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1,818 records · Page 60

Theoretical design and performance of three-dimensional, pillared FeS 2 cathodes

Three-dimensional (3D) electrode design can provide improved capacities and rate capabilities over conventional two-dimensional electrodes by enhancing electrical and ionic transport. Here, expanding upon our previous modeling efforts for conversion chemistry lithium-ion batteries, we develop a pseudo-four-dimensional (P4D) approach that is subsequently used to investigate the design of a pillared FeS 2 electrode. The model considers transport in three dimensions with an additional “fourth” dimension corresponding to the solid-state lithium transport within the active material particles. Additionally, we allow for expansion of the active material during the conversion reaction to understand how internal stresses impact the electrochemical performance of the cell. By optimizing the model with respect to areal capacity, we are able to predict areal capacities up to 16.8 mAh/cm 2 for an areal current density of 1.78 mA/cm 2 and a 103% improvement for the three-dimensional electrodes over planar electrodes of equal volume. Despite the promising results, our simulations suggest that 3D design may be difficult for conversion cathode materials due to the large internal stresses that arise during conversion. Nevertheless, the model is robust and adaptable to other materials that may be more suitable for 3D electrodes due to a lesser change in volume during discharge.

Conversion cathode materials

Thermodynamically informed priors for uncertainty propagation in first-principles statistical mechanics

Here, this work demonstrates how first-principles statistical mechanics approaches within a Bayesian framework can quantify and propagate uncertainties to downstream thermodynamic calculations. To address the issue of Bayesian prior selection, knowledge of 0 K ground states in the material system of interest is incorporated into the prior. The effectiveness of this framework is shown by creating a phase diagram for the fcc zirconium nitride system, including confidence intervals on order-disorder transition temperatures.

Bayesian methods

Robust Heat-Flux Sensors for Coal-Fired Boiler Extreme Environments

In this project, robust heat-flux measurement systems were developed. The heat-flux sensors utilize thermoelectric effects to directly transduce the heat-flux inputs to analog electrical voltage signals. They were constructed from dedicated materials that can withstand temperatures of at least 1000°C and maintain adequate performance at these conditions for prolonged periods of time. The proposed approaches took into account numerous considerations, including system cost, sensor head resilience, sensor footprint, data accuracy, response time, and maintenance requirements. Through modern thermoelectric materials design, methodical materials selection and rigorous testing in materials characterization labs and medium-scale fire research facilities, we have demonstrated functioning laboratory prototypes, upon which one could base industrial heat-flux sensing platforms capable of operating in the challenging high-temperature, corrosive environments of the boilers of coal-fired power plants. A distributed sensor array for heat-flux measurements throughout the furnace water-wall, the superheater area and the economizer coils can provide critical data for the power plant control systems to increase efficiency, improve safety and reduce down times. For example, the combined heat-flux sensor/control systems can contribute to the optimization of burner and boiler operations under flexible loads, the optimization of heat-exchange conditions and overall reduction of heat rate and emissions, the prediction of imminent overheating conditions, and the optimization of the soot-blowing protocols.

20 FOSSIL-FUELED POWER PLANTS

A Multi-Tier Autonomous Aerial Architecture for Wildfire Detection, Characterization, and Communication in Infrastructure-Denied Environments

Wildfire response depends on how fast an ignition can be confirmed and located, especially in remote regions where ground-based communication and monitoring may be limited. Geostationary sensors provide frequent observations but at kilometer-scale resolution, which is too coarse to resolve small fires in remote terrain. Ground camera networks require sightlines and infrastructure that back-country areas lack. To address these limitations, this work proposes a Multi-Tier Autonomous Wildfire Intelligence System that combines wide-area monitoring with targeted, high-resolution sensing. A solar-powered high-altitude long endurance (HALE) platform operating at approximately 60,000 ft provides persistent wide-area thermal and optical surveillance, running onboard edge inference to screen candidate ignitions and reduce false positives and downlink bandwidth. When a candidate ignition is detected, low-altitude uncrewed aircraft systems (UAS) can be deployed to conduct localized observations, including high-resolution imaging and atmospheric measurements such as wind and plume observation. By combining persistent detection with local sensing, the proposed architecture is designed to provide first responders with timely, high-resolution information about fire location and behavior to aid in emergency decision making.

Wildfire management, UAS, drones

Coercivity enhancement in hematite/permalloy heterostructures across the Morin transition

Interfacial effects between antiferromagnetic (AFM) and ferromagnetic (FM) materials have long been a center of magnetism studies. Aside from the exchange bias occurring at the AFM/FM interface, controlling the coercivity is another significant topic in magnetic recordings. The coercivity of FM materials is often determined through varying grain size, alloy composition, density of defects, etc., which is set during material growth and offers limited room for modification after growth. Hematite (α - Fe 2 O 3 ) is an AFM material that undergoes a temperature-controlled spin-flip transition, the so-called Morin transition. This transition gives an extra degree of freedom making hematite an intriguing component to study the exchange coupling when interfaced with an FM material. Here, in this work, changes in the magnetic properties of soft magnetic permalloy (Ni 81 Fe 19 , or Py) thin films grown on hematite were studied across the Morin transition. Surprisingly, these samples showed a remarkable change in coercivity during the Morin transition. We attribute this effect to the magnetic domain mixture of hematite during the Morin transition. Our findings present a novel method of controlling the coercivity of plain ferromagnetic thin films.

Antiferromagnet

Derivation of low-energy Hamiltonians for heavy-fermion materials

Here, by utilizing a multiorbital periodic Anderson model with parameters obtained from ab initio band structure calculations, combined with degenerate perturbation theory, we derive effective Kondo-Heisenberg and spin Hamiltonians that capture the interaction among the effective magnetic moments. This derivation encompasses fluctuations via both nonmagnetic 4⁢𝑓 0 and magnetic 4⁢𝑓 2 virtual states, and its accuracy is confirmed through comparison with experimental data obtained from CeIn 3 . The significant agreement observed between experimental results and theoretical predictions underscores the potential of deriving minimal models from first-principles calculations for achieving a quantitative description of 4⁢𝑓 materials. Moreover, our microscopic derivation unveils the underlying origin of anisotropy in the exchange interaction between Kramers doublets, shedding light on the conditions under which this anisotropy may be weak compared to the isotropic contribution.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry

Audible Noise Modeling of Hydrogen Release Sonic Hazards in Rail Maintenance Facilities

This study implemented validated literature models to predict audible noise due to pressurized gaseous hydrogen releases through a thermally-activated pressure relief device (TPRD) and attached vent stack. A literature survey discovered limited hydrogen-specific noise prediction models validated by experiments. However, empirical noise prediction models for air flowing through pipes and valves were identified. These empirical models were used to predict noise levels and compared against hydrogen noise data reported in two studies: one experimental study of noise from hydrogen leaking through a pipe and another which modeled hydrogen flowing through a solenoid valve during a fuel cell vehicle refueling. The valve flow model was then applied to predict noise for hydrogen releases through a TPRD. Results show that hydrogen releases through a TPRD can produce harmful noise levels varying from 134 to 150 dB. However, further model validation and additional experimental data are needed to improve prediction confidence and accuracy.

08 HYDROGEN

Method for implant and anneal for high voltage field effect transistors

In an example, the present invention provides a method of forming a semiconductor device on a gallium and nitrogen containing material. The method includes providing a substrate member comprising a surface region, the substrate member comprising a gallium and nitrogen bearing material. The method includes causing an implanted species to electrically activate the implant profile while removing one or more crystalline damage from the epitaxial material to change the amorphous state to a single crystalline state, and thereby creating a substantially electrically activated crystalline material.

Shealy, James R.

Advanced Non-Intrusive Instrumentation for Propulsion Engines

Changes in engine technology such as higher temperatures, higher tip speeds, new metal/composite/ceramic materials together with radical changes in design philosophy will require amongst other prerequisites the ability to measure and to monitor key internal gas and structural characteristics. The symposium papers presented non-intrusive measurement and analysis technologies in the following categories: Laser Point Measurements (11); Absorption and Infrared Techniques (4); Paints - Surface Sensors (6); Laser Induced Fluorescence (6); Mechanical (7); Films (5); Laser Planar Measurement (9); and a Keynote Address.

Propulsion

Pressure induced modification of electronic and magnetic properties of MnCrNbAl and MnCrTaAl

Spin-gapless semiconductor (SGS) is a new class of material that has been studied recently for potential applications in spintronics. This material behaves as an insulator for one spin channel, and as a gapless semiconductor for the opposite spin. In this work, we present results of a computational study of two quaternary Heusler alloys, MnCrNbAl and MnCrTaAl that have been recently reported to exhibit spin-gapless semiconducting electronic structure. In particular, using density functional calculations we analyze the effect of external pressure on electronic and magnetic properties of these compounds. It is shown that while these two alloys exhibit nearly SGS behavior at optimal lattice constants and at negative pressure (expansion), they are half-metals at equilibrium, and magnetic semiconductors at larger lattice constant. At the same time, reduction of the unit cell volume has a detrimental effect on electronic properties of these materials, by modifying the exchange splitting of their electronic structure and ultimately destroying their half-metallic/semiconducting behavior. Thus, our results indicate that both MnCrNbAl and MnCrTaAl may be attractive for practical device applications in spin-based electronics, but a potential compression of the unit cell volume (e.g. in thin-film applications) should be avoided.

Materials Science

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.

Chemistry

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE

2,5‐Dimercapto‐1,3,4‐Thiadiazole (DMCT)‐Based Polymers for Rechargeable Metal–Sulfur Batteries

Organosulfur materials are a sustainable alternative to the present-day layered oxide cathodes in lithium-based batteries. One such organosulfur material that was intensely explored from the 1990s to early 2010s is 2,5-dimercapto-1,3,4-thiadiazole (DMCT). However, research interest declined as the electrode reactions with DMCT were assumed to be too sluggish to be practical. Armed with the advances in metal–sulfur batteries, we revisit DMCT-based materials in the form of poly[tetrathio-2,5-(1,3,4-thiadiazole)], referred to as pDMCT-S. With an appropriate choice of electrode design and electrolyte, pDMCT-S cathode paired with a Li-metal anode shows a capacity of 715 mA h g −1 and a Coulombic efficiency of 97.7% at a C/10 rate, thus quelling the concerns of sluggish reactions. Surprisingly, pDMCT-S shows significantly improved long-term cyclability compared to a sulfur cathode. Investigations into the origin of the stability reveals that the discharge product Li-DMCT in its mesomeric form can strongly bind to polysulfides, preventing their dissolution into the electrolyte and shuttling. This unique mechanism solves a critical problem faced by sulfur cathodes. Encouragingly, this mechanism results in a stable performance of pDMCT-S with Na-metal cells as well. In conclusion, this study opens the potential for exploring other organic materials that have inherent polysulfide sequestering capabilities, enabling long-life metal–sulfur batteries.

2,5-dimercapto-1,3,4-thiadiazole

Prototype Demonstration of an Integrated Solar Concentrator System and Carbothermal Reactor Using Solar Energy to Extract Oxygen from Regolith

The Carbothermal Reduction Demonstration (CaRD) project was an effort to develop a prototype system to demonstrate the extraction of oxygen from simulated lunar regolith using concentrated solar energy and a carbothermal reaction. The prototype consisted of a deployable solar concentrator capable of tracking the sun, carbothermal reactor, fluid system, gas analysis, and a solar concentrator control system consisting of avionics and software. These subsystems were developed by multiple NASA centers and a private industry partner, Sierra Space. The various teams worked together to define requirements and interfaces to successfully assemble the complex system and demonstrate an integrated solar carbothermal process. The solar concentrator developed at Glenn Research Center (GRC) was designed to be stowed for a launch environment then deployed on the lunar surface. It utilized a crossed dragone configuration of composite mirrors to direct horizontal sunlight onto a target 90° from the incoming sunlight. The key performance parameters for the solar concentrator were efficiency and power density. The carbothermal reactor was developed by Sierra Space through a separate project called the Carbothermal Oxygen Production Reactor (COPR) where it successfully demonstrated a fully automated process in a thermal vacuum environment. The fluid system needed for the carbothermal reaction was also developed by Sierra Space and successfully demonstrated in the same thermal vacuum test. The gas analysis system was developed at Kennedy Space Center (KSC) and was required to determine the amount of oxygen extracted during each test. The gas analysis system was based on the Mass Spectrometer Observing Lunar Operations (MSOLO) instrument. Avionics and software for the CaRD prototype were also developed at KSC and based on experience with MSOLO avionics and software. The control system was designed to stow, deploy, track the sun, and perform beam alignment of the concentrated light. The prototype subsystems were integrated and tested at Johnson Space Center’s (JSC) Energy Systems Test Area. A heliostat was used to direct sunlight toward the prototype in a way that is representative of the sunlight conditions at the south pole of the Moon. When concentrated sunlight was focused on simulated lunar regolith within the reactor, the gas analysis team confirmed the presence of carbon monoxide gas, which confirmed that a solar carbothermal reaction took place. The key performance parameter for the integrated prototype was grams of oxygen extracted per kilowatt hour of energy arriving at the concentrator primary mirror. The prototype design successfully demonstrated end-to-end capability and further steps to achieve a flight capable system have been defined. With lunar data, engineers would be able to design a scaled-up system capable of extracting oxygen from regolith at useful quantities for crew life support and rocket propellant. On the long term, this method of In-Situ Resource Utilization could be used to drastically reduce the cost and risk of a sustained human presence on the Moon by reducing the amount of oxygen that would have to be delivered.

Oxygen from Regolith

Asymmetric pathways for lithium extraction and recovery based on the two-phase equilibrium of layered oxides

Electrochemical intercalation offers a promising platform for Li + extraction. However, only limited types of electrode materials have been investigated. The challenge to broaden and tailor materials for electrochemical intercalation-based Li + extraction lies in the lack of understanding of material’s response upon co-intercalation of multiple ions, therefore, paired process design to enable reversible Li + extraction and recovery. Here, we showcase the design of asymmetric ion pathways for Li + extraction and recovery for host material with complex Li + and Na + interaction using layered cobalt oxide as a model material. The two-phase equilibrium of Na 0.48 CoO 2 and Li 0.94 CoO 2 governs Li + selectivity when a high depth of intercalation is achieved (low vacancy level). We show that the relative rate between ion exchange and intercalation is critical to determine the ion pathways. The relationship can be quantitatively compared using the average pseudo ion exchange rate (C pseudoIX ) and the intercalation rate (C inter ). The ion pathways at the three regimes with C pseudoIX > C inter , C pseudoIX ~ C inter , and C pseudoIX < C inter are constructed. By selecting the optimized ion pathway and particle size, we demonstrate 9.7×10 4 Li + selectivity with 99% purity Li + recovery from an initial 1:1000 Li: Na molar ratio solution using 115 mAh/g specific capacity.

electrochemistry

Equivalent System Mass Comparison of ECLSS CO2 Removal Technologies

This project employs equivalent system mass (ESM) analyses to evaluate and compare state-of-the-art spacecraft CO2 removal systems. The ESM methodology converts sizing characteristics such as volume, power, and cooling requirements into a unified metric of mass, allowing for simple quantitative comparison of equipment impacts on a system level. Comparison of six high TRL technologies – the Carbon Dioxide Removal System (CDRS), Thermal Amine Scrubber (TAS), Carbon Dioxide Removal Assembly (CDRA), Four-Bed CO2 Scrubber (4BCO2), CO2 and Humidity Control Swing Bed (CHC), and the Carbon Dioxide Removal by Ionic Liquid Sorbent (CDRILS) – was achieved using an existing ESM spreadsheet tool that formerly ranked such technologies for consideration on Gateway. Fidelity of the tool was increased through revision with the most up-to-date sizing and performance data for each technology. ESM values were then estimated using NASA-supplied Lunar, Martian, and low-Earth orbit habitat infrastructure costs. This paper details the ESM approach used, lists major performance variables for each CO2 removal option, and summarizes conclusions of the technology comparison. The results are intended to act as a guide to assist programs in their selection of CO2 technologies best suited for a specific vehicle or mission.

Madeleine C Oliver

Complex-Concentrated Anion Doping Enables Ultra-Stable Lattice Oxygen and Structural Integrity in Lithium-Rich Layered Oxide Cathodes

Lithium- and manganese-rich layered oxides (LMR) stand out as next-generation lithium-ion cathode chemistries, which harness both transition-metal and lattice-oxygen redox processes to deliver exceptional capacity and energy density. However, their full potential is hindered by intrinsic oxygen instability and structural degradation, resulting in pronounced voltage fade and capacity decay. Here, we present a complex-concentrated anion-doping paradigm in which multiple anions, F, Br, and S, are incorporated into the oxygen sublattice to enhance oxygen-redox and structural stability. X-ray absorption spectroscopy and aberration-corrected scanning transmission electron microscopy confirm ultra-stable local oxygen coordination environments during long-term cycling, with detrimental phase transformations and oxygen-loss-induced cavitation dramatically inhibited. Notably, we show that the characteristic LiTM6 transition metal (TM) honeycomb ordering is preserved even after electrochemical cycling. Concurrently, this strategy yields an unprecedented volume change of only 0.63% upon charging to 4.8 V vs. Li+/Li, achieving the first zero-strain LMR cathode. The resulting LMR cathode delivers ultralow voltage fade (1 mV per cycle during the first 100 cycles and becomes negligible in subsequent cycles) and outstanding energy retention (93% after 200 cycles) in a pouch cell configuration. Our complex-concentrated anion-doping concept establishes a broadly applicable strategy for resolving chemo-mechanical failure mechanisms in ceramic intercalation electrodes for next-generation energy storage.

Li-ion batteries