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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enhancing Cycle Life in Superoxide‐Based Na–O 2 Batteries by Reducing Interface Reactivity

Abstract Sodium–oxygen (Na–O 2 ) batteries are considered a promising energy storage alternative to current state‐of‐the‐art technologies owing to their high theoretical energy density, along with the natural abundance and low price of Na metal. The chemistry of these batteries depends on sodium superoxide (NaO 2 ) or peroxide (Na 2 O 2 ) being formed/decomposed. Most Na–O 2 batteries form NaO 2 , but reversibility is usually quite limited due to side reactions at interfaces. By using new materials, including a highly active catalyst based on vanadium phosphide (VP) nanoparticles, an ether/ionic liquid‐based electrolyte, and an effective sodium bromide (NaBr) anode protection layer, the sources of interface reactivity can be reduced to achieve a Na–O 2 battery cell that is rechargeable for 1070 cycles with a high energy efficiency of more than 83%. Density functional theory calculations, along with experimental characterization confirm the three factors leading to the long cycle life, including the effectiveness of the NaBr protective layer on the anode, a tetraglyme/EMIM‐BF 4 based electrolyte that prevents oxidation of the VP cathode catalyst surface, and the EMIM‐BF 4 ionic liquid aiding in avoiding electrolyte decomposition on NaO 2 .

Azaribeni, Adel [Department of Chemical and Biolog↗
DOE OSTI2022

Mechanically robust high magnetic-performance Sm-Co sintered magnets through microstructure engineering

Samarium-cobalt (Sm-Co) sintered magnets have high magnetic energy densities, great resistance to demagnetization and corrosion, and excellent thermal stability in a wide temperature range (–50–550 °C). However, the utilization of these magnets is restricted by their brittleness. Based on micromechanical and the Zener pinning model, Sm-Co sintered magnets with improved mechanical properties have been designed and fabricated via microstructure engineering. A small amount of fine Sm 2 O 3 particulates (0–3 wt%) has been incorporated into Sm 2 (CoFeCuZr) 17 sintered magnets to refine the grain size by up to approximately 50% (from 45 to 22 µm) and narrow the grain size distribution. Doping with 3 wt% Sm 2 O 3 increased the flexural strength by 62% while maintaining magnetic performance. Both grain-refined unimodal microstructure and heterogeneous laminated coarse/fine grain microstructure were formed by strategically designed assemblies of Sm 2 O 3 -added Sm-Co powder feedstock mixtures. The technology is compatible with existing magnet manufacturing processes. Numerical micromechanics simulation indicates that the fracture is dominated by intragranular mode. The mechanical strength is mainly enhanced by the additive-induced grain refinement, while the small amount of Sm 2 O 3 addition has a small direct positive contribution. Finally, these magnets will be more cost-effective, efficient, and robust for various functional applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Nanocrystalline Materials: Synthesis, Characterization, Properties, and Applications

Nanostructuring is a commonly employed method of obtaining superior mechanical properties in metals and alloys. Compared to conventional polycrystalline counterparts, nanostructuring can provide remarkable improvements in yield strength, toughness, fatigue life, corrosion resistance, and hardness, which is attributed to the nano grain size. In this review paper, the current state-of-the-art of synthesis methods of nanocrystalline (NC) materials such as rapid solidification, chemical precipitation, chemical vapor deposition, and mechanical alloying, including high-energy ball milling (HEBM) and cryomilling was elucidated. More specifically, the effect of various process parameters on mechanical properties and microstructural features were explained for a broad range of engineering materials. This study also explains the mechanism of grain strengthening using the Hall-Petch relation and illustrates the effects of post-processing on the grain size and subsequently their properties. This review also reports the applications, challenges, and future scope for the NC materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Lorentz Symmetry Group, Retardation and Energy Transformations in a Relativistic Engine

In a previous paper, we have shown that Newton’s third law cannot strictly hold in a distributed system of which the different parts are at a finite distance from each other. This is due to the finite speed of signal propagation which cannot exceed the speed of light in vacuum, which in turn means that when summing the total force in the system the force does not add up to zero. This was demonstrated in a specific example of two current loops with time dependent currents, the above analysis led to suggestion of a relativistic engine. Since the system is effected by a total force for a finite period of time this means that the system acquires mechanical momentum and energy, the question then arises how can we accommodate the law of momentum and energy conservation. The subject of momentum conservation was discussed in a pervious paper, while preliminary results regarding energy conservation where discussed in some additional papers. Here we give a complete analysis of the exchange of energy between the mechanical part of the relativistic engine and the field part, the energy radiated from the relativistic engine is also discussed. We show that the relativistic engine effect on the energy is 4th-order in 1c and no lower order relativistic engine effect on the energy exists.

42 ENGINEERING↗
DOE OSTI2025

Egg yolk as a model for gelation: From rheometry to flow physics

Egg yolks are an excellent model for studying sol-gel transitions, particularly the power law viscoelasticity that defines the critical point of gelation. However, prior studies lack comprehensive datasets and fail to visualize flow behavior linked to temperature and time-dependent linear and nonlinear rheology. Here, we present a detailed dataset characterizing egg yolk viscoelasticity across temperature, time, and forcing amplitude using oscillatory shear, step strain, step stress, and constant high strain rate. Novel protorheology visualizations link rheological properties with observable flow behavior. Our findings highlight the nuanced determination of the critical gel point, emphasizing observation timescale dependencies. We compare methods to identify critical temperatures for gelation, including power law viscoelasticity, moduli crossover, diverging zero-shear viscosity, and emerging equilibrium elastic modulus, while visualizing flow consequences near these transitions. Egg yolk is an accessible, realistic, and nontoxic material relevant to the physicist and the chef alike, making it ideal for understanding the rheology of critical gels. By integrating protorheology photos and videos with rigorous rheometric data, we deepen the understanding of critical gels, with broader impacts for studying other materials with sol-gel transitions.

Marsh, Maxwell C. [Department of Mechanical Scienc↗
DOE OSTI2022

FEMTOSECOND LASER DRILLING OF SILICON NITRIDE

Ceramics are employed in various engineering applications including electronics, semiconductors, automotive and aerospace industry, among others. Ceramics are desirable materials due to their high hardness, thermal resistance, chemical, thermal, and mechanical properties. A drawback of engineering ceramics is the manufacturing process. Ceramics are hard and brittle and conventional drilling or cutting methods can damage the material. These challenges in manufacturing have limited the application of ceramics in certain engineering applications. The non-contact machining of ceramics through ultrashort pulsed laser drilling offers a possible solution to the manufacturing issues. The advantage of laser drilling is the ability to machine hard and brittle materials. A laser, pulsed in the time regime of femtoseconds (fs), drills/machines sharp and well-defined holes and cuts. Additionally, the short pulse width results in limited melting, cracking, or heat transfer to the surrounding material, as well as no residual stresses. This non-contact process can greatly aid in the machining of hard materials. This study focuses on drilling into silicon nitride to produce zero-taper through holes. With this, other thicknesses can be explored, to achieve zero-taper holes.

Femtosecond laser ablation, Ceramics, Silicon Nitr↗
DOE OSTI2022

High thermal conductivity in wafer-scale cubic silicon carbide crystals

High thermal conductivity electronic materials are critical components for high-performance electronic and photonic devices as both active functional materials and thermal management materials. We report an isotropic high thermal conductivity exceeding 500 W m –1 K –1 at room temperature in high-quality wafer-scale cubic silicon carbide (3C-SiC) crystals, which is the second highest among large crystals (only surpassed by diamond). Furthermore, the corresponding 3C-SiC thin films are found to have record-high in-plane and cross-plane thermal conductivity, even higher than diamond thin films with equivalent thicknesses. Our results resolve a long-standing puzzle that the literature values of thermal conductivity for 3C-SiC are lower than the structurally more complex 6H-SiC. We show that the observed high thermal conductivity in this work arises from the high purity and high crystal quality of 3C-SiC crystals which avoids the exceptionally strong defect-phonon scatterings. Moreover, 3C-SiC is a SiC polytype which can be epitaxially grown on Si. We show that the measured 3C-SiC-Si thermal boundary conductance is among the highest for semiconductor interfaces. These findings provide insights for fundamental phonon transport mechanisms, and suggest that 3C-SiC is an excellent wide-bandgap semiconductor for applications of next-generation power electronics as both active components and substrates.

42 ENGINEERING↗
DOE OSTI2022

Dynamic aspects of graphene deformation and fracture from approximate density functional theory

Graphene is one of the most intriguing two-dimensional carbon materials. Its mechanical strength and failure are key concerns for materials engineering and applications. Despite the success of fracture mechanics, the mechanism of how pristine materials fail remains an elusive problem. While many theoretical studies based on molecular dynamics using empirical forcefields have tried to address this question, atomic-scale mechanics are not clearly understood. Especially, a widely employed bond-breaking approach based on the critical bond length has not been rigorously tested. Here, utilizing molecular dynamics simulations with density functional based tight binding, we investigate how the failure of the pristine material initiates. The Wiberg bond order (W BO ) to estimate the change of chemical bonds shows a transition from sp 2 (W BO ~ 1.33) to sp 3 (WBO < 1.0) during the deformation. However, it reveals that a single threshold value for either the WBO or bond length is insufficient to decide failure of pristine material without free edges or defects. Instead, collective behaviors of the local atomic group govern the fracture initiation of pristine graphene. Overall, our study provides dynamic mechanical responses based on quantum mechanics, which have not been captured by empirical forcefields, opening opportunities to design properties by precisely coupling the mechanics and quantum mechanics.

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

Dry electrodes with a printed cellulose–graphene ink for low-profile strain sensors in electromyography

Dihydrolevoglucosenone, commonly known as Cyrene, is a renewable and fully biodegradable cellulose-waste derived, environmentally friendly solvent, presenting a non-toxic alternative to N-methyl-2-pyrrolidone (NMP). Currently, solution-based processing of graphene and other similar van der Waals solids favor toxic solvents such as NMP, limiting their use for biosensing. However, with the use of Cyrene, bio-compatible printable devices are possible, and studies have already demonstrated its use in temperature and other biosensing methods through screen-printing. Screen-printing unfortunately often requires masks that constrain the minimum acquirable feature size to be above hundreds of centimeters and wastes material, adding to process complexity and cost. Conversely, inkjet-printing is an attractive alternative for the maskless patterning of hierarchically assembled structures, with micron length scales attainable. Graphene's high conductivity positions it ideally for long-wear sensors such as dry electrodes or respiration monitors. Here, we demonstrate the potential of Cyrene-based graphene inks through few-layer inkjet printing on flexible substrates for the first time, to produce non-toxic conductors toward a strain-mediated mechanism for biosensing, used to detect bodily motion for wearable electronics. The challenges overcome in this study include engineering ink chemistry and printing parameters such that Cyrene's relatively high viscosity compared to typical inkjet solvents, still allows for droplet ejection in a conventional material printer, yielding well-resolved clean line-edges in contrast to other solvents that exhibit diffuse line-edges possibly from stray droplets and ink-splashing. Temperature-dependent transport measurements on the inkjet-printed Cyrene-based graphene films showed the conductivity to be largely temperature-invariant but at lower temperatures below 100 K, conductivity decreased, likely as a result of increased inter-membrane separation arising from thermal contraction. Additionally, temperature-dependent Raman spectroscopy showed the red-shift in the G-band, 2D-band and D-band peaks, as temperature increased. As a result, by validating flexion motion detection of the proximal interphalangeal joint demonstrated in this study, our work is the first of its kind to successfully additively manufacture inkjet-printed Cyrene-based graphene strain sensors on flexible substrates for bio-sensing and wearables.

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

Extending wire-arc directed energy deposition using non-gravity aligned (NGA) torch methods

For standard Additive Manufacturing (AM) processes, traditional path planning typically relies on a 2.5-dimensional approach. In wire-arc directed energy deposition (DED), commonly referred to as wire-arc additive manufacturing (WAAM), this 2.5D approach inherently limits final near net shape due to the stair-step effect and restricts the maximum overhang angle achievable without part degradation. To achieve better near net shape and part quality, a 3D planning approach that modulates the tool tip position and angle without process changes is demonstrated in components containing up to 105° of unsupported overhang. The experimental methods are validated with half and fully enclosed cylinder sections containing 90° of overhang. The non-gravity aligned methods are then applied to a commercial WAAM system for a composite tool mold demonstrator part. As a result, the methods developed in this paper enable expansion of WAAM system capabilities to parts containing large overhangs without compromising the net shape or material structure of the resulting parts and without the need for a part positioner.

Additive manufacturing↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
DOE OSTI2023

Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems

We consider a high-dimensional nonlinear computational model of a dynamical system, parameterized by a vector-valued control parameter, in the presence of uncertainties represented by an uncontrolled parameter modeled by a vector-valued random variable, and possibly with stochastic excitation. The objective is to construct a statistical surrogate model where the input is any deterministic value of the control parameter, and the output is a vector-valued observation of the computational model, which is a random vector whose probability measure is updated using a target dataset. To construct this statistical surrogate model, the stochastic response of the computational model must be built, which is a vector-valued time-discretized stochastic process in high dimension, depending on the control parameter. It is assumed that the computational cost of a single evaluation of the deterministic model is high. For the probabilistic updating, we consider a subset of the components of the observation of the computational model, defined as the “identification observation” of the computational model, for which a small target dataset is available. Therefore, the target dataset is associated with partial observability, corresponding to an incomplete data case. Given a prior probability model of the random control and uncontrolled parameters, a training dataset is constructed, consisting of realizations of the random triplet composed of the stochastic response, the random identification observation, and the random control parameter. Since the computational cost of a single evaluation of the deterministic model is assumed to be large, the training dataset is also of small size. The main challenges in this problem are the high dimensionality, partial observability leading to incomplete data in the target dataset for the identification observation of the computational model (which is not sufficient to identify the computational stochastic responses), and the availability of a small training dataset. To address these challenges, we propose a methodology based on statistical methods for constructing necessary reduced representations, direct probabilistic learning under constraints using probabilistic learning on manifolds (PLoM) constrained by the target dataset, and the use of a weak formulation of the Fourier transform of probability measures. Statistical conditioning is also employed to explore the learned dataset. The constructed predictive statistical surrogate model can be implemented in the context of online computation. Here, we apply this approach to a problem of nonlinear stochastic dynamics in high dimensions within the framework of deformable solids mechanics.

Engineering↗
DOE OSTI2025

Secure biosystems design in Saccharomyces cerevisiae establishes effective biocontainment strategies and mechanisms of escape

The widespread application of recombinant DNA and synthetic biology approaches for microbial metabolic engineering pursuits has motivated the development of biocontainment strategies, targeting safe and secure deployment of genetically modified microorganisms (GMMs). However, the design rules and mechanistic drivers governing biocontainment efficacy, as well as impacts of biocontainment upon microbial fitness, remain to be comprehensively evaluated, hindering predictive design and application of these strategies. We have developed a platform for high-resolution analysis of a transactivated kill switch in laboratory and industrial strains of Saccharomyces cerevisiae to assess modes of biocontainment escape and establish design rules for development of kill switch systems in diverse microbes. A camphor-regulated, RelE toxin system was systematically deployed to assess the impacts of differential kill switch copy number and ploidy in laboratory vs industrial strains. CRISPR-mediated integration of the biocontainment system at various loci revealed rapid escape events driven, in part, by mutations to both the Cam-transactivator (cam-TA) and RelE toxin. Genetic engineering enabled recapitulation of escape phenotypes, confirming mechanisms of escape and establishing structure-function relationships in the cam-TA system. Interestingly, genomic resequencing of escape mutants also revealed a series of off-target mutations, implicating additional modes of kill switch escape. Multi-copy integration of the kill switch system mitigated these effects by orders of magnitude, without compromising the biosynthetic capacity of the microbes, but proved insufficient to establish sustained biocontainment. The resultant data define a series of key design rules for next-generation biocontainment strategies and add to a growing foundational knowledge base targeting establishment of secure biosystems designs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Anion Vacancies Coupling with Heterostructures Enable Advanced Aerogel Cathode for Ultrafast Aqueous Zinc‐Ion Storage

As a potential cathode material, manganese-based sulfide has recently attracted increasing interest due to its many advantages in aqueous zinc-ion storage. Unfortunately, some challenges such as sluggish kinetics, unstable structure, and controversial phase transition mechanism during the energy storage process hinder its practical application. Herein, inspired by density functional theory (DFT) calculations, a novel 3D sulfur vacancy-rich and heterostructured MnS/MXene aerogel is designed, and used as a cathode for aqueous Zn-ion batteries/hybrid capacitors (ZIBs/ZICs) for the first time. Thanks to the synergistic modification strategy of sulfur vacancies and heterostructures, the as-constructed MnS/MXene//Zn ZIBs exhibit significantly enhanced electrochemical properties, especially outstanding rate capability and cyclic stability. More encouragingly, the as-assembled MnS/MXene//porous carbon (PC) ZICs exhibit an ultrahigh energy density, a high power density, and a splendid cycling lifespan. Most notably, systematic kinetic analyses, ex situ characterizations, and DFT calculations illustrate that MnS/MXene first irreversibly converts into MnO x @ZnMnO 3 /MXene, and then undergoes a reversible conversion from MnO x @ZnMnO 3 /MXene to MnOOH@ZnMn 2 O 4 /MXene, accompanied by the co-insertion/extraction of H + and Zn 2+ . Further, the synergistic modification strategy of sulfur vacancies and heterostructures and the thorough mechanistic study proposed in this work offer valuable guidance for designing and exploiting high-performance cathodes in aqueous zinc-based energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2020

A machine learning approach to fracture mechanics problems

Analytical and empirical solutions to engineering problems are usually preferred because of their convenience in applications. However, they are not always accessible in complex problems. A new class of solutions, based on machine learning (ML) models such as regression trees and neural networks (NNs), are proposed and their feasibility and value are demonstrated through the analysis of fracture toughness measurements. It is found that both solutions based on regression trees and NNs can provide accurate results for the specific problem, but NN-based solutions outperform regression-tree-based solutions in terms of their simplicity. Here this example demonstrates that ML solutions are a major improvement over analytical and empirical solutions in terms of both reliable functionality and rapid deployment. When analytical solutions are not available, the use of ML solutions can overcome the limitations of empirical solutions and substantially change the way that engineering problems are solved.

36 MATERIALS SCIENCE↗
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↗