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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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This project improved the understanding of how electrochemical reactions cause degradation when perovskite solar cells are operated in reversed bias. Atomic layer deposition of
The capture of volatile radioiodine from nuclear fuel reprocessing off-gas streams remains a critical challenge due to the high volatility, long half-life of 129I, and biological uptake of iodide from the environment. Although silver-based sorbents provide strong iodine chemisorption, their high cost and regulatory classification as mixed radioactive-hazardous waste motivate the development of alternative materials. Here, we report a silver-free Cu2O-Ti3C2Tx MXene hybrid for iodine gas capture at 150 °C. Structural and compositional analyses confirm the formation of Cu2O nanoparticles on Ti3C2Tx nanosheets and their subsequent conversion to thermodynamically stable CuI upon static iodine gas exposure, achieving an iodine mass loading of up to 1115 mg/g. These results demonstrate the potential of Cu2O-Ti3C2Tx MXene as a copper-based alternative to silver sorbents for elevated-temperature iodine gas capture.
Molecular beam epitaxy (MBE) has been used to create high-quality, large-scale two-dimensional van der Waals (2D vdW) materials. However, due to the strong adhesion between the substrate and deposited materials, the peel-off and dry transfer of MBE-grown vdW films onto other substrates has been challenging. This limits the study and use of MBE films for heterogeneous integration including stacked and twisted heterostructures. In this work, we develop a polymer-assisted dry transfer method and successfully perform full-film transfer of various MBE-grown 2D vdW materials including transition metal dichalcogenides (TMD), topological insulators (TI) and 2D magnets. In particular, we transfer air-sensitive 2D magnets, characterize their magnetic properties, and compare them with as-grown materials. The results show that the transfer technique does not degrade the magnetic properties, with the Curie temperature and hysteresis loops exhibiting similar behaviors after the transfer. Our results enable further development of heterogeneous integration of 2D vdW materials based on MBE growth.
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This Special Issue focuses on noise associated with Advanced Air Mobility (AAM), an emerging class of predominantly electric distributed-propulsion aircraft designed for urban and regional transportation. As these vehicles move toward certification and deployment, noise has become a central challenge for regulatory approval and public acceptance, particularly due to operations in densely populated areas and at low altitudes. The 24 contributions in this issue address three key aspects of AAM noise: prediction, measurement, and human perception. Prediction studies span a wide range of modeling fidelities, from high-resolution simulations to improved semi-analytical approaches, and examine complex aeroacoustic mechanisms including rotor interactions, turbulence ingestion, and broadband noise generation. Measurement studies, largely at model scale, provide new insights into tonal and broadband noise characteristics across configurations and operating conditions, while supporting model validation. Perception-focused contributions investigate annoyance, sound quality metrics, and auralization, emphasizing the role of context and operational factors in shaping human response. Together, these works highlight the interdisciplinary nature of AAM noise research and the need for integrated approaches to enable quieter vehicle design.
Polyvinyl chloride (PVC) is ubiquitous yet challenging to recycle due to its tendency to thermally decompose above 250 °C, releasing toxic, corrosive chlorinated compounds, and its inability to melt. Here, we report a catalytic strategy for PVC upcycling at 160 °C using gallium liquid metal particles (Ga-LMP) featuring a dynamic Ga-GaOOH core–shell architecture. These catalysts enable concurrent dechlorination and hydrogen evolution, yielding up to 7% H2 (based on initial hydrogen atoms in PVC) along with a highly dechlorinated (>95%) carbonaceous solid and aqueous HCl. Mechanistic investigations combining X-ray photoelectron spectroscopy, infrared spectroscopy, solid-state NMR, inelastic neutron scattering, and ab initio molecular dynamics reveal a synergistic interplay between Gaδ+ sites in the GaOOH shell and metallic Ga0 in the core. Cationic Ga initiates C–Cl bond activation and HCl formation, while progressive reduction of the shell exposes Ga0 sites that promote C–H activation and H2 evolution. Control experiments with a Ga salt and bulk Ga liquid metal confirmed that neither oxidation state alone can achieve both transformations efficiently. This work establishes a dynamic dual-site paradigm for liquid metal catalysis, in which the in situ evolution and coexistence of oxidized and metallic species enable sequential and cooperative bond activation pathways. These findings provide a general design principle for novel liquid metal catalysts that target challenging polymer transformations under mild conditions.
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Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.
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Tomographic imaging of time-evolving samples is a challenging yet important task for various research fields. At the nanoscale, current approaches face limitations of measurement speed or resolution due to lengthy acquisitions. We developed a dynamic nanotomography technique based on sparse dynamic imaging and 4D tomography modeling. We demonstrated the technique, using ptychographic x-ray computed tomography as its imaging modality, on resolving the in situ hydration process of polymer electrolyte fuel cell (PEFC) catalyst. The technique provides a 40-time increase in temporal resolution compared to conventional approaches, yielding 28 nm half-period spatial and 12 min temporal resolution. The results allow a quantitative characterization of the water intake process inside PEFC catalysts with nanoscale resolution, which is crucial for understanding their electrochemical mechanisms and optimizing their performance. Our technique enables high-speed operando nanotomography studies and paves the way for wider application of dynamic tomography at the nanoscale.
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Nylons, a major class of synthetic polyamides, are widely used due to their excellent mechanical strength, thermal stability, and chemical resistance. Conventional nylon production relies on the polymerization of lactams or stoichiometric nylon salts. However, applying these approaches to unconventional precursors such as bio-derived glutaric acid produces polymers with low molecular weight and limited applications. To address these challenges, we demonstrated that chemically-synthesized nylon diads enable the production of higher-molecular-weight polyamides compared with traditional salts. We then identified a biosynthetic approach using amide synthetases to convert unprotected bifunctional substrates into nylon-relevant diads. Using a cofactor regeneration system, enzymatic diad synthesis was scaled to produce sufficient material for laboratory-scale characterization and solid-state polymerization. Amide synthetases demonstrated broad substrate scope, catalyzing the regioselective assembly of diverse nylon-relevant diacids, diamines, and ω-amino acids. This strategy offers a novel route to synthesize challenging nylon monomers and advances production of bioderived nylons.
Material selection is a key part of the National Aeronautics and Space Administration (NASA) spacecraft fire safety management plan. Non-flammable textiles are necessary to ensure large-scale flame propagation events do not occur inside a spacecraft. An increased use of textiles and other softgood material is crucial to the pursuit of exploration spaceflight to reduce mass and volume. Exploration spaceflight missions benefit from enriched oxygen cabin environments (>21% O2) by allowing a reduced prebreathe protocol before Extravehicular Activity (EVA). However, materials become exponentially more flammable the higher the O2 levels become. Recent testing within the agency has revealed the lack of commercial-off-the-shelf (COTS) materials that can meet safety requirements in oxygen-enriched environments. Most of the fibers and textiles developed during Apollo (100% O2 cabin environment) and Skylab (>70% O2 cabin environment) are no longer commercially available due to those Programs ending and the discontinuation of raw materials or closure of the original manufacturers. As NASA returns to higher oxygen concentrations inside spacecraft, non-flammable textile development efforts have begun to meet the agency’s needs. This paper discusses those efforts including overall fire safety approach, priorities, interactions with industry, flammability testing and expected challenges.
Accurate predictions of aeroheating are critical for designing thermal protection systems for planetary entry vehicles. For larger vehicles, turbulence in the boundary layer can substantially increase convective heating. This turbulence must be accurately modeled to ensure the thermal protection system is sufficient. The majority of hypersonic turbulence model development and validation focuses on boundary layers developing over flat-plates or sharp cones; these cases are substantially different than the boundary layer that develops over the heatshield of a blunt body traveling at hypersonic speeds. Planetary missions often use blunt body geometries, such as the 70-degree sphere-cone favored by Mars missions or the 45-degree sphere-cone planned for the upcoming DAVINCI mission. Due to smaller vehicle size and the lower velocities in the stagnation region, planetary entry vehicles have relatively low Reynolds numbers. Surface curvature and high enthalpy gradients create additional challenges. These difficulties must be addressed to obtain high accuracy needed for the ambitious planetary missions in the upcoming decade. This work focuses on both assessing and improving Reynolds-averaged Navier-Stokes (RANS) turbulence models for blunt-body geometries typical of planetary entry vehicles, with a focus on one-equation and two-equation formulations.
The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.