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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Reinforcement of double-exchange ferromagnetic coupling by Ru in La{sub 1.24}Sr{sub 1.76}Mn{sub 2-y}Ru{sub y}O{sub 7} manganite system

Highlights: • Effect of Mn-site doping by Ru has been studied in La{sub 1.24}Sr{sub 1.76}Mn{sub 2-y}Ru{sub y}O{sub 7}. • Electrical resistance, magnetoresistance and magnetic properties were measured. • Ru substitution enhances the ferromagnetism and metallicity. • Results were interpreted by the ferromagnetically coupled Ru with Mn ions in Mn–O–Ru network. - Abstract: The effect of Mn-site doping on magnetic and transport properties in the bilayer manganites La{sub 1.24}Sr{sub 1.76}Mn{sub 2-y}Ru{sub y}O{sub 7} (y = 0.0, 0.04, 0.08 and 0.15) has been studied. The undoped compound La{sub 1.24}Sr{sub 1.76}Mn{sub 2}O{sub 7} exhibits a ferromagnetic metal to paramagnetic insulator transition at T{sub C} = 130 K and the substitution of Ru shifts the transition temperatures to higher temperature values. The increased metal–insulator transition by Ru substitution, obtained from temperature dependence of resistivity measurements, indicates that the Ru substitution enhances the metallic state at low temperature regime and favours the Mn–Ru pairs in the Ru doped samples. Moreover, the activation energy values calculated from the temperature dependence of resistivity curves suggest that the Ru substitution weakens the formation of polarons. The increased magnetoresistance ratio from 108% to 136% by Ru substitution, measured at 5 K, points out that the Ru substitution also enhances the inter-grain tunneling magnetoresistance. Thus, the ferromagnetic order and metallic state in La{sub 1.24}Sr{sub 1.76}Mn{sub 2}O{sub 7} system have been enhanced by the presence of Ru in the Mn-site. These reinforcements of ferromagnetic metallic state and magnetoresistance have been interpreted by the ferromagnetically coupled high spin states of Ru with Mn ions in the Mn–O–Ru network.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Dynamic tensile deformation and damage of B 4 C-reinforced Al composites: Time-resolved imaging with synchrotron x-rays

Dynamic tensile experiments are conducted on 15% and 30% in weight percentage B 4 C/Al composites with a split Hopkinson tension bar, along with high-speed synchrotron x-ray digital image correlation (XDIC) to map strain fields at μ m and μ s scales. As manifested by bulk-scale stress – strain curves, a higher particle content leads to a higher yield strength but lower ductility. Strain field mapping by XDIC demonstrates that tension deformation and tensile fracture, as opposed to shear and shear failure, dominate deformation and failure of the composites. The fractographs of recovered samples show consistent features. The particle-matrix interfaces are nucleation sites for strain localizations, and their propagation and coalescence are diffused by the Al matrix. The reduced spacing between strain localization sites with increasing particle content, facilitates their coalescence and leads to decreased ductility. Furthermore, designing a particle-reinforced, metallic-matrix composite with balanced strength and ductility should consider optimizing the inter-particle distance as a key par

36 MATERIALS SCIENCE↗
DOE OSTI2021

Improvement of interfacial adhesion of unidirectional textile grade carbon fiber (TCF) with unsized, epoxy and urethane sizing reinforced in thermoset urethane composites

This work considers a unique wide tow (450k filaments) form of low-cost carbon fiber intended for non-aerospace applications. TCF is currently produced in epoxy and urethane sizing, and there is a need to understand its resulting composite properties. In this work, the interfacial adhesion of sized TCF reinforced in thermoset urethane (TSU) composites are examined through the surface, thermal and mechanical characterization techniques. Atomic force microscopy (AFM) results showed an increase in surface roughness for urethane (276%) and epoxy sized (78%) versus unsized TCF. XPS results showed 531% increase in O in epoxy sized TCF and 250% N content in urethane sized TCF compared to unsized TCF. The surface energy of epoxy and urethane sized TCF is enhanced by 78% and 96%, respectively compared to unsized TCF. The storage modulus showed improvement for urethane (23%) and epoxy (21%) sized than unsized TCF-TSU composites. The flexural, interlaminar shear strength (ILSS), and impact properties of urethane sized TCF increase by 24%, 50%, and 273%, respectively, than unsized TCF. The results demonstrate that the surface and thermal properties correlate with the mechanical properties of TCF-TSU composites and sizing enhances the wettability of the composites.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Harnessing biomass-derived reinforcements for sustainable flame-resistant composite

Bio-based composites containing natural fiber reinforcements have been integral to developing innovative, low-cost, and low-energy technologies for transportation applications in marine, automotive, and other industries. The low density of natural fibers gives these materials a high specific stiffness and strength, which can lead to significant weight savings in composites. This study focused on a specialty type of wood fiber treated with boric acid (≤ 7%) sourced from Northern White Pine in the USA, marketed as TimberFill. TimberFill is a high aspect ratio, fibrillated wood fiber that is produced commercially for wood fiber insulation; it is chemically treated to improve both its flame retardance and water resistance. In the present study, TimberFill was processed into non-woven mats using a process akin to papermaking and then infiltrated with epoxy resin. Biochar from wood waste was also incorporated into the epoxy resin as a low-cost filler and functional additive. The effects of biochar concentration on composites’ flame retardance (17% improvement in burn rate), thermal conductivity (improved by 9%), and mechanical performance (33% improvement in modulus) are herein presented. Additionally, it explores the plasticizing effect of the boric acid used in the treatment of TimberFill on the thermal and mechanical properties of the resulting composites, specifically on the thermal expansion coefficient.

Joshy, Joslin [ORNL]↗
DOE OSTI2019

The effects of staged mixing on the dispersion of reinforcing fillers in elastomer compounds

Kinetically mixed reinforcing fillers are dispersed by accumulated strain. Processing conditions such as mixing geometry, intensity and the duration of mixing are key elements that control accumulated strain. A simple method to modify mechanical mixing is the mixing schedule which involves the timing of filler additions to a compound. For example, it is known that simultaneous addition of oil with filler is disadvantageous to dispersion, while the incorporation of oil after filler addition enhances dispersion. The simplest case of mixing schedule involves timed filler additions into an elastomer, for instance in a single stage or in two-stages. This study reported herein explores the effects of staged mixing on nanoscale dispersion based on a colloidal model. Dispersion is quantified using a pseudo-thermodynamic approach coupled with ultra-small angle X-ray scattering. It was found that single stage mixing yields optimal nano-dispersion for carbon-coated silica and carbon black in polybutadiene using an internal mixer.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Reprocessable polyhydroxyurethane networks reinforced with reactive polyhedral oligomeric silsesquioxanes (POSS) and exhibiting excellent elevated temperature creep resistance

The rapid development of covalent adaptable networks or vitrimers shows promise for addressing the long-standing recycling issues associated with conventional, permanently cross-linked thermosets. At the same time, it is important to demonstrate that properties of reprocessable polymer networks can be optimized to meet the ongoing demand for high-performance materials. We have fabricated reprocessable polyhydroxyurethane (PHU) network composites reinforced with reactive polyhedral oligomeric silsesquioxanes (POSS). With functionalized POSS serving as a fraction of the cross-linkers, the PHU–POSS network nanocomposites exhibit significantly enhanced storage modulus at the rubbery plateau region relative to the neat PHU network. With up to 10 wt% POSS loading, these network composites can undergo melt-state reprocessing at 140 °C with 100% property recovery associated with cross-link density. We also show that hydroxyurethane dynamic chemistry leads to excellent creep resistance at elevated temperature up to 90 °C and is unaffected by reactive incorporation of POSS. In conclusion, this study demonstrates the effectiveness of POSS as nanofillers for designing high-performance, organic-inorganic dynamic PHU networks with excellent reprocessability.

42 ENGINEERING↗
DOE OSTI2026

Ambient-Pressure Chemical Recycling of Thermoset Carbon Fiber-Reinforced Polymers via Low-Temperature Solvolysis: Techno-Economic and Life-Cycle Assessment

Carbon fiber-reinforced polymer (CFRP) waste is rapidly accumulating, with global volumes projected to exceed 500,000 tons annually by 2050. Virgin carbon fiber production is extremely energy- and cost-intensive, and growing demand has intensified supply-chain vulnerabilities associated with precursor availability and reliance on critical materials. Scalable recycling technologies therefore offer not only environmental benefits but also opportunities to reduce material costs and strengthen domestic composite supply chains. This study introduces an efficient, mild chemical recycling method for epoxy-amine CFRPs, utilizing acetic acid (80 wt%) and zinc acetate (1 wt%) at atmospheric pressure and low temperature (~110 °C). The solvolysis process preserves residual resin (~40%) on recycled carbon fibers (rCF), enhancing interfacial bonding in remanufactured composite materials. Recycled fibers retain ~90% of their mechanical properties, and recovered oligomers show potential for reuse in epoxy formulations, supporting material circularity. Techno-economic analysis (TEA) and life-cycle assessment (LCA) confirm the scalability and sustainability of this process.

Recycling CFRP↗
DOE OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Carbon nanotube (CNT) reinforced 316L stainless steel composites made by laser powder bed fusion: Microstructure and wear response

Carbon nanotube (CNT) reinforced 316L stainless steel (SS) composites were fabricated by laser powder bed fusion (LPBF) additive manufacturing (AM). This study focuses on microstructure evolution, hardness, and wear behavior. The wear behavior for AM 316L SS and 1 wt% CNT/316L SS was studied by a dry sliding test under normal loads of 10, 15, and 25 N. The addition of CNT to 316L SS led to the change of solidification mode from cellular/columnar to dendritic growth, producing grain refinement. Nanoscale dendrites were observed. Although TEM characterization confirmed the existence of CNT in cell interior, most CNT were damaged, resulting in carbon material segregation along cellular/dendritic boundaries. The wear rate was found dramatically decreased due to the improved hardness. Compared to the adhesive and abrasive wear in AM 316L SS, the wear mechanism of CNT/316L composite was adhesion and oxidative wear. Furthermore, enhanced cellular/dendritic boundary strengthening by carbon segregation was the main strengthening mechanism of CNT/316L composite.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Textile-Grade Carbon Fiber-Reinforced Polycarbonate Composites: Effect of Epoxy Sizing

In this work, epoxy-sized textile-grade polyacrylonitrile (PAN) carbon fiber (TCF) with 450 K filaments (CFTF, ORNL) was reinforced in the polycarbonate (PC) matrix using a compression molding technique. The epoxy sizing effect on the surface, thermal, and mechanical properties of TCF–PC was investigated. X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy results of the TCF–PC composite show the absence of an oxirane group (epoxy-sized TCF), which suggests a covalent bond formation between the oxirane ring and carbonyl groups from TCF and PC. Dynamic mechanical analysis results confirm a strong immobilized interface (b > 1) present between TCF and PC. The tensile, flexure, compression, and interlaminar shear strength of TCF–PC composites were 323 ± 53, 371 ± 31, 397 ± 87, and 36 ± 3 MPa, correspondingly. The fracture analysis through scanning electron microscopy shows good wettability between TCF and the PC matrix which validates the results obtained from surface and thermal characterization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Hierarchical Reinforcement Learning of a Short-Range Bond-Order Potential for Silica: Analytic Embedding of Coordination with Classical Efficiency

Reinforcement learning (RL) has recently emerged as a data-efficient strategy to parametrize short-range interatomic potentials. Building on our past RL optimization of pairwise silica models, we extend the framework to a bond-order (Tersoff-type) potential that provides an analytic embedding of local coordination through a three-body term. A hierarchical RL workflow combining continuous-action Monte Carlo Tree Search and property-based rewards efficiently explores the 26-dimensional parameter space, sequentially optimizing lattice parameters, densities, angles, and cohesive energies of 21 silica polymorphs. The resulting models, Q-Tersoff and ML-Tersoff, reproduce the energetic ordering of low-energy phases and capture the angular correlations and amorphous structure factors of silica with improved fidelity over pairwise force fields, while remaining orders of magnitude faster than high-dimensional machine-learned potentials. Both models underperform for elastic constants and high-energy frameworks, delineating the limits of the current analytic form. The approach establishes a general and interpretable route to angle-aware, short-range potentials that bridge physics-based and machine-learned descriptions of silicate materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ab Initio-Based Bond Order Potential for Arsenene Polymorphs Developed via Hierarchical Reinforcement Learning

Arsenene, a less-explored two-dimensional material, holds the potential for applications in wearable electronics, memory devices, and quantum systems. This study introduces a bond-order potential model with Tersoff formalism, the ML-Tersoff, which leverages multireward hierarchical reinforcement learning (RL), trained on an ab initio data set. This data set covers a spectrum of properties for arsenene polymorphs, enhancing our understanding of its mechanical and thermal behaviors without the complexities of traditional models requiring multiple parameter sets. Our RL strategy utilizes decision trees coupled with a hierarchical reward strategy to accelerate convergence in high-dimensional continuous search spaces. Unlike the Stillinger-Weber approach, which demands separate formalisms for buckled and puckered forms, the ML-Tersoff model concurrently captures multiple properties of the two polymorphs by effectively representing the local environment, thereby avoiding the need for different atomic types. Here, we apply the ML model to understand the mechanical and thermal properties of the arsenene polymorphs and nanostructures. We observe an inverse relationship between the critical strain and temperature in arsenene. Thermal conductivity calculations in nanosheets show good agreement with ab initio data, reflecting a decrease in thermal conductivity attributable to increased anharmonic effects at higher temperatures. We also apply the model to predict the thermal behavior of arsenene nanotubes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene

Here, we introduce a multi-reward reinforcement learning (RL) approach to train a flexible bond-order potential (BOP) for 2D phosphorene based on ab initio training data sets. Our approach is based on a continuous action space Monte Carlo tree search algorithm that is general and scalable and presents an efficient multiobjective optimization scheme for high-dimensional materials design problems. As a proof-of-concept, we deploy this scheme to parametrize multiple structural and dynamical properties of 2D phosphorene polymorphs. Our RL-trained BOP model adequately captures the structure, energetics, transformation barriers, equation of state, elastic constants, and phonon dispersions of various 2D P polymorphs. We use this model to probe the impact of temperature and strain rate on the phase transition from black (α-P) to blue phosphorene (β-P) through molecular dynamics simulations. A decrease in critical strain for this phase transition with increase in temperature is observed, and the underlying atomistic mechanisms are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Studying Transient Phenomena in Thin Films with Reinforcement Learning

Neutron reflectometry has long been a powerful tool to study the interfacial properties of energy materials. Recently, time-resolved neutron reflectometry has been used to better understand transient phenomena in electrochemical systems. Those measurements often comprise a large number of reflectivity curves acquired over a narrow q range, with each individual curve having lower information content compared to a typical steady-state measurement. In this work, we present an approach that leverages existing reinforcement learning tools to model time-resolved data to extract the time evolution of structure parameters. Further, by mapping the reflectivity curves taken at different times as individual states, we use the Soft Actor-Critic algorithm to optimize the time series of structure parameters that best represent the evolution of an electrochemical system. We show that this approach constitutes an elegant solution to the modeling of time-resolved neutron reflectometry data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Structural and Dynamical Roles of Bound Polymer Chains in Rubber Reinforcement

The addition of nanofillers to rubber matrices is a powerful route to improve the mechanical properties. Here, we focus on a molecular understanding of basic mechanisms that are important for the reinforcement in rubbers. The key role in this process is ascribed to bound rubber (BR) that engages with the matrix as well as with adjacent nanofillers. To date, this understanding has been impeded by the lack of experimental tools to directly probe the BR chains buried in a polymer matrix composed of the same polymer. To tackle this challenge, we combine neutron scattering/spectroscopy techniques with isotope-labeling and molecular dynamics simulations. The system is a simplified carbon-black-filled polybutadiene. The combined experimental and computational results provide new insights into the local structural and dynamical heterogeneities of BR chains and their interactions with the matrix polymer, highlighting (i) the structural partition of the bound chains into three components (i.e., trains, loops, and tails) and their fractions; (ii) their dynamical hierarchies, i.e., the trains that remain immobile on the filler surface, the loops that are fairly large and hence allow the interdigitation of matrix chains, and the tails with their unique characteristics to reach far out into the matrix and entangle with matrix chains. These multiple roles of the constituent components of the BR chains promote the formation of a well-developed adhesive polymer–filler interface, enhancing the elastic property of a filled rubber. Finally, the comprehensive understanding derived and validated by the model rubber will be translatable to many other polymer nanocomposites.

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

Multiscale Mechanical Characterization of Mineral-Reinforced Wood Cell Walls

Studying the multiscale mechanics of bio-based composites offers unique perspectives on underlying structure–property relations. Cellular materials, such as wood, are highly organized, hierarchical assemblies of load-bearing structural elements that respond to mechanical stimuli at the microscopic, mesoscopic and macroscopic scale. In this study, we modified oak wood with nanocrystalline ferrihydrite, a widespread ferric oxyhydroxide mineral, and characterized the resulting mechanical properties of the composite at various levels of organization. Ferrihydrite nanoparticles were deposited inside the wood cell wall by an in situ chemical reaction, resulting in increased stiffness and hardness of the functionalized secondary cell wall, as evidenced by region-specific nanoindentation tests under an electron microscope. Chemically modified and pristine wood samples were characterized by using atomic force microscopy in the bimodal frequency modulation mode, which produced topographical images from the cellular ultrastructure with high lateral resolution and localized nanomechanical information across distinct cell wall layers. In conclusion, despite mineral reinforcement at the cell wall level, the macroscopic fracture behavior examined through three-point flexural testing remained unchanged upon modification, as cell–cell adhesion could be impaired by harsh chemical conditions.

Cells↗
DOE OSTI2022

Melt State Reinforcement of Polyisoprene by Silica Nanoparticles Grafted with Polyisoprene

Here, we systematically vary the nanoparticle (NP) dispersion state in composites formed by mixing polyisoprene homopolymers with polyisoprene grafted silica particles, and demonstrate how creep measurements allow us to overcome the limitations of small amplitude oscillatory shear (SAOS) experiments. This allows us to access nearly 13 orders in time in the mechanical response of the resulting composites. We find that a specific NP morphology, a percolating particle network achieved at intermediate graft densities, significantly reinforces the system and has a lower NP percolation loading threshold relative to other morphologies. These important effects of morphology only become apparent when we combine creep measurements with SAOS re-emphasizing the role of synergistically combining methods to access the mechanical properties of polymer nanocomposites over broad frequency ranges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Amplifying Nanoparticle Reinforcement through Low Volume Topologically Controlled Chemical Coupling

Here, we present a streamlined method to covalently bond hydroxylated carbon nanotubes (CNOH) within a polyphenol matrix, all achieved through a direct, solvent-free process. Employing an extremely small concentration of CNOH (0.01% w/w) along with topologically contrasting linkers led to a maximum of 5-fold increase in modulus and a 25% enhancement in tensile strength compared to the unaltered matrix, an order of magnitude greater reinforcement (w/w) compared to state-of-the-art melt-processed nanocomposites. Through dynamic mechanical analysis, low field solid-state nuclear magnetic resonance spectroscopy, and molecular dynamics simulations, we uncovered the profound influence of linker’s conformational degrees of freedom on the segmental dynamics and therefore the material’s properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗