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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fluid modeling of low-temperature plasmas

Fluid models are essential for understanding and predicting low-temperature plasma (LTP) behavior in various scientific and industrial settings. This paper provides an introductory tutorial on fluid modeling of LTPs, covering model formulation, implementation, and computational simulations. The tutorial focuses on five main components of the formulation of LTP fluid models: fluid flow, energy, chemistry, electromagnetism, and material properties, as well as in essential aspects of model implementations, including multiscale phenomena, multiphysics coupling, and numerical convergence. Designed for students and early-career researchers, this work offers a practical foundation for developing and using fluid models, from in-house computational codes to commercial software, bridging fundamental theory with real-world applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

The Second Skin: A Wearable Sensor Suite that Enables Real-Time Human Biomechanics Tracking Through Deep Learning

Objective: Real-time determination of human kinematics and kinetics could advance biomechanics research and enable valuable applications of biofeedback and generalizable exoskeleton control. Here, this work aims to investigate a taskindependent, user-independent method for obtaining precise realtime joint state estimation across lower-body joints during a wide variety of tasks. Methods: We developed a generalizable sensing approach using a suit comprised of inertial measurement units (IMUs) and pressure insoles. With the suit, we collected a dataset of 33 tasks commonly performed during construction and hazardous waste cleanup (N = 10). We then trained deep learning user-independent, task-agnostic models to estimate joint lowerbody kinematics and dynamics using only worn sensor data. We likewise computed joint kinematics and dynamics analytically from sensor data to serve as a comparison tool for model results. Results: Our models achieved overall angle estimation root-meansquared-errors (RMSE) of 6.56±.92°, 8.60±1.01°, 7.58±.89°, and 6.00±.73° compared to 13.9±.1.3°, 15.31±1.0°, 10.76±.70°, and 7.56±.48° via analytical methods at the lower back, hip, knee, and ankle, respectively. Likewise, our models achieved overall normalized moment estimation RMSEs of .207±.069 Nm/kg, .242±.044 Nm/kg, .202±.038 Nm/kg, and .193±.034 Nm/kg compared to .306±.036 Nm/kg, .407±.021 Nm/kg, 1.18 ±.022 Nm/kg, and 1.73±.071 Nm/kg via analytical methods at the lower back, hip, knee, and ankle, respectively. Conclusion: These results are comparable to other state-of-the-art wearable sensing systems, establishing deep learning as a viable sensing approach that generalizes to new users and tasks. Significance: This work shows promise for enabling accurate real-world biomechanical data collection and enhancement of biofeedback systems and wearable robot control.

Casey, Ryan T. F. [Georgia Institute of Technology↗
DOE OSTI2025

Third-order photon correlations extract single-nanocrystal multiexciton properties in solution

Colloidal semiconductor nanocrystals are considered promising materials for high-flux optical applications, including lasing, light-emitting diodes, biological imaging, and quantum optics. In high-flux applications, multiexcitons can significantly contribute to emission, influencing its brightness, spectral purity, and kinetics. As a result, understanding and controlling multiexciton emission in colloidal nanocrystal materials is of the utmost importance. In the past, single-nanocrystal photon correlation methods have been applied to understand biexciton and triexciton efficiencies, lifetimes, and spectra. While powerful, such methods suffer from user selection bias and require stable emission from single nanocrystals. To compensate for this shortcoming, second-order correlation methods were developed to extract sample-averaged biexciton properties from a solution of nanocrystals. Until now, however, the analogous third-order solution photon correlation methods remained unexplored. In this work, we present a pair of third-order photon correlation techniques to obtain the sample-averaged single-nanocrystal triexciton quantum yield and lifetime in a solution-phase experiment. These techniques derive from the relationship between the Poisson probability of nanocrystal photon absorption and the intrinsic probability of nanocrystal photon emission. We validate the theoretical background of these techniques by creating a numerical model to simulate the diffusion and emission of many nanocrystals in solution. Our simulations confirm that the average triexciton quantum yield and triexciton lifetime can be extracted from a solution of nanocrystals. These techniques will enable researchers to gain a better understanding of the fundamental multiexciton properties of colloidal nanocrystals.

Horowitz, Jonah R. [Massachusetts Institute of Tec↗
DOE OSTI2025

MultiTaskDeltaNet: change detection-based image segmentation for operando ETEM with application to carbon gasification kinetics

Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.

08 HYDROGEN↗
DOE OSTI2025

New Deformation Mechanisms in Nanocrystalline Nano-porous Small Scale Metals as Defined by Kinetically-driven Microstructures

This report describes recent advances in the design and fabrication of nanostructured metallic pillars with hierarchical microstructures using a novel nanoscale additive manufacturing approach. Through a hydrogel-infusion-based two-photon lithography (TPL) method, we successfully fabricated 3D nickel nanopillars exhibiting both nanocrystalline and nanoporous features. The resulting “bamboo-like” internal architecture comprises 30–50 nm grains and voids with similarly scaled pores. These geometrically tunable pillars, with diameters ranging from ~130 to 550 nm, serve as an ideal platform for probing deformation mechanisms in structurally heterogeneous metals at the nanoscale.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Design of facilitated dissociation enables timing of cytokine signalling

Protein design has focused on the design of ground states, ensuring that they are sufficiently low energy to be highly populated. Designing the kinetics and dynamics of a system requires, in addition, the design of excited states that are traversed in transitions from one low-lying state to another. This is a challenging task because such states must be sufficiently strained to be poorly populated, but not so strained that they are not populated at all, and because protein design methods have focused on generating near-ideal structures. Here we describe a general approach for designing systems that use an induced-fit power stroke to generate a structurally frustrated and strained excited state, allosterically driving protein complex dissociation. X-ray crystallography, double electron–electron resonance spectroscopy and kinetic binding measurements show that incorporating excited states enables the design of effector-induced increases in dissociation rates as high as 5,700-fold. We highlight the power of this approach by designing rapid biosensors, kinetically controlled circuits and cytokine mimics that can be dissociated from their receptors within seconds, enabling dissection of the temporal dynamics of interleukin-2 signalling.

deformation dynamics↗
DOE OSTI2025

Quasicrystal stability and nucleation kinetics from density functional theory

The aperiodic order of quasicrystals bridges the amorphous and crystalline regime, so it has remained unclear whether quasicrystals are metastable or stable phases of matter. Density functional theory is often used to evaluate thermodynamic stability, but quasicrystals are long-range aperiodic and their energies cannot be calculated using conventional ab initio methods. Here, in this work, we perform first-principles calculations on quasicrystal nanoparticles of increasing size, from which we can directly extrapolate their bulk and surface energies. Using this technique, we determine with high confidence that the icosahedral quasicrystals ScZn 7.33 and YbCd 5.7 are ground-state phases, thus revealing that translational symmetry is not a necessary condition for the zero-temperature stability of inorganic solids. Although we found the ScZn 7.33 quasicrystal to be thermodynamically stable, we show on a mixed thermodynamic and kinetic phase diagram that its solidification from the melt is limited by nucleation, which illustrates why even stable materials may be kinetically challenging to grow. Our techniques broadly open the door to first-principles investigations into the structure–bonding–stability relationships of aperiodic materials.

density functional theory↗
DOE OSTI2024

Semi-Lagrangian nodal discontinuous Galerkin method for the BGK model

In this work, we propose a semi-Lagrangian (SL) nodal discontinuous Galerkin (DG) solver for the BGK equation. The BGK model was introduced by Bhatnagar, Gross, and Krook [1] as a relaxation model for the fundamental Boltzmann equation [5], which describes the kinetic dynamic of rarefied gases with a probability distribution function. The challenges of designing efficient numerical schemes for the Boltzmann equation mainly come from its high dimensionality and complicated nonlinear collision operator. The BGK model gains interests since it has much lower computational cost, due to the relatively simple structure of the relaxation operator in replacement of the collision operator, while simultaneously preserving several important physical properties, such as macroscopic quantities and dissipation of entropy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Linear gyrokinetic simulations of toroidal Alfvén eigenmodes in the Mega-Amp Spherical Tokamak

Linear gyrokinetic (GK) simulations using the Gyrokinetic Toroidal Code (GTC) have been performed to investigate Toroidicity-driven Alfvén Eigenmodes (TAEs) driven by the neutral beam injection (NBI) induced fast ions in the Mega-Amp Spherical Tokamak (MAST) to identify the non-perturbative and kinetic effects of thermal plasma. A specific TAE in MAST discharge 26887, with an on-axis NBI power of approximately 1.5 MW and plasma current around 800 kA, exhibited frequency chirping, and the tangential soft x-ray camera array resolved the radial mode structure peaked near |q|=1.5. Various excitation methods were used in the GTC linear simulations, illustrating this code's capability to realistically represent the mechanisms and behaviors of fast ion-driven TAEs in spherical tokamaks. The radial structures from these GK simulations closely match measurements and calculations performed using the NOVA ideal MHD code, though with the frequencies approximately 10 kHz lower, likely due to various kinetic and non-perturbative effects. The simulations measured the damping rates due to continuum damping, radiative damping, and ion Landau damping, revealing that ion Landau damping has the most significant contribution to the total damping rate of the TAE. A comparison of growth rates of TAEs excited by fast ion Maxwellian and slowing-down distributions shows that the TAEs excited by a fast ion anisotropic pitch distribution (as part of the slowing-down distributions) are more unstable compared to those excited by a Maxwellian distribution with an equivalent fast ion beta. This shows that the use of fast ion anisotropy alters the number of fast ions to be in shear Alfvén resonance, and hence, it can greatly affect the stability of TAEs. These tests can be performed with the GTC but impossible with ideal MHD simulations, highlighting the necessity of kinetic simulations such as the GTC for a precise prediction of the TAE stability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Effective direct steam regeneration of bis-iminoguanidine solid sorbent used for carbon dioxide capture

A cost-effective, energy-efficient sorbent regeneration process for phase-changing guanidines used for CO 2 capture was developed based on direct-steam stripping. This approach enhances the regeneration rate, simplifies the overall CO 2 capture process, and reduces the energy cost compared to conventional conductive thermal regeneration. A direct-steam sorbent regeneration reactor was developed, demonstrating that aqueous bis(iminoguanidines) (BIG) sorbents, e.g., methylglyoxal-bis(iminoguanidine) (MGBIG) and glyoxal-bis(iminoguanidine) (GBIG), could be efficiently regenerated with up to ~ 99 % CO 2 recovery through direct-steam stripping. Using low-temperature steam at 100 °C, a 4.5 times faster regeneration rate for GBIG carbonate sorbent (e.g., 30 min for 10 g) was demonstrated compared to conductive-heating (e.g., 135 min for 10 g) at 130 °C. Additionally, fully regenerated MGBIG converts into an aqueous MGBIG solution when the steam condenses onto the sorbent surface. Condensed steam with the guanidine can be easily recycled as an aqueous solution into the gas–liquid contactor to achieve a continuous-flow CO 2 -capture process. Molecular dynamics simulation was employed to provide a better understanding of the process. Higher heat transfer rates from steam to guanidine carbonate, compared to air heating, were attributed to the vibration resonance of water molecules within MGBIG with that of vapor molecules and the effective transfer of kinetic energy from vapor to solid. Technoeconomic analysis demonstrated that direct-steam stripping significantly decreases the CO 2 capture cost by 50 % compared to traditional conductive heating methods. Further, enhanced mass transfer facilitated by low-temperature steam and subsequent condensation effectively heats up the H 2 O-containing BIG-carbonate crystals, facilitating the desorption of CO 2 from the solid crystals, thereby leading to fast, effective, and energy-efficient sorbent regeneration.

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

Local reduced-order modeling for electrostatic plasmas by physics-informed solution manifold decomposition

Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. Here, in this work, we develop data-driven reduced-order models (ROMs) for collisionless electrostatic plasma dynamics, based on the kinetic Vlasov-Poisson equation. Our ROM approach projects the equation onto a linear subspace defined by the proper orthogonal decomposition (POD) modes. We introduce an efficient tensorial method to update the nonlinear term using a precomputed third-order tensor. We capture multiscale behavior with a minimal number of POD modes by decomposing the solution manifold into multiple time windows and creating temporally local ROMs. We consider two strategies for decomposition: one based on the physical time and the other based on the electric field energy. Applied to the 1D1V Vlasov–Poisson simulations, that is, prescribed E-field, Landau damping, and two-stream instability, we demonstrate that our ROMs accurately capture the total energy of the system both for parametric and time extrapolation cases. The temporally local ROMs are more efficient and accurate than the single ROM. In addition, in the two-stream instability case, we show that the energy-windowing reduced-order model (EW-ROM) is more efficient and accurate than the time-windowing reduced-order model (TW-ROM). With the tensorial approach, EW-ROM solves the equation approximately 90 times faster than Eulerian simulations while maintaining a maximum relative error of 7.5% for the training data and 11% for the testing data.

Electrostatic plasmas↗
DOE OSTI2024

X-ray induced synthesis of beta tin (β-Sn)

The destabilization of molecular structures via hard X-rays has been previously utilized to synthesize novel compounds. Here, in this study, we report that the monochromatic X-ray induced decomposition of tin(II) oxalate (SnC 2 O 4 ) at ambient and 0.6 GPa pressures lead to the formation of beta tin (β-Sn). At 1 GPa, only the degradation of SnC 2 O 4 crystal structure is observed without any indication of β-Sn at the end of irradiation. The maximum transformation yield is achieved at 0.6 GPa suggesting the critical role of intermolecular distance in X-ray induced synthesis of β-Sn. Moreover, a modified Avrami equation is utilized to describe the kinetics and geometry of structural synthesis at ambient and 0.6 GPa. The obtained results demonstrate that X-ray irradiation can induce photochemical synthetic pathways different from conventional methods (e.g., high pressure, temperature, stoichiometric mixing) and that high pressure (HP) can be considered a tool to control X-ray induced photochemistry.

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

C–C Bond Formation during Electrochemical CO 2 Reduction on Pristine Cu(100) Unlikely to Involve Adsorbed CO at Any Potential

Formation of hydrocarbons containing two or more carbon atoms (C 2+ ) during heterogeneous electrochemical CO and CO 2 reduction (ECOR and ECO 2 R) only occurs, among pure metals, on Cu electrodes. Moreover, the activity and selectivity is facet dependent, with Cu(100) generally preferentially forming ethylene over methane. Previously, we found via quantum-mechanics-based modeling that, unlike standard density functional theory, more accurate correlated wavefunction methods predict that non-electroactive coupling pathways involving two adsorbed COs (*CO) or a *CO and a *COH to form C–C bonds on Cu(100) are kinetically inhibited, with the former also thermodynamically unfavorable. Here, we extend that embedded complete active space second order perturbation theory (ECASPT2) study, further showing that electrochemical coupling of two *COs to form an anionic dimer [OC*–*CO] (1+δ)– , followed by protonation to form [OC*–*COH] δ− , is not kinetically competitive with the reduction of *CO to *COH at relevant ECO/CO 2 R potentials. Our simulations therefore suggest that the ability of Cu(100) to electrochemically synthesize C 2+ molecules from CO and CO 2 is unlikely to be via *CO, at least on pristine Cu(100). Instead, hydrogenated CO species (*COH, *CH x OH, or *CH x ) are most likely to be the key intermediates in C–C bond formation.

Martirez, John Mark P. [Princeton Plasma Physics L↗
DOE OSTI2024

Bridging length scales in hard materials with ultra-small angle X-ray scattering – a critical review

Owing to their exceptional properties, hard materials such as advanced ceramics, metals and composites have enormous economic and societal value, with applications across numerous industries. Understanding their microstructural characteristics is crucial for enhancing their performance, materials development and unleashing their potential for future innovative applications. However, their microstructures are unambiguously hierarchical and typically span several length scales, from sub-ångstrom to micrometres, posing demanding challenges for their characterization, especially for in situ characterization which is critical to understanding the kinetic processes controlling microstructure formation. This review provides a comprehensive description of the rapidly developing technique of ultra-small angle X-ray scattering (USAXS), a nondestructive method for probing the nano-to-micrometre scale features of hard materials. USAXS and its complementary techniques, when developed for and applied to hard materials, offer valuable insights into their porosity, grain size, phase composition and inhomogeneities. We discuss the fundamental principles, instrumentation, advantages, challenges and global status of USAXS for hard materials. Using selected examples, we demonstrate the potential of this technique for unveiling the microstructural characteristics of hard materials and its relevance to advanced materials development and manufacturing process optimization. We also provide our perspective on the opportunities and challenges for the continued development of USAXS, including multimodal characterization, coherent scattering, time-resolved studies, machine learning and autonomous experiments. Our goal is to stimulate further implementation and exploration of USAXS techniques and inspire their broader adoption across various domains of hard materials science, thereby driving the field toward discoveries and further developments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next

Chemical kinetic modeling plays a foundational role in fields ranging from energy to environmental science, pharmaceuticals, and advanced materials. The past two decades have seen remarkable progress, particularly in modeling gas-phase reactions for thermochemical processes, leading to impactful industrial applications such as steam cracking and air quality management. However, new challenges are emerging. The successful development of systematic methodologies for the description of gas-phase kinetics opens the possibility to apply the same approach to the study of more challenging systems. Here, we review recent advances, including ab initio transition state theory-based master equation estimation of elementary rates, automated mechanism generation, machine-learning-assisted kinetics, and uncertainty quantification, and discuss the advances needed to apply the same methodological approach in areas such as heterogeneous catalysis, electrochemistry, liquid-phase and solid-state reactivity, and multiscale model integration. We advocate for the development of targeted tools, especially methods that go beyond empirical tuning toward first-principles-based predictions. We highlight the need for accessible software and AIaugmented workflows to democratize modeling for industry and academia alike. In this perspective, we call attention to not only what has worked but also what remains unsolved, advocating to avoid overemphasizing successes in scientific works at the expense of realism. The next decade should focus on predictive capability, physical accuracy, and community infrastructure (e.g., databases and services) to enable innovation across diverse fields. We argue that kinetic modeling, properly equipped, can accelerate discovery far beyond its traditional domains.

ab initio calculations↗
DOE OSTI2025

Coupled cluster and dislocation dynamics modeling of microstructure evolution in irradiated materials

We develop here a coupled cluster and dislocation dynamics framework to study the microstructure evolution of irradiated materials. The framework not only accounts for the three dimensional diffusion of radiation-generated clusters, but also their interaction with dislocation networks and the resultant climb motion of discrete dislocations within finite crystals. The framework is solved with a superposition solution scheme, and is applied to investigate the evolution of the irradiation-induced dislocation loops in zirconium (Zr), considering the effects of various bias factors including the diffusion anisotropy difference (DAD) of interstitials and interstitial clusters, the dislocation bias of defects to discrete dislocation segments, and the production bias of defects from the radiation cascade. We find that the DAD is the most critical factor influencing the kinetics of the loop evolution in Zr, while the recombination/interaction of mobile defects can induce a strong spatial dependence of the loop evolution together with the DAD. Here, the method is also adopted to study the evolution of interstitial $\langle$a$\rangle$ and vacancy $\langle$c$\rangle$ dislocation loop ensembles consistent with the microstructure observed during irradiation-induced growth of Zr. Our findings not only reveal the spatial dependence of the size and ellipticity of the dislocation loops, but also suggest a limit on the anisotropy factor of interstitials to reproduce the co-growth of $\langle$a$\rangle$ and $\langle$c$\rangle$ loops in zirconium, in good agreement with experimental observations and other simulation results.

Bias factors↗
DOE OSTI2026

Co 2 P-Pt Heterostructure Interfaces for Electrocatalytic Hydrogen Evolution

Pt-based electrocatalysts are effective for the hydrogen evolution reaction (HER); however, their limited ability to facilitate water dissociation and suboptimal hydrogen binding energy (H BE ) in alkaline electrolytes result in slow reaction kinetics, which hinders their cost-efficiency and practical applications. This study reports the synthesis of Co 2 P-Pt heterostructure nanorods using a seed-mediated growth method, producing a high density of Co 2 P-Pt interfacial sites. Density functional theory (DFT) calculations indicate that electronic interactions at these interfaces optimize H BE on Pt, while the interfacial sites promote water dissociation. The Co 2 P-Pt nanorods demonstrate an overpotential of 14 mV at 10 mA cm −2 for the HER, highlighting the potential of precisely engineered metal-metal phosphide interfaces for enhancing electrocatalytic efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Recyclable Design for Retaining High Solar Absorptivity of the Media in CSP

Efficient thermal energy storage is pivotal to lowering the levelized cost of electricity (LCOE) for Concentrating Solar Power (CSP) plants. In solid-particle systems, however, prolonged high-temperature service degrades particle solar absorptivity, eroding overall efficiency. This project demonstrates a hydrogen-assisted recovery process that reliably restores absorptivity to >90 %, offering a practical route to sustain long-term CSP performance. Bench-scale investigations mapped the reduction kinetics of optically faded particles across hydrogen concentrations, temperatures, and residence times. Coupling mass-spectrometric monitoring with machine-learning optimization minimized energy demand while maximizing absorptivity gain. The resulting process window—moderate hydrogen partial pressures, 15–30 min dwell times, and temperatures well below initial calcination levels—cuts energy consumption well below that of incumbent re-blackening methods. A prototype recovery reactor processed multiple 2 kg batches with repeatable outcomes, confirming scalability and operational robustness. Integrated techno-economic analysis indicates material and operating cost reductions exceeding 15 % relative to conventional particle replacement or chemical re-coating, translating directly into lower LCOE for next-generation CSP facilities. By uniting fundamental reaction-kinetics insight with pragmatic engineering, this work advances the solid-particle pathway, delivering a cost-effective, field-deployable solution to one of CSP’s key durability challenges and strengthening the commercial outlook for high-temperature renewable power.

14 SOLAR ENERGY↗