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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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1,097 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Statistical Uncertainty of Inhalation Dose Coefficients: Impact of Particle Deposition in ICRP 66 Human Respiratory Tract Model

Inhaled radioactive materials can pose a long-term health concern, as the material can be incorporated into the body’s metabolic pathways and remain in organs and tissues for extended durations. During the retention period, the radioactive material may localize in a source organ and irradiate adjacent target organs and tissues. Distribution of these materials changes over time, requiring biokinetic modeling to evaluate their movement through various tissues and organs. The evolving distribution depends on multiple inputs characterizing the inhaled material, such as particle size and size distribution, particle density, aspect ratio, specific radionuclide, the chemical form, and solubility. In addition, biological parameters such as breathing rate, breathing type (nasal or nasal/oral), respiratory system morphometry, tidal volume, functional residual capacity, and anatomical dead space all influence material transport. These aerosol properties and physiological characteristics of the respiratory tract jointly define a range of initial conditions that influence the time-dependent distribution of radioactive material. To evaluate both uncertainty in the initial conditions of inhalation exposure and the final output (committed effective dose) from biokinetic models, a Python-based software tool, Radiological Exposure Dose Calculator (REDCAL), was developed to propagate uncertainty within the human respiratory tract model. Focusing on deposition fraction uncertainty, the primary objective was to characterize the initial activity distribution across respiratory regions as a function of anticipated particle sizes and distributions. The impact of the deposition fraction uncertainty was propagated to committed effective dose coefficients for selected radionuclides in a companion publication. For each particle size, a lognormal distribution, characterized by its geometric mean as defined within ICRP Publication 66, serves as the basis for introducing uncertainty into the physical processes governing deposition in various lung regions. Finally, this study addresses the deposition process and examines how uncertainty in deposition mechanisms affects activity distribution in the airways, ultimately presenting the expected range and standard deviation of deposited activity as a function of particle size.

International Commission on Radiological Protectio↗
DOE OSTI2026

Integrated Magnetics for Multiport Inductive and Conductive Power Transfer Architecture for Electric Vehicles

This paper proposes a shared magnetic arrangement for an electric-vehicle charger that supports both conductive and inductive charging while preserving independent power control at the two ports. In the proposed implementation, the high-frequency transformer used for the conductive path and the resonant inductor required by the inductive path are realized in an integrated form. By combining the magnetic structure and primary-side converter hardware, the charger can deliver improved hardware utilization and higher power density without a corresponding increase in cost or implementation complexity. The paper describes the magnetic realization and presents finite-element and circuit-level simulation results to confirm minimal interaction between the two charging paths. The study demonstrates that the proposed charger is a viable option for flexible, high-power EV charging systems.

Mukherjee, Subho [ORNL] (ORCID:0009000672297925)↗
DOE OSTI2024

X-ray linear dichroic tomography of crystallographic and topological defects

Abstract The functionality of materials is determined by their composition 1–4 and microstructure, that is, the distribution and orientation of crystalline grains, grain boundaries and the defects within them 5,6 . Until now, characterization techniques that map the distribution of grains, their orientation and the presence of defects have been limited to surface investigations, to spatial resolutions of a few hundred nanometres or to systems of thickness around 100 nm, thus requiring destructive sample preparation for measurements and preventing the study of system-representative volumes or the investigation of materials under operational conditions 7–15 . Here we present X-ray linear dichroic orientation tomography (XL-DOT), a quantitative, non-invasive technique that allows for an intragranular and intergranular characterization of extended polycrystalline and non-crystalline 16 materials in three dimensions. We present the detailed characterization of a polycrystalline sample of vanadium pentoxide (V 2 O 5 ), a key catalyst in the production of sulfuric acid 17 . We determine the nanoscale composition, microstructure and crystal orientation throughout the polycrystalline sample with 73 nm spatial resolution. We identify and characterize grains, as well as twist, tilt and twin grain boundaries. We further observe the creation and annihilation of topological defects promoted by the presence of volume crystallographic defects. The non-destructive and spectroscopic nature of our method opens the door to operando combined chemical and microstructural investigations 11,18 of functional materials, including energy, mechanical and quantum materials.

Science & Technology - Other Topics↗
DOE OSTI2025

Light-triggered electrochemical biosensor using singlet oxygen for self-powered operation and glucose detection

This study introduces a light-activated sensing strategy that integrates photosensitization with electrochemical detection. The sensor employs Eosin Y, a photosensitizer that generates singlet oxygen ( 1 O 2 ) via type II photosensitization. Immobilized within a thin polymer matrix on a carbon working electrode, Eosin Y produces 1 O 2 , under green light (520 nm) illumination, initiating a redox process that yields a measurable current. To incorporate biosensing capabilities and enable self-powered operation, this 1 O 2 – mediated process was coupled with glucose oxidase (GOx) to construct a fully operational glucose biosensor. The addition of glucose reverses the current flow by causing GOx to compete for electrons, with the resulting current magnitude correlating with glucose concentration providing a sensitive measure of glucose. The biosensor, as proof-of-principle, demonstrated excellent performance over a range of glucose concentrations (0–73 mM), achieving a detection limit (LOD) of 2.8 mM for steady state photocurrent under oxygen-saturated conditions. This platform leverages light and 1 O 2 as stimuli for tunable, on-demand signal control, offering a novel approach for adaptive, real-time biosensing technologies.

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

Topological prethermal strong zero modes on superconducting processors

Abstract Symmetry-protected topological phases 1–4 cannot be described by any local order parameter and are beyond the conventional symmetry-breaking model 5 . They are characterized by topological boundary modes that remain stable under symmetry respecting perturbations 1–4,6–8 . In clean, gapped systems without disorder, the stability of these edge modes is restricted to the zero-temperature manifold; at finite temperatures, interactions with mobile thermal excitations lead to their decay 9–11 . Here we report the observation of a distinct type of topological edge mode 12–14 , which is protected by emergent symmetries and persists across the entire spectrum, in an array of 100 programmable superconducting qubits. Through digital quantum simulation of a one-dimensional disorder-free stabilizer Hamiltonian, we observe robust long-lived topological edge modes over up to 30 cycles for a wide range of initial states. We show that the interaction between these edge modes and bulk excitations can be suppressed by dimerizing the stabilizer strength, leading to an emergent U(1) × U(1) symmetry in the prethermal regime of the system. Furthermore, we exploit these topological edge modes as logical qubits and prepare a logical Bell state, which exhibits persistent coherence, despite the system being disorder-free and at finite temperature. Our results establish a viable digital simulation approach 15–18 to experimentally study topological matter at finite temperature and demonstrate a potential route to construct long-lived, robust boundary qubits in disorder-free systems.

Science & Technology - Other Topics↗
DOE OSTI2026

Strain and Defect-Tailored Magnetotransport in NiCo 2 O 4 Thin Films and Freestanding Membranes

Magnetic spinel NiCo 2 O 4 is promising for developing spintronic applications due to its high magnetic Curie temperature, high spin polarization, fast spin dynamics, and strain-tunable magnetic anisotropy, while its electronic and magnetic properties depend sensitively on epitaxial strain and disorder. Here, in this study, we use epitaxial NiCo 2 O 4 thin films and freestanding NiCo 2 O 4 membranes as model systems to reveal the complex interplay of strain and defects in determining the metallicity and magnetotransport properties of the ferrimagnetic spinel. NiCo 2 O 4 on perovskite substrates and NiCo 2 O 4 membranes exhibit insulating behaviors and spin canting, in sharp contrast to the metallic NiCo 2 O 4 films on spinel substrates that possess strong perpendicular magnetic anisotropy. Anisotropic magnetoresistance studies provide critical information about disorder-induced spin scattering and strain-induced tetragonal magnetocrystalline anisotropy, which is corroborated by comprehensive electron microscopy characterizations. Our study presents a promising venue for designing flexible magnetic memory, sensor, and spintronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Actuating Liquid Crystals Rapidly and Reversibly by Using Chemical Catalysis

Abstract Microtubules and catalytic motor proteins underlie the microscale actuation of living materials, and they have been used in reconstituted systems to harness chemical energy to drive new states of organization of soft matter (e.g., liquid crystals (LCs)). Such materials, however, are fragile and challenging to translate to technological contexts. Rapid (sub‐second) and reversible changes in the orientations of LCs at room temperature using reactions between gaseous hydrogen and oxygen that are catalyzed by Pd/Au surfaces are reported. Surface chemical analysis and computational chemistry studies confirm that dissociative adsorption of H 2 on the Pd/Au films reduces preadsorbed O and generates 1 ML of adsorbed H, driving nitrile‐containing LCs from a perpendicular to a planar orientation. Subsequent exposure to O 2 leads to oxidation of the adsorbed H, reformation of adsorbed O on the Pd/Au surface, and a return of the LC to its initial orientation. The roles of surface composition and reaction kinetics in determining the LC dynamics are described along with a proof‐of‐concept demonstration of microactuation of beads. These results provide fresh ideas for utilizing chemical energy and catalysis to reversibly actuate functional LCs on the microscale.

Chemistry↗
DOE OSTI2026

Resolving root causes of experiment discrepancies guided by machine learning

Abstract Scientists rely on accurate experimental data to explain nature and then harness this knowledge for applications addressing human needs. However, discrepancies between experiments of the same observable can impede scientific progress if one does not understand the underlying causes. Here, we developed a process that unravels data discrepancies by first using Bayesian machine learning to relate discrepancies to few of many, potentially biasing metadata features that encode experiment procedures. This machine learning output guides human experts to study discrepancy causes by simulating suspicious aspects of historical experiments or designing modern ones to address open questions. The study findings then lead to rejecting or correcting historical data on firm scientific bases. This process is demonstrated for the energy spectrum of neutrons emitted promptly (<1 ns) after fission of 252 Cf, a trusted nuclear physics Standard. It reduces the spread in experimental 252 Cf spectra by up to a factor of 6.

Neudecker, D. (ORCID:0000000339200627)↗
DOE OSTI2026

PIP-II LLRF Master Oscillator and Precision Reference Line - Station Level Design and Testing

The PIP-II superconducting linac at Fermilab requires a highly stable RF Reference Line to maintain phase syn- chronization throughout the accelerator. Temperature- induced changes in the electrical length of long coaxial cables can introduce phase drift and measurement errors. The reference-line architecture mitigates these effects by phase averaging the forward and reflected RF signals, while a phase-locked loop anchors the system to the mas- ter oscillator. This work focuses on the characterization, validation, and mechanical integration of station-level RF assemblies using CAD modeling, vector network analyzer measurements, and spectrum analyzer testing. PID-controlled thermal plates will stabilize critical RF components and further reduce temperature-dependent phase and amplitude variations. These methods sup- port repeatable, standardized designs that can be reliably integrated across the different reference-line stations.

Mosher, Alexander [U. Illinois, Chicago]↗
DOE OSTI2025

Magnetically and optically active edges in phosphorene nanoribbons

Abstract Nanoribbons, nanometre-wide strips of a two-dimensional material, are a unique system in condensed matter. They combine the exotic electronic structures of low-dimensional materials with an enhanced number of exposed edges, where phenomena including ultralong spin coherence times 1,2 , quantum confinement 3 and topologically protected states 4,5 can emerge. An exciting prospect for this material concept is the potential for both a tunable semiconducting electronic structure and magnetism along the nanoribbon edge, a key property for spin-based electronics such as (low-energy) non-volatile transistors 6 . Here we report the magnetic and semiconducting properties of phosphorene nanoribbons (PNRs). We demonstrate that at room temperature, films of PNRs show macroscopic magnetic properties arising from their edge, with internal fields of roughly 240 to 850 mT. In solution, a giant magnetic anisotropy enables the alignment of PNRs at sub-1-T fields. By leveraging this alignment effect, we discover that on photoexcitation, energy is rapidly funnelled to a state that is localized to the magnetic edge and coupled to a symmetry-forbidden edge phonon mode. Our results establish PNRs as a fascinating system for studying the interplay between magnetism and semiconducting ground states at room temperature and provide a stepping-stone towards using low-dimensional nanomaterials in quantum electronics.

Science & Technology - Other Topics↗
DOE OSTI2026

Atomic Evolution of Hydrogen Intercalation Wave Dynamics in Palladium Nanocrystals Revealed by Liquid-Phase Transmission Electron Microscopy

Solute-intercalation-induced phase separation creates spatial heterogeneities in host materials, a phenomenon ubiquitous in batteries, hydrogen storage, and other energy devices. Despite many efforts, probing intercalation processes at the atomic scale has been a significant challenge. By utilizing liquid-phase transmission electron microscopy (TEM), we study hydrogen (de)intercalation in palladium nanocrystals as a model system and have achieved unprecedented atomic-resolution imaging of hydrogen intercalation wave dynamics. Our observations reveal that intercalation wave mechanisms, instead of shrinking-core mechanisms, prevail at ambient temperature for palladium nanocubes ranging from ∼60 nm down to ∼10 nm. Systematic image analysis uncovers the atomic evolution of the hydrogen intercalation wave, transitioning from nonplanar and inclined boundaries to those closely aligned with {100} planes. Our kinetic Monte Carlo simulations demonstrate that the observed intercalation wave dynamics correspond to sorption pathways minimizing the lattice mismatch strain at the phase boundary. In conclusion, unveiling the atomic intercalation pathways holds profound implications for engineering intercalation-mediated devices and advancements in energy sciences.

Lee, Daewon [Lawrence Berkeley National Laboratory↗
DOE OSTI2026

Flame stabilization in DME spray flames under engine-relevant conditions characterized by OH* chemiluminescence and formaldehyde laser-induced fluorescence

The transient and quasi-steady flame structures of Dimethyl Ether (DME) fuel sprays, produced by a single-hole injector (Spray D), were investigated using Planar Laser-Induced Fluorescence (PLIF) and chemiluminescence imaging in a constant-volume chamber under Engine Combustion Network (ECN) Spray A conditions (900 K ambient temperature, 60 bar ambient pressure, 1500 bar injection pressure, and 22.8 kg/m 3 ambient density). Low-temperature chemical reaction zones were visualized using formaldehyde (CH 2 O) PLIF with 355 nm excitation, while high-temperature flame regions were captured via chemiluminescence imaging of excited-state hydroxyl radicals (OH*). Both transient and quasi-steady flame structures clearly show the transition from CH 2 O to OH*, highlighting the progression from low- to high-temperature combustion, while the position of the flame is displaced for DME compared to reference hydrocarbon n-dodecane. Homogeneous reactor calculations with detailed chemistry and using adiabatic mixing for initial temperature show that CH 2 O peaks are significantly higher for DME at the same equivalence ratio, with a higher heat-release during the cool-flame regime with respect to the fuel heating value. Thus, the cool-flame dynamic as a precursor to high-temperature combustion and flame stabilization exhibit distinct behavior for DME relative to conventional hydrocarbons, and these phenomena are effectively resolved through the soot-free nature of DME and the high-speed, time-resolved diagnostics.

CH2O laser-induced fluorescence↗
DOE OSTI2025

Tree tensor network hierarchical equations of motion based on time-dependent variational principle for efficient open quantum dynamics in structured thermal environments

In this work, we introduce an efficient method, TTN-HEOM, for exactly calculating the open quantum dynamics for driven quantum systems interacting with highly structured bosonic baths by combining the tree tensor network (TTN) decomposition scheme with the bexcitonic generalization of the numerically exact hierarchical equations of motion (HEOM). The method yields a series of quantum master equations for all core tensors in the TTN that efficiently and accurately capture the open quantum dynamics for non-Markovian environments to all orders in the system–bath interaction. These master equations are constructed based on the time-dependent Dirac–Frenkel variational principle, which isolates the optimal dynamics for the core tensors given the TTN ansatz. The dynamics converges to the HEOM when increasing the rank of the core tensors, a limit in which the TTN ansatz becomes exact. We introduce TENSO, tensor equations for non-Markovian structured open systems, as a general-purpose Python code to propagate the TTN-HEOM dynamics. We implement three general propagators for the coupled master equations: two fixed-rank methods that require a constant memory footprint during the dynamics and one adaptive-rank method with a variable memory footprint controlled by the target level of computational error. We exemplify the utility of these methods by simulating a two-level system coupled to a structured bath containing one Drude–Lorentz component and eight Brownian oscillators, which is beyond what can presently be computed using the standard HEOM. Our results show that the TTN-HEOM is capable of simulating both dephasing and relaxation dynamics of driven quantum systems interacting with structured baths, even those of chemical complexity, with an affordable computational cost.

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

Integration and Optimization of Loads, and Renewables for Grid-Interactive Efficient Buildings

Deploying advanced building control strategies in small- and medium-sized commercial buildings (SMBs) is critical but constrained by two major challenges: (1) the lack of sensor and control infrastructure (SCI) and (2) the significant engineering effort required for implementation and configuration. While these barriers limit large-scale adoption in SMBs, they remain underexplored in the literature, which has largely emphasized feasibility over scalability.

25 ENERGY STORAGE↗
DOE OSTI2026

Tunable magnetic excitations in the honeycomb antiferromagnet (Co,Ni)⁢TiO3

The solid solution of the honeycomb antiferromagnet (AFM) Co0.5⁢Ni0.5⁢TiO3 (CNTO) was synthesized by mixing a 1:1 molar ratio of CoTiO3 (CTO) and NiTiO3 (NTO). Powder neutron scattering was used to determine the structure and magnetic spectrum, where the nuclear and magnetic structures resemble those of the end members. The Néel temperature (T𝑁 = 27 ± 1 K), the ordered magnetic moment (M = 2.29 ± 0.03 𝜇𝐵 per magnetic ion), and lattice parameters are intermediate between those of the two. From inelastic neutron scattering, it is observed that the magnon density of states (MDOS) extends up to 13 meV, and the interaction can be described by an XXZ-type magnetic Hamiltonian. Above 15 meV, spin-orbit excitons (SOEs) are observed similarly to those in CTO, but with modified spin-orbit crystal-field splitting because of the substitution of Ni at Co sites, and the disorder induced by the Ni substitution modifies the spin-orbit crystal field. Thus, the magnetic dynamics of the CNTO cannot be described as a simple average of the two parent compounds.

Rathnayaka, Srimal [University of Virginia, Charlo↗
DOE OSTI2024

Using scalable computer vision to automate high-throughput semiconductor characterization

Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Science & Technology - Other Topics↗
DOE OSTI2025

Highly textured metal anodes for stable aqueous batteries: Fabrication and characterization

We report a purely mechanical “cold-compression flow” method for fabricating Zn, Sn, and In substrates with tunable crystallographic textures. Using textured Zn as a model system, we investigate Zn electrocrystallization and demonstrate correlated growth of crystalline films with correlation lengths from tens to hundreds of micrometers. At 5 milliamperes per square centimeter (mA/cm 2 ), capacities between 20 and 82 milliampere hours per square centimeter (mA·hour/cm 2 ) are achieved depending on substrate texture level. At higher currents (40 mA/cm 2 ), capacities reach up to 604 mA·hour/cm 2 . Rotating disk electrode studies show that dominantly (002) textured Zn substrates exhibit enhanced corrosion resistance and reduced interphase passivation. We introduce an effective Damköhler number (Da*) to concisely describe morphological evolution during electrocrystallization across substrates with different textures. High-texture (002) Zn substrates substantially enhance performance in high-capacity (~20 mA·hour/cm 2 ) symmetric Zn||Zn cells and full cells (Zn||δ-MnO 2 and Zn||I 2 ), enabling fast-charging and prolonged energy storage in coin and pouch rechargeable Zn battery formats.

Science & Technology - Other Topics↗
DOE OSTI2026

Boron Coordination in Multicomponent Glasses: Analytical Models and Machine Learning With Uncertainty

Borosilicate glasses are extensively used in a variety of applications from kitchenware to nuclear waste immobilization due to the strong network formed by the Si-O-B bond that makes it resistant to chemical corrosion and gives it a low thermal expansion. Boron, however, exists in both trigonal BO3 and tetrahedral BO4 bonds in glass systems, which impacts the chemical durability and thermal resistance of the glass, amongst other properties. Boron coordination (N4), or the ratio of the amount of BO4 to BO3 within a glass, may aid in predicting these properties but is difficult to derive without experimental data due to the complexity of impacts from varied glass compositions and processing factors. For this reason, compositional models have been developed to predict boron coordination, but the models typically include a limited number of glass components. To help fill this gap in the models, in this work, a diverse multicomponent glass dataset of 809 glasses is compiled from a literature search, and then a number of analytical and machine learning (ML) models are trained on the dataset. Previously developed modified Bernstein and modified Du Stebbins analytical models were fitted to update parameters with the new dataset. Then, partially Bayesian neural networks, Gaussian process regressor, and heteroskedastic deterministic neural networks were evaluated. The ML models examined all have different strategies to overcome the potential for overfitting as a result of a limited training dataset, and return results that account for model uncertainty, which can be valuable for understanding model reliability. For the first time, cooling rate is introduced as an input parameter for ML models, showing consistent improvements in performance and solidifying the importance of including parameters outside of composition alone for N4 prediction. The machine learning models examined here show promise in accurate predictions of boron coordination in borosilicate glasses, all achieving R2 values of 0.91.

boron coordination↗