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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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At least 55 records · Page 3

Elucidation of Local Ordering and Atomic-Scale Structure in Polymer-Derived SiOC

Silicon oxycarbide (SiOC) is a versatile ceramic material with tunable microstructure and compositions that can be modulated through precursor chemistry and processing conditions. Though there are several noteworthy uses of SiOC across a range of application spaces, the difficulties in elucidating the short- to medium-range order within these materials have limited the maturation of strategies to precisely control SiC x O 4–x compositions for user-tailored applications. In this contribution, we implement a range of synchrotron scattering and spectroscopy methods coupled with stochastic modeling techniques to elucidate changes in local chemistry and structure associated with the pyrolysis of a commercially available SiOC polymer precursor. Stochastic modeling approaches provide valuable insights into decoupling local Si–O and Si–C environments while confirming predominate heterogeneous phases in materials. Using pyrolysis temperatures between 250 to 800 °C results in a heterogeneous material predominately composed of SiOC and amorphous SiO 2 domains. At 1100 °C, redistribution of Si–C pairs in the SiOC network and Si–O from the SiO 2 domains create a more ordered SiOC phase with local cubic SiC-like ordering. In addition, residual carbon leads to a detectable carbon phases at 1100 °C that persist at higher temperatures. These efforts address the difficulties of obtaining atomic-scale insights into the local structure and nanoscale heterogeneities in SiOC, providing pathways toward establishing structure–property relationships for future materials development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Silicon-On-Silicon Carbide Platform for Integrated Photonics

Silicon carbide (SiC)'s nonlinear optical properties and applications to quantum information have recently brought attention to its potential as an integrated photonics platform. However, despite its many excellent material properties, such as large thermal conductivity, wide transparency window, and strong optical nonlinearities, it is generally a difficult material for microfabrication. Here, it is shown that directly bonded silicon-on-silicon carbide can be a high-performing hybrid photonics platform that does not require the need to form SiC membranes or directly pattern in SiC. The optimized bonding method yields defect-free, uniform films with minimal oxide at the silicon–silicon–carbide interface. Ring resonators are patterned into the silicon layer with standard, complimentary metal–oxide–semiconductor (CMOS) compatible (Si) fabrication and measure room-temperature, near-infrared quality factors exceeding 10 5 . The corresponding propagation loss is 5.7 dB cm -1 . The process offers a wafer-scalable pathway to the integration of SiC photonics into CMOS devices.

36 MATERIALS SCIENCE↗

Chemical speciation correlated with microstructural heterogeneity of interdicted uranium materials

Two uranium powders seized by law enforcement in Victoria, Australia, have been characterized by established nuclear forensic methods in a previously published study. Here, in this work, the results of further characterization by a scanning transmission x-ray microscope (STXM) operating in the soft x-ray regime are reported. STXM images are used to estimate the elemental distribution in micrometer-scale particles of each powder, and oxygen K-edge absorption spectra are used to determine the chemical state of uranium. The results of the current study are consistent with the previous analysis; the first powder is found to be a potassium-uranium hydrate, while the second powder is determined to be a mixture of uranium oxides primarily consisting of UO 3 .

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Abstract Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

36 MATERIALS SCIENCE↗

Deposit and Evaluate Material Across Extremes of Process Windows

Additive manufacturing (AM) can be a viable pathway to fabricate near-net-shape parts for next generation nuclear reactors. Stainless steel 316H (SS 316H) has been identified as a material of interest in the Advanced Materials and Manufacturing Technologies program to accelerate the use of AM for nuclear energy applications. Heterogeneity in material performance is one of the biggest challenges to qualification of AM SS 316H for in-service applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

CatTestHub: A benchmarking database of experimental heterogeneous catalysis for evaluating advanced materials

The ability to quantitatively compare newly evolving catalytic materials and technologies is hindered by the widespread availability of catalytic data collected in a consistent manner. While certain catalytic chemistries have been widely studied across decades of scientific research, quantitative comparisons based on literature information is hindered by variability in reaction conditions, types of reported data, and reporting procedures. Here, we present CatTestHub, an open-access database dedicated to benchmarking experimental heterogeneous catalysis data. Combining systematically reported catalytic activity data for selected probe chemistries, with relevant material characterization and reactor configuration information, the database provides a collection of catalytic benchmarks for distinct classes of active site functionality. Through key choices in data access, availability, and traceability, CatTestHub seeks to balance the fundamental information needs of chemical catalysis and the FAIR data design principles. Details of the database architecture and the means through which to navigate it are presented, highlighting examples of catalytic insights readily drawn from the available benchmarking data. In its current iteration, CatTestHub spans over 250 unique experimental data points, collected over 24 solid catalysts, that facilitated the turnover of 3 distinct catalytic chemistries. Here, a roadmap is presented through which to expand the open-access platform that serves as a community wide benchmark, primarily through continuous addition of kinetic information on select catalytic systems by members of the heterogeneous catalysis community at large.

Benchmark↗

Formation of Composite SiC/SiC Joints by Embedded Wire Chemical Vapor Deposition

The joining of ceramic monoliths or composites to date has primarily been limited to the formation of brittle monolithic joints using heterogeneous (dissimilar) materials, similar to brazing in metals. The development of a damage-tolerant joint layer by SiC fiber reinforcements is demonstrated here. Tube workpieces made of SiC fiber-SiC matrix composite are joined using a nonwoven SiC fiber mat densified by embedded wire chemical vapor deposition (EWCVD), creating a fiber-reinforced weld-like joint by homogeneous joining. EWCVD uses a localized heating method to target deposition and growth to the joint region specifically, while minimizing thermal damage to the surrounding composite tube material. X-ray computed tomography (XCT) is used to nondestructively characterize as-made joints for relative density, adhesion, and composition. In situ XCT analysis during mechanical testing revealed crack deflections in the bonding layer, which indicates a toughening mechanism typical of ceramic matrix composite phase. Gas permeation testing of these proof-of-concept composite joints identified relatively high leak rates in comparison to fully coated SiC/SiC composite tube workpieces. In conclusion, the novelty of the composite joining method and current technology challenges, including gas permeability, are discussed in comparison with traditional ceramic joints and materials.

SiC↗

Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems

Here, we present a novel physics-informed neural networks (PINNs) framework for modeling interface problems, termed Interface PINNs (I-PINNs). I-PINNs uses different neural networks for any two subdomains separated by a sharp interface such that the neural networks differ only through their activation functions while the other parameters remain identical. The performance of I-PINNs, conventional PINNs, and other existing domain-decomposition PINNs methods such as extended PINNs (XPINNs) and multi-domain PINN (M-PINN) is compared through several one-dimensional, two-dimensional, and three-dimensional benchmark elliptic interface problems. The results demonstrate that I-PINNs provides a root-mean-square-error accuracy, at least two orders of magnitude better than conventional PINNs and XPINNs at approximately one-tenth of the computational cost of conventional PINNs and half the cost of XPINNs. Additionally, while I-PINNs and M-PINN provide comparable accuracies, M-PINN is found to be approximately 50% more expensive.

42 ENGINEERING↗

Design optimization of lightweight automotive seatback through additive manufacturing compression overmolding of metal polymer composites

With the growing demand for enhanced automotive fuel efficiency and environmental sustainability, there is a need for lightweighting automotive components through innovative design and manufacturing processes. Here, this study leverages a combination of numerical iterative design optimization and hybrid additive manufacturing–compression molding (AM-CM) technique for metal polymer composites to lightweight an automotive seatback. The AM-CM process enables robust mechanical interlocking between metals and composites, boasting high stiffness and strength with low overall density. Replacing metallic components with such metal polymer composites allows for comparable mechanical performance while significantly reducing the overall weight. First, the automotive seatback design space is reduced to critical load carrying regions using topology optimization and high stress concentration areas are identified using finite element analysis. Next, a lightweight metal polymer subcomponent is designed for a high stress concentration region. The full seatback frame with spatially heterogeneous material-specific design is then iteratively optimized to enable enhanced stiffness with minimal weight. Overall, the automotive seatback frame designed with location-specific metal, polymer, and metal polymer composite materials weighs 20% less than the metal-only design while exhibiting similar stiffness.

36 MATERIALS SCIENCE↗

Long-Range Metal–Sorbent Interactions Determine CO 2 Capture and Conversion in Dual-Function Materials

Carbon capture and utilization involve multiple energy- and cost-intensive steps. Dual-function materials (DFMs) can reduce these demands by coupling CO 2 adsorption and conversion into a single material with two functionalities: a sorbent phase and a metal for catalytic CO 2 conversion. The role of metal catalysts in the conversion process seems salient from previous work, but the underlying mechanisms remain elusive and deserve deeper investigation to achieve maximum utilization of the two phases. Here, for this work, preformed colloidal Ru nanoparticles were deposited onto a “NaOx”/Al 2 O 3 sorbent to prepare prototypical DFMs with controlled phases for CO 2 capture and hydrogenation to CH 4 . Ru addition was found to double the high-temperature CO 2 adsorption capacity by activating the “NaOx”/Al 2 O 3 sorbent phase during a reductive pretreatment step. Most importantly, low Ru loadings were sufficient to ensure maximum CO 2 adsorption and conversion. This was attributed to the key role of the metal–sorbent interactions, wherein Ru was required to hydrogenate strongly bound CO 2 on the “NaO x ”/Al 2 O 3 sorbent to CH 4 via the H 2 activated on Ru. This interaction facilitated rate-determining carbonate migration and subsequent hydrogenation at the metal–sorbent interface. Overall, Ru controlled the CO 2 hydrogenation reaction rate, while the “NaO x ”/Al 2 O 3 sorbent dictated the CO 2 uptake capacity. By controlling metal–sorbent interactions at the molecular level, we demonstrate the critical role of the two phases and their synergy, facilitating the design of DFMs with maximum CO 2 capture and conversion efficiency.

carbon capture↗

The ORNL Moderator Test Station Science Case

Oak Ridge National Laboratory (ORNL) hosts two world-leading slow neutron sources, the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR), and is currently developing the technical design for a Second Target Station (STS) for the SNS. Upon completion of the STS project, ORNL will be uniquely positioned to optimize each of its three neutron sources, the SNS First Target Station (FTS), the STS, and HFIR, in a complementary way. Among the essential aspects of a re-imagined FTS and the current STS design are high-brightness parahydrogen moderators—moderators which are optimized for high per-unit-area neutron brightness rather than integrated-across-large-area neutron intensity. The high-brightness moderators proposed for the STS will, for the brightness metric, significantly outperform the coupled moderators currently on the FTS for appropriately optimized neutron beamlines, provided the moderating hydrogen is converted to near-equilibrium levels of parahydrogen (approximately 99.8% at 20 K). The original FTS moderators, by contrast, were conservatively designed to be relatively insensitive to the exact ortho:para ratio, with a consequent loss in performance. As a result, a redesign of the FTS moderators assuming fully converted parahydrogen could result in significant performance improvements on the FTS coupled moderators, and more consistent performance over time for all hydrogen moderators. This “parahydrogen problem” is a long-standing challenge for the effective implementation of hydrogen cold moderators at high-power neutron sources. In addition, the development of new moderator concepts, whether based on previously unused materials, structured heterogeneous arrays, or even simply on changes in overall shape and size is significantly restricted at a large-scale production facility intended to use the resulting neutron beams. Accordingly, moderators for production neutron sources are often designed in a very conservative, low-risk fashion, even though this compromises the absolute neutronic performance. Advanced moderator concepts worthy of study include features that could not be tested without redesigning and redeploying the entire existing reflector, shielding, and neutron beamline installation, making such development efforts far more expensive than building a stand-alone test facility.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Microwave-Assisted Activation of Mo/HZSM5 in Methane Dehydroaromatization

Natural gas flaring is a significant challenge for oil and gas producers. One viable option to mitigate flaring is the conversion of natural gas into valuable chemicals. Applying microwave energy to heterogenous catalyst materials offers advantages in terms of higher rates and product selectivities in comparison to traditional thermal systems which can be designed for on-demand chemical production in modular reactor systems. Methane dehydroaromatization (MDHA) produces transportable, liquid aromatic products directly from natural gas, which is ideal for remote production but is hindered by low thermodynamic yields and rapid deactivation. The stability and performance of the Mo/ZSM5 catalyst is often determined by the state and nature of Mo in the catalyst, which in turn is dictated by the temperature and gaseous environment during catalyst activation. In this study, the Mo/HZSM5 catalyst was activated under three different heating modes (thermal, microwave E-field, and microwave H-field) and four different gas environments (CO, He, CH4+H2, and H2). All the activated catalysts were characterized using different tools and performance evaluated in MDHA.

methane dehydroaromatization↗

Linking structural and rheological memory in disordered soft materials

Linking the macroscopic flow properties and nanoscopic structure is a fundamental challenge to understanding, predicting, and designing disordered soft materials. Under small stresses, these materials are soft solids, while larger loads can lead to yielding and the acquisition of plastic strain, which adds complexity to the task. In this work, we connect the transient structure and rheological memory of a colloidal gel under cyclic shearing across a range of amplitudes via a generalized memory function using rheo-X-ray photon correlation spectroscopy (rheo-XPCS). Our rheo-XPCS data show that the nanometer scale aggregate-level structure recorrelates whenever the change in recoverable strain over some interval is zero. The macroscopic recoverable strain is therefore a measure of the nano-scale structural memory. We further show that yielding in disordered colloidal materials is strongly heterogeneous and that memories of prior deformation can exist even after the material has been subjected to flow.

Kamani, Krutarth M. [Univ. of Illinois at Urbana-C↗

Designs for scalable construction of hybrid quantum photonic cavities

Nanophotonic resonators are central to numerous applications, from efficient spin–photon interfaces to laser oscillators and precision sensing. A leading approach consists of photonic crystal (PhC) cavities, which have been realized in a wide range of dielectric materials. However, translating proof-of-concept devices into a functional system entails a number of additional challenges, inspiring new approaches that combine resonators with wavelength-scale confinement and high quality factors; scalable integration with integrated circuits and photonic circuits; electrical or mechanical cavity tuning; and, in many cases, a need for heterogeneous integration with functional materials such as III–V semiconductors or diamond color centers for spin–photon interfaces. Here we introduce a concept that generates a finely tunable PhC cavity at a selected wavelength between two heterogeneous optical materials whose properties satisfy the above requirements. The cavity is formed by stamping a hard-to-process material with simple waveguide geometries on top of an easy-to-process material consisting of dielectric grating mirrors and active tuning capability. We simulate our concept for the particularly challenging design problem of multiplexed quantum repeaters based on arrays of cavity-coupled diamond color centers, achieving theoretically calculated unloaded quality factors of 106, mode volumes as small as 1.2(λ/neff)3, and maintaining >60% total on-chip collection efficiency of fluorescent photons. We further introduce a method of low-power piezoelectric tuning of these hybrid diamond cavities, simulating optical resonance shifts up to ∼760 GHz and color center fluorescence tuning of 5 GHz independent of cavity tuning. These results will motivate integrated photonic cavities toward larger scale systems-compatible designs.

Greenspon, Andrew S. (ORCID:0000000296317568)↗

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics–Informed Neural Networks

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. Furthermore, the improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

59 BASIC BIOLOGICAL SCIENCES↗

Heterogeneity of the Dominant Causes of Performance Loss in End-of-Life Cathodes and Their Consequences for Direct Recycling

Recycling Li-ion batteries from electric vehicles is critical for reducing costs and supporting the development of a domestic battery supply chain. Direct recycling of cathodes, like LiNixMnyCozO2 (NMC), is attractive due to its low cost, energy use, and emissions compared to traditional recycling techniques. However, a comprehensive understanding of the active material properties at end-of-life is needed to guide direct recycling processes and the performance-dependent reuse applications. Here, NMC material from an end-of-life commercial pouch cell is characterized and bench-marked against pristine non-cycled counterparts with respect to capacity, impedance, crystallography, morphology, and microstructure to identify major degradation modes and understand variability in the end-of-life material. The spatial heterogeneity of each property throughout the cell is also quantified. While the degraded material demonstrated similar capacity as the pristine, its impedance and rate capability are severely diminished. Furthermore, samples from the periphery of the electrode layers showed more severe performance loss compared to samples extracted from central regions. The dominant culprit of performance loss is the material microstructure, where the magnitude of particle cracking showed the strongest correlation to the impedance components that are most unfavorably impacted. This work suggests severe cracks in cathode active materials are the primary challenge that direct recycling methods must overcome.

25 ENERGY STORAGE↗

AC electrical transport detection of magnetic properties of small, granular, and heterogeneous samples

Experimental constraints and sample limitations can preclude ideal measurements of electrical transport properties of materials. In such situations, AC electrical transport methods are often employed due to a significant increase in signal-to-noise ratio they can provide. However, dynamic effects that are not often accounted for may be present that may modify the signals in these measurements. In particular, dynamic filtering effects are prominent in small, granular, and heterogeneous materials. We demonstrate that a lock-in amplifier based circuit can distinguish between these DC transport and AC filtering effects. Here, we further demonstrate that this filtering can reveal distinct signatures of magnetic transitions while providing a measure of sample quality.

47 OTHER INSTRUMENTATION↗

Growth in heterogeneous, evolving macromolecular networks: toward functional, biomimetic material

This report summarizes research carried out by the Balazs and Matyjaszewski Labs at Pitt and CMU, respectively, during 2025-26, supported by the DOE grant. It has produced the following 3 research articles and 2 review publications that acknowledged grant ER45998: 1. Computational Modeling of Hyperbranched Polymers 2. Synthesis of Structurally Tailored Networks 3. Sustainable and Oxygen-Tolerant Catalysis 4. A review paper on Current Status and Outlook for ATRP 5. A review paper on Future Directions for Atom Transfer Radical Polymerization

Balazs, Anna (ORCID:0000000255552692)↗