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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

In Situ MOF Pyrolysis Construction of Hierarchical Porous Co‐Nanoparticles/Carbon Cloth Composites for Enhanced Electromagnetic Wave Shielding and Absorption

The development of high-performance electromagnetic protection materials integrating broadband absorption and effective shielding capabilities is hindered by challenges in simultaneously optimizing multiple electromagnetic properties through conventional material designs. This study pioneers a hierarchical porous Co nanoparticle/carbon cloth (Co/CC) composite via controlled annealing of a Co-MOF precursor on carbon cloth. The Co-MOF served a dual role as both magnetic source and pore-forming agent, enabling in situ generation of uniformly dispersed Co nanoparticles and creation of abundant pores/interfaces on the CC fibers during pyrolysis. This unique architecture synergistically enhanced dielectric loss (via interfacial/dipolar polarization) and magnetic loss (via natural resonance, exchange interactions, and eddy currents), significantly improving impedance matching. The hierarchical pores further functioned as integrated “absorption–reflection” units for efficient electromagnetic energy attenuation. Consequently, the Co/CC composite annealed at 800°C (Co/CC-800) achieves minimum reflection loss (−40.69 dB) and 120% effective absorption bandwidth extension (6.16 GHz) as a filler, and exhibits superior electromagnetic interference shielding effectiveness (46.66 dB) as an integrated component. Significantly, Co/CC-800 demonstrated robust photothermal and electrothermal conversion capabilities, ensuring operational stability in ice-covered and humid harsh environments. This work pioneers a pore-structure-mediated strategy to harmonize dielectric–magnetic synergy, providing a new paradigm for designing advanced multifunctional electromagnetic protection materials.

dielectric‐magnetic synergy↗
DOE OSTI2025

Frontal polymerization of thermosets to enable vacuum-formed structural electronics

Material design and accessible manufacturing are often at odds with each other, calling for creative solutions to adapt high-performance materials to available processes. This challenge is represented well by in-mold electronics, an innovative approach to the manufacture of 3D circuitry and electronic components that offers game-changing advantages. In-mold electronics relies on vacuum forming processes, which are historically limited to thermoplastics. Extending these methods to include thermosets would enable manufacturing of robust components with desirable properties. Here, we provide a solution to make thermoset materials amenable to vacuum forming. Specifically, an ambient polymerization is used to transition a liquid monomeric solution to an elastomeric gel. These free-standing gels can then be vacuum formed, and the reaction can be completed via frontal polymerization. Thermoset materials produced with this method have properties that provide benefits over traditionally employed thermoplastic substrates and enable 3D device integration into environmentally demanding architectural, automotive, and extraterrestrial structures.

Fowler, Hayden Elise [Sandia National Laboratories↗
DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

High-power test of a C-band linear accelerating structure with an RFSoC-based LLRF system

Normal conducting linear particle accelerators consist of multiple rf stations with accelerating structure cavities. Low-level rf (LLRF) systems are employed to set the phase and amplitude of the field in the accelerating structure and to compensate for the pulse-to-pulse fluctuation of the rf field in the accelerating structures with a feedback loop. The LLRF systems are typically implemented with analog rf mixers, heterodyne-based architectures, and discrete data converters. There are multiple rf signals from each of the rf stations, so the number of rf channels required increases rapidly with multiple rf stations. With a large number of rf channels, the footprint, component cost, and system complexity of the LLRF hardware will increase significantly. To meet the design goals of being compact and affordable for future accelerators, we have designed the next-generation LLRF (NG-LLRF) with a higher integration level based on RFSoC technology. The NG-LLRF system samples rf signals directly and performs rf mixing digitally. Further, the NG-LLRF has been characterized in loopback mode to evaluate the performance of the system and has also been tested with a standing-wave accelerating structure, a prototype for the Cool Copper Collider (C 3 ) with a peak rf power level up to 16.45 MW. The loopback test demonstrated amplitude fluctuation below 0.15% and phase fluctuation below 0.15°, which are considerably better than the requirements of C 3 . The rf signals from the different stages of the accelerating structure at different power levels are measured by the NG-LLRF, which will be critical references for the control algorithm designs. The NG-LLRF also offers flexibility in waveform modulation, so we have used rf pulses with various modulation schemes, which could be useful for controlling some of the rf stations in accelerators. In this paper, the high-power test results at different stages of the test setup will be summarized, analyzed, and discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction

Abstract The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior skill than traditional physics-based NWP. However, among these leading DL models, there is a wide variance in both the training settings and architecture used. Further, the lack of thorough ablation studies makes it hard to discern which components are most critical to success. In this work, we show that it is possible to attain high forecast skill even with relatively off-the-shelf architectures, simple training procedures, and moderate compute budgets. Specifically, we train a minimally modified Swin Transformer V2 (SwinV2) on ERA5 data and find that it attains superior skill in terms of mean-square errors of deterministic forecasts when compared against the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS). Almost all DL–NWP systems share a core set of hyperparameters and design decisions. To aid and expedite future DL–NWP research, we present an in-depth, systematic exploration of different loss functions, model sizes and depths, patch sizes, and multistep training objectives. We also examine the model performance with metrics beyond the typical accuracy (ACC) and RMSE and investigate how the performance scales with model size. Through our open-source code, scoring pipelines, and models, we share our findings on key aspects of the training pipeline. These ablations reduce the necessity for expensive hyperparameter tuning and lower the barrier to entry for future DL–NWP research. Significance Statement This study investigates the potential of using large-scale transformer-based models for weather prediction, showing that it is possible to achieve high forecast accuracy with simpler, off-the-shelf architectures. By training a minimally modified SwinV2 transformer on ERA5 data, we show that the model achieves competitive forecast skill in terms of mean-square error for key variables, outperforming the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS) at all lead times. Our findings suggest that effective training strategies, such as multistep fine-tuning and channel-weighted losses, significantly enhance the model’s performance. However, we also highlight that these improvements come with trade-offs in other areas, such as ensemble spread and high-frequency spatial detail. This work highlights the promise of deep learning in improving weather forecasts, which could lead to better preparedness and response to weather events, ultimately benefiting society by providing more reliable weather predictions.

Willard, Jared D. [Lawrence Berkeley National Labo↗
DOE OSTI2026

Optics and Systems Design of the Ring-to-Second Target Transport Beam-Line for the SNS Second Target Station

The Second Target Station (STS) project at the Spallation Neutron Source (SNS) is being developed to provide world-leading cold neutron brightness for next-generation neutron scattering experiments. The STS Accelerator Systems (AS) scope includes the design and implementation of the Ring-to-Second Target (RTST) proton beam transport line, which extracts 1.3 GeV proton beam pulses from the existing Ring-to-Beam Transport (RTBT) system and delivers them to the STS target. The RTST design emphasizes operational reliability [high reliability], low activation [minimum activation of components and the tunnel], maintainability, and compatibility with existing SNS infrastructure through extensive reuse of proven RTBT systems and components. The beamline includes a new extraction region, a transport lattice consisting of dipole, quadrupole, and corrector magnets, beam instrumentation systems, vacuum systems, personnel protection systems, and radiation shielding systems. Beam optics and particle tracking studies were performed using PyORBIT to validate extraction trajectories, beam transport, and target beam spot requirements [60–90 cm² beam spot area]. This paper presents the optics design philosophy, extraction system architecture, transport lattice design, instrumentation strategy, vacuum system approach, and radiation protection integration for the RTST beamline. Particle tracking simulations indicate successful beam transport without beam loss under nominal operating conditions. The RTST is designed to transport 1.3 GeV proton beam pulses at repetition rates up to 15 Hz, delivering nominal beam power of 700 kW to the Second Target Station.

Baron, Alex [ORNL]↗
DOE OSTI2024

5G Securely Energized and Resilient: (5G-SER) (Final Report)

NREL's work on 5G integration with physical power systems (versus simulated systems demonstrated in task 3) under 5G-Securely Energized and Resilient (5G-SER) achieved a major milestone in with the completion of Task 4 activities. After many months (6+) of planning and development along with scaling challenges along the way, a full 5G end-to-end network with physical hardware components (physical inverter, physical power panel, physical battery, etc.) was deployed in a containerized environment. In parallel, a distributed controls architecture for a microgrid powering 5G resources was also successfully modified from its previous instantiation for a simulated environment to work with these physical components. These accomplishments set the stage to use newly setup 5G technology with physical microgrid components to test the feasibility of 5G wireless with physical systems to enable resilient communications between controls and distributed solar and storage resources while exploring ways to configure 5G components to survive power disturbances. The results detailed in the report below show that even utilizing physical components, 5G wireless systems were able to provide resilient results that were similar to the results from the simulated environment Task 3).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment

X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.

Wang, Cong↗
DOE OSTI2025

All 2D Material Printed Diodes and Circuits on Paper for Sustainable Electronics

Sustainable electronics aim to reduce environmental impact by using ecofriendly materials, energy-efficient manufacturing, and recyclable components. However, existing approaches rely on complex, resource-intensive methods, rare metals, or nanomaterials with limited stability as well as plastic substrates, raising sustainability issues. Solution-processed two-dimensional (2D) materials offer a promising alternative: water-based and biocompatible conductive, semiconductive, and insulating 2D material inks can be produced with scalable techniques and are suitable for the fabrication of fully printed devices on low-cost and biodegradable paper substrates. However, 2D material only and fully printed diodes on paper have not yet been reported. Here, we demonstrate fully inkjet-printed 2D material-based diodes on paper using metal-insulator-semiconductor and metal-insulator-metal-semiconductor architectures. Water-based graphene and MoS2 inks, prepared by liquid-phase exfoliation, are used for the metallic and insulating films, while electrochemical exfoliation is used to produce the semiconducting MoS2 ink. The highest forward-to-reverse current ratio obtained is 330 (at ±2 V), while the forward current density is 1 mA/cm2 (at 1 V), making the diode performance comparable to the best solution-processed diodes reported so far. However, in contrast to previous works, fabrication occurs entirely at room temperature and ambient pressure, without using high-pressure sputtering, thermal evaporation, and any precious metal ink. The devices maintain stable performance under bending up to strain of 4% over 10,000 cycles. Finally, the diodes are successfully integrated with other 2D-material based electrical components to realize fully printed RC circuits, differentiators, integrators, and AC-to-DC converters onto paper, hence demonstrating the suitability of our approach for sustainable and disposable integrated circuits.

2D materials↗
DOE OSTI2026

A High Power Density and High Efficiency Traction Drive Based on a Segmented Inverter and an Axial and Radial Flux Hybrid Machine

Eaton Corporation and Oak Ridge National Laboratory (ORNL) collaborated to develop a high performance traction drive system aimed at increasing power density and overall efficiency. The system integrates Eaton’s hybrid flux motor—combining radial and axial flux paths to convert end-winding and structural elements into torque-producing components—with ORNL’s high-power-density segmented inverter. This motor design achieves higher torque density within the same footprint as traditional radial flux machines by effectively utilizing end turns. ORNL’s segmented inverter reduces the DC bus capacitor requirement by over 50% compared to standard voltage source inverters (VSIs), achieving power densities exceeding 100 kVA/L. The dual-module architecture also enables six-phase operation, aligning with Eaton’s motor design. Together, the motor and inverter offer a compact, efficient traction drive system with the potential for significant reductions in system weight and improvements in powertrain efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

The Concept and Role of Reference Architectures In NIF LRU Refurbishment Factories within LLNL

The National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) operates one of the most advanced laser systems in the world, relying on a vast number of optical components and Line Replaceable Units (LRUs) to maintain its functionality. Over time, these components degrade due to operational wear, necessitating refurbishment to sustain performance. However, many NIF LRU refurbishment factories have been “mothballed” or suffer from aging infrastructure, inconsistent work flows, and inefficiencies due to different approaches to production control and management. This paper explores the concept of reference architecture as a standardized framework to guide the redevelopment and restructuring of NIF LRU refurbishment factories. By establishing a common reference architecture, the refurbishment process can achieve reduced inefficiencies, produce quality products, and enhanced coordination across factories. This paper evaluates existing reference architectures, particularly those that integrate technical architecture, business architecture, customer context perspectives, and proposes tailored reference architecture for NIF LRU refurbishment factories.

42 ENGINEERING↗
DOE OSTI2025

Preliminary Plan to Inform Testing of a Heat Exchanger Test Article

This report presents a preliminary plan to guide the qualification testing of advanced heat exchanger (HX) components for nuclear-to-industrial heat transfer applications. The objective is to establish a defensible, physics-based methodology that integrates computational modeling, targeted experimentation, and in-service inspection considerations to demonstrate component performance and reliability under representative reactor conditions. The analysis identifies Sodium-cooled Fast Reactor (SFR) and High-Temperature Gas-cooled Reactor (HTGR) systems as reference configurations in terms of temperature, pressure, and chemical environment. Within these operating envelopes, dominant degradation mechanisms— including creep–fatigue interaction, flow-induced vibration, corrosion, and diffusion-bond deterioration—were evaluated to define test requirements. A comprehensive computationalexperimental framework is proposed to support life prediction and qualification activities. The framework couples high-fidelity structural-mechanics, thermal-hydraulic, and fluid-structure interaction models with accelerated degradation testing to produce a traceable linkage between microstructural evolution, mechanical performance, and remaining useful life (RUL). The approach adheres to established Verification, Validation, and Uncertainty Quantification (VVUQ) standards (ASME V&V 10/20; NUREG-2152) and incorporates a digital-twin architecture for continuous model refinement through data assimilation. The plan further outlines testing methodologies, including pre-test analyses, test-loop design parameters, and sensor placement strategies that maximize information yield while maintaining mechanistic fidelity. Complementary sections describe in-service inspection (ISI), on-line monitoring (OLM), and structural-health-monitoring (SHM) techniques applicable to compact HX geometries typical of advanced reactors. Collectively, these activities establish the technical foundation for demonstrating 40-60-year equivalent service life of advanced heat exchangers in support of the U.S. Department of Energy’s Advanced Reactor and Integrated Energy Systems programs. The forthcoming phase will execute the defined pre-test analyses, initiate hardware fabrication, and implement the integrated testing campaign to validate the proposed qualification methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Topology-Dependent Performance of Free-Space Photonic Quantum Networks Under Noise

Photonic quantum communication enables secure and high-fidelity information transfer beyond classical limits, with direct relevance to emerging quantum networks operating in free-space environments. While physical-layer models of depolarizing noise, Gamma–Gamma turbulence statistics, entanglement swapping, and decoy-state QKD security bounds are individually well established, prior work typically treats these components in isolation or under fixed network assumptions. In this work, we develop a unified topology-aware analytical framework that simultaneously integrates free-space optical link budgets, turbulence-induced visibility degradation, depolarizing qubit noise, multi-hop entanglement cascade dynamics, teleportation fidelity thresholds, CHSH nonlocality certification, and asymptotic decoy-state secret key rate bounds across star, mesh, and ring graph structures. Rather than introducing new physical channel models, we demonstrate that identical physical links exhibit fundamentally different end-to-end performance once embedded within different network topologies. Mesh architectures minimize visibility cascade through hop-count reduction but incur quadratic hardware scaling. Star topologies minimize link count but concentrate noise and synchronization overhead at the hub. Ring configurations offer linear hardware scaling with multiplicative fidelity degradation. The results establish topology as a first-order design parameter in near-term free-space quantum networks operating without full quantum repeater infrastructures. While motivated by distributed multi-agent architectures, the framework applies broadly to terrestrial, airborne, and satellite-assisted photonic quantum communication systems.

QKD↗
DOE OSTI2025

Coupling magnons to an opto-electronic parametric oscillator

Hybrid magnonic systems have emerged as versatile modular components for quantum signal transduction and sensing applications owing to their capability of connecting distinct quantum platforms. To date, the majority of the magnonic systems have been explored in a local, near-field scheme, due to the close proximity required for realizing a strong coupling between magnons and other excitations. This constraint greatly limits the applicability of magnons in developing remotely coupled, distributed quantum network systems. On the contrary, opto-electronic architectures hosting self-sustained oscillations have been a unique platform for long-haul signal transmission and processing. Here, we integrated an opto-electronic oscillator with a magnonic oscillator consisting of a microwave waveguide and a Y 3 Fe 5 O 12 (YIG) sphere, and demonstrated strong and coherent coupling between YIG’s magnon modes and the opto-electronic oscillator’s characteristic photon modes—revealing the hallmark anti-crossing gap in the measured spectrum. In particular, the photon mode is produced on-demand via a nonlinear, parametric process as stipulated by an external seed pump. Both the internal cavity phase and the external pump phase can be precisely tuned to stabilize either degenerate or nondegenerate auto-oscillations. Our result lays out a new, hybrid platform for investigating the long-distance coupling and nonlinearity in coherent magnonic phenomena, which may be found useful in constructing the future “distributed hybrid magnonic systems.”

36 MATERIALS SCIENCE↗
DOE OSTI2026

Lab-Scale Cable-Driven Parallel Robot Prototype for Automated Prefabricated Component Manipulation

This paper presents the design and evaluation of a lab-scale cable-driven parallel robot (CDPR) developed as a flexible platform for automated installation of prefabricated components onto exterior building envelopes. Traditional manual installation methods for prefabricated components, which depend on scaffolding, cranes, cherry pickers, and verbal coordination, are not only labor-intensive and error-prone but also face significant limitations in dense urban environments due to site access constraints. To address these challenges, we developed a lab-scale CDPR platform capable of autonomously transporting building envelope components from a designated pickup zone to their target installation location, minimizing the need for human intervention. This study describes the system’s mechanical design, actuation architecture, real-time feedback system, and control strategy of the CDPR, and evaluates its performance in a laboratory environment. The robot’s actuation system uses torque control for end-effector manipulation. The robot’s real-time pose feedback comes from a construction-grade total station and a wireless inertial measurement unit (IMU), which together support precise end-effector control. Experimental results demonstrate the successful integration of the hardware, sensing, state estimation, and control subsystems. Preliminary tests showed that our lab-scale prototype can position the end effector with an error of less than 3 mm, which is a level of precision not previously achieved by existing CDPRs in construction applications. The key findings are twofold: (1) torque-only control is necessary but not sufficient for minimizing final pose error, and (2) incorporating real-time pose feedback can achieve the desired placement accuracy.

Liu, Yifang [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2025

Real-Time Lifetime Prediction of Semiconductor Devices Using Hardware-in-the-Loop

This paper presents a unique approach to enable real-time lifespan prediction of semiconductor power modules using a Hardware-in-the-Loop (HIL) system. By integrating the module's overall loss characteristics-specifically switching and conduction losses-with a thermoelectric model of the thermal management system, this research demonstrates that the model can dynamically estimates the junction temperature profile of the semiconductor devices in response to a changing torque demand profile for the motor drive system. This capability enables continuous monitoring of the module's operational time and cumulative stress induced on the devices to compute accumulated remaining lifetime or time-to-failure (TTF). This study provides an architectural framework for the HIL system with high-fidelity component models of multiple physical domains, allowing simulation of dynamic behaviors of a closely-coupled motor drive system. The advanced real-time computation and measurement functionalities of the HIL system allow for both dynamic lifetime calculations based on simulated data and aggregate lifetime predictions utilizing historical data. Moreover, this paper details an algorithm that not only computes cumulative damage but also synthesizes these data into a comprehensive aggregated lifetime metric. This methodology can enhance the maintenance scheduling strategies and operational reliability of semiconductor devices in critical applications, ultimately extending their service life while optimizing performance.

hardware-in-the-loop (HIL)↗
DOE OSTI2026

Differential Privacy in Grid Kitchen: Implementation & Software Documentation

Sharing of power grid feeder models faces significant challenges due to the potential risk of exposing sensitive operational information. Traditional anonymization techniques have shown notable limitations in other sensitive domains, as evidenced by documented re-identification attacks that combine supposedly anonymized datasets with auxiliary information, raising concerns that similar vulnerabilities could affect power grid data. Consequently, there is a pressing need for a more rigorous privacy protection strategy that not only delivers formal mathematical guarantees but also preserves the analytical value of the shared models. To address this challenge, we have enhanced the Grid Kitchen framework by implementing differential privacy mechanisms within the distribution model dehydration pipeline. This implementation carefully calibrates and applies noise to sensitive attributes in feeder models according to configurable privacy levels—low, moderate, and high—each offering different balances between data utility and privacy protection. Our approach uses established noise functions (Gaussian for continuous data and Discrete Laplace for integer values) with parameters carefully calibrated so that the impact of individual data points is effectively masked in the final output. The integration leverages our Noise Catalog, which we developed to categorize feeder model properties by component type, data type, and sensitivity. This catalog guides the application of appropriate noise functions and privacy parameters ($\varepsilon$ and $\delta$) to each attribute, ensuring consistent privacy protection across the model while maintaining its structural integrity and analytical usefulness. This implementation also includes evaluation tools that allow model owners to assess the impact of privacy-preserving transformations before sharing data with external parties. This report provides documentation for the differential privacy capabilities added to the Grid Kitchen project. It includes a primer on differential privacy concepts and their importance in modern data sharing, details the architecture of our implementation, explains the privacy modes and parameter configurations, and offers practical guidance on using the code for applying differential privacy to grid feeder models. Through examples and code snippets, we demonstrate the effective application of these privacy-enhancing technologies, enabling utility operators and researchers to confidently share grid data while protecting sensitive information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Covalent adaptable networks for electrolyte–binder integration in recyclable lithium metal batteries

Lithium-metal batteries (LMBs) are considered a promising next-generation energy storage technology due to their exceptionally high energy density. However, the development of solid polymer electrolytes and cathode binders for LMBs faces critical challenges, including interfacial instability, poor recyclability, and growing environmental concerns. In particular, current systems often rely on non-recyclable components featuring permanently crosslinked networks and polyfluoroalkyl substances (PFAS), such as poly(vinylidene fluoride) (PVDF), which cause battery waste and environmental harm. Herein, we introduce a multifunctional covalent adaptable network (CAN) platform based on thermally reversible Diels–Alder (DA) chemistry, designed for dual functionality as a CAN-based electrolyte (CAE) and a CAN-based cathode binder. The CAE achieves high ionic conductivity and strong storage modulus (1.4 mS cm −1 and ∼ 10 5 Pa at room temperature, respectively) and enables stable long-term cycling in symmetric Li||Li cells for over 2000 h with low overpotential. When it is applied as a cathode binder in LiFePO 4 (LFP) composite electrodes (C-LFP), the CAN matrix significantly reduces interfacial resistance and enhances discharge capacity compared to conventional PVDF-based systems. Thermal treatment induces self-healing at the cathode–electrolyte interface, further improving contact and yielding a discharge capacity of 150 mAh g −1 at 0.5 C. Moreover, the dynamic CAN architecture allows efficient recovery and reuse of lithium salts from spent electrolytes through retro-DA reactions under mild conditions (∼80 °C), establishing a low-energy, cost-effective recycling pathway. In conclusion, this work presents a scalable and sustainable strategy for high-performance LMBs by integrating recyclability, interfacial healing, and PFAS-free design, offering a holistic solution aligned with circular economy principles and next-generation battery demands.

Diels–Alder↗