Results for “infrared (IR) imagery” — Page 3

NASA NTRS2026

Assessing the Adhesion of Nanofibrous PVDF-HFP as Passive Thermal Control Coatings for the Extraterrestrial Storage of Cryogenic Propellants

Cryogenic propellant passive thermal management techniques in space are essential complements to active thermal management methods, helping reduce absolute dependence on the latter. Particularly, materials with superior optical properties (high solar reflectance and infrared emittance) would be beneficial for passive thermal control to reject most of the incident solar radiation and enhance thermal emission from the storage tank, leading to self-cooling. We developed a nanofibrous, porous passive thermal control material by electrospinning polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) co-polymer onto an aluminum foil substrate. The material exhibits a very high solar reflectance (>99%) and infrared emittance (~86%) when characterized in the ultraviolet-visible-near infrared and mid-infrared wavelengths. This study focuses on the adhesion of the nanofibrous PVDF-HFP to the substrate. We incorporated in-lab fabricated and commercial adhesives, under normal room conditions, between the nanofibers and the aluminum foil substrate to improve their adhesion. Some samples were also exposed to space-like conditions – vacuum and thermal cycling, to investigate their effects on the strengths of adhesives. Subsequently, adhesion peel tests were conducted on all the samples to assess the peel strengths of the bonds. Peel test strengths after space environment conditioning of the adhesives were generally higher than those observed in the pristine, unaged samples. The results are discussed in this study.

Chieloka Ibekwe↗

NASA NTRSDate not supplied

Kalman Filtering and RTS Smoothing for Arc-Jet Sample Edge Tracking

Accurate and temporally consistent measurements of test-article recession are required to characterize material response during arc-jet ablation experiments. However, image-based boundary measurements are often affected by segmentation noise, brightness variations, and frame-to-frame variability. This work extends arcjetCV [1] with filtering methods for tracking the evolving boundaries of hemispherical and ISO-Q test articles. Two boundary-tracking approaches based on Kalman filtering [2] were implemented. The first applies a point-wise Kalman filter followed by a Rauch–Tung–Striebel smoother [3] to individual boundary-point locations. The second applies the same filtering and smoothing framework to a reduced set of geometry-dependent shape parameters. The point-wise method reduces local frame-to-frame fluctuations while preserving spatial details along the detected boundary. The shape-parameter method provides a compact and geometrically constrained estimate of the sample contour. Figure 1 illustrates the two approaches for a hemispherical test article. Both methods improve temporal consistency and support more robust estimation of surface recession from arc-jet imagery. The two filtering approaches provide complementary representations of boundary evolution and improve the reliability of image-based recession measurements during arc-jet experiments.

recession measurement↗

NASA NTRS2026

Diamond-Loaded Polyimide Aerogel Scattering Filters and Their Applications in Astrophysical and Planetary Science Observations

Infrared-blocking, aerogel-based scattering filters have a broad range of potential applications in astrophysics and planetary science instruments in the far-infrared, sub-millimeter, and microwave regimes. This paper demonstrates the ability of conductively-loaded, polyimide aerogel filters to meet the mechanical and science instrument requirements for several experiments, including the Cosmology Large Angular Scale Surveyor (CLASS), the Experiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM), and the Sub-millimeter Solar Observation Lunar Volatiles Experiment (SSOLVE). Thermal multi-physics simulations of the filters predict their performance when integrated into a cryogenic receiver. Prototype filters have survived cryogenic cycling to 4 K with no degradation in mechanical properties. Measurement of total hemispherical reflectance and transmittance, as well as cryogenic tests of the aerogel filters in a full receiver context, allow estimates of the integrated infrared emissivity of the filters. Knowledge of the emissivity will help instrument designers incorporate the filters into future experiments in planetary science, astrophysics, and cosmology.

Kyle R Helson↗

NASA NTRSDate not supplied

Artemis I Space Launch System Base Heat Shield Thermal Protection System Performance

The Space Launch System (SLS) Core Stage base heat shield experienced the highest external heating environments on the entire launch vehicle during Artemis I ascent flight. This result was consistent with design predictions. The base heat shield experiences P50 cork combustion dynamics at low altitudes, plume-induced recirculation at moderate altitudes and then in-space base flow physics out to Main Engine Cut-Off (MECO). The base heat shield thermal protection system (TPS) is composed of a P50 cork ablator which is bonded to a substrate. The heat shield protects the gimbal actuation system, RS-25 turbomachinery systems and other aft section sensitive components during ascent. This paper estimates the base heat shield TPS performance from Artemis I using flight data from the NASA Langley Research Center’s Scientifically Calibrated In-Flight Imagery (SCIFLI) Airborne Multispectral Imager (SAMI), development flight instrumentation (DFI) and other TPS recession flight measurements. Predictions from computational and ground test-derived engineering ablation models and observations are also applied. Since no base heat shield substrate thermocouple data were obtained for Artemis I, an estimate of the TPS performance data is derived here. This data assesses thermal margin of the SLS Core Stage base heat shield and best informs the Artemis II Crewed mission to the moon.

aerothermodynamics↗

DOE OSTI2026

ClassNMSW- a real-time classification approach for non-recycled municipal solid waste using hyperspectral imaging

Real-time classification of non-recycled municipal solid waste (NMSW) is essential for efficient valorization. This study introduces ClassNMSW, a comprehensive framework for classifying 22 NMSW subclasses under industrial constraints by using hyperspectral imaging (HSI). A primary innovation of this work is the development of a variance-controlled spectral extraction algorithm. Unlike traditional methods that rely on simple averaging, this approach systematically investigates the extent of pixel extraction to minimize the loss of critical chemical information while maximizing data reduction thus ensuring high spectral fidelity with low computational cost. The approach developed in this work integrates automated, computer-vision-based background removal, eliminating the need for the manual thresholding common in current literature. To resolve ambiguities among chemically similar subclasses, a tiered classification and multi-camera fusion strategy (NIR17 and NIR22) is implemented. Results demonstrate that ClassNMSW achieves an object-wise weighted accuracy of 98.70% for single-sensor configurations and 100% under sensor fusion. A novel rolling-window strategy satisfies desired end-to-end latency of <2 s, satisfying the strict deterministic requirements of high-speed industrial sorting environments. The ClassNMSW framework provides a scalable foundation for advancing circularity and resource recovery in large-scale waste valorization operations.

99 - GENERAL AND MISCELLANEOUS↗

NASA NTRS1994

Orbital Processing of High-Quality CdTe Compound Semiconductors

CdZnTe crystals were grown in one-g and in micro-g for comparative analysis. The two micro-g crystals were grown in the Crystal Growth Furnace during the First United States Microgravity Laboratory mission (USML-1). The samples were analyzed for chemical homogeneity, structural perfection, and optoelectronic performance (infrared transmission). Fourier Transform Infrared (FTIR) transmission of both ground and flight materials showed that the infrared transmission was close to theoretical, 63% versus 66%, suggesting that the material was close to the stochiometric composition during both the ground and flight experiments. Infrared microscopy confirmed that the principal precipitates were Te and their size (1-10 microns) and density suggested that the primary flight and ground base samples experienced similar cooling rates. Macrosegregation was predicted, using scaling analysis, to be low even in one-g crystals and this was confirmed experimentally, with nearly diffusion controlled growth achieved even in the partial mixing regime on the ground. Radial segregation was monitored in the flight samples and was found to vary with fraction solidified, but was disturbed due to the asymmetric grvitational and thermal fields experienced by the flight samples. The flight samples, however, were found to be much higher in structural perfection than the ground samples produced in the same furnace under identical growth conditions except for the gravitational level. Rocking curve widths were found to be substantially reduced, from 20/35 (one-g) to 9/20 (micro-g) for the best regions of the crystals. The full width at half maximum (FWHM) of 9 arc seconds is as good as the best reported terrestrially for this material. The ground samples were found to have a fully developed mosaic structure consisting of subgrains, whereas the flight sample dislocations were discrete and no mosaic substructure was evident. The defect density was reduced from 50-100,000 (one-g) to 500-25000 EPD (micro-g). These results were confirmed using rocking curve analysis, synchrotron topography, and etch pit analysis. The low dislocation density is thought to have resulted from the near-absence of hydrostatic pressure which allowed the melt to solidify with minimum or no wall contact, resulting in very low stress being exerted on the crystal during growth or during post-solidification cooling.

D J Larson, Jr.↗

NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗

DOE OSTI2026

Optical Vibrational Spectroscopic Investigation of Natural and Synthetic Analogs of Uranyl Oxyhydroxyhydrate Minerals

Uranyl oxy-hydroxy-hydrate minerals are common alteration products of uraninite (UO2+x) which is chemically and structurally analogous to uranium dioxide nuclear fuel. Therefore, structural and spectroscopic investigations of these alteration minerals and their analogous anthropogenic counterparts can provide insight into the environmental behavior of nuclear fuel cycle materials. Previously, we compiled available vibrational spectroscopic data for uranyl minerals in the Compendium of Uranium Raman and Infrared Experimental Spectra (CURIES) and found that only 37% of known uranyl oxy-hydroxy-hydrate minerals had spectra readily available in the literature and existing databases for inclusion therein. Furthermore, no available infrared spectra for this mineral group were included in CURIES. To expand our understanding of the spectroscopic features of uranyl oxy-hydroxy-hydrates and the structural origins thereof, we collected, and now include in CURIES, Raman and infrared spectra for an additional 12 uranyl hydroxide phases. To better understand the impact of structural and compositional variations of these phases on their spectroscopic features, we compare Raman spectra of different anion sheet topological groups and of analogous phases hosting different counter cations. We identify spectroscopic variations related to differences in equatorial bonding and structural changes as a result of cation substitution. We also prepare a uranyl hydroxide phase via hydrolysis of uranyl fluoride (UO2F2) as an analog of hydrolysis reactions that occur in nuclear fuel cycle materials; and we find that the alteration product of UO2F2, despite chemical and structural similarities to uranyl oxy-hydroxy-hydrate minerals, is readily distinguishable from related mineral phases using Raman spectroscopy. In this work, we provide new insights into the structural origins of spectroscopic features in uranyl oxy-hydroxy-hydrate minerals, improve the average Raman spectrum for this group of minerals, and thereby improve capabilities for identifying these mineral species and related anthropogenic phases using Raman spectroscopy.

Barth, Brodie [ORNL] (ORCID:0000000256142601)↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

DOE OSTI2023

Dual atom catalysts for rapid electrochemical reduction of CO to ethylene

Strong CO adsorption and facile CO dimerization are the key challenges in electrochemical CO2 reduction towards multi-carbon (C2+) products. We recently showed that CoPc immobilized on a single-walled carbon nanotube can selectively reduce CO2 to methanol. This is enabled through molecular strain, which dramatically improves the CO adsorption energy to CoPc, which in turn facilitates methanol formation. We now examine the extended Phthalocyanine (PcEx) dual atom catalyst (DAC), which is intrinsically strained and contains two catalyst centers, making it a candidate for reducing CO to C2+ products. Using Quantum Mechanics (QM), we screened 20 elements embedded in the PcEx, seeking catalysts with weak hydrogen binding, strong CO binding, and facile CO dimerization. We identi>ied Fe, Ru, Co, and Ir as the best performers and subsequently evaluated the entire CO to C2H4 mechanism (9 steps) using each of these elements as catalysts. In terms of limiting potential and overall exergonicity, we identi>ied CoPcEx as the best catalyst, followed by IrPcEx. We then examined the full CO to C2H4 mechanism on the bimetallic IrCoPcEx catalyst using grand canonical QM to obtain the reaction energetics as a function of applied potential. We conclude that the bimetallic IrCoPcEx is most promising for ef>iciently converting CO to ethylene.

Musgrave, Charles B.↗

NASA NTRS2026

Artemis Suit Material Optical Property Testing of Dust Exposed Fabrics

This paper highlights one aspect of NASA’s ongoing technology-infusion effort to design, fabricate, and test a next-generation outer shell fabric for a lunar Extravehicular Activity (EVA) space suit, a critical component of sustained lunar exploration. Managing thermal loads on the Moon is essential for astronaut safety and suit performance. The suit’s exterior fabric directly influences heat gain and loss through its optical properties: low solar absorptivity minimizes sunlight absorption, while high infrared emissivity aids radiative cooling. Lunar regolith complicates this balance. Its fine, abrasive particles possess unique optical behavior that can lower reflectivity and raise emissivity when embedded in or adhered to fabric surfaces, degrading thermal control and increasing the risk of overheating or cooling inefficiency. To quantify these effects, the Artemis Suit Materials (ASM) team measured solar absorptance and infrared emissivity of clean and dust-soiled Ortho Fabric, establishing beginning-of-life (BOL) and end-of-life (EOL) benchmarks. EOL conditions were simulated with a rotary tumbler abrasion process using lunar dust simulant and ceramic media to reproduce cumulative wear expected during surface operations. Tests also included unmodified fabrics and a fabric/film laminate system containing titanium dioxide to evaluate potential improvements in dust resistance and optical performance. Results from these evaluations provide critical insight into how lunar dust alters fabric thermal behavior and inform the design of bespoke suit materials that maintain required optical properties throughout mission life, supporting safe and effective long-duration EVA on the lunar surface.

Textile↗

NASA NTRS2026

Artemis Suit Material Optical Property Testing of Dust Exposed Fabrics

This paper highlights one aspect of NASA’s ongoing technology-infusion effort to design, fabricate, and test a next-generation outer shell fabric for a lunar Extravehicular Activity (EVA) space suit, a critical component of sustained lunar exploration. Managing thermal loads on the Moon is essential for astronaut safety and suit performance. The suit’s exterior fabric directly influences heat gain and loss through its optical properties: low solar absorptivity minimizes sunlight absorption, while high infrared emissivity aids radiative cooling. Lunar regolith complicates this balance. Its fine, abrasive particles possess unique optical behavior that can lower reflectivity and raise emissivity when embedded in or adhered to fabric surfaces, degrading thermal control and increasing the risk of overheating or cooling inefficiency. To quantify these effects, the Artemis Suit Materials (ASM) team measured solar absorptance and infrared emissivity of clean and dust-soiled Ortho Fabric, establishing beginning-of-life (BOL) and end-of-life (EOL) benchmarks. EOL conditions were simulated with a rotary tumbler abrasion process using lunar dust simulant and ceramic media to reproduce cumulative wear expected during surface operations. Tests also included unmodified fabrics and a fabric/film laminate system containing titanium dioxide to evaluate potential improvements in dust resistance and optical performance. Results from these evaluations provide critical insight into how lunar dust alters fabric thermal behavior and inform the design of bespoke suit materials that maintain required optical properties throughout mission life, supporting safe and effective long-duration EVA on the lunar surface.

space↗

DOE OSTI2025

Carbon Utilization and Storage Partnership of the Western United States

This technical report documents research conducted under DOE Award No. DE-FE0031837 focused on evaluating the feasibility of carbon capture, utilization, and storage (CCUS) systems in the central and western United States. The project integrated geologic characterization, reservoir simulation, infrastructure modeling, and economic analysis to assess CO₂ storage potential near industrial sources and develop strategies for transport and sequestration. The work included subsurface modeling, risk assessment, monitoring and verification (MRV) planning, and evaluation of regulatory pathways such as EPA Underground Injection Control (UIC) Class VI permitting and IRS 45Q tax credit eligibility. Results demonstrate the viability of multiple storage approaches, including saline formations, enhanced coalbed methane recovery, and basalt mineralization, supported by data-driven workflows and regional analyses. The project also produced permitting templates, technology transfer activities, and stakeholder engagement efforts to support deployment readiness. These findings contribute to the development of scalable, economically viable CCUS systems and provide a repeatable framework for future carbon management projects.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Stellar Population Properties in the Stellar Streams around SPRC047

Abstract We have investigated the properties (e.g., age, metallicity) of the stellar populations of a ringlike tidal stellar stream (or streams) around the edge-on galaxy SPRC047 (z= 0.031) using spectral energy distribution (SED) fits to integrated broadband aperture flux densities. We used visual images in six different bands and Spitzer/IRAC 3.6μm data. We have attempted to derive best-fit stellar population parameters (metallicity, age) in three noncontiguous segments of the stream. Due to the very low surface brightness of the stream, we have performed a deconvolution with a Richardson–Lucy–type algorithm of the low spatial resolution 3.6μm IRAC image, thereby reducing the effect of the point-spread function aliasedemissionfrom the bright edge-on central galaxy at the locations of our three stream segments. Our SED fits that used several different star formation (SF) history priors, from an exponentially decaying SF burst to continuous SF, indicate that the age–metallicity–dust degeneracy is not resolved, most likely because of inadequate wavelength coverage and low signal-to-noise ratios of the low surface brightness features. We also discuss how future deep visual–near-infrared observations, combined with absolute flux calibration uncertainties at or below the 1% level, complemented by equally well absolute flux-calibrated observations in ultraviolet and mid-infrared bands, would improve the accuracy of broadband SED fitting results for low surface brightness targets, such as stellar streams around nearby galaxies that are not resolved into stars.

Astronomy & Astrophysics↗

DOE OSTI2026

Dark Energy Survey Year 6 results: Clustering redshifts and importance sampling of self-organized-maps 𝑛⁡(𝑧) realizations for 3 × 2 ⁢pt samples

This work is part of a series establishing the redshift framework for the 3 × 2 ⁢pt analysis of the Dark Energy Survey Year 6 (DES Y6). For DES Y6, photometric redshift distributions are estimated using self-organizing maps (SOMs), calibrated with spectroscopic and many-band photometric data. To overcome limitations from color-redshift degeneracies and incomplete spectroscopic coverage, we enhance this approach by incorporating clustering-based redshift constraints (clustering-z, or WZ) from angular cross-correlations with BOSS and eBOSS galaxies and eBOSS quasar samples. We define a WZ likelihood and apply importance sampling to a large ensemble of SOM-derived 𝑛⁡(𝑧) realizations, selecting those consistent with the clustering measurements to produce a posterior sample for each lens and source bin. The analysis uses angular scales corresponding to 1.5–5 Mpc to optimize signal-to-noise ratio while mitigating modeling uncertainties and marginalizes over redshift-dependent galaxy bias and other systematics informed by the N-body simulation CARDINAL . While a sparser spectroscopic reference sample limits WZ constraining power at 𝑧 >1.1, particularly for source bins, we demonstrate that combining SOM with WZ improves redshift accuracy and enhances the overall cosmological constraining power of DES Y6. As a result, we estimate an improvement in 𝑆 8 of approximately 10% for cosmic shear and 3 ×2⁢pt analysis, primarily due to the WZ calibration of the source samples.

Cosmological parameters↗

DOE OSTI2026

Computational discovery of a tetragonal MBene phase: diamond-shaped boron lattices, viable MAB precursors, and selective CO 2 reduction

MBenes are a class of two-dimensional transition-metal borides derived from layered MAB phases. Here, we use first-principles calculations to predict a new tetragonal MB (1 : 1) MBene phase featuring a diamond-shaped B–B lattice and systematically assess its phase stability, synthetic accessibility, and catalytic performance towards the CO 2 reduction reaction (CO 2 RR). Four tetragonal configurations are benchmarked against known hexagonal and orthorhombic phases, and a multi-tier screening identifies six robust members (CrB, FeB, MoB, WB, IrB, and PtB). Exploration of the I4/mmm MAB space reveals 11 viable precursors, including the experimentally synthesized Ir 2 ZnB 2 , which validates our screening approach. CO 2 RR free-energy diagrams show that CrB favors CH 3 OH while FeB, MoB, WB, IrB, and PtB preferentially yield HCOOH. The corresponding limiting potentials are competitive with, and in some cases superior to, those reported for state-of-the-art orthorhombic and hexagonal MBenes. These findings expand the MBene landscape by establishing a tetragonal phase with structural, synthetic, and catalytic promise.

Lu, Linguo [University of Puerto Rico, San Juan, P↗

NASA NTRS1985

Passive Remote Sensing of Stratospheric and Mesospheric Winds

A passive infrared sensor is described for remote sounding of the wind field in the stratosphere and mesosphere from near Earth orbital spacecraft. The instrument uses gas correlation spectroscopy together with electro-optic phase modulation techniques to measure winds in the 20- to 120-km altitude range globally, both in the day and at night, and with a vertical resolution of better than the atmospheric scale height. Measurement of temperature and the amounts of key atmospheric species may also be made simultaneously and in coincident fields of view with the wind observations. The sensor is currently being developed at the Jet Propulsion Laboratory as a candidate for the upcoming NASA Earth Observation System.

Daniel J Mccleese↗