an essential macronutrient for agriculture, yet global reliance on finite phosphorite reserves raises concerns about long-term food and nutrient security. Struvite (MgNH 4 PO 4 ·6H 2 O), a mineral recovered from municipal wastewater, represents a sustainable and circular source of P, but uncertainty remains regarding its nutrient availability relative to conventional fertilizers. This study evaluated the fertilizer value and environmental safety of electrochemically recovered struvite (RecoP) in comparison with diammonium phosphate (DAP), triple superphosphate (TSP), and rock phosphate (RockP). Nutrient availability, potential contaminant concentrations, and soil P and nitrogen (N) dynamics were assessed using standardized chemical analyses and an 8-week incubation experiment across three soils with contrasting soil types. RecoP contained 14.7% citrate-soluble P, significantly greater than RockP and comparable to DAP and TSP. Across all soils, RecoP increased both readily available and moderately labile P pools (p < 0.01), matching the performance of conventional fast-release fertilizers. Despite containing approximately half the total of DAP, RecoP stimulated nitrification rates that were 2.2 times greater than the control and exceeded those of TSP, suggesting that fertilizer-derived N bioavailability, rather than total N loading, governed microbial N transformation responses. Concentrations of potentially toxic elements in RecoP were consistently well below US Environmental Protection Agency regulatory limits thresholds. Collectively, these results indicate that RecoP functions as a readily available source of both P and N, comparable to widely used conventional fertilizers, while meeting environmental safety standards. RecoP therefore represents a viable component of sustainable nutrient management strategies that support P recycling and agricultural productivity.

Kharal, Sudarshan [Ohio Univ., Athens, OH (United ↗
DOE OSTI2025

Automation for Electric Vehicle Battery Pack Disassembly

Battery‐electric vehicles (EVs) are growing exponentially. The demand for these batteries is expected to increase sevenfold by 2035. The EV batteries reach their end of life when the capacity fades to 70%–80% of new, with some being removed from the primary applications with even lower levels of degradation. These batteries can be used in less demanding applications. The disassembly process is currently manual, slow, unsafe, and expensive. Automation is needed to increase the throughput. EV battery packs feature various continually changing designs and form factors, which limit the usefulness of deterministically programmed robotic solutions. The conceptual robotic disassembly of EV batteries has attracted the attention of researchers. However, while many approaches have been proposed, practical implementations are lacking. Here, we review proposed concepts for EV battery disassembly and describe the selected approach, with elements of partial solutions validated in a laboratory setting, including the selection of commercial solutions, the development of custom end effectors, and methodologies for detection, localization, and classification of fasteners. The computer vision tasks employed an overhead 2D camera to detect the type of battery pack and approximate localization of fasteners, and a 3D camera mounted on the robotic arm for precise localization (position and tilt) and classification.

Islam, Abu [Rochester Inst. of Technology, Rochest↗