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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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246 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler [National Renewable Energy Lab. (NR↗
DOE OSTI2026

Atomic- and Molecular-Scale Interphase Engineering for High-Performance Solid-State Batteries

Solid-state batteries (SSBs) promise a decisive advance beyond conventional Li-ion systems, yet their development remains constrained by persistent solid–solid interfacial instabilities that degrade performance and durability. Interfaces between solid electrolytes and both cathodes and Li metal often exhibit poor wettability, limited physical contact, and high charge–transfer resistance, leading to chemical decomposition, mechanical failure, and impedance growth. Overcoming these limitations requires interphase engineering with atomic-scale precision—capabilities that conventional coating methods cannot reliably deliver. Atomic layer deposition (ALD) and molecular layer deposition (MLD) uniquely meet this need by enabling ultrathin, conformal, and composition-tunable films that stabilize reactive surfaces, suppress parasitic reactions, and regulate Li-metal morphology. Importantly, this Perspective highlights ALD/MLD systems that have already demonstrated effectiveness in liquid-electrolyte cells and discusses how these validated strategies can be deliberately translated to solid-state architectures. By grounding future directions in experimentally proven concepts rather than speculative hypotheses, we outline how atomic- and molecular-scale design principles can accelerate the development of robust, high-performance SSB technologies.

atomic and molecular layer deposition↗
DOE OSTI2023

Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing

While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called “thermal neuristors”, which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. In conclusion, this study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Atomistic Simulation of Glasses and Amorphous Materials: Challenges and Opportunities for the Next Decade

Atomistic simulations have become indispensable tools for understanding glass structure, dynamics, and properties, yet persistent challenges limit their predictive power. This perspective examines three interconnected issues, namely glass formation procedures, interatomic potential development, and machine learning applications, which emerged from the 5th International Workshop on Challenges of Atomistic Simulations of Glasses and Amorphous Materials. We identify convergent community priorities for (i) standardized validation protocols, (ii) curated benchmark datasets with complete metadata, and (iii) open repositories for glasses. A systematic was forward is provided by a hierarchical validation framework for assessing the structural fidelity, property prediction, and behavioral realism of simulation techniques. Looking ahead, transformative advances are promised by the fusion of classical techniques with machine learning based approaches, for instance, by integrating swap Monte Carlo with machine-learning (ML) potentials, leveraging foundation models through transfer learning, and finetuning ML potentials with experimental data. Progress depends on the community committing to validated models, reproducible protocols, and sustained data sharing.

Krishnan, N. M. Anoop↗
DOE OSTI2025

Erosion of high-Z refractory coatings for helicon plasma source window

Proto-MPEX (Materials Plasma Exposure eXperiment), a linear plasma device (LPD), using a high power radio frequency (RF) (⩾100 kW, 13.56 MHz) helicon plasma source, has suffered RF sheath-induced window erosion. The sputtered impurities from the window surface transport toward the downstream target affecting plasma-material interaction studies. The rectified sheath voltage formed was high enough to cause window erosion by light ions due to its low-Z components (e.g. Si 3 N 4 and AlN). Hence, we are proposing impurity mitigation strategies, which involve the application of a Faraday screen to lower the rectified sheath voltage and the application of high-Z refractory coatings on the existing window plasma-facing surface. In this work, we report the erosion of two different coatings, i.e., tantalum oxide (Ta 2 O 5 ) and hafnium oxide (HfO 2 ) on a silicon nitride (Si 3 N 4 ) window material under conditions of low-energy deuterium (D) ion impact, well below the Ta 2 O 5 sputtering energy threshold (i.e. 250 eV). The test samples were RF-biased and exposed to a high D-ion fluence (∼10 26 m −2 ) in Plasma Interaction with Surface Component Experimental Station (PISCES-A) LPD. The experimentally measured sputtering yields of the high-Z refractory coatings below the sputtering energy threshold of Ta 2 O 5 indicate an increased erosion due to the plasma impurities, especially due to oxygen. Improving the vacuum level could reduce the oxygen impurities and increase the lifespan of the coated helicon window for plasma operation. Post-surface analysis indicates no preferential erosion of oxygen or enrichment of the high-Z surface component. This is likely due to an effective energy transfer from the impurity ion for sputtering of the high-Z component in the coating. With the reduced rectified sheath voltage at the window surface and improved vacuum conditions, the proposed high-Z refractory coatings offer a promising solution for reducing window erosion in future MPEX plasma operations at ORNL.

RF bias↗
DOE OSTI2026

The Future Polarized Target Program at Jefferson Lab

Polarized targets have played a crucial role in Jefferson Lab's exploration of nuclear structure over the past four decades. The three original experimental halls have seen 19 separate installations of polarized solid or gas targets for use in the particle physics scattering experiments, and this trend will continue in the next decade. Five polarized target systems are in preparation for use at JLab in the coming years. Hall B will see the use of two solid polarized targets, one longitudinally polarized to the beam, the other transversely, as well as a novel 3He gas polarized target. In Hall C, new experiments will augment the tensor polarization in dynamically polarized solids. Plans are under development to bring a polarized solid target to Jefferson Lab's photon beam hall, Hall D, for the first time. To support these efforts, the JLab polarized target group is building a test laboratory to develop dynamic nuclear polarization techniques, as well as an apparatus to irradiate target material using electrons from JLab's injector test facility. In this talk, we will explore the development progress and plans for each of these efforts.

Maxwell, James [Thomas Jefferson National Accelera↗
DOE OSTI2025

Electric‐Field‐Driven Reversal of Ferromagnetism in (110)‐Oriented, Single Phase, Multiferroic Co‐Substituted BiFeO 3 Thin Films

Abstract While multiferroic materials are attractive systems for the promise of ultra‐low‐power‐consumption computational technologies, electric‐field‐induced magnetization reversal is a key challenge for realizing devices at scale. Though significant research efforts have been working toward the realization of a material which couples ferroelectricity and ferromagnetism, there are few, even composite, systems which are practical for device scale applications at room temperature. Co‐substituted multiferroic BiFe 0.9 Co 0.1 O 3 is a promising candidate system, due to coupled ferroelectricity and weak ferromagnetism at room temperature. Here, it is theoretically indicated that the ferroic orders in this material are statically coupled, where an in‐plane 109° ferroelectric switching event can result in the reversal of this out‐of‐plane component of magnetization, and the electric field‐induced magnetization reversal is experimentally observed. Such an in‐plane poling configuration is particularly desirable for device applications.

Chemistry↗
DOE OSTI2025

AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks

We propose the artificial intelligence velocimetry-thermometry (AIVT) method to reconstruct a continuous and differentiable representation of the temperature and velocity in turbulent convection from measured three-dimensional (3D) velocity data. AIVT is based on physics-informed Kolmogorov-Arnold networks and trained by optimizing a loss function that minimizes residuals of the velocity data, boundary conditions, and governing equations. We apply AIVT to a set of simultaneously measured 3D temperature and velocity data of Rayleigh-Bénard convection, obtained by combining particle image thermometry and Lagrangian particle tracking. This enables us to directly compare machine learning results to true volumetric, simultaneous temperature and velocity measurements. We demonstrate that AIVT can reconstruct and infer continuous, instantaneous velocity and temperature fields and their gradients from sparse experimental data at a high resolution, providing an additional approach for understanding thermal turbulence.

Science & Technology - Other Topics↗
DOE OSTI2024

Elucidation of Design Criteria for V‐based Redox Mediators: Structure‐Function Relationships that Dictate Rates of Heterogeneous Electron Transfer

Redox mediators are attractive solutions for addressing the stringent kinetic stipulations required for efficient energy conversion processes. In this work, we compare the electrochemical properties of four vanadium complexes, namely [V(acac) 3 ], [V 6 O 7 (OMe) 12 ], [ n Bu 4 N] 3 [V 6 O 13 (TRIS NO2 ) 2 ], and [ n Bu 4 N] 5 [V 18 O 46 (NO 3 )] in non-aqueous solutions on glassy carbon electrodes. The goal of this study is to investigate the electron transfer kinetics and diffusivity of these compounds under identical experimental conditions to develop an understanding of structure-function relationships that dictate the physicochemical properties of vanadium oxide assemblies. Complex selection was dictated by two criteria – (1) nuclearity of the transition metal complexes (2) distribution of electron density in the native electronic configuration. In conclusion, our analyses establish that electronic communication between metal centers significantly impacts charge transfer kinetics of these vanadium-based compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Autonomous phototaxis of hydrogel swimmers

The design of synthetic soft matter capable of emulating the complex behaviors of living organisms, such as sensing and adapting to their environment, remains an important challenge in developing biomimetic materials. Functionalized hydrogels are ideal candidates for such materials since they are highly responsive to their environment and can be operated in water. In this work, we investigate a hybrid bonding hydrogel composed of peptide amphiphile supramolecular nanofibers covalently attached to a photoresponsive network, in which high-aspect-ratio ferromagnetic nanowires are aligned along the length of the sample, designed to swim under oscillating magnetic fields. This hybrid hydrogel swimmer can autonomously swim toward a light source by utilizing photoinduced interactions between supramolecular and covalent networks reminiscent of phototactic swimming in living systems. Using a combination of experimental techniques and a continuum model incorporating photochemistry, magnetoelasticity, and hydrodynamics, we explain the swimming mechanism and predict phototactic behavior. Our work highlights the potential role of hybrid bonding polymers, which leverage the interplay between supramolecular assemblies and covalent networks. We demonstrate how these polymers can be tailored to react dynamically to their environment, paving the way for developing intelligent and autonomous robotic systems.

Science & Technology - Other Topics↗
DOE OSTI2026

All-Polyester Multilayer Plastics (‘All-Polyester MLPs’): A Redesign for Inherently Recyclable Plastics

Multilayer plastics (MLPs) are widely used in modern packaging because they combine multiple functions such as oxygen and moisture barriers, mechanical strength, puncture resistance, and heat sealability into lightweight and cost-effective packaging solutions. These attributes are essential for food, beverage, pharmaceutical, and consumer goods packaging. However, conventional commercial MLPs typically consist of five to twelve layers made from chemically incompatible materials, including polyolefins, polyethylene terephthalate, nylon, ethylene-vinyl alcohol (EVOH), adhesives, and tie layers. This complexity makes MLPs extremely difficult to recycle. As a result, the vast majority of multilayer plastics (MLPs) are disposed of through landfilling or incineration. This disposal pathway perpetuates demand for virgin material production, thereby driving high industrial energy consumption, increasing greenhouse gas (GHG) emissions, and contributing to the long-term accumulation of plastic waste. The scope of work included: (1) Design and fabrication of all-polyester multilayer structures using commercially relevant processing methods; (2) Experimental validation of barrier, mechanical, and sealing performance; (3) Demonstration of both mechanical and chemical recycling pathways; and (4) Comprehensive techno-economic analysis (TEA) and life-cycle assessment (LCA) to quantify cost, energy, and environmental impacts.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Stoichiometry dependent properties of cerium hydride: An active learning developed interatomic potential study

Cerium hydride has a variety of interesting properties, including a known lattice contraction and densification with increasing hydrogen content. However, precise stoichiometric control is not experimentally straightforward and ab initio approaches are not computationally feasible for many properties such as melting and low temperature diffusion. Therefore, we develop a machine-learned interatomic potential for cerium hydride that is valid for H to Ce ratios from 2.0 to 3.0. A query-by-committee active learning approach is used to develop the training set. Leveraging classical molecular dynamics simulations, we assess a range of properties and provide fundamental mechanisms for the trends with stoichiometry. Finally, a majority of the properties follow the trend of lattice contraction, being governed by the stronger lattice binding induced by adding octahedral atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Terahertz conductivity of two-dimensional materials: a review

Two-dimensional (2D) van der Waals materials are shaping the landscape of next-generation devices, offering significant technological value thanks to their unique, tunable, and layer-dependent electronic and optoelectronic properties. Time-domain spectroscopic techniques at terahertz (THz) frequencies offer noninvasive, contact-free methods for characterizing the dynamics of carriers in 2D materials. They also pave the path toward the applications of 2D materials in detection, imaging, manufacturing, and communication within the increasingly important THz frequency range. In this paper, we overview the synthesis of 2D materials and the prominent THz spectroscopy techniques: THz time-domain spectroscopy, optical-pump THz-probe technique, and optical pump–probe THz spectroscopy. Through a confluence of experimental findings, numerical simulation, and theoretical analysis, we present the current understanding of the rich ultrafast physics of technologically significant 2D materials: graphene, transition metal dichalcogenides, MXenes, perovskites, topological 2D materials, and 2D heterostructures. Finally, we offer a perspective on the role of THz characterization in guiding future research and in the quest for ideal 2D materials for new applications.

2D materials↗
DOE OSTI2024

A Perspective on the Impact of Group Delay Dispersion in Future Terahertz Wireless Systems

This article discusses the challenges and opportunities of managing group delay dispersion (GDD), and its relation to the performance standards of future sixth-generation (6G) wireless communication systems utilizing terahertz frequency waves. The unique susceptibilities of 6G systems to GDD are described, along with a quantitative description of the sources of GDD, including multipath, rough surface scattering, intelligent reflecting surfaces, and propagation through the atmosphere. An experimental case-study is presented that confirms previous models quantifying the impact of atmospheric GDD. Several GDD manipulation strategies are presented, illustrating their hindered effectiveness in the 6G context. Conversely, some benefits of leveraging GDD to enhance 6G systems, such as improved security and simplified hardware, are also discussed. Finally, a perspective on using photonic GDD control devices is provided, revealing quantitative benefits that may unburden existing equalization schemes. Here, the article argues that GDD will uniquely and significantly impact some 6G systems, but that its careful consideration along with new mitigation strategies, including photonic devices, will help optimize system performance. The conclusion provides a perspective to guide future research in this area.

Strecker, Karl [Oklahoma State University, Stillwa↗
DOE OSTI2026

Excitons in van der Waals magnetic materials

Two-dimensional magnetic semiconductors provide a unique platform where long-range magnetic order coexists with strongly bound excitons. Because excitonic states and magnetic moments originate from the same electronic orbitals and couple via intrinsic exchange interactions, optical excitations in these systems exhibit pronounced sensitivity to magnetic order. Recent experiments show unusually strong magneto-optical responses and direct exciton–magnon coupling, establishing new routes for controlling light–matter interactions with spin degrees of freedom. Here, this Review surveys key developments, focusing on representative material systems, experimental signatures, and theoretical frameworks used to describe these phenomena. We conclude with perspectives on how this rapidly evolving field could enable next-generation optoelectronic and quantum technologies leveraging the coupled dynamics of light, charge and spin.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An evolutionarily conserved tryptophan cage promotes folding of the extended RNA recognition motif in the hnRNPR ‐like protein family

Abstract The heterogeneous nuclear ribonucleoprotein (hnRNP) R‐like family is a class of RNA binding proteins in the hnRNP superfamily with diverse functions in RNA processing. Here, we present the 1.90 Å X‐ray crystal structure and solution NMR studies of the first RNA recognition motif (RRM) of human hnRNPR. We find that this domain adopts an extended RRM (eRRM1) featuring a canonical RRM with a structured N‐terminal extension (N ext ) motif that docks against the RRM and extends the β‐sheet surface. The adjoining loop is structured and forms a tryptophan cage motif to position the N ext motif for docking to the RRM. Combining mutagenesis, solution NMR spectroscopy, and thermal denaturation studies, we evaluate the importance of residues in the N ext –RRM interface and adjoining loop on eRRM folding and conformational dynamics. We find that these sites are essential for protein solubility, conformational ordering, and thermal stability. Consistent with their importance, mutations in the N ext –RRM interface and loop are associated with several cancers in a survey of somatic mutations in cancer studies. Sequence and structure comparison of the human hnRNPR eRRM1 to experimentally verified and predicted hnRNPR‐like proteins reveals conserved features in the eRRM.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Energy filtering–induced ultrahigh thermoelectric power factors in Ni 3 Ge

Traditional thermoelectric materials rely on low thermal conductivity to enhance their efficiency but suffer from inherently limited power factors. Innovative pathways to optimize electronic transport are thus crucial. Here, we achieve ultrahigh power factors in Ni 3 Ge-based systems through an unconventional thermoelectric materials design principle. When overlapping flat and dispersive bands are engineered to the Fermi level, charge carriers can undergo intense interband scattering, yielding an energy filtering effect similar to what has long been predicted in certain nanostructured materials. Via a multistep DFT-based screening method developed here, we find a family of L1 2 -ordered binary compounds with ultrahigh power factors up to 11 mW m −1 K −2 near room temperature, which are driven by an intrinsic phonon-mediated energy filtering mechanism. Our comprehensive experimental and theoretical study of these intriguing materials paves the way for understanding and designing high-performance scattering-tuned metallic thermoelectrics.

Science & Technology - Other Topics↗
DOE OSTI2026

Development of Ternary Transition Metal Oxide Catalysts for Oxygen Evolution Reaction

Electrochemical water splitting, a promising method for green hydrogen production, is currently hindered by the high cost of precious-group metal (PGM)-based catalysts for the oxygen evolution reaction (OER). This study addresses this challenge by advancing the development of catalysts for OER, focusing on the development of high-performance PGM-free catalysts using nickel-iron-cobalt (NiFeCo)-based aerogels. The catalysts were synthesized via a sol-gel method and critical point drying to achieve a highly porous structure with an exceptionally high surface area. The designed catalyst structure provides an ideal platform for maximizing catalytic active sites and enhancing mass transport kinetics. Co has been systematically incorporated into the current PGM-free state-of-the-art, NiFeOx catalyst, and the metal ratios have been optimized. In addition to the experimental studies, density functional theory calculations were performed to study the material’s properties of this ternary catalyst and the effect of Co addition on enhancing OER catalysis.

Mizrahi, Michal [Bar-Ilan University, Ramat Gan, I↗