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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Lightweight, Flexible Electromagnetic Shielding Composite Films Reinforced with Recycled Carbon Fibers and Carbon Nanofillers

Lightweight, flexible polymer composites are widely adopted in modern electronic devices and systems for high-efficiency electromagnetic interference (EMI) shielding. Reinforcing polymer composites with conductive fillers offers a promising alternative to conventional metal-based shielding material thanks to their low density, tunable properties, and flexibility. Herein, lightweight, flexible ultra-high molecular weight polyethylene composite films reinforced with recycled carbon fibers (rCFs) and carbon-based nanofillers, including graphene nanoplatelets (GNPs) and carbon nanotubes, are reported for EMI shielding applications. The incorporation of these nanofillers significantly reduces the required content of rCFs and improves processability while maintaining comparable shielding effectiveness. The addition of these nanofillers with rCFs enhances the EMI shielding effectiveness by up to 15 dB. Incorporating 1 wt% GNPs can replace 5 wt% rCFs and achieve comparable EMI shielding performance, while 5 wt% of either nanofillers can substitute for 10 wt% rCFs. Numerical modeling of electromagnetic wave transmission reveals that increasing nanofiller concentrations enhances both reflection and absorption losses, with absorption consistently dominating across all levels. Furthermore, this study not only provides insight into the synergistic contributions of rCFs and carbon nanofillers to shielding effectiveness but also paves the way for the design of sustainable, lightweight EMI shielding composite films for applications in electric vehicles, medical equipment, and portable electronics.

carbon nanofiller↗
DOE OSTI2025

Polypropylene Composites Reinforced With Recycled Waste Cellulosic Fiber/Fine Mixture: The Impact of Cellulose Sieving on Performance

This study explores how a sieving step of waste cellulosic fiber and fine (WCFF) mixture affects the performance of WCFF‐loaded polypropylene (PP) composites and whether the separation of fines from fibers offers an added benefit. The WCFF samples were downsized, and four different filler size ranges were sieved using a series of mesh sizes from 4 to 0.85 mm. The WCFF/PP composites were then compounded at 20 wt.% loading of WCFF using a twin‐screw extruder. Incorporating WCFF increased the tensile strength to 41.28 MPa and the modulus to 3207 MPa, accounting for 28% and 38% enhancements, respectively. Interestingly, the greatest improvements were associated with the nonsieved WCFF case, and the sieved WCFF fibers provided only marginal enhancements over virgin PP. The outperformance of nonsieved WCFF was attributed to the synergistic reinforcement of hybrid fibers and fines as well as the maintenance of longer fibers in the system. However, the strain at break and impact strength of PP decreased after introducing WCFF. Moreover, the complex viscosity and storage modulus increased with an increase in the filler size, due to the formation of a more effective percolative network. The PP's crystallinity exhibited a relatively strong dependency on the sieving, where WCFF samples with short‐aspect‐ratio fillers promoted the crystallinity significantly. It was also found that the WCFF degradation onset temperature increased once it was incorporated into PP. This study suggests that waste cellulosic feedstocks can be utilized as a reinforcement without additional sieving to manufacture high‐performance and cost‐effective composites.

36 MATERIALS SCIENCE↗
DOE OSTI2025

New class of tritium breeders for fusion applications: Metal-reinforced composite breeders

Commercial fusion reactors operating on a D-T fuel cycle will require a steady supply of tritium to maintain the burning plasma required for continuous power generation. Since tritium has a short half-life, there is negligible natural abundance which necessitates fusion reactors to produce their own source of tritium. Tritium is most easily produced by surrounding a fusion reactor core with lithium (Li), which reacts under the intense neutron flux leaving the reactor core to form tritium and helium. Due to the hazards and technical challenges associated with surrounding a fusion reactor core with many tons of molten Li, other Li-bearing tritium breeder materials have been pursued. Unfortunately, most of the liquid breeders historically examined are exceedingly corrosive to reactor structural materials while many solid breeders in the form of ceramics are forced to make tradeoffs between Li content and mechanical integrity. In this work, to break the historic limit between Li-density and mechanical integrity of traditional solid breeders, a new class of solid tritium breeders is developed: metal-reinforced composite (MERC) breeders. Specifically, the high Li-density of lithium oxide (Li 2 O) is exploited through the addition of a metal reinforcing phase, producing a composite breeder material exhibiting high splitting tensile strength and quasi-ductility with a Li-density greater than other leading solid breeder candidates, including lithium orthosilicate (Li 4 SiO 4 ) and lithium metatitanate (Li 2 TiO 3 ). Mechanical testing, microstructural characterization, and neutronic simulation results are presented and discussed in light of fusion reactor design considerations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Genetic algorithms↗
DOE OSTI2025

Origin of Heating-Induced Softening and Enthalpic Reinforcement in Elastomeric Nanocomposites

Molecular simulations demonstrate that the enthalpic softening of elastomeric nanocomposites upon heating can arise naturally from a Poisson’s ratio mismatch between elastomer and nanoparticle networks, providing a more parsimonious explanation for this phenomenon than the widely accepted interpretation based on glassy interparticle bridging. Despite a century of use, the mechanism of nanoparticle-driven mechanical reinforcement of elastomers is unresolved. Here, a major hypothesis attributes it to glassy interparticle bridges, supported by an observed inversion of the variation of the modulus E(T) on heating – from entropic stiffening in elastomers to enthalpic softening in nanocomposites. Here, molecular simulations reveal that elastomer enthalpic softening can instead emerge from a competition over the preferred volumes between elastomer and nanoparticulate networks. A theory for this competition accounting for softening of the bulk modulus on heating predicts the simulated E(T) inversion, suggesting that reinforcement is driven by a volume-competition mechanism unique to cocontinuous systems of soft and rigid networks.

Biopolymers↗
DOE OSTI2026

Chemical Recycling of Carbon Fiber-Reinforced Nylon Composites

Nylon-based fiber-reinforced composites are widely used in various sectors due to their strength, durability, and lightweight properties. Despite their widespread use, recycling these composites is difficult due to the inability to separate fibers and thermal instability of nylon at high temperatures. Consequently, most nylon composites are landfilled, leading to significant economic loss. Current fiber recovery methods (i.e., pyrolysis) are energetically inefficient and preclude recovery of the matrix. Dissolution methods, such as using hexafluoroisopropanol (HFIP), allow recovery of polymer and fiber but are economically taxing and require extensive safety infrastructure. Herein we report tailored glycolysis of nylon-6 composites, resulting in separated constituent fibers and nylon-6 oligomers. Deconstruction kinetics reveal nylon’s molecular weight reductions from 32,600 to 2000 g/mol, while SEM and tensile testing confirm recovered fiber integrity. In conclusion, this approach offers a pathway to reclaim high-value materials from nylon composites, providing a strategy for the chemical recycling of fiber-reinforced composites.

Zheng, Jackie [Univ. of Tennessee, Knoxville, TN (↗
DOE OSTI2024

High-strength 3D printed poly(lactic acid) composites reinforced by shear-aligned polymer-grafted cellulose nanofibrils

This work demonstrates the application of pilot-scale surface functionalization of cellulose nanofibrils (CNFs) by aqueous grafting-through polymerization and subsequent spray drying in 3D printed poly(lactic acid) (PLA) composites. Grafted-CNF composites attain an ultimate tensile strength of 88 ± 3 MPa and a tensile modulus of elasticity of 7.8 ± 1.3 GPa in the printing direction at 20 wt% reinforcement loading. These increases, 42% and 139% over neat PLA, respectively, represent the strongest reported 3D printed CNF/PLA composite to date in the literature. The mechanisms behind these improvements are investigated by comparisons to neat PLA and unmodified spray-dried CNF/PLA controls using melt rheology, dynamic mechanical analysis, and assessment of the reinforcement dispersion. These experiments reveal that improved network formation and shear-induced alignment of the grafted CNFs facilitate the remarkable tensile properties of the printed composites.

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

Cyclic olefin copolymer-based reinforced anion exchange membranes for water electrolyzers

Anion exchange membranes (AEMs) have emerged as a promising technology for water electrolysis in hydrogen production since they offer significant cost reduction in choices of electrocatalysts and bipolar plates. However, AEMs satisfying multiple requirements of high ionic conductivity, good chemical stability, robust mechanical properties, scalable synthesis, and low manufacturing costs are rare. Herein, we introduce quaternary ammonium functionalized cyclic olefin copolymers (COCs) as a new class of chemically stable and low-cost AEM materials. To further enhance the mechanical robustness, we prepared reinforced composite AEMs by impregnating the ionically functionalized COC into a mechanically robust matrix. The resulting reinforced composite membrane exhibits a high hydroxide conductivity of 127 mS cm −1 and excellent mechanical strength. In water electrolyzers, the MEA demonstrated outstanding performance, achieving a current density of 2.24 A cm −2 at 1.8 V, attributable to high conductivity, enhanced mechanical properties, and good alkaline stability of the composite membrane. These results indicate that the COC-based AEMs demonstrate good potential for application in AEM electrolyzers.

08 HYDROGEN↗
DOE OSTI2025

Thermophysical Properties of Ti3SiC2 MAX Phase Composites with SiC Reinforcement

In the present work, dense (∼100%) Ti 3 SiC 2 composites (TSC) are processed along with 20 vol% of SiC reinforcement (TSC20) via spark plasma sintering at 1400°C, 40 MPa, 15 min, and dynamic vacuum environment. Thermal expansion of both the composites increases from RT to 1273 K and linear fitting of data yields coefficient of thermal expansion (CTE) of 9.4 × 10 −6 K −1 for TSC which decreases to 8.3 × 10 −6 K −1 for TSC20. With increase in temperature from RT to 773 K, specific heat for both TSC and TSC20 composites is observed to increase from 598-850 J.kg −1 .K −1 , whereas thermal diffusivity and thermal conductivity values decrease with testing temperature. SiC reinforcement in Ti 3 SiC 2 resulted in improved thermal diffusivity from 12.7 to 18.7 mm 2 .s −1 and thermal conductivity from ∼57 to ∼79 W.m −1 .K −1 at RT. However, with increase in temperature (773 K), thermal diffusivity and conductivity decrease, and values get closer for both TSC and TSC20 composites. Further extrapolation of thermal conductivity data showed cross-over at ∼973 K due to domination of phonon-phonon scattering and thus lower values of thermal conductivity for TSC20 than TSC. Therefore, reduced CTE and higher thermal conductivity of TSC20 make it a viable choice for applications in high temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The influence of exposure to early-life adversity on agency-modulated reinforcement learning

Agency beliefs influence how humans learn from different contexts and outcomes. Research demonstrates that stressors, such as exposure to early-life adversity (ELA), are associated with both agency beliefs and learning, but how these processes interact remains unclear. The current study investigated whether exposure to ELA influences agency and interacts with reinforcement learning in adults. Replicating prior behavioral and computational work, ELA resulted in decreased learning, while increased adversity severity was associated with decreased latent agency beliefs. These findings suggest that exposure to adversity in childhood has a nuanced impact on reinforcement learning and agency beliefs in adulthood.

Neurosciences & Neurology↗
DOE OSTI2025

Integrated Routing and Traffic Signal Control for CAVs via Reinforcement Learning Approach

Incorporating Connected and Automated Vehicles (CAVs) into urban traffic networks presents opportunities and challenges for traffic management systems. This paper aims to develop an integrated routing and traffic signal control system designed explicitly for CAVs, utilizing a Reinforcement Learning (RL) approach. The objective is to enhance traffic flow and improve overall transportation efficiency in the controlled areas. We propose an innovative framework that employs the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Q-network (DQN), to dynamically adjust the number of vehicles in the routes and the duration of traffic signals. Our simulation results demonstrate that a DQN agent successfully optimizes the number of vehicles in the routes and traffic signal timings of traffic signal controllers, eventually reducing total travel time. The study illustrates the potential usage of RL-based systems in managing routing and traffic signals for CAVs, offering a promising opportunity for future urban traffic management strategies.

Park, Jiho [New York University]↗
DOE OSTI2024

Federated Deep Reinforcement Learning for Decentralized VVO of BTM DERs

The future of grid control requires a hybrid approach combining centralized and decentralized methods to fully utilize the potential of smart edge devices with artificial intelligence (AI) capabilities. This paper aims to develop and evaluate a federated deep reinforcement learning (FDRL) framework for decentralized adaptive volt-var optimization (VVO) of behind-the-meter (BTM) distributed energy resources (DERs). First, this paper models a single deep reinforcement learning (DRL) agent using the Markov Decision Process (MDP) framework for decentralized adaptive VVO of BTM DERs. Two DRL algorithms, soft actor-critic (SAC) and twin-delayed deep deterministic policy gradient (TD3), are compared for their effectiveness in optimizing VVO. Results show that TD3 outperforms SAC, achieving a 71.3% improvement in mean reward. Finally, the DRL agent is deployed within the FDRL framework, using the Flower platform, to enhance learning, provide adaptive control, and ensure data privacy for BTM DERs.

Ravi, Abhijith↗
DOE OSTI2025

Quantum Reinforcement Learning for Volt-VAR Control in Power Distribution Systems

Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Deep Reinforcement Learning Based Control of Wind Turbines for Fast Frequency Response

In order to fulfill vital auxiliary grid services, such as load regulation, spin and non-spin reserve provision, and frequency support during emergencies, there is often a requirement for certain wind farms to operate in de-loaded modes. Leveraging the swift response capabilities of wind farms, this study demonstrates that reserving power in de-loaded modes can significantly enhance power grid stability and reliability during system contingencies. Controlling wind farms optimally for frequency support is intricate due to the nonlinearity of models and controllers and the complexity of wind farm interactions with power systems. Here, to address this challenge, this paper introduces a novel approach that integrates wind turbines into reinforcement learning-based solutions for frequency response. This innovative methodology utilizes the state-of-the-art reinforcement learning algorithm known as the surrogate-gradient-based evolutionary strategy. The proposed learning-based algorithm provides continuous control of wind farm output to rapidly stabilize system frequency and prevent unnecessary trips of under-frequency load shedding relays. To facilitate efficient training, parallel computing techniques are employed. The proposed methodology is evaluated on a modified IEEE-39 bus system, and simulation results reveal its efficacy in reliably supporting power system frequency and preventing the need for unnecessary load shedding.

Gao, Wei [Argonne National Laboratory (ANL), Argon↗
DOE OSTI2025

A Two-Stage Quantum Reinforcement Learning Method for Multi-Objective Transmission Switching

Multi-objective transmission switching (MO-TS) problems involve the strategic reconfiguration of network topology to simultaneously optimize multiple objectives. As the system scale increases, finding feasible solutions becomes increasingly challenging due to the problem's nonlinearity and high computational complexity. To address these challenges, this paper proposes a two-stage quantum reinforcement learning method that leverages potential quantum advantages for MO-TS. In the first stage, candidate switching lines are identified using a graph-theoretical approach to reduce the problem's dimensionality. The second stage introduces a quantum-classical reinforcement learning framework, where a learnable measurement-based CNN-ResVQC architecture is developed to effectively reduce the input dimension for quantum processing, mitigate vanishing gradients, and enhance trainability while improving the quantum circuit's flexibility in modeling complex decision policies for MO-TS. Numerical studies on IEEE 14-bus, 57-bus, and 118-bus systems demonstrate that the proposed algorithm achieves superior training stability and faster convergence with approximately 1% of the network parameters required by classical algorithms, highlighting its effectiveness, efficiency, and scalability. Furthermore, the practicality is validated through its stable convergence under three common quantum noise channels.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Flax fiber-reinforced fatty acid vitrimer biocomposite with enhanced chemical recyclability

Here, this study unveils a sustainable, easily recyclable biocomposite, leveraging the dynamic nature of covalently adaptive bonds in a vitrimer matrix. The fabrication involved a fatty acid-derived vitrimer as the polymer matrix and multi-layered, nonwoven flax mat as reinforcing scaffold. The incorporation of these fibers significantly improved the mechanical performance of the vitrimer matrix uniformly. The ester-based covalently adaptive network plays a crucial role in enabling exceptional fiber-matrix bonding, as well as recyclability. The vitrimer matrix dissolves in ethylene glycol through transesterification, facilitating complete material recovery and biocomposite recycling without compromising the original properties of the matrix and reinforcing fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Overview of Elastic Orthotropic Fiber Reinforced Polymer Modular Damage Model for Library of Advanced Materials for Engineering (LAMÉ)

This memo includes the documentation sections for the Library of Advanced Materials for Engineering (LAMÉ) manual for a fiber reinforced polymer composite damage model (Elastic_Orthotropic_FRP_Modular_Damage) in advance of the next Sierra/SM and LAMÉ release consistent with the transition of the model from development to a production capability. This new model capability provides both a set of physically based orthotropic damage criteria as well as orthotropic material softening for woven fiber reinforced polymer composite materials. The documentation sections include short sections on theory, implementation, verification, and user guidance to prescribe the model in a Sierra/SM input file.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Tailored Silicone Network Architecture for Ultimate Mechanical Reinforcement

Hydrosilylation cured silicone elastomers are subject to reaction inefficiency, leading to incomplete and non-uniform crosslink networks, restricting the potential of mechanical reinforcement. This work investigates pre-synthesized, functional PDMS architectures as additives to improve ultimate mechanical performance relative to conventional single-step curing. Three custom, functional structures were prepared: a partially crosslinked PDMS scaffold (Structure A), a bottle-brush PDMS (Structure B), and a star-shaped PDMS derived from an MQ resin (Structure C). Rheological characterization was used to identify the ultimate design space and proper stoichiometric ratio for Structure A, and confirm successful formation of all structures for suitable incorporation into a base silicone formulation at 30wt%. Mechanical tests indicated that all three structures increased in ultimate tensile strength relative to their single-step counterparts, with Structure A providing additional improvements to toughness (432 vs. 258 kJ/m3) and ultimate elongation (158 vs. 115%). Furthermore, Structure B remained very soft in the unfilled state, while Structure C provided hardness (23 vs. 18 Shore A) and stiffness (780 vs. 420 kPa Young’s modulus) increases. In silica filled systems, Structure A retained increased strength but reduced elongation, while Structure B indicated strong reinforcement in terms of strength, toughness, and stiffness. Thermal analysis on the cure profiles of these materials suggested that pre-formation of network architectures enable a more complete reaction than a single-step process (15.9 vs. 15.1 J/g). Ultimately, these results indicate that tailoring PDMS architecture before the final cure can improve ultimate mechanical properties via improved network development in silicone elastomers. Furthermore, this work offers a promising strategy for designing higher-performance, more tunable silicone formulations.

36 MATERIALS SCIENCE↗