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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Transformer coupled toroidal wave-heated remote plasma sources operating in Ar/NF 3 mixtures

Remote plasmas are used in semiconductor device manufacturing as sources of radicals for chamber cleaning and isotropic etching. In these applications, large fluxes of neutral radicals (e.g. F, O, Cl, H) are desired with there being negligible fluxes of potentially damaging ions and photons. One remote plasma source (RPS) design employs toroidal, transformer coupling using ferrite cores to dissociate high flows of moderately high pressure (up to several Torr) electronegative gases. In this paper, results are discussed from a computational investigation of moderate pressure, toroidal transformer coupled RPS sustained in Ar and Ar/NF 3 mixtures. Operation of the RPS in 1 Torr (133 Pa) of argon with a power of 1.0 kW at 0.5 MHz and a single core produces a continuous toroidal plasma loop with current continuity being maintained dominantly by conduction current. Operation with dual cores introduces azimuthal asymmetries with local maxima in plasma density. Current continuity is maintained by a mix of conduction and displacement current. Operation in NF 3 for the same conditions produces essentially complete NF 3 dissociation. Electron depletion as a result of dissociative attachment of NF 3 and NF x fragments significantly alters the discharge topology, confining the electron density to the downstream portion of the source where the NFx density has been lowered by this dissociation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Describing Function Analysis of Transformer Magnetizing Inductance for Direct Power Control of Back-to-Back Modular Multilevel Converters with Advanced Grid Support

This paper provides a detailed investigation into the application of describing function-based analysis for assessing transformer magnetizing inductance and its impact on system performance. The focus is on a back-to-back modular multilevel converter architecture, designed to interconnect systems operating at different frequencies. The study explores the implementation of a Direct Power Control strategy, examining its effects on transformer magnetizing inductance saturation and offering effective mitigation techniques. Furthermore, the integration of advanced grid support functionalities is highlighted, demonstrating how these enhancements bolster the converter's ability to improve grid stability and power quality, positioning it as a robust solution for modern power systems. The proposed approach is validated through extensive computer simulations based MAT LAB/Simulink domain, supported by significant case study results, confirming its practical effectiveness.

back-to-back modular multilevel converters (B2B- M↗
DOE OSTI2025

Design, Control, and Protection of a 13.2 kV, 1 MVA Solid State Transformer for Electric Vehicle Extreme Fast Charging Station

In this article, a medium-voltage (MV) ac-dc solid state transformer (SST) for electric vehicle (EV) extreme fast charging (XFC) station is proposed. The SST adopts a cascaded H-bridge (CHB)-based structure where the active front end (AFE) power stages are connected in input-series followed by dual active bridge (DAB) converters connected in an output-parallel configuration providing galvanic isolation through a high-frequency transformer (HFT). The SST is rated for 1 MVA and connects directly to a three-phase 13.2 kV MV ac grid through ac switchgear and outputs 750-V dc. At the dc bus, several dc/dc converters are connected, each of which can charge an EV based on its battery capacity. A novel decentralized control architecture of the SST is adopted in this work which simplifies the MV dc link voltage and module-level power balancing. In addition, the local and central protection designs of the SST are presented which identify and respond to the internal fault of the system. Finally, the experimental validations of the SST hardware prototype are presented up to the rated voltage. Furthermore, this article details the design and implementation of the MV SST addressing the challenges of an isolated MV class power converter for connecting directly to the MV ac grid with unique controller architecture, distributed protection framework, and SST constructional features.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Flexible Transformers for Resilient and Adaptable Power Systems

This paper presents experience with grid ready flexible transformer unit, which is in service for two years without any difficulty. Transformer unit is capable of changing short circuit impedance on load and it is equipped with state-of-the-art monitoring system.

field validation↗
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 OSTI2025

Probing Electrode Transformation under Dynamic Operation for Alkaline Water Electrolysis

Alkaline water electrolyzers (AWEs) play a pivotal role in the realm of large-scale hydrogen production. However, AWEs face significant challenges in electrode degradation particularly under dynamic operating conditions, induced by reverse current phenomenon during frequent startup/shutdown. Herein, this study aims to rationalize the degradation mechanisms of AWEs under these conditions. A three-electrode membrane electrode assembly (MEA) setup is first utilized to decouple polarization behaviors of anode and cathode in AWEs. Following a proposed accelerated stress testing protocol, the setup allows for tracking individual electrode performance transformations during frequent reverse current operation. Integrating operando cell studies with in situ and post-mortem characterizations, it is showed that continuous formation of highly active species, nickel (oxy)hydroxides, improves the anode performance for oxygen evolution reaction. On the contrary, irreversible oxidation of nickel to β-nickel hydroxide results in a severe degradation of cathode, leading to material dissolution, poor electrical conductivity and loss of catalytic activity for hydrogen evolution reaction. These results provide insights in nickel-based electrode transformation mechanisms for alkaline water electrolysis and indicate that cathode with higher redox reversibility can potentially improve durability of AWEs under dynamic conditions.

08 HYDROGEN↗
DOE OSTI2024

Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds

Abstract Quantifying the relative influence of factors and processes controlling riverine ecosystem function is essential to predicting future conditions under global change. Dissolved organic matter (DOM) is a fundamental component of riverine ecosystems that fuels microbial food webs, influences nutrient and light availability, and represents a significant carbon flux globally. The heterogeneous nature of DOM molecular composition and its propensity for interaction (i.e., functional diversity) can characterize riverine ecosystem function across spatiotemporal scales. To investigate fundamental drivers of DOM diversity, we collected seasonal water samples from 42 nested locations within five watersheds spanning multiple watershed sizes (~ 5 to 30,000 km 2 ) across the United States. Patterns in DOM molecular richness, aromaticity, relative abundance of N-containing formulas, and putative biochemical transformations derived from high-resolution mass spectrometry were assessed across gradients of explanatory variables associated with watershed characteristics (e.g., watershed area, water residence time, land cover). We found that putative biochemical transformations were more strongly related to explanatory variables across watersheds than common bulk DOM parameters and that watershed area, surface water residence time and derived Damköhler numbers representing DOM reactivity timescales were strong predictors of DOM diversity. The data also indicate that catchment-specific land cover factors can significantly influence DOM diversity in diverging directions. Overall, the results highlight the importance of considering water residence time and land cover when interpreting longitudinal patterns in DOM chemistry and the continued challenge of identifying generalizable drivers that are transferable across watershed and regional scales for application in Earth system models. This work also introduces a Findable Accessible Interoperable Reusable (FAIR) dataset (> 300 samples) to the community for future syntheses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Reverse martensitic transformation on the corrosion behavior of a 2304 lean duplex stainless steel

This study investigates the influence of the reverse martensitic transformation on the corrosion behavior of 2304 lean duplex stainless steel (LDSS) at low temperatures (400–600°C). The steel underwent cold rolling, followed by isochronal annealing for 0.5 h. Phase transformations and microstructures were analyzed using X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. Corrosion resistance was evaluated using a syringe cell with cyclic potentiodynamic polarization in 0.6 M NaCl solution and double-loop electrochemical potentiokinetic reactivation in 0.5 M H 2 SO 4 + 0.001 M KSCN solution. A bimodal breakdown potential behavior, associated with either pitting or transpassivity, was observed in the as-received (AR) and samples annealed at 400°C. Pitting in the AR sample was associated with Al–Mg–Si–Ca–O inclusions, while pitting on the samples annealed at 400°C was linked to local reduced passive film resistance. Severe cold rolling enhanced the pitting resistance of 2304 LDSS. Selective electrochemical etching revealed preferential corrosion at phase interfaces and within the austenite phase. Additionally, annealing at 500°C to 600°C led to a marked decrease in pitting resistance and increased sensitization. Electron backscatter diffraction analysis of the corroded pits in the annealed specimens indicated that pits tend to nucleate and grow in α'-martensite/austenite regions. Furthermore, the nanostructure formed during low-temperature reversion, characterized by dislocation-cell martensite with high dislocation density and stacking faults, may adversely affect corrosion resistance due to heterogeneities associated with alloying element redistribution.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A microstructural signature of the coesite-quartz transformation: New insights from high-pressure experiments and EBSD

Ultra-high pressure (UHP) metamorphism is difficult to identify in continental crust as few petrological barometers are suitable for dominantly felsic lithologies. In such cases, burial to extreme depths is commonly identified through the preservation of coesite, a high-pressure polymorph of SiO 2 that typically forms at depths exceeding ∼ 100 km (i.e., > 2 GPa pressure). Unfortunately, coesite readily transforms to quartz upon exhumation, meaning that UHP terranes may often be overlooked. While some studies have suggested that quartz may inherit an orientation signature indicative of former coesite, both the specific nature of this signature and the conditions favouring its development remain uncertain. Here, to address this problem, we combine electron backscatter diffraction analysis of natural and experimental samples to explore microstructural evolution across the coesite-quartz phase transformation. We demonstrate that neighbouring domains of quartz commonly feature an 84 ± 4° rotation of [c] axes around the pole of a common {m} plane. This orientation relationship is a product of epitaxy, whereby the {$11\bar{2}2$} Japan twin plane in quartz nucleates on the (010) plane in coesite. In supercell simulations, the nucleation of Japan twins can be explained by the energetically favourable alignment of quartz tetrahedra on parental coesite tetrahedra. Through experiments, we demonstrate that this signature emerges over a broad range of conditions, regardless of the availability of nucleation sites (e.g., grain boundaries) or the density of crystal lattice defects (e.g., dislocations). Overall, our work provides a quantitative and unambiguous tool for identifying UHP terranes from quartz in isolation.

Coesite↗
DOE OSTI2025

Phosphate-modulated transformation of Sb(V)-bearing ferrihydrite under microbial iron- and sulfate-reducing conditions

Antimony (Sb) is a toxic metalloid that poses environmental risks in terrestrial and aquatic systems. The fate of the Sb(V) oxyanion, Sb(OH) 6 − , is governed by complex biogeochemical processes, including immobilization by ferric (Fe(III)) oxides, reduction by sulfide, and the less-explored competitive adsorption with other anions such as phosphate (PO 4 3− ). Here, this study investigates the interplay between such mechanisms in controlling the behavior of Sb(V) associated with ferrihydrite (Fh) under Fe(III)- and sulfate-reducing conditions, with a particular emphasis on the role of phosphate in influencing Sb(V) mobility and transformation. Anoxic reactors that contained Sb(V)-coprecipitated Fh, varying PO 4 3− concentrations (0, 0.2, and 2 mM), and sulfate, were inoculated with a microbial community sourced from Sb-contaminated soil. Additionally, abiotic reactors with either Sb(V)-adsorbed or Sb(V)-coprecipitated Fh and different PO 4 3− loadings (0–100 mM) were created to investigate the competitive adsorption mechanisms in the absence of microbial activity. Results from the abiotic reactors suggest that Sb(V) is likely incorporated into the Fh structure, with only minor amounts remaining surface-bound and extractable by PO 4 3− . In the biotic reactors, microbial Fe(III) and sulfate reduction were more extensive in the presence of PO 4 3− . At 0.2 mM PO 4 3− , microbial activity transformed Fh into siderite and led to the complete reduction of Sb(V) to Sb(III) as stibnite (Sb 2 S 3 ). At 2 mM PO 4 3− , the greater coverage by PO 4 3− stabilized Fh and decreased the extent of both Fe(III) and Sb(V) reduction, and shifted the reduced products to mackinawite and Sb(III) adsorbed onto Fh, in addition to stibnite formation. This study demonstrates that while PO 4 3− may not directly compete with Sb(V) for sorption sites, it can influence Sb mobility in Fe(III)- and sulfate-reducing environments by enhancing microbial activity and altering the mineralization pathways.

Microbial Fe(III) and sulfate reduction↗
DOE OSTI2026

Beyond interpolation: Physics-inspired gating transformers for extrapolating irradiation conditions to novel nuclear fuels

The qualification of advanced nuclear fuels relies on irradiation experiments in test reactors that emulate commercial conditions. Designing these tests requires accurate prediction of key irradiation quantities, particularly heat generation rate and burnup, yet obtaining them typically involves computationally expensive multi-step simulation workflows. We propose a physics-inspired gating transformer (PIGT) that integrates an inverse-square, distance-based attenuation into the encoder representation to bias attention toward physically relevant spatial relationships while retaining data-driven flexibility. Using MiniFuel irradiation data from the High Flux Isotope Reactor at Oak Ridge National Laboratory, we benchmark against ensemble methods, feedforward and recurrent networks, convolutional models, and standard transformers. While baseline models perform well under interpolation, they exhibit a pronounced generalization gap when evaluated on fuels not included in the training set. The proposed model consistently improves extrapolative accuracy and stability, yielding the strongest performance on unseen fuel configurations. These results indicate that a lightweight physics structure embedded within attention mechanisms can substantially improve robustness, enabling more reliable surrogate predictions to accelerate the design of nuclear fuel irradiation experiments.

Fuel qualification↗
DOE OSTI2025

Scalable Implementation of Mean-Field and Correlation Methods Based on Lie-Algebraic Similarity Transformation of Spin Hamiltonians in the Jordan–Wigner Representation

Recent work has highlighted that the strong correlation inherent in spin Hamiltonians can be effectively reduced by mapping spins to Fermions via the Jordan−Wigner transformation (JW). The Hartree−Fock method is straightforward in the Fermionic domain and may provide a reasonable approximation to the ground state. Correlation with respect to the Fermionic mean field can be recovered based on Lie-algebraic similarity transformation (LAST) with two-body correlators. Specifically, a unitary LAST variant eliminates the dependence on site ordering, while a nonunitary LAST yields size-extensive correlation energies. Whereas the first recent demonstration of such methods was restricted to small spin systems, we present efficient implementations using analytical gradients for the optimization with respect to the mean-field reference and the LAST parameters, thereby enabling the treatment of larger clusters, including systems with local spins s > $\frac{1}{2}$.

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

SCF Framework, HF Stability, and RPA Correlation for Jordan–Wigner-Transformed Spin Hamiltonians on Arbitrary Coupling Topologies

Mapping spins to fermions via the Jordan–Wigner (JW) transformation can render mean-field (Hartree–Fock, HF) descriptions effective for strongly correlated spin systems. As established in recent work, the application of such approaches is not limited by the nonlocal structure of JW strings or by site ordering because string operators can be absorbed into Thouless rotations of a Slater determinant, and the variational optimization of a unitary Lie-algebraic similarity transformation removes any ordering dependence. Leveraging these ideas, we develop a self-consistent field (SCF) scheme that expresses the mean-field energy as a functional of the single-particle density matrix, providing an alternative to gradient-based optimization of Thouless parameters. We derive the analytical orbital Hessian to diagnose HF stability and compute the ground-state correlation energy through the random-phase approximation (RPA). Benchmark results for the XXZ and J 1 –J 2 model on one- and two-dimensional lattices demonstrate that RPA significantly improves mean-field accuracy.

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

Structural Transformation and Degradation of Cu Oxide Nanocatalysts during Electrochemical CO 2 Reduction

The electrochemical CO 2 reduction reaction (CO 2 RR) holds enormous potential as a carbon-neutral route to the sustainable production of fuels and platform chemicals. The durability for long-term operation is currently inadequate for commercialization, however, and the underlying deactivation process remains elusive. A fundamental understanding of the degradation mechanism of electrocatalysts, which can dictate the overall device performance, is needed. In this work, we report the structural dynamics and degradation pathway of Cu oxide nanoparticles (CuO x NPs) during the CO 2 RR by using in situ small-angle X-ray scattering (SAXS) and X-ray absorption spectroscopy (XAS). The in situ SAXS reveals a reduction in the size of NPs when subjected to a potential at which no reaction products are detected. At potentials where the CO 2 RR starts to occur, CuO x NPs are agglomerated through a particle migration and coalescence process in the early stage of the reaction, followed by Ostwald ripening (OR) as the dominant degradation mechanism for the remainder of the reaction. As the applied potential becomes more negative, the OR process becomes more dominant, and for the most negative applied potential, OR dominates for the entire reaction time. The morphological changes are linked to a gradual decrease in the formation rate for multicarbon products (C 2 H 4 and ethanol). Other reaction parameters, including reaction intermediates and local high pH, induce changes in the agglomeration process and final morphology of the CuO x NPs electrode, supported by post-mortem ex situ microscopic analysis. The in situ XAS analysis suggests that the CuO x NPs reduced into the metallic state before the structural transformation was observed. The introduction of high surface area carbon supports with ionomer coating mitigates the degree of structural transformation and detachment of the CuO x NPs electrode. These findings show the dynamic nature of Cu nanocatalysts during the CO 2 RR and can serve as a rational guideline toward a stable catalyst system under electrochemical conditions.

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

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗
DOE OSTI2025

Feature engineering descriptors, transforms, and machine learning for grain boundaries and variable-sized atom clusters

Abstract Obtaining microscopic structure-property relationships for grain boundaries is challenging due to their complex atomic structures. Recent efforts use machine learning to derive these relationships, but the way the atomic grain boundary structure is represented can have a significant impact on the predictions. Key steps for property prediction common to grain boundaries and other variable-sized atom clustered structures include: (1) describing the atomic structure as a feature matrix, (2) transforming the variable-sized feature matrix to a fixed length common to all structures, and (3) applying a machine learning algorithm to predict properties from the transformed matrices. We examine how these steps and different combinations of engineered features impact the accuracy of grain boundary energy predictions using a database of over 7000 grain boundaries. Additionally, we assess how different engineered features support interpretability, offering insights into the physics of the structure-property relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Alshaabi, Thayer [Howard Hughes Medical Institute,↗
DOE OSTI2024

Physics-constrained machine learning for electrodynamics without gauge ambiguity based on Fourier transformed Maxwell’s equations

We utilize a Fourier transformation-based representation of Maxwell’s equations to develop physics-constrained neural networks for electrodynamics without gauge ambiguity, which we label the Fourier–Helmholtz–Maxwell neural operator method. In this approach, both of Gauss’s laws and Faraday’s law are built in as hard constraints, as well as the longitudinal component of Ampère–Maxwell in Fourier space, assuming the continuity equation. An encoder–decoder network acts as a solution operator for the transverse components of the Fourier transformed vector potential, $\hat{A}_⟂(k,t)$, whose two degrees of freedom are used to predict the electromagnetic fields. This method was tested on two electron beam simulations. Among the models investigated, it was found that a U-Net architecture exhibited the best performance as it trained quicker, was more accurate and generalized better than the other architectures examined. We demonstrate that our approach is useful for solving Maxwell’s equations for the electromagnetic fields generated by intense relativistic charged particle beams and that it generalizes well to unseen test data, while being orders of magnitude quicker than conventional simulations. We show that the model can be re-trained to make highly accurate predictions in as few as 20 epochs on a previously unseen data set.

97 MATHEMATICS AND COMPUTING↗