SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A range of advanced imaging techniques are employed to study the micron- and nano-scale morphology of a polymer/non-fullerene acceptor blend. Cryo-electron microscopy in particular reveals nanoscale variations in molecular orientation and order.
Abstract The Vera C. Rubin Observatory will, over a period of 10 yr, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an active-optics system (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this, Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wave front. We have designed and integrated a deep-learning (DL) model for wave-front estimation into the AOS pipeline. In this paper, we compare the performance of this DL approach to Rubin’s baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the DL approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the DL model is 40× faster, the median error 2× better under ideal conditions, 5× better in the presence of vignetting by the Rubin camera, and 14× better in the presence of blending in crowded fields. In addition, the DL model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin.
This work summarizes the process to compute and analyze the thermal conductivity and mechanical properties of 3D woven TPS materials such as 3MDCP (3D Mid-Density Carbon-Phenolic). The PuMA [1,2] software, developed at NASA Ames, was used to characterize different 3MDCP samples from their constituents' data, averaging their thermal conductivity and elastic properties in the three main directions to obtain their effective orthotropic thermo-mechanical properties. This was performed in multiple steps: firstly, TPS samples were digitally reconstructed using micro computed tomography (µCT) and their constituents were segmented; then, the porous matrix phase was analyzed at the micro-scale and these results were used, along with the fibers’ constituents information, to model the tows at the meso-scale; finally, results from the constituents were homogenized and used to model the thermo-mechanical behavior of the unit cell at the macro-scale. Additionally, to gain a better understanding of the tows’ morphology and distribution of the carbon-phenolic blended fibers, microscopy images of the tows’ cross-section were segmented using deep learning techniques and analyzed with PuMA. This workflow can be applied to any TPS material to computationally obtain its thermo-mechanical properties, enabling more informed TPS design and manufacturing choices.
This work summarizes the process to compute and analyze the thermal conductivity and mechanical properties of 3D woven TPS materials such as 3MDCP (3D Mid-Density Carbon-Phenolic). The PuMA software, developed at NASA Ames, was used to characterize different 3MDCP samples from their constituents' data, averaging their thermal conductivity and elastic properties in the three main directions to obtain their effective orthotropic thermo-mechanical properties. This was performed in multiple steps: firstly, TPS samples were digitally reconstructed using micro computed tomography (µCT) and their constituents were segmented; then, the porous matrix phase was analyzed at the micro-scale and these results were used, along with the fibers’ constituents information, to model the tows at the meso-scale; finally, results from the constituents were homogenized and used to model the thermo-mechanical behavior of the unit cell at the macro-scale. Additionally, to gain a better understanding of the tows’ morphology and distribution of the carbon-phenolic blended fibers, microscopy images of the tows’ cross-section were segmented using deep learning techniques and analyzed with PuMA. This workflow can be applied to any TPS material to computationally obtain its thermo-mechanical properties, enabling more informed TPS design and manufacturing choices.
Ammonia is a promising hydrogen carrier due to its favorable storage and transport characteristics. However, its direct use in combustion systems is limited by low flammability and potential for high nitrogen oxide emissions. To better understand ammonia combustion, researchers conducted experiments using a flat flame burner and measured species profiles using tunable-diode-laser-absorption-spectroscopy and chemiluminescence imaging. They tested three flame conditions with different ammonia-hydrogen blends and oxygen levels. The results were compared to simulations using various kinetic mechanisms, including one that accounts for excited species chemistry. The goal is to provide direct information about species profiles in a simple system, isolating chemical kinetics from fluid dynamic effects, which can inform the development of more efficient and low-emission combustion systems using ammonia.
Ammonia is viewed as a viable hydrogen carrier due to favorable storage and transport characteristics. While it can be re-converted to hydrogen at point-of-use via thermal catalytic cracking, direct utilization in combustion systems can result in reduced costs and improved efficiency. A major barrier to this approach is the low flammability and potential for high nitrogen oxide emissions, driven by fuel-bound nitrogen and complex kinetic pathways. While a number of kinetic mechanisms currently exist for simulating ammonia combustion, a major need continues to be direct information about species profiles in easy-to-model systems capable of isolating chemical kinetics from multi-dimensional fluid dynamic effects. This paper reports on recent species measurements made in a flat flame burner using a combined tunable-diode-laser-absorption-spectroscopy (TDLAS) and chemiluminescence imaging approach. Three flame conditions were included representative of NH3/H2 blends, partially cracked NH3 (inc. N2), and 100% NH3 with enhanced air (30% oxygen). Two NIR distributed feedback (DFB) laser diodes were used to determine H2O concentration and temperature (via ratio thermometry) at various radial and axial positions, after which an inverse Abel transform was used to infer centerline values. Companion images were collected using a Princeton Instrument PI-MAX intensified camera equipped with a 105mm UV lens and multiple filter sets targeting OH*, NH*, and NH2* emission. Results were compared to companion Cantera burner-stabilized flame simulations using various kinetic mechanisms. A recent mechanism including excited species chemistry was also included, to investigate whether excited and ground state profiles exhibit significant differences.
The compatibility among different ethylene−vinyl alcohol (EVOH) copolymer grades plays a crucial role in determining how easily they can be recycled when present together in a mixed waste stream. In this work, melt miscibility and cocrystallization in 50/50 wt/wt binary blends of EVOH copolymers with differing ethylene content were studied. Blends with up to a 21 mol % difference in composition were analyzed. Thin films of miscible blends appeared homogeneous in the melt as observed under an optical microscope, whereas AFM height images showed that immiscible blends underwent surface roughening due to phase separation. For copolymers with modest composition differences, the blend behaved as a single component, showing single, narrow melting and crystallization peaks in DSC thermograms, indicating cocrystallization at the level of crystal stems. Interestingly, even phase-separated blends demonstrated partial cocrystallization due to partial miscibility of the components in the molten phase-separated domains. The extent of cocrystallization is governed by the difference in freezing points of the components; rapid cooling increased cocrystallization in the blends. The maximum compositional difference allowed for miscibility is not constant but depends on the blend’s average comonomer content, in agreement with the “miscibility window” calculated from literature pressure−volume−temperature behavior for the individual EVOH copolymers and regular mixing theory. This study helps illuminate the effect of copolymer composition on miscibility, as well as cocrystallization behavior in blends of random copolymers with cocrystallizable units.
The Charon/Pluto mass ratio is a fundamental but poorly known parameter of the two-body system. Previous values for the mass ratio have ranged from 0.0837 plus or minus 0.0147 (Null et al., 1993, Astron. J. 105, 2319-2335) to 0.1566 plus or minus 0.0035 (Young et al., 1994, Icarus 108,186-199). We report here a new determination of the Charon/Pluto mass ratio, using five sets of groundbased images taken at four sites in support of Pluto occultation predictions. Unlike the Null et al. and Young et A determinations, where the centers of light for Pluto and Charon could be determined separately, this technique examines the motion of the center of light of the blended Pluto-Charon image. We compute the offsets of the observed center-of-light position of Pluto-Charon from the ephemeris position of the system and fit these offsets to a model of the Pluto-Charon system. The least-squares fits to the five data sets agree within their errors, and the weighted mean mass ratio is 0.117 plus or minus 0.006. The effects of errors in the Charon light fraction, semimajor axis, and ephemeris have been examined and are equal to only a small fraction of the formal error from the fit. This result is intermediate between those of Null et al., and Young et al. and matches a new value of 0.124 plus or minus 0.008 by Null and Owen (1996, Astron. J. 111, 1368-1381). The mass ratio and resulting individual masses and densities of Pluto and Charon are consistent with a collisional origin for the Pluto-Charon system.
The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR
Phase-diverse speckle (PDS) is a novel imaging technique intended to overcome the degrading effects of atmospheric turbulence on fine-resolution imaging. As its name suggests, PDS is a blend of phase-diversity and speckle-imaging concepts. PDS reconstructions on solar data were validated by simulation, by demonstrating internal consistency of PDS estimates, and by comparing PDS reconstructions with those produced from well accepted speckle-imaging processing. Several sources of error in data collected with the Swedish Vacuum Solar Telescope (SVST) were simulated: CCD noise, quantization error, image misalignment, and defocus error, as well as atmospheric turbulence model error. The simulations demonstrate that fine-resolution information can be reliably recovered out to at least 70% of the diffraction limit without significant introduction of image artifacts. Additional confidence in the SVST restoration is obtained by comparing its spatial power spectrum with previously-published power spectra derived from both space-based images and earth-based images corrected with traditional speckle-imaging techniques; the shape of the spectrum is found to match well the previous measurements. In addition, the imagery is found to be consistent with, but slightly sharper than, imagery reconstructed with accepted speckle-imaging techniques.
The Advanced Cockpit Evaluation System (ACES) includes communication, computing, and display subsystems, mounted in a van, that synthesize out-the-window views to approximate the views of the outside world as it would be seen from the cockpit of a crewed spacecraft, aircraft, or remote control of a ground vehicle or UAV (unmanned aerial vehicle). The system includes five flat-panel display units arranged approximately in a semicircle around an operator, like cockpit windows. The scene displayed on each panel represents the view through the corresponding cockpit window. Each display unit is driven by a personal computer equipped with a video-capture card that accepts live input from any of a variety of sensors (typically, visible and/or infrared video cameras). Software running in the computers blends the live video images with synthetic images that could be generated, for example, from heads-up-display outputs, waypoints, corridors, or from satellite photographs of the same geographic region. Data from a Global Positioning System receiver and an inertial navigation system aboard the remote vehicle are used by the ACES software to keep the synthetic and live views in registration. If the live image were to fail, the synthetic scenes could still be displayed to maintain situational awareness.
Recycling crosslinked polyurethanes (PUs) is accomplished through mechanical or chemical processes that are energy-intensive or produce plastics of lesser value. Polymer recycling processes are notably intolerant of polymer mixtures, yet the ability to reprocess and compatibilize two or more crosslinked PUs together will make this process more amenable to mixed waste streams while offering an opportunity to tune the properties of the recycled polymer products. Here, we blend a rigid polyester PU and a soft polyether PU using twin-screw extrusion to yield materials with tunable mechanical properties based on the feed composition. Their material properties were compared to those of compression-molded reprocessed blends and blends where the monomers were mixed prior to synthesis. The extruded materials showed similar mechanical and thermal properties to newly prepared blends and had higher-value mechanical properties compared to the samples reprocessed via compression molding. The morphologies of the blends were observed using phase imaging via atomic force microscopy to show that there is less phase separation in the extruded materials compared to compression-molded blends. The mechanical properties of these materials were tunable from soft to elastomeric to rigid based on the feed composition, and this tunability was demonstrated through four consecutive reprocessing cycles, through which the mechanical properties were steadily varied from rigid to soft by incorporating increasing amounts of soft polyether PU material. Here, this blending method for reprocessing mixed waste compatibilizes different PUs and provides a means to tune the mechanical properties of a PU product, even if starting from waste streams of varying compositions. As such, this process represents an intriguing new approach for polymer reprocessing.
A dual-hopper feed system that was developed for the Big Area Additive Manufacturing (BAAM) system allows for transitioning between different materials while maintaining continuous deposition. This technique creates a step-change in material feedstock by switching the pellet feeding system to alternate which hopper is currently supplying material, allowing for multi-material construction. The step-change in feedstock material produces a transition region that is characterized by a compositional gradient and blended internal morphology. Initial cross-sectional imaging of the transition region revealed a non-homogenous blend of materials with distinct domains of each material, likely due to incomplete mixing within the screw. This study used a carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS) and an unfilled ABS to characterize the internal structure and to correlate it to mechanical performance by tracking microhardness across cross-sections of the transition region.
To date, chemical imaging of polymers and polymer blends has been primarily accomplished using time-of-flight secondary ion mass spectrometry (ToF-SIMS) to directly visualize the distribution of components in a complex material with spatial resolution ranging from 100 nm to 5 μm. However, in many cases, this resolution falls far short of visualizing interfaces directly. To overcome these limitations, recent work has focused on developing a SIMS detection system based on a helium ion microscope (HIM) enabling chemical imaging with a demonstrated ~14 nm spatial resolution. Here, we utilize HIM-SIMS for differentiation between the olefin-based polymers of polyethylene (PE) and polypropylene (PP). We illustrate both analyses for separating PE and PP using specific mass fragment ratios as well as demonstrate spatially resolved imaging of phase-separated domains within PE. Altogether, we demonstrate the abilities of HIM-SIMS as a multimodal chemical technique for imaging and quantification of polyolefin interfaces, which could be more broadly applied to the analysis of more complex polymeric systems.
Ionomer strongly influences the performance of proton exchange membrane fuel cells (PEMFCs), affecting catalyst activity and reactant transport within the electrodes. While recent work on high oxygen permeability ionomers (HOPI) has demonstrated improved performance compared to conventional perfluorosulfonic acid (PFSA) ionomers such as Nafion™, there have also been reports of increased cracking in fabricated electrodes. We investigated the effects of blending HOPI with Nafion™ ionomer dispersions when fabricating cathode catalyst layers (CCLs). Small-angle x-ray scattering suggests that the ionomers mix well, and adsorption measurements indicate that HOPI adsorbs less strongly to the carbon-supported platinum (Pt) catalyst, and in blends, the Nafion™ ionomer exhibits a greater degree of adsorption. Imaging CCLs revealed a decrease in crack formation in blended samples as HOPI content decreased, with 14% HOPI having the lowest crack density. In a membrane electrode assembly (MEA) using a high surface area carbon support, the 14% HOPI blend exhibited similar performance to 100% HOPI. However, similar performance enhancements were not achieved with a medium surface area carbon support. These findings suggest a path for low-crack CCLs with enhanced oxygen transport, while highlighting a need for further investigation of ionomer blending towards efficient and durable PEMFCs.
The next generation of imaging surveys, including the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), Euclid, and the Nancy Grace Roman Space Telescope, will provide unprecedented constraints on cosmology using weak gravitational lensing. To fully exploit this statistical power, shear measurement methods must achieve sub- per cent accuracy while mitigating systematic biases from noise, the point-spread function (PSF), blending, and shear-dependent detection. The analytical calibration framework (AnaCal) has demonstrated such accuracy but requires adding noise to images, reducing effective depth. We introduce Deep-Field Analytical Calibration (DEEP-FIELD AnaCal), an extension of AnaCal that uses deep-field images to compute shear responses while preserving the statistical power of wide-field data. We validate DEEP-FIELD AnaCal on isolated and blended galaxy image simulations with LSST-like conditions, finding it meets the stringent requirement of multiplicative bias $|m| < 3\times 10^{-3}$ at 99.7 per cent confidence. Compared to standard AnaCal applied to wide-field images, DEEP-FIELD AnaCal increases the effective galaxy number density from 17 to 30 arcmin$^{-2}$ for simulated 10-yr LSST data. With deep fields $10\times$ longer than the wide field, we find pixel noise variance in shear estimation is reduced by 30 per cent and overall uncertainty by $\sim 25~{{\ \rm per\ cent}}$. Finally, using the LSST Deep Drilling Fields strategy, we assess sample variance and find an equivalent calibration uncertainty of $\lesssim 0.3~{{\ \rm per\ cent}}$. These results demonstrate that DEEP-FIELD AnaCal offers a promising path to achieve the required shear calibration for upcoming weak lensing surveys.
We map the average line-of-sight velocities of the X-ray-emitting mass in the supernova remnant Cassiopeia A using measurements of the centroid of the Si-Healpha blend with the Advanced CCD Imaging Spectrometer on the Chandra X-Ray Observatory. This 4 inch scale map confirms the broad trends noted in previous studies, namely, that the line-of-sight velocity scale is roughly 2000-3000 kilometers per second with relatively more blueshifted material in the southeastern region of the remnant than in the northwest; new details are that the northwestern region consists of two arcs, with the southernmost one representing the most redshifted X-ray-emitting material in Cas A. These results are consistent with contemporaneous results from XMM-Newton. The X-ray patterns resemble the complex velocity patterns measured at optical wavelengths for much denser ejecta, and they support the growing body of evidence that the explosion and subsequent evolution of Cas A were highly asymmetrical.
Explore the source record for details and available documents.