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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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Several aspects of lithium primary cell technology are discussed with respect to aerospace application. Particular attention is given to the statistical analysis of battery data and accelerated testing.
High-energy X-ray observations have transformed our understanding of astrophysical systems where non-thermal processes dominate, from shock-accelerated particles in supernova remnants to relativistic jets and hot coronae in active galactic nuclei. NuSTAR’s pioneering focusing images of the hard X-ray sky have demonstrated the immense scientific potential of this band, while also highlighting how limited angular resolution constrains our ability to fully characterize complex physical processes in faint extended sources and resolving faint sources in crowded sky regions. In this presentation, I will discuss a pathway to overcome these challenges through NASA Marshall Space Flight Center’s replicated NiCo full-shell X-ray mirror technology. Specifically, I will discuss the SuperHERO hard X-ray telescope to achieve better than 10-arcsecond half-power diameter (HPD) angular resolution on a balloon flight observing the Crab Nebula, and the High EneRgy Observatory for Imaging X-rays (HEROIX) Medium-Class Mission concept to achieve an integrated effective area of 570 cm2 at 30 keV with 5 arcsec HPD angular resolution.
Abstract Elastic nonlinearity observed in consolidated granular media can be attributed to the combination of slow and fast effects, which give rise to hysteresis and relaxation of both modulus and damping after the sample is perturbed. A consequence is a high level of complexity in the measurements of the sample linear and nonlinear elastic parameters. The results of experiments are dependent on the experimental protocol that is adopted to measure the relevant quantities and it is hard to quantify parameters with accuracy and repeatability. Here we focus on examining Nonlinear Resonant Ultrasound Spectroscopy, showing experimentally the role of slow dynamics in the process and quantifying/discussing its influence on the quantification of nonlinearity. We also propose a model to describe the process, which shows that different contributions to nonlinearity (e.g., classical and hysteretic) could be due to physical features (defects) relaxing with different relaxation times.
Performance results of a digital test signal-generator hardware-demonstration unit are reported. Capabilities available include baseband and intermediate frequency (IF) spectrum generation, for which test results are provided. Repeatability in the setting of a given signal-to-noise ratio (SNR) when a baseband or an IF spectrum is being generated ranges from 0.01 dB at high SNR's or high data rates to 0.3 dB at low data rates or low SNR's. Baseband symbol SNR and carrier SNR (P c /N o ) accuracies of 0.1 dB were verified with the built-in statistics circuitry. At low SNR's that accuracy remains to be fully verified. These results were confirmed with measurements from a demodulator synchronizer assembly for the baseband spectrum generation, and with a digital receiver (Pioneer 10 receiver) for the IF spectrum generation.
This work introduces a completely rewritten version of the programRMCProfile(version 7), big-box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature ofRMCProfile7is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid-body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back-Fourier transform are now also available.RMCProfile7is freely available for download at https://rmcprofile.ornl.gov/.
Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity—electronic energy gaps—is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6% accuracy in cuprate superconductor Bi 2 Sr 2 CaCu 2 O 8+δ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems.
Advanced Si photovoltaic architectures incorporate different materials and processing pathways that influence degradation modes. Ultraviolet-induced degradation (UVID) is an understudied degradation mode for advanced cell architectures and is of increasing concern to industry due to growing adoption of UV-transparent encapsulation and bifacial technologies. In order to adopt new and evolving technologies confidently, novel component materials and processing techniques must be evaluated and designed for long-term stability, in addition to the conventional design focus on efficiency. In this work, a study protocol framework is presented for the rapid screening of unencapsulated devices against UVID. Unencapsulated passivated emitter rear contact (PERC) and tunnel oxide passivated contact (TOPCon) devices were aged under different UV irradiance intensities and measured via conventional nondestructive electrical characterization methods to assess performance degradation. Based on the results, protocol efficacy and recommendations for further study are discussed. As a result, this work is part of a broader effort to develop rapid screening processes that cut across architectures and exposure conditions to aid module manufacturers in vetting new materials choices for long-term stability.
The effective operation of atomic layer deposition (ALD) feeding system is the premise of realizing specific ALD processes. In the present work, a detailed computational fluid dynamics (CFD) model of the feeding system has been developed and validated, which accounts for the roles of ALD valves and manifolds. A numerical simulation of the compressible fluid flow and heat/mass transfer within the feeding system was conducted. The dosing amounts and the spatiotemporal distributions of the precursors can be accurately predicted using the CFD model, as validated by experimental results. Different precursors, operating conditions, and structures of the feeding system were simulated and analyzed to examine the operating flexibility of the feeding system. The simulation results can be adopted as the upstream boundary conditions for simulations of the ALD process in the reaction chamber. The substrate-scale simulation indicates that the effect of the feeding system on the film deposition is highly related to the surface kinetics of ALD. The present work can serve as a guide for the development and optimization of different ALD-based processes via proper operation and even the design of the feeding system.
Low thermal budget (LTB) annealing has become increasingly critical for shallow junctions as semiconductor devices are scaled down. Plasma Enhanced Annealing (PEA) has emerged as a promising LTB annealing process, however its mechanisms remain unclear. In this study, arsenic and boron implanted silicon wafers were subjected to helium or argon ion bombardment generated by annealing plasmas under controlled ion energies and doses in a home-built annealing reactor. The structural and electrical changes were characterized by four-point probe measurements, secondary ion mass spectrometry, Raman spectroscopy and spectroscopic ellipsometry. A pronounced reduction in sheet resistance, accompanied by a decrease in the damage layer thickness demonstrates the surface selective annealing capability of PEA process. Comparative studies using He and Ar plasmas and related ion bombardment reveal a dependence of activation efficacy on both ion species and the dopant implantation profile. Furthermore, a multi-step annealing mechanism is proposed, consisting of an initial (low dose) structural recovery stage followed by the generation of “extended” defects and concluded at large doses by an enhanced dopant activation. The proposed annealing kinetics are thought to be controlled by ion flux, dose and energy. Finally, these results highlight PEA as an effective, surface-selective, LTB approach for shallow-dopant activation and near surface annealing, and provide mechanistic insights into PEA processes.
The laser altimeter, consisting of a Q switched ruby laser, transmitting optics, counting timer, receiving optics, and a photomultiplier is described. Harmonic analysis of the mission data is also presented.
This paper will delineate the criteria required for the selection of batteries as a power source for space experiments. Four basic types of batteries will be explored, lead acid, silver zinc, alkaline manganese and nickel cadmium. A detailed description of the lead acid and silver zinc cells while a brief exploration of the alkaline manganese and nickel cadmium will be given. The factors involved in battery selection such as packaging, energy density, discharge voltage regulation, and cost will be thoroughly examined. The pros and cons of each battery type will be explored. Actual laboratory test data acquired for the lead acid and silver zinc cell will be discussed. This data will include discharging under various temperature conditions, after three months of storage and with different types of loads. A description of the required maintenance for each type of battery will be investigated. The lifetime and number of charge/discharge cycles will be discussed.
Growth in power requirements and the complexity of emerging distribution systems are creating the need for new solid-state products for many applications. The qualification requirements, testing standards and life-cycle management practices of solid-state devices used in protection applications is not established. The project will leverage newly developed accelerated testing capabilities design specifically for protection applications to generate large data sets that will enable advanced digital techniques for data analysis and embedded health monitoring.
Landsat 10 will be the upcoming mission in the 50+ year Landsat series of Earth observation platforms. The centerpiece of the observatory will be a super-spectral imager known as the Landsat Instrument Suite (LandIS). The sensor will feature 26 spectral channels from the visible through near-, short-wave, and thermal infrared wavelengths with spatial resolutions of 10, 20, and 60 meters on the ground, depending on the band. These enhancements over the legacy Landsat instruments will ensure data continuity with the existing archive and will expand upon the core Landsat capabilities to enable new applications in Earth science. After a competitive procurement, NASA selected the design submitted by the Raytheon Company for the LandIS instrument. The innovative Raytheon instrument concept utilizes an advanced whiskbroom architecture to fulfill the strict radiometric, spatial, and geometric image quality requirements demanded by the Landsat 10 mission and fits within restrictive mass, volume, and power constraints. The instrument will continue the Landsat directive to image all daylit land and near-shore water areas, along with select nighttime imaging. On-board calibration source data will ensure high radiometric and geometric accuracy and stability consistent with previous missions to enable continuity in data products available to users. This paper discusses the driving requirements for LandIS and provides a description of the chosen design and operations concept of the instrument.
Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.
The orbit data for the Intelsat 4 spacecraft is described. The performance and characteristics of the battery used for this spacecraft are discussed. Graphs are presented which relate battery voltage to various eclipse seasons.
It is a critical national imperative to transform the buildings sector, yet innovation is impeded by deployment failures that leave promising technologies stranded. Conventional market reports and techno-economic analysis provide an insufficient understanding of markets and resource allocation for emerging building technologies. They omit crucial commercialization factors such as ecosystem maturity and adoption friction, where the coordinated participation of a network of suppliers, contractors, financiers, regulators, and integrators is required to scale solutions. This study addresses these gaps by introducing an evaluation framework grounded in front-line data from six years of the DOE's IMPEL incubator, comprising experience from 300 building-sector innovators and the adjacent, complex ecosystem. Our methodology synthesizes top-down market analysis with bottom-up, practitioner-level data across five megatrends: (M1) Affordable materials and industrialized construction; (M2) Healthy and efficient mechanical systems; (M3) Intelligent building operations; (M4) Buildings as grid assets; and (M5) High-density power and cooling for data centers and therein identify twelve "white space" technology opportunities. Next, we develop a multi-criteria scoring rubric to rank these opportunities based on parameters, i.e., Affordability, Quality of Life, Reliability, and Security, yielding composite ‘Demand’ and ‘Maturity’ indices. Our results indicate that the most significant white spaces may not be incremental products but a new class of ‘Ecosystem Enablers’, such as logistics platforms, orchestration layers, and automated compliance software that solve structural deployment gaps. This paper summarizes this transparent, evidence-based, practitioner-informed evaluation framework for policymakers and investors to re-evaluate policy and resource allocation and unlock scalable market transformation.
There is a growing body of theoretical and experimental evidence suggesting that the uniformity, rate, adhesion, quality, and other key properties of Chemical Vapor Deposition (CVD) diamond coatings are influenced by the gas mixing kinetics in the thermal plasma environment of the reaction chamber. The implementation of, for example, Microwave Plasma Enhanced Chemical Vapor Deposition (MPECVD) process in microgravity has, thus, been suggested. Such a diamond deposition system, which overcomes the limitations of present systems and which is distiguishable from them by the use of high pressure MPECVD and recirculation of the would be effluent hydrogen and carbon, is described. Given the key fact that there is nothing in the effluent of the MPECVD process that is truly a byproduct or 'waste', the system can, at least in principle, lend itself to being closed loop yet dynamic. The exhaust contains hydrogen and carbon species which can be recirculated to the plasma reactor, that is, since there are no unusable reaction byproducts, the effluent can be fed back to the reaction chamber with no detriment, thus allowing deployment in a microgravity environment.
Maintaining optimal team functioning is critical for long-duration space exploration missions, yet traditional monitoring methods, such as self-reports and wearable sensors, often impose operational burdens or suffer from bias. This paper investigates a non-intrusive speech-based artificial intelligence (AI) framework to predict degradations in team functioning using data from the Human Exploration Research Analog (HERA) of the U.S. National Aeronautics and Space Administration (NASA). Using acoustic features, linguistic descriptors, and semantic embeddings, we evaluate static non-linear and temporal machine learning models to predict both objective (task accuracy) and subjective (self-reported efficacy and cohesion) team functioning outcomes. Results indicate that temporal models outperform static approaches, with prediction of objective task accuracy in Team Interaction Battery (TIB) improving from near chance to 71%. Self-reported outcomes, including team efficacy and cohesion, are predicted more reliably than task performance, achieving balanced accuracies of up to 85.56% and 78.12%, respectively, and are found to be most strongly associated with acoustic features. In a second interdependent task, the MMSEV–EVA, accuracies of up to 78% are achieved using temporal models with acoustic features. Furthermore, incorporating just 1–2 days of team-specific historical data systematically improved performance, and acoustic markers from informal pre-task interactions provided modest predictive gains. Finally, while automated preprocessing yielded viable accuracy, humancorrected data provided moderate performance gains, though transcription error rates did not significantly correlate with model performance. These findings highlight the potential of speech as a passive, high-fidelity monitoring tool for autonomous habitats.