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At least 145 records · Page 8

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NASA NTRS2026

Beyond Melting: Amorphous Bonding for Joining and Consolidation

Crystallization may be the hidden constraint in thermoplastic composite manufacturing. It requires tightly controlled cooling, induces residual stresses through shrinkage, and introduces path-dependent behavior that complicates predictive modeling yet remains essential for structural performance. This work asks: can bonding be achieved without relying on melt-driven crystallization? To address this, thin (5–20 μm) polyetherimide (PEI) interlayers are pre-healed to slow-cooled polyaryletherketone (PAEK) in two contexts. The first, Thermabond®, is sub-melt joining of low melt-PAEK laminates. Results show that bond quality is governed primarily by processing (i.e., adequate healing and film handling) rather than modest changes in interlayer thickness. This concept is then extended to laminate-scale manufacturing through an architecture known as OATMEAL (Out-of-autoclave Amorphous/semicrystalline Thermoplastic Material for Energy-efficient Aerospace-grade Laminates). PEI is healed to carbon fiber reinforced polyetheretherketone (PEEK) at the prepreg and excess PEI is then ablated from the surface. Crystallinity is developed off-line during prepreg fabrication, while subsequent consolidation occurs below the melt temperature to preserve it. Cross-ply warpage experiments show that, contrary to intuition, repeated amorphous interfaces reduce global curvature by lowering the effective stress lock-in temperature and eliminating crystallization shrinkage from the lamina response. Correspondingly, laminate behavior is accurately predicted using classical laminate theory (CLT) with a single effective stress-free temperature, whereas conventional CF/PEEK requires accounting for crystallization-driven effects. By decoupling interfacial healing from crystallization, OATMEAL enables sub-melt consolidation, reduces energy consumption by up to 75%, and increases manufacturing throughput by fivefold. These results demonstrate that amorphous bonding is not only a joining strategy, but a pathway to more predictable and scalable thermoplastic composite manufacturing.

solidification↗

DOE OSTI2021

Implementing Zero Energy Design with the U.S. Department of Energy Solar Decathlon

The U.S. Department of Energy Solar Decathlon® Design Challenge is a collegiate competition that challenges student teams to design high performance buildings that push the boundaries of the industry. In the 2020 competition, DOE is piloting Design Partners, a low-risk opportunity for builders and building owners to harness student innovation and explore zero energy design for current or upcoming projects. Design Partners provide a student team of architects and engineers with project requirements. By the end of the Challenge, Design Partners receive a zero energy alternative and cost estimate for their project. The collaboration allows Design Partners to incorporate innovative concepts such as grid-interactivity, resilience, and low embodied carbon in a low-risk environment and provides the future generation of engineers and architects with invaluable experience designing a building for a client under real-world circumstances. Attendees will leave understanding the “perfect storm” of policy, technology, health, and economic trends that make zero energy buildings desirable and feasible and the value of the U.S. DOE Solar Decathlon to industry. They will also get a sneak peek at innovative solutions 2020 Design Partner pilot projects are bringing to the building industry and how becoming a Design Partner in 2021 could benefit their organization.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

Large-scale continuous carbon/glass fiber additive-compression molded composites

Additive Manufacturing (AM) or 3-D printing has advanced from small-scale desktop printers to large-scale printers. Most of the present large-scale printers utilize feedstock materials in the form of pellets to create composite structures. To create structurally robust composite parts, reinforcements in the form of short fibers (carbon or glass) are often used to impart mechanical properties to the printed parts. However, poor mechanical properties in Z-direction and high porosity of pellets-based printed composites compared to composite manufactured using traditional methods are serious concerns. The authors report a combined approach in the present work, where fiber reinforced composites are printed with a high-throughput continuous fiber deposition method followed by a secondary compression molding process. A specially designed end-effector mounted on a robotic arm is used to print composite preforms. Continuous comingled fibers (Thermofiber 12K CF-PA12, Thermofiber 12K S2-PA12, and Hybrid Thermofiber 12K CF-PA12+PEEK PA6) embedded in the thermoplastic nylon matrix are printed to create composite preform plaques. The printed preforms were further compression molded (CM) using a hydraulic hot-press to create highly consolidated composite parts. The mechanical properties of the continuous fiber composites produced by this combined approach are improved significantly due to the highly aligned continuous fibers and reduced porosity. Flexural strength, flexural modulus, and tensile modulus of AM-CM Thermofiber 12K CF-PA12 UD sample were 615.37 MPa, 75.65 GPa, and 122.23 GPa, respectively.

Kumar, Vipin↗

DOE OSTI2023

PRINTED, PLANAR MICROWAVE CONNECTOR WITH MULTIPLE SIGNAL LINES

Microwave connectors with a planar geometry have been demonstrated using additive manufacturing techniques. This planar form factor is expected to enable connectors to be integrated directly with other components onto a printed circuit board. The connectors were developed with multiple signal lines that will increase the density of signal connections to PCBs, eliminating the physical constraints of single-signal COTS microwave connectors. This work developed electromagnetic models (using Ansys HFSS) of these planar connectors to predict performance and optimize connector design. Materials and printing processes were developed to fabricate the connectors based on high-temperature PEEK thermoplastics. The multiple signal connectors were designed to operate over the 1-6GHz band and are built upon the single signal printed planar geometry connector introduced in a previous paper [1].

Additive Manufacturing↗

DOE OSTI2023

What is Geotherrmal?

Get fast facts about geothermal energy, including a peek at its role in the natural world, home heating and cooling, and power production.

GEOTHERMAL ENERGY↗

DOE OSTI2025

Mechanical and Thermal Characterization of Additively Manufactured Carbon/Nylon 12 and Carbon/PEEK Composites

This study explores additive manufacturing of carbon fiber-reinforced thermoplastic composites using the Composite-Based Additive Manufacturing (CBAM) process. Carbon/Nylon 12 and Carbon/PEEK composites were fabricated and evaluated through mechanical (compression, tensile, flexural, and impact) and thermal (DSC and TGA) tests. Carbon/PEEK exhibited superior mechanical performance, with 97.5% higher tensile strength, 79.8% higher elastic modulus, and 59.6% higher flexural strength compared to Carbon/Nylon 12. Thermal testing showed that Carbon/PEEK had higher thermal stability, beginning degradation at 350 °C versus 298 °C for Carbon/Nylon. These results indicate that CBAM-fabricated Carbon/PEEK composites are suitable for applications requiring high strength and temperature resistance.

Additive manufacturing↗

DOE OSTI2020

DeepMerge: Classifying high-redshift merging galaxies with deep neural networks

In this work, we investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a "pristine" data set and that with noise form a "noisy" data set. The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy. The CNN outperforms a Random Forest classifier, which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, $M_{20}$ statistics etc.), which are commonly used when classifying merging galaxies. We also investigate the selection effects of the classifier with respect to merger state and star formation rate, finding no bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation Mapping) from the results to further assess and interrogate the fidelity of the classification model.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

The crystal structure of Grindelia robusta 7,13-copalyl diphosphate synthase reveals active site features controlling catalytic specificity

Diterpenoid natural products serve critical functions in plant development and ecological adaptation and many diterpenoids have economic value as bioproducts. The family of class II diterpene synthases catalyzes the committed reactions in diterpenoid biosynthesis, converting a common geranylgeranyl diphosphate precursor into different bicyclic prenyl diphosphate scaffolds. Enzymatic rearrangement and modification of these precursors generate the diversity of bioactive diterpenoids. We report the crystal structure of Grindelia robusta 7,13-copalyl diphosphate synthase, GrTPS2, at 2.1 Å of resolution. GrTPS2 catalyzes the committed reaction in the biosynthesis of grindelic acid, which represents the signature metabolite in species of gumweed (Grindelia spp., Asteraceae). Grindelic acid has been explored as a potential source for drug leads and biofuel production. The GrTPS2 crystal structure adopts the conserved three-domain fold of class II diterpene synthases featuring a functional active site in the γβ-domain and a vestigial α-domain. Substrate docking into the active site of the GrTPS2 apo protein structure predicted catalytic amino acids. Biochemical characterization of protein variants identified residues with impact on enzyme activity and catalytic specificity. Specifically, mutagenesis of Y457 provided mechanistic insight into the position-specific deprotonation of the intermediary carbocation to form the characteristic 7,13 double bond of 7,13-copalyl diphosphate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Pseudogap behavior in charge density wave kagome material ScV 6 Sn 6 revealed by magnetotransport measurements

Over the last few years, significant attention has been devoted to studying the kagome materials A V 3 Sb 5 (A = K, Rb, Cs) due to their unconventional superconductivity and charge density wave (CDW) ordering. Recently $ScV_{6}Sn_{6}$ was found to host a CDW below ≈ 90 K, and, like A V 3 Sb 5 , it contains a kagome lattice comprised only of V ions. Here we present a comprehensive magnetotransport study on $ScV_{6}Sn_{6}$. We discovered several anomalous transport phenomena above the CDW ordering temperature, including insulating behavior in interlayer resistivity, a strongly temperature-dependent Hall coefficient, and a violation of Kohler’s rule. All these anomalies can be consistently explained by a progressive decrease in carrier densities with decreasing temperature, suggesting the formation of a pseudogap. Our findings suggest that high-temperature CDW fluctuations play a significant role in determining the normal state electronic properties of $ScV_{6}Sn_{6}$.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Giant coercivity and enhanced intrinsic anomalous Hall effect at vanishing magnetization in a compensated kagome ferrimagnet

Ferrimagnets that can be driven to magnetic compensation show promise for use in spintronics as they exhibit a finite anomalous Hall effect at zero magnetic field without having a substantial magnetic moment. Compensated ferrimagnet spintronic devices with both a large anomalous Hall effect and a high coercivity would be simultaneously easy to read and difficult to erase. The kagome ferrimagnet TbMn 6 Sn 6 has been reported to host a large intrinsic anomalous Hall effect. Here, we demonstrate that doping the Mn sites with Cr drives the system toward magnetic compensation. For nearly compensated compositions at low temperatures, giant coercive fields exceeding 14 T are observed. Additionally, Cr doping markedly enhances the intrinsic anomalous Hall effect, which can be attributed to a shift in the Fermi level. Our results extend the range of unique magnetic states observed in kagome materials, demonstrating that chemical doping is an effective strategy to tune and realize these states.

DeStefano, Jonathan M. [Univ. of Washington, Seatt↗

DOE OSTI2020

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era

The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era. We discuss challenges for discovery in large and complex data sets; challenges and requirements for the next stage of development of statistical methodologies and algorithmic tool sets; how we might change our paradigms of collaboration and education; and the ethical implications of scientists' contributions to widely applicable algorithms and computational modeling. We start with six distinct recommendations that are supported by the commentary following them. This white paper is related to a larger corpus of effort that has taken place within and around the Petabytes to Science Workshops (https://petabytestoscience.github.io/).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

DeepMerge: Studying Distant Merging Galaxies with Deep Neural Networks

The hierarchical merging of galaxies is a probe of the cosmos to test the canonical ?CDM cosmology paradigm. A particularly interesting period is "cosmic high noon" at redshifts z? 2?3, during which star formation rates were the highest, and significant amounts of stellar mass assembled into galaxy-scale bodies. Detecting galaxy mergers in observations by conventional automated methods (which use extracted parameters of galaxy structure - asymmetry, clumpiness, concentration etc.) or by visual inspection has proven to be quite time-consuming and prone to errors. Convolutional Neural Network (CNNs) are a primary representative of deep learning algorithms which are used in computer vision tasks by training to detect features in images. CNNs were already used for classification of low-redshift merging galaxies [1, 2]. Here we will use CNN to to learn directly from images (without the need to extract morphology parameters) of distant merging galaxies in order to distinguish between merging and non-merging objects.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Reimagining Heating, Ventilation, and Air Conditioning for New Manufactured Homes

Manufactured homes represent one of the most affordable paths to home ownership for American households, but minimum-efficiency equipment and poor-quality home installation can lead to excess energy use and high operating costs. Slipstream, with partners from the Florida Solar Energy Center (FSEC), Northwest Energy Works, and Washington State University, identified and tested manufactured home HVAC innovations targeted for new construction offering the most promising mix of improved energy performance and likelihood of industry uptake.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Catalytic Reduction of Graphene Oxide Membranes and Water Selective Channel Formation in Water–Alcohol Separations

Graphene oxide (GO) is a promising membrane system for chemical separation applications due to its 2-D nanofluidics properties and an ability to control interplanar spacing for selectivity. The permeance of water, methanol (MeOH) and isopropyl alcohol (IPA) through 5 µm thick membranes was found to be 0.38 ± 0.15, 0.33 ± 0.16 and 0.42 ± 0.31 LMH/bar (liter/m2·h·bar), respectively. Interestingly, the permeance of a water–alcohol mixture was found to be dramatically lower (~0.01 LMH/bar) than any of its components. Upon removing the solvent mixture, the transmembrane flux of the pure solvent was recovered to near the original permeance. The interlayer space of a dried GO membrane was found to be 8.52 Å, which increased to 12.19 Å. 13.26 Å and 16.20 Å upon addition of water, MeOH and IPA. A decrease in d-space, about 2 Å, was consistently observed when adding alcohol to water wetted GO membrane and an optical color change and reduction in permeance. A newly proposed mechanism of a partial reduction of GO through a catalytic reaction with the water–alcohol mixture is consistent with experimental observations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Identification of Galaxy Shreds in Large Photometric Catalogs Using Convolutional Neural Networks

Contamination from galaxy fragments, identified as sources, is a major issue in large photometric galaxy catalogs. In this paper, we prove that this problem can be easily addressed with computer vision techniques. We use image cutouts to train a convolutional neural network (CNN) to identify cataloged sources that are in reality just star-formation regions and/or shreds of larger galaxies. The CNN reaches an accuracy ~98% on our testing data sets. We apply this CNN to galaxy catalogs from three among the largest surveys available today: the Sloan Digital Sky Survey, the DESI Legacy Imaging Surveys, and the Panoramic Survey Telescope and Rapid Response System Survey. We find that, even when strict selection criteria are used, all catalogs still show a ~5% level of contamination from galaxy shreds. Our CNN gives a simple yet effective solution to clean galaxy catalogs from these contaminants.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Extending the SAGA Survey (xSAGA). I. Satellite Radial Profiles as a Function of Host-galaxy Properties

We present "Extending the Satellites Around Galactic Analogs Survey" (xSAGA), a method for identifying low-z galaxies on the basis of optical imaging and results on the spatial distributions of xSAGA satellites around host galaxies. Using spectroscopic redshift catalogs from the SAGA Survey as a training data set, we have optimized a convolutional neural network (CNN) to identify z < 0.03 galaxies from more-distant objects using image cutouts from the DESI Legacy Imaging Surveys. From the sample of >100,000 CNN-selected low-z galaxies, we identify >20,000 probable satellites located between 36–300 projected kpc from NASA-Sloan Atlas central galaxies in the stellar-mass range $9.5\lt \mathrm{log}({M}_{\star }/{M}_{\odot })\lt 11$. We characterize the incompleteness and contamination for CNN-selected samples and apply corrections in order to estimate the true number of satellites as a function of projected radial distance from their hosts. Satellite richness depends strongly on host stellar mass, such that more-massive host galaxies have more satellites, and on host morphology, such that elliptical hosts have more satellites than disky hosts with comparable stellar masses. We also find a strong inverse correlation between satellite richness and the magnitude gap between a host and its brightest satellite. The normalized satellite radial distribution between 36–300 kpc does not depend on host stellar mass, morphology, or magnitude gap. The satellite abundances and radial distributions we measure are in reasonable agreement with predictions from hydrodynamic simulations. Our results deliver unprecedented statistical power for studying satellite galaxy populations and highlight the promise of using machine-learning for extending galaxy samples of wide-area surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Target Selection and Sample Characterization for the DESI LOW-Z Secondary Target Program

Abstract We introduce the DESI LOW- Z Secondary Target Survey, which combines the wide-area capabilities of the Dark Energy Spectroscopic Instrument (DESI) with an efficient, low-redshift target selection method. Our selection consists of a set of color and surface brightness cuts, combined with modern machine-learning methods, to target low-redshift dwarf galaxies ( z < 0.03) between 19 < r < 21 with high completeness. We employ a convolutional neural network (CNN) to select high-priority targets. The LOW- Z survey has already obtained over 22,000 redshifts of dwarf galaxies ( M * < 10 9 M ⊙ ), comparable to the number of dwarf galaxies discovered in the Sloan Digital Sky Survey DR8 and GAMA. As a spare fiber survey, LOW- Z currently receives fiber allocation for just ∼50% of its targets. However, we estimate that our selection is highly complete: for galaxies at z < 0.03 within our magnitude limits, we achieve better than 95% completeness with ∼1% efficiency using catalog-level photometric cuts. We also demonstrate that our CNN selections z < 0.03 galaxies from the photometric cuts subsample at least 10 times more efficiently while maintaining high completeness. The full 5 yr DESI program will expand the LOW- Z sample, densely mapping the low-redshift Universe, providing an unprecedented sample of dwarf galaxies, and providing critical information about how to pursue effective and efficient low-redshift surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

A Deep, High-angular-resolution 3D Dust Map of the Southern Galactic Plane

We present a deep, high-angular-resolution 3D dust map of the southern Galactic plane over 239° < l < 6° and ∣ b∣ < 10° built on photometry from the DECaPS2 survey, in combination with photometry from VISTA Variables in the Via Lactea, the Two Micron All Sky Survey, and “Unofficial” Wide-field Infrared Survey Explorer and parallaxes from Gaia Data Release 3 where available. To construct the map, we first infer the distance, extinction, and stellar types of over 700 million stars using the brutus stellar inference framework with a set of theoretical MESA Isochrone and Stellar Tracks (MIST) stellar models. Our resultant 3D dust map has an angular resolution of 1′ , roughly an order of magnitude finer than existing 3D dust maps and comparable to the angular resolution of the Herschel 2D dust emission maps. We detect complexes at the range of distances associated with the Sagittarius-Carina and Scutum-Centaurus arms in the fourth quadrant, as well as more distant structures out to a maximum reliable distance of d ≈ 10 kpc from the Sun. The map is sensitive up to a maximum extinction of roughly A V ≈ 12 mag. We publicly release both the stellar catalog and the 3D dust map, the latter of which can easily be queried via the Python package dustmaps. When combined with the existing Bayestar19 3D dust map of the northern sky, the DECaPS 3D dust map fills in the missing piece of the Galactic plane, enabling extinction corrections over the entire disk ∣b∣ < 10°. Our map serves as a pathfinder for the future of 3D dust mapping in the era of LSST and Roman, targeting regimes accessible with deep optical and near-infrared photometry but often inaccessible with Gaia.

Milky Way galaxy↗