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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Performance of cross-flow turbines with varying blade materials and unsupported blade span

Cross-flow turbines could play a larger role in the diversification of the global energy supply if the impact of more cost-competitive design choices on performance and rotor dynamics was better understood. This study focuses on rotor performance and blade strain measurements while varying the following parameters: blade materials and blade free end length by changing strut support position. Towing tank experiments were performed with a modular 1-meter diameter cross-flow turbine consisting of three NACA 0018 blades with two support struts. One strut was fixed at the lower end of the turbine, while the second strut was adjustable, thereby changing the length of the free end. The blade materials tested were carbon, E-glass, and hollow E-glass fiber composites, in decreasing order of stiffness and cost. High-resolution distributed fiber optic sensors were embedded in two of the three rotor blades for each material and provided hundreds of strain measurements per blade. Turbine performance and blade strain were measured while varying tow speed and tip speed ratio. Performance tests were conducted at towing speeds sufficiently high for the performance to be independent of Reynolds number. E-glass blades and carbon blades performed similarly for the most rigid strut configurations. Higher strain was measured on the E-glass blades, and their performance was reduced for less rigid configurations compared to the carbon fiber blades. The performance of the highly deflective hollow E-glass blades was lower overall and became even more degraded for longer unsupported blade span. Furthermore, the results provide insight into the use of various blade materials in cross-flow turbines and guidance on allowable free end length for each material type.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Material Recovery Facilities (MRFs) in the United States: Operations, revenue, and the impact of scale

An analysis was conducted using nationwide survey data to evaluate how material recovery facilities (MRFs) operations vary regionally and with scale. The survey characterized materials, processes, and energy use involved with operations, and revenue for recyclables. This is the first nationwide analysis of MRFs in the US that accounts for mass processed, energy consumed, and revenue. Of a population of 521 MRFs, 48 responses representing MRFs from five US regions were received and analyzed (9.2 % response rate). Responses were analyzed by size according to yearly mass of inbound materials (small: <1,000 Mg/year, medium: 1,000–10,000 Mg/year, and large: >10,000 Mg/year). Most MRFs identify as single-stream; source from residences; utilize tipping floors, picking lines, baling and warehousing; and are powered by electricity. Most revenue and inbound mass (>50%) came from fiber (cardboard and paper). Glass had little revenue, and plastics were difficult to transition to market. Percent residue ranged from 1-39%, averaged <20%, and increased as the mass of inbound material increased. Large MRFs reported more sources of material, employed advanced sorting technology, had greater plastics revenue (33% versus 5% for small MRFs), and had more market access for plastics compared to small MRFs. Large MRFs had two orders of magnitude less annual electricity consumption per Mg recyclables than small MRFs (5–90 kWh/Mg versus ∼300–550 kWh/Mg). Results demonstrate environmental and economic benefits of larger-scale MRFs, which could be implemented more broadly in the US through regional hub-and-spoke arrangements for collecting and processing recyclables, lowering energy consumption and increasing revenue for recyclables.

Hub-and-Spoke↗
DOE OSTI2024

Reactive Carbon Capture: Cooperative and Bifunctional Adsorbent-Catalyst Materials and Process Integration for a New Carbon Economy

To say the least, releasing CO 2 into the atmosphere is reaping undue environmental consequences given the ever-present increase in severe global weather events over the past five years. However, it can be argued that–at least in the confines of current technological capabilities–the atmospheric release of CO 2 is somewhat unavoidable given that even shifting toward clean energy sources–such as solar, nuclear, wind, battery, or H 2 power–incurs an initial carbon requirement by way of manufacturing the very production abilities through which “clean” energy is generated. Even years from now, experts agree that energy production will be diversified and–as the global population continues to drive the growth of global energy consumption–thermal power derived from carbon combustion is likely to remain one intrinsic energetic source, of which CO 2 will always be a byproduct. In this context, it is the responsibility of the scientific community to devise improved pathways of carbon management such that (i) the consequences of combustion on the global environment are reduced and (ii) carbon fuels can be leveraged in a sustainable fashion. In this Account, we discuss a pivotal perspective shift on CO 2 emissions derived from a considerable breakthrough in material science from our work on shape engineering of nanoporous adsorbents and catalysts. This account details the development of materials which no longer vilify CO 2 emissions as a valueless combustion byproduct, instead providing a path for them to become a potential feedstock. In more specific terms, this work details the development of structured, cooperative “bifunctional” materials (BFMs) comprised of (i) a high-temperature adsorbent and (ii) a heterogeneous catalyst that enable single-bed CO 2 capture and utilization in oxidative ethane dehydrogenation (ODHE), oxidative propane dehydrogenation (ODHP), and dry methane reforming (DMR) processes. This Account begins with the conceptual development of the BFMs in the powdered state, followed by detailing the first-ever reports of structuring the materials into facile honeycomb contactors by 3D printing. The Account then summarizes the impressive performance of the 3D-printed BFMs, specifically focusing on how their catalysts (metal oxides and perovskites) influence their reactive CO 2 capture performances in ODHE, ODHP, and DMR processes. Such promise of CO 2 -as-fuel offers a glimpse into the future of a diversified energy economy, in which CO 2 /fuel looping can play an important role. A major factor in achieving this future is, of course, developing an appropriately active catalyst; an account of whose first breakthroughs in material science are detailed herein.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Characterizing Electrode Materials and Interfaces in Solid-State Batteries

Solid-state batteries (SSBs) could offer improved energy density and safety, but the evolution and degradation of electrode materials and interfaces within SSBs are distinct from conventional batteries with liquid electrolytes and represent a barrier to performance improvement. Over the past decade, a variety of imaging, scattering, and spectroscopic characterization methods has been developed or used for characterizing the unique aspects of materials in SSBs. These characterization efforts have yielded new understanding of the behavior of lithium metal anodes, alloy anodes, composite cathodes, and the interfaces of these various electrode materials with solid-state electrolytes (SSEs). This review provides a comprehensive overview of the characterization methods and strategies applied to SSBs, and it presents the mechanistic understanding of SSB materials and interfaces that has been derived from these methods. This knowledge has been critical for advancing SSB technology and will continue to guide the engineering of materials and interfaces toward practical performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Upcycling of Polystyrene Waste to Poly(ionic liquid) Materials

The C–H functionalization of commodity polymers could be a promising approach for upcycling plastic waste into advanced materials, which can alter the properties of the original materials through the introduction of different functionalities onto the existing backbone structures. In this study, waste polystyrene (PS) was modified by Friedel–Crafts acylation using 4-chlorobutyryl chloride (4-CBC) followed by reaction with N-alkylimidazoles to form cationic polyelectrolytes. These methods provide access to what are essentially poly(ionic liquid) (poly(IL)) materials with properties that are distinctly different from those of the PS from which they were formed. In one notable example, the glass transition temperature (T g ) of imidazolium-functionalized PS was ∼16 °C, which is a nearly 90 °C reduction from PS. This is also evidenced by macroscopic mechanical properties where the poly(IL) product is highly elastic in stark contrast to brittle PS. Moreover, the resulting ionomers showed self-healing behaviors in the presence of a “free” IL further contributing to the utility of the materials. Here, the methods in this work can open opportunities to utilize waste PS to obtain a vast array of poly(IL) materials with highly tailored structures and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dynamic Interfacial Design in Adaptive Hybrid Materials Enables Reversible and Tunable Mechano-Optic Smart Responses

Next-generation polymeric materials are shifting toward adaptive and interactive behaviors of living systems; however, designing materials that can reversibly modulate optical properties under mechanical deformation while maintaining mechanical robustness remains a key challenge. Here, we report a mechanically robust vitrimer-based adaptive hybrid material (AHM) that exhibits a stretch-induced reversible transparency-to-opacity transition, enabled by the integration of dynamic interactions at the polymer–silica nanoparticle interface and controlled nanoparticle self-assembly. The AHM combines boronic ester–functionalized polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (S-Bpin) with diol-functionalized silica nanoparticles (diol-SiNPs) to form a hybrid network hosting both dynamic boronic ester and hydrogen-bonding interactions. These reversible linkages facilitate controlled nanoparticle self-assembly and enable strain-induced nanoparticle alignment/aggregation. Upon stretching, SiNP-rich domains align and aggregate within the polymer matrix, while local modulus mismatch between stiff aggregated SiNP/borylated-styrene-rich regions and the softer elastomeric midblock induces surface microwrinkle formation. These internal aggregates and surface wrinkles cooperatively enhance light scattering, producing the opaque state under strain. Furthermore, the tailored AHM exhibits high toughness, thermomechanical stability, reprocessability, and programmable shape-memory behavior. This work presents a dynamic interfacial design strategy for mechanically robust, optically reconfigurable, and reusable soft materials for adaptive optics, smart windows, sensing, soft robotics, and circular smart-material platforms.

adaptive hybrid materials↗
DOE OSTI2025

Electrode Separation and Froth Flotation for the Recovery of Li-ion Battery Cathode Materials

Increasing use of Li-ion batteries (LIBs) will significantly increase the quantity of LIB waste generated from end-of-life batteries. Spent LiCoO 2 (LCO) battery cathodes contain significant quantities of valuable metals, making them good candidates for recycling. Typical recycling processes rely on high temperatures (pyrometallurgy) and chemical leachants (hydrometallurgy) to completely decompose the cathode and enable material recovery. Direct recycling processes and recovery of active material from electrode scrap do not benefit from such destructive recycling, and an efficient means of separating the cathode active material from the inactive components (e.g., polyvinylidene fluoride (PVDF) binder, conductive carbon) is needed. Here, we explore PVDF removal from LCO cathodes by two separate methods, evaluating their suitability for coupling to subsequent separation by froth flotation (FF). We show that washing electrodes in the solvent PolarClean (PC) dissolves PVDF away from LCO cathodes, enabling separation of the remaining LCO and conductive carbon by FF. The process is suitable for use with both pristine and cycled LCO electrodes, and the pristine recovered material can be used to prepare electrodes capable of cycling at 140 mAh/g. In conclusion, this demonstrates the suitability of coupled PC washing and FF separation for recycling both end-of-life batteries and scrap electrode material.

Batteries↗
DOE OSTI2024

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

36 MATERIALS SCIENCE↗
DOE OSTI2024

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Abstract Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.

36 MATERIALS SCIENCE↗
DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A metastable pentagonal 2D material synthesized by symmetry-driven epitaxy

Most two-dimensional (2D) materials experimentally studied so far have hexagons as their building blocks. Only a few exceptions, such as PdSe 2 , are lower in energy in pentagonal phases and exhibit pentagons as building blocks. Although theory has predicted a large number of pentagonal 2D materials, many of these are metastable and their experimental realization is difficult. Here, in this paper, we report the successful synthesis of a metastable pentagonal 2D material, monolayer pentagonal PdTe 2 , by symmetry-driven epitaxy. Scanning tunnelling microscopy and complementary spectroscopy measurements are used to characterize this material, which demonstrates well-ordered low-symmetry atomic arrangements and is stabilized by lattice matching with the underlying Pd(100) substrate. Theoretical calculations, along with angle-resolved photoemission spectroscopy, reveal monolayer pentagonal PdTe 2 to be a semiconductor with an indirect bandgap of 1.05 eV. Our work opens an avenue for the synthesis of pentagon-based 2D materials and gives opportunities to explore their applications such as multifunctional nanoelectronics.

interfaces↗
DOE OSTI2025

Higher-order factorization machine for accurate surrogate modeling in material design

Efficient and robust optimization is important in material science for identifying optimal structural parameters and enhancing material performance. Surrogate-based active learning algorithms have recently gained great attention for their ability to efficiently navigate large, high-dimensional design spaces. Among surrogate models, 2 nd -order factorization machine (FM) models are widely employed as the surrogate model in active learning algorithms due to their balance between simplicity and effectiveness. However, their quadratic nature limits their capacity to capture complex, higher-order interactions among variables, often leading to suboptimal solutions. To overcome this limitation, we propose an active learning scheme integrating a 3 rd -order FM model, capable of modeling three-variable interactions and more intricate relationships in material systems. We comprehensively evaluate the surrogate modeling performance of the 3 rd -order FM case using various objective functions. Furthermore, we examine the optimization reliability and efficiency of the 3 rd -order FM-based active learning in a real-world material design task (e.g., nanophotonic structures for transparent radiative cooling). Our study shows that the 3 rd -order FM outperforms the 2 nd -order model in both surrogate accuracy and optimization performance, highlighting higher-order models’ promises for material design and optimization problems.

Factorization machine↗
DOE OSTI2024

Analytical model for laser-induced transient grating measurements of thermal diffusivity in non-opaque materials

The thermal transport and elastic properties of materials are often measured using the laser-induced transient grating spectroscopy (TGS) technique. The analysis of the TGS signal usually involves fitting well-known expressions, derived assuming the limiting cases of opaque or transparent materials, to the measured data. In this paper, the system of thermoelastic equations is analytically solved for an isotropic homogeneous material assuming finite laser penetration depth, which is an important consideration when the penetration depth is on the order of the acoustic wavelength. The need to use such a solution is discussed and compared to the expression for opaque material. The solution is benchmarked against TGS signal measured on single-crystal silicon with {100} surface orientation and is found to significantly improve the accuracy of the calculated thermal diffusivity as compared to using the expression for opaque material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Large radiation back-flux from Monte Carlo simulations of fusion neutron–material interactions

Abstract Fusion power reactors will generate intense neutron fluxes into plasma-facing and structural materials (SMs). Radiation back-fluxes, generated from neutron–material interactions under these fluxes, can dramatically impact the plasma dynamics, e.g. by seeding runaway electrons during disruptions via Compton scattering of background electrons by wall-emitted gamma radiation. Here, we quantify these back-fluxes, including neutrons, gamma rays, and electrons, using Monte Carlo calculations for a range of SM candidates and first wall (FW) thicknesses. The radiation back-flux magnitudes are remarkably large, with neutron and gamma radiation back-fluxes on the same order of magnitude as the incident fusion neutron flux. Electron back-fluxes are two orders of magnitudes lower, but are emitted at sufficiently high energies to impact the sheath and boundary plasma dynamics. Material configuration plays a key role in determining back-flux magnitudes. The SM chiefly determines the neutron back-flux magnitude, while the FW thickness principally attenuates the gamma ray and electron back-fluxes. In addition to prompt back-fluxes, which are emitted immediately after fusion neutrons impact the surface, significant delayed gamma ray and electron back-fluxes arise from nuclear decay processes in the activated materials. These delayed back-flux magnitudes range from 2% to 7% of the prompt back-fluxes, and remain present during transients when fusion no longer occurs. During disruptions, build-up of delayed gamma radiation back-flux represents potential runaway electron seeding mechanisms, posing additional challenges for disruption mitigation in a power reactor compared with non-nuclear plasma operations. This work highlights the impact of these radiation back-fluxes plasma performance and demonstrates the importance of considering back-flux generation in materials selection for fusion power reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Optimizing spin qubit coherence through materials codesign

The evolution of defect-based spin qubit systems is currently transitioning from fundamental studies and proof-of-concept demonstrations into applications in the burgeoning field of quantum technology. Within this context, new challenges emerge, in particular, the need to understand and engineer the fundamental materials that form the hardware building blocks critical for the scalability and wide-scale adoption of such technologies. While earlier discussions have often focused on qubits within idealized systems, major limitations on spin coherence and optical properties arise from effects imposed by the nonideality of the surrounding host matrix. Decoherence can stem from a variety of sources, including other qubits, nuclear spins, and parasitic point- and extended defects, which interact with the qubit via magnetic and electric fields, photons, phonons, and strain. In this article, we focus on the relevant sources and mechanisms through which decoherence occurs and provide potential mitigation strategies via the synergistic integration of first-principles simulations and materials synthesis and engineering. We aim to provide a tangible link between material properties and material functions thereby enabling materials-by-design.

ab initio simulations↗
DOE OSTI2026

Micro-architected material design for mechanical response

Rapid advances in additive manufacturing (AM) have enabled the creation of micro-architected materials—also known as mechanical metamaterials—with unprecedented control over fine-scale geometries and arrangements of multiple material constituents. These “materials” can achieve unique and extraordinary effective mechanical properties through their complex architectures rather than composition alone. A key challenge is to design for these bespoke effective mechanical responses within the constraints of available AM techniques (i.e., given a set of desired effective properties), identify a (often nonunique) micro-architecture and selection of material constituents that achieves them. Two main strategies have emerged. Gradient-based methods use sensitivity analysis to iteratively refine candidate designs, while data-driven methods learn micro-architecture-constituent relationships from existing examples to propose new designs. This article reviews these design approaches for micro-architected materials with tailored mechanical responses that can be fabricated by AM as well as their applications.

Spadaccini, Christopher M [Lawrence Livermore Nati↗
DOE OSTI2024

Advanced and functional composite materials via additive manufacturing: Trends and perspectives

Additive manufacturing (AM) has many advantages over conventional subtractive manufacturing methods. The cost-effective AM allows for precise fabrication of complex structures with less material waste, making it a popular manufacturing process in many applications. The recent development of AM materials has advanced significantly. Further, by precisely controlling material distribution and microstructural features, AM facilitates the creation of new composites with specific requirements. AM techniques have contributed considerably to integrating unique functionalities for various applications. This review emphasizes multiple categories of materials, including metal alloys, polymer-based composites, and sustainable composites, as well as applications of sensing materials and strategies and emerging artificial intelligence and machine learning.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Energy Materials Chemistry Integrating Theory, Experiment and Data Science (Final Report)

The Energy Materials Chemistry Integrating Theory, Experiment and Data Science (EM-CITED) project is a multidisciplinary research effort focused on accelerating discovery of scientific knowledge via incorporation of data science and artificial intelligence in materials chemistry research. The project aims to advance materials chemistry-aware data science to unify theory and experiment knowledge streams. The work resulted in foundational AI frameworks for materials chemistry – Deep Reasoning Networks (DRNets), Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP), and Material-to-Spectrum (Mat2Spec) prediction – as well as a host of strategies for accelerated scientific discoveries through principled incorporation of data science in computational and experimental research.

36 MATERIALS SCIENCE↗