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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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The thermoelectric compatiblity factor is used to rationally select materials for a segmented thermoelectric generator. the thermoelectric potential is used for the exact analytic expressions for materials with temperature dependent thermoelectric properties.
NASA is designing a Ground Validation Segment (GVS) as one of its contributions to the Global Precipitation Measurement (GPM) mission. The GPM GVS provides an independent means for evaluation, diagnosis, and ultimately improvement of the GPM spaceborne measurements and precipitation products. NASA's GPM GVS concept calls for a combination of direct observations executed within a Multidimensional Observing Volume (MOV) and model-based analyses executed by a Satellite Simulator Model (SSM). The MOV consists of ground-based instruments that measure local surface and atmospheric properties required for GPM validation. The SSM utilizes MOV measurements in a forward numerical model. The goal of the SSM forward modeling is calculation of the following properties: top-of-atmosphere microwave radiative quantities to within sensor noise of those measured by the GPM Core Satellite, precipitation quantities identical to those generated by the standard GPM precipitation retrieval algorithms, and quantitative/objective error estimates of both sets of quantities. At present, the GVS is in the early design stage and various scenarios have been generated to assess how it will be used in the GPM era. The GPM GVS will be operational in the year prior to the launch of the GPM core satellite, which has a launch date scheduled for December 2010.
Wavefront aberrations will lower the DFS fringe visibility and DFS signal SNR. The specific effect of wavefront aberration on DFS depends on the aberration type. Due to the "pixel spatial filter" effect the extracted DFS signal can tolerate moderate amount of wavefront aberrations..By averaging piston detection results from multiple traces extracted from fringe images DFS can further increase its robustness against wavefront aberration. Line-of-sight jitter can cause significant loss of fringe visibility will affect more on the aberrated system..Modeling results have shown that gravity sag on the JWST segment mirror during I&T will lower the DHS fringe visibility by factor of 2-3X and lower the DHS signal intensity by factor of approx.5X. Under gravity sag the RMS DHS detection error approx. 100 nm.
Next generations lightweight, high resolution, high throughput optics for x-ray astronomy requires integration of very thin mirror segments into a lightweight telescope housing without distortion. Thin glass substrates with linear dimension of 200 mm and thickness as small as 0.4 mm can now be fabricated to a precision of a few arc-seconds for grazing incidence optics. Subsequent implementation requires a distortion-free deposition of metals such as iridium or platinum. These depositions, however, generally have high coating stresses that cause mirror distortion. In this paper, we discuss the coating stress on these thin glass mirrors and the effort to eliminate their induced distortion. It is shown that balancing the coating distortion either by coating films with tensile and compressive stresses, or on both sides of the mirrors is not sufficient. Heating the mirror in a moderately high temperature turns out to relax the coated films reasonably well to a precision of about a second of arc and therefore provide a practical solution to the coating problem.
Joint Polar Satellite System (JPSS) Ground Segment Support to the GOSAT-3 Mission introduction, mission parameters, assumptions and constraints.
A problem of using mixture-of-Gaussian models for unsupervised texture segmentation is that multimodal textures cannot be well represented by a single Gaussian cluster.
Highly efficient, segmented thermoelectric unicouple incorporating advanced thermoelectric materials with superior thermoelectric figures of merit are currently being developed at the Jet Propulsion Laboratory (JPL).
A new version of a segmented thermoelectric unicouple incorporating advanced thermoelectric materials with superior thermoelectric figures of merit has been recently proposed and is currently being developed at the Jet Propulsion Laboratory.
Coronagraphs (together with starshades) are an important tool to directly image and characterize exoplanets, and enable the search for biomarkers in reflected light on potentially habitable worlds. Their performance and efficiency has steadily been improving over the past several decades, but has not yet reached performance limits. In particular, the expected exoplanet yield for missions such as the Astro2020-recommended “IR/O/UV Flagship” can still be improved by factors of at least 2-3, simply by continued improvements in coronagraph performance, before they plateau due to physics limits. One possible architecture that can enable at least a part of this improvement is the Phase-Induced Amplitude Apodization Complex Mask Coronagraph (PIAACMC). Itoffersthe advantages of high throughput, small inner working angle (IWA),and almost noloss inPSF sharpness, and natively supports obstructed and segmented apertures, which is essential to the Astro2020 “IR/O/UV Flagship”mission. Historically, key disadvantages of PIAA have been poor tolerance to stellar angular sizes and maturity, but latest designs and demonstrations have made significant strides in this respect.In this paper, we present the current status and overview of our program to mature the PIAACMC technology. We first review PIAACMC designs for LUVOIR-A and B, which resultedin improved expected yield of Exo-Earths relative tothe baselines for both mission concepts. In particular, for LUVOIR-B, the yield improves from 28 to 42due to improvedtolerance to stellar angular size in our design. The improvement in yield is marginal for LUVOIR-A, but our design enables detecting planets around smaller diameter stars (nearby K-dwarfs and/or farther FG stars). We also describe our modeling and vacuum demonstrationsfor the LUVOIR-A aperture(which is more challengingthan LUVOIR-B due to the central obstruction).The demonstration included a LUVOUR-A pupil mask, an on-axis PIAA set of 2 mirrors with central holes, a Boston Micromachines DM, a patterned CMC mask, Lyot stop, and supporting masks and optics. Demonstrations were conducted at JPL’s High Contrast Imaging Testbed (HCIT) using several characterization andwavefront control techniques (primarily standard EFC, several experimental techniques were attempted, as well as speckle nulling). So far, our results include 1.9e-8 raw contrast in 10% broadband light between 3.5 and 8 l/D; 4.1e-8 and 1.6e-9 coherent contrasts in monochromatic light between 2-4 and 4-8 l/D, respectively. We also present measurements and analysis of sensitivity to tip/tilt jitter and stellar angular size. Finally, we compare our test results to models, present an analysis of our limiting factors, and explorefuture prospectsfor improvement based on validated models.
Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at https://github.com/vitorgrizzi/nugraph_phys/tree/main_phys.
Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.
Metallic fuels, particularly U—10Zr, are promising candidates for next-generation sodium-cooled fast reactors. Irradiation of nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequently forms microstructural pores, deteriorating fuel performance. Due to the massive amount of pores and complex phases formed, a quantitative description of fission gas pores is not yet available, preventing the development of microstructure-informed fuel performance modeling for fuel qualification. This paper applied a pre-trained deep learning model to ~10,260 high magnification scanning electron microscopy images. This method increased the accuracy of fission gas pore segmentation and allows statistical features to be extracted which cannot be achieved manually. A pre-trained decision tree model worked on the segemenation results and further classified the pores into different categories to produce a correlation between the pores, movement of lanthanides, and temperature gradient during irradiation. Finally, this paper emphasizes the potentials of machine learning models to accelerate fuel research, development, and qualification for advanced reactors.
The magnetized near detector (ND280) of the T2K long-baseline neutrino oscillation experiment has been recently upgraded aiming to satisfy the requirement of reducing the systematic uncertainty from measuring the neutrino–nucleus interaction cross section, which is the largest systematic uncertainty in the search for leptonic charge-parity symmetry violation. A key component of the upgrade is SuperFGD, a 3D segmented plastic scintillator detector made of approximately 2,000,000 optically-isolated 1 cm 3 cubes. The SuperFGD cube unit shows promising optical performance, including a high light yield of about 40 photoelectrons (p.e.) per channel, a low cube-to-cube crosstalk rate below 3%, and a sub-nanosecond time resolution of 0.96 ns. By combining tracking and stopping power measurements of final state particles, this novel detector enables precise 3D-imaging of GeV neutrino interactions with reduced systematic uncertainties. A detailed Geant4 based optical simulation of the SuperFGD building block, i.e. a plastic scintillating cube read out by three wavelength shifting fibers, has been developed and validated with the different datasets collected in various beam tests. In this manuscript the description of the optical model as well as the comparison with data are reported.
Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.
A segmented PFCB aryl ether copolymer was synthesized directly from two aryl TFVE monomers having different reactivities and the blocky copolymer structure was confirmed by 19 F NMR spectroscopic data.
Small-angle neutron scattering experiments revealed elongation-induced demixing in polymer blends. Such concentration fluctuations can enhance or reduce the local segmental friction and thereby affect the rheological behavior.
Solvent-free, solid polymer electrolytes (SPEs) are promising candidates for next-generation, electrochemical energy storage systems due to their potential to enhance safety and performance, enable flexible device architectures, and streamline manufacturing processes. Conventional SPEs suffer from limited ionic conductivity due to the strong coupling between ion transport and (generally slow) polymer segmental relaxation. The realization of superionic conduction in SPEs, in which ions move faster than the structural relaxation of the polymers, requires a shift in design principles to promote this type of decoupled ion motion. In this perspective, we discuss how polymer architecture, ion–ion correlations, and ion–polymer interactions can unlock superionic behavior. We highlight several key design features, such as crystallinity, bulky side groups, high molecular weight, and percolating ionic aggregation, with a focus on creating low-barrier transport pathways in various polymer systems. We also demonstrate opportunities to combine polymer chemistry and data science through high-throughput and automated screening approaches to reveal how phase behavior, ion dynamics, and ionic interactions govern transport, thereby potentially enabling data-driven discovery of superionic polymer electrolyte materials.
The design of functionalized surfaces for interactions with biological systems is critical across sectors such as healthcare, energy, and agriculture. Tailoring materials for specific applications, such as antifouling and antimicrobial surfaces, demands a comprehensive understanding of topology and chemistry across multiple length and time scales on both biological and materials systems. This work presents the development and characterization of nanostructured surfaces with controlled topographies and chemistries that enhance bacterial membrane disruption, reduce biofilm formation, and improve antimicrobial and antifouling capabilities. Two specific use cases will be presented - the use of cellulose nanocrystals (CNCs) for bacterial growth inhibition and the development of antifouling surfaces to prevent protein and bacterial adsorption [1-4]. By leveraging large language models (LLMs) for image segmentation and training [5], we enable automated analysis of terabyte-scale cryogenic electron microscopy (cryoEM) datasets. This analysis provides statistical insights into the biotic/abiotic interface and facilitates automated electron microscopy experiments to mitigate time and dose. The integration of cryogenic electron tomography (cryoET) and cryogenic focused ion beam (cryoFIB) milling enables high-resolution, near-native-state imaging and 3D reconstructions of bio/material interfaces [6]. Orthogonal characterization techniques and computational modeling further enhances our understanding, offering a robust platform for the design and optimization of next-generation functional surfaces [7].