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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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Pressure drop and heat transfer properties for normal weight and lightweight pervious concrete
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Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications
The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of medium-entripy alloys (easily extendable to multi-principal-element alloys), such as Ti–Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co–Ni-X (X = Al, Mg, V). Ti–Nb–Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40–50 %. We attributed results in molybdenum-rich alloys to slow hydrogen kinetics, as validated by our pressure-composition-temperature (PCT) isotherm experiments on pure Ti and Ti 5 Mo 95 alloys. Density functional theory (DFT) and molecular dynamics (MD) simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. Here, to aid material selection, we present two periodic tables illustrating elemental effects on (a) H 2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.
A green ultra-lightweight chemically foamed concrete for building exterior: A feasibility study
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An automated and lightweight framework for electrolyte diagnostics using quantitative microelectrode voltammetry
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Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN
Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality and are the stealthiest attacks to detect in CAN, recent work has devoted attention to compare frameworks for detecting masquerade attacks in CAN. However, most existing works report offline evaluations using CAN logs already collected using simulations that do not comply with the domain’s real-time constraints. Here we contribute to advance the state of the art by presenting a comparative evaluation of four different non-deep learning (DL)-based unsupervised online intrusion detection systems (IDS) for masquerade attacks in CAN. Our approach differs from existing comparative evaluations in that we analyze the effect of controlling streaming data conditions in a sliding window setting. In doing so, we use realistic masquerade attacks being replayed from the ROAD dataset. We show that although evaluated IDS are not effective at detecting every attack type, the method that relies on detecting changes in the hierarchical structure of clusters of time series produces the best results at the expense of higher computational overhead. We discuss limitations, open challenges, and how the evaluated methods can be used for practical unsupervised online CAN IDS for masquerade attacks.
Lightweight, Passive Radiative Cooling to Enhance Concentrating Photovoltaics
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Development of a lightweight, portable, waterproof, and low power stem respiration system for trees
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Lightweight Zr1.2V0.8NbTi Al high-entropy alloys with high tensile strength and ductility
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Mini-DAQ: A lightweight, low-cost, high resolution, data acquisition system for wave energy converter testing
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Synthesis of sustainable, lightweight and electrically conductive polymer brushes grafted multi-layer graphene oxide
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Inventions, innovations, and new technologies: Flexible and lightweight thin-film solar PV based on CIGS, CdTe, and a-Si:H
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A Fireproof, Lightweight, Polymer–Polymer Solid-State Electrolyte for Safe Lithium Batteries
Safety issues in lithium-ion batteries have raised serious concerns due to their ubiquitous utilization and close contact with the human body. Replacing flammable liquid electrolytes, solid-state electrolytes (SSEs) is thought to address this issue as well as provide unmatched energy densities in Li-based batteries. However, among the most intensively studied SSEs, polymeric solid electrolyte and polymer/ceramic composites are usually flammable, leaving the safety issue unattended. Here, we report the first design of a fireproof, ultralightweight polymer–polymer SSE. The SSE is composed of a porous mechanic enforcer (polyimide, PI), a fire-retardant additive (decabromodiphenyl ethane, DBDPE), and a ionic conductive polymer electrolyte (poly(ethylene oxide)/lithium bis(trifluoromethanesulfonyl)imide). The whole SSE is made from organic materials, with a thin, tunable thickness (10–25 μm), which endorse the energy density comparable to conventional separator/liquid electrolytes. The PI/DBDPE film is thermally stable, nonflammable, and mechanically strong, preventing Li–Li symmetrical cells from short-circuiting after more than 300 h of cycling. LiFePO 4 /Li half cells with our SSE show a high rate performance (131 mAh g –1 at 1 C) as well as cycling performance (300 cycles at C/2 rate) at 60 °C. Most intriguingly, pouch cells made with our polymer–polymer SSE still functioned well even under flame abuse tests.
Inverse Design of Lightweight Broadband Reflector for Relativistic Lightsail Propulsion
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Accurate additive manufacturing of lightweight and elastic carbons using plastic precursors
Despite groundbreaking advances in the additive manufacturing of polymers, metals, and ceramics, scaled and accurate production of structured carbons remains largely underdeveloped. This work reports a simple method to produce complex carbon materials with very low dimensional shrinkage from printed to carbonized state (less than 4%), using commercially available polypropylene precursors and a fused filament fabrication-based process. The control of macrostructural retention is enabled by the inclusion of fiber fillers regardless of the crosslinking degree of the polypropylene matrix, providing a significant advantage to directly control the density, porosity, and mechanical properties of 3D printed carbons. Using the same printed plastic precursors, different mechanical responses of derived carbons can be obtained, notably from stiff to highly compressible. This report harnesses the power of additive manufacturing for producing carbons with accurately controlled structure and properties, while enabling great opportunities for various applications.
Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part I: design principles and rapid down-selection
Abstract Refractory metal-based multi-principal element alloys (MPEAs) are compelling materials for high-temperature (1000–2000 K) structural applications. However, only a minuscule fraction of their vast and heterogeneous compositional design space has been explored, leaving many potentially interesting alloys undiscovered. In this two-part work, a large region of the 11-element Al-Cr-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr design space is computationally explored to identify refractory MPEAs with simultaneously high yield strength or specific yield strength and body-centered cubic (BCC) solid solution stability. In Part I , two case studies illuminate key factors and considerations in the yield strength versus phase stability tradeoff, provide guidelines for narrowing the expansive design space, and identify many candidates predicted to be stronger than refractory MPEAs reported to date, with BCC phase stability. Our findings indicate that medium entropy ternary alloys can outperform alloys with more elements and highlight the importance of exploring regions away from the equiatomic center of composition space.
Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part II: comprehensive ternary design and validation
Abstract Here the discovery of refractory multi-principal element alloys (MPEAs) with high-temperature strength and stability is pursued within a constrained and application-relevant design space. A comprehensive approach is developed and applied to explore all 165 ternary systems in the Al-Ce-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr family. A subset of ternary systems that contain large areas in composition–temperature space with high strength and robust BCC phase stability is found. Twelve sets of high-performing alloys are identified, each set optimized for one combination of phase constraint, optimization target, and temperature range. Preliminary mechanical tests support the viability of the method. This work highlights the importance of considering phase stability, exploring non-equiatomic regions of composition space, and applying application-relevant constraints. Parts I and II provide three down-selection techniques for identifying high-performing BCC refractory MPEAs, design guidelines, and many candidates predicted to have BCC phase stability and strengths 2–3 times higher than any reported to date.