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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Extraction of lithium from battery recycling wastewater using synergistic D2EHPA and TBP

The recycling of spent lithium-ion batteries (LIBs) poses significant challenges, including the generation of large volumes of chemically complex wastewater. The composition of this wastewater is influenced by both the intrinsic chemistry of the batteries and the specific recycling processes employed. Notably, this wastewater contains economically valuable components, such as lithium, which can be recovered. Here, in this study, a solvent extraction (SX) process was investigated as a method to recover lithium from battery recycling wastewater, especially from anode-washing stage. Initially, various commercial extractants were evaluated, including di(2-ethylhexyl)phosphoric acid (D2EHPA), mono-2-ethylhexyl (2-ethylhexyl)phosphonate (PC88A), bis(2,4,4-trimethylpentyl)phosphinic acid (Cyanex 272), 2-hydroxy-5-nonylacetophenone oxime (LIX 84-I), tri-butyl phosphate (TBP), and their combinations. Among these, D2EHPA + TBP demonstrated a synergism to advance and maximize the lithium extraction. Subsequently, the effects of key parameters, including D2EHPA concentration, TBP concentration, contact time, initial pH, and aqueous-to-organic (A/O) phase ratio, were systematically investigated and optimized. A two-stage SX approach was employed to enhance lithium recovery. Under the optimized conditions of 30.0 vol% D2EHPA, 10.0 vol% TBP, 10 min of contact time, and a 1:1 A/O phase ratio, a lithium extraction efficiency of more than 88% in a two-stage solvent extraction was achieved. Lithium was subsequently stripped from the loaded organic solution using sulfuric acid (H 2 SO 4 ). Using 2.0 M H 2 SO 4 , lithium stripping was achieved after two counter-current stripping stages at an organic-to-aqueous (O/A) phase ratio of 6:1. This stripping process enriched the lithium concentration by a factor of four compared to the original lithium concentration in the anode-washing wastewater. The recyclability of the synergistic D2EHPA + TBP system was also evaluated over four extraction-stripping cycles. The results demonstrated that the system maintained high extraction and stripping efficiencies.

D2EHPA↗
DOE OSTI2025

Ambient Rare Earth Metal Electrodeposition in Nitrogen-Coordinated Silylamide Electrolyte

The transition to a sustainable, low-carbon economy demands energy-efficient and environmentally benign methods for rare earth metal (REM) production. Furthermore, while high-temperature molten salt electrolysis remains energy-intensive, corrosive, and environmentally unfriendly, emerging room-temperature processes based on conventional ionic liquids are also hindered by high viscosity and chemical instability. In this study, a nitrogen-coordinated, water-, oxygen-, and fluorine-free silylamide-based electrolyte is presented as a promising system for room-temperature REM electrodeposition. Derived from commercially available lithium silylamide precursors, the system enables facile synthesis, broad electrochemical stability, and tunable metal–ligand interactions. Electrochemical analysis reveals high voltammetric stripping reversibility, stable cathodic and anodic potentials, and selective neodymium (Nd) deposition at appreciable current densities (>1 mA/cm 2 ), with minimal parasitic reactions. Bulk experiments produced high-purity Nd with reproducible performance across multiple batches. Scaled deposition yielded over 1 g of Nd with near-theoretical mass efficiency (0.40 mg/C) and >90% purity. Using sacrificial dysprosium (Dy) and Nd metal anodes, the system maintained constant Nd loading during extended deposition and enabled cathodic codeposition of stripped anode material, demonstrating the electrolyte’s dual functionality for REM electrorefining. Collectively, this silylamide platform offers a compelling combination of electrochemical robustness, chemical resilience, and process scalability for sustainable ambient REM recovery.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Aqueous Carbon Capture Using Guanidinium-Functionalized Hollow Fiber Sorbent Contactors

As part of the growing suite of technologies aimed at combatting rising temperatures, negative emissions technologies have become a powerful tool in the global effort to minimize the consequences of human-induced climate change. Among these, carbon removal from aqueous sources, which contain much higher carbon concentrations than the atmosphere, remains largely unexplored. Indeed, developing robust and efficient carbon capture materials for usage in complex aqueous environments remains a significant challenge. Here, we explore the potential of functionalizing polyvinylidene fluoride (PVDF) hollow fiber contactors grafted with a guanidinium-derived polymer sorbent for carbon removal from aqueous sources, including saline waters. Computational screening against amine-based analogs is utilized to identify guanidinium as a promising motif for bicarbonate (HCO 3 – ) ions binding. To leverage this finding, synthesis of a guanidinium polymer and subsequent covalent grafting onto PVDF hollow fibers is employed to structured polymer–sorbent–grafted hollow fiber contactors. Our prototype achieves an initial HCO 3 – removal of 34% with an increase to 98% after four cycles. The functionalized fibers demonstrate aqueous stability over 13 adsorption/desorption cycles in model NaHCO 3 solutions where regeneration is facilitated by a mild pH swing. Importantly, the system maintains selective performance in the presence of competitive chloride ions over multiple cycles; carbon removal remained above 10% even at high (10:1) NaCl/NaHCO 3 ratios. These findings demonstrate the feasibility of sorbent-based aqueous carbon removal and highlight its potential as a promising approach for negative emissions.

carbon capture↗
DOE OSTI2026

Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning

Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range, conventional terahertz-based detection methods often struggle in real-world environments, where variations in sample geometry, thickness, and packaging can lead to inconsistent spectral responses. In this study, we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays, our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz, supporting practical, stand-off operation. By analyzing individual time-domain pulses with deep neural networks, the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals—including pharmaceutical excipients and explosive compounds—resulted in an average classification accuracy of 99.42% at the pixel level. Notably, the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings, demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.

Imaging and sensing↗
DOE OSTI2025

Closed-Loop Control of Active Nematic Flows

Stabilizing and shaping autonomous flows of active fluids is a fundamental challenge and a prerequisite for applications. We embed a light-responsive microtubule-based nematic in a proportional-integral control loop that adjusts the applied light intensity in response to real-time measurements of the spatially averaged flow speed. The self-regulating hardware-software-wetware system maintains a target flow speed against external or internal perturbations, including protein aging and aggregation, sample-to-sample variability, and temperature variation. Varying the controller’s gains reveals antagonistic roles between feedback and intrinsic processes, leading to nontrivial dynamics observed in fluctuation spectra. In particular, oscillations emerge from the interplay between the controller, motor binding kinetics, and active hydrodynamic relaxation. Accounting for the underlying binding timescale, our coarse-grained model and nematohydrodynamics simulations corroborate these observations. This work provides insight into the coupled dynamics of controlled active matter, laying the foundation for spatiotemporal patterning of active stress to generate and stabilize new dynamical configurations.

Active nematics↗
DOE OSTI2025

Synthesis and characterization of electron beam irradiation-induced damage in polycrystalline metal thin films

High-energy physics research, industrial sterilizing, and material processing depend extensively on electron beam accelerators. Exit windows are crucial components of such electron accelerator systems, maintaining vacuum integrity inside the machine while providing mechanical strength, thermal stability, and radiation resistance at the beam-target interface. In this study, thin metallic films of Ni, Ti, Cr, and V were explored for use in electron beam exit windows, and their properties were compared with the properties of their bulk counterparts. Simulation results of metal foils predicted Ti to exhibit less beam power dissipation compared to Ni. However, Ni possesses superior mechanical and structural properties compared to Ti. The performance of these films under electron beam irradiation was examined by depositing thin layers of these films on silicon and metallic substrates using magnetron sputtering and exposing them to e-beam irradiation in a controlled setup. The deposited films were subjected to a dose of approximately 66 kGy at a beam energy of 10 MeV and characterized prior to and postexposure to the beam using field emission scanning electron microscopy, atomic force microscopy, x-ray diffraction (XRD), and nanoindentation. Particular emphasis was given to characteristics like the grain structure, surface morphology, dislocation density, and hardness. XRD patterns revealed irradiation-induced changes in peak intensities, while the crystallinity remained largely unchanged. Nanoindentation results showed that the pristine and irradiated Ti and Ni films were twice as hard when compared to bulk Ti and Ni samples, regardless of the substrate type used (Si or bulk Ti, or Ni). These results emphasize the superior mechanical properties of thin metal films compared to their bulk counterparts. In conclusion, this study advances the optimization of thin film materials for robust and efficient e-beam applications, ensuring improved durability and operational reliability.

AFM↗
DOE OSTI2026

Cyber-Informed Engineering for Strategic Planning

The CIE Guide for Engaging Organizational Leadership: Board and C-Suite describes how to apply CIE at the senior-level of organizational management. This guidance integrates CIE concepts into theory and practice of guiding coalition leadership to improve permeation of CIE concepts throughout organizational culture. This guide incorporates feedback from CIE community of practice volunteers in a study of how business administrative and strategic planning and management guidance and course materials can be applied to CIE implementation throughout an organization's principles and processes. This guide defines the interests guiding senior leadership, managers and supervisors, and technicians and workers involved in creating and operating and maintaining systems, and cultural/ historical/ political assumptions or influences shaped the landscape that could facilitate or impede development of CIE. The included guidance for organizational strategic management and leadership can be imbedded into contract guidance based on the CIE implementation guide and lessons learned from industry application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Closed-loop control of active nematic flows

Stabilizing and shaping autonomous flows of active fluids is a fundamental challenge and a prerequisite for applications. We embed a light-responsive microtubule-based nematic in a proportional-integral control loop that adjusts the applied light intensity in response to real-time measurements of the spatially averaged flow speed. The self-regulating hardware/software/wetware system maintains a target flow speed against external or internal perturbations, including protein aging and aggregation, sample-to-sample variability, and temperature variation. Varying the controller’s gains reveals antagonistic roles between feedback and intrinsic processes, leading to nontrivial dynamics observed in fluctuation spectra. In particular, oscillations emerge from the interplay between the controller, motor binding kinetics, and active hydrodynamic relaxation. Accounting for the underlying binding timescale, our coarse-grained model and nematohydrodynamics simulations corroborate these observations. This work provides insight into the coupled dynamics of controlled active matter, laying the foundation for spatiotemporal patterning of active stress to generate and stabilize new dynamical configurations.

Nishiyama, Katsu [Brandeis Univ., Waltham, MA (Uni↗
DOE OSTI2024

PDV Sensitivity [Slides]

The concept of PDV sensitive is not new. But we tend not to discuss sensitivity in a rigorous, quantified way. That should change. Indeed there is a natural definition, which is just the energy of light from the target within the analysis window that gives a SNR of 10 in the frequency domain. Systems designers, diagnostic engineers, and operators should use the language of sensitivity: (1) Absolute sensitivity: nW * ns; (2) Launch power: mW or dBm; (3) Relative surface return: dB; (4) Relative sensitivity: dB * ns. This will help us design better systems, choose the correct system for the job, maintain our systems, and tune our systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

With the rapid digitization of the world, an increasing number of real-world applications are turning to nonEuclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamic systems composed of CMOS components, have emerged as a new approach that harnesses the inherent power of natural annealing within dynamic systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are inherently binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees effective natural annealing poses daunting scalability concerns. To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamic system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamic system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and component structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamic system while maintaining high accuracy. Evaluations with three diverse GL applications across six real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 102× to 106× speedups and 500× energy reduction over Graph Neural Networks on GPUs, with 5% - 20% accuracy enhancement.

Song, Ruibing↗
DOE OSTI2024

A Roadmap for a Lightning Modeling Grand Challenge

This document is a roadmap for building an interconnected model of the physical processes that produce a lightning discharge, and its observable optical and radio signals. We call this a Lightning Modeling Grand Challenge, recognizing that significant effort and coordination of human and financial resources is required to realize the capability. The roadmap serves to outline the coordination of resources necessary to enable stitching together existing knowledge and model components to make a lightning prediction, and to test these predictions with observations. Such a capability does not currently exist. The roadmap is motivated not only by a spirit of scientific inquiry, but by practical challenges faced by US Federal and societal stakeholders. Advancements in lightning observations have outpaced our tests of integrated understanding, leaving many stakeholders unsure how to design their missions to properly detect and discriminate lightning, and unsure how to apply the sometimes-disagreeing lightning signals from diverse instruments. The time is right to connect existing theories and models to support stakeholders in understanding the signals they observe, for needs as diverse as climate monitoring, national security, weather forecasting, public safety, and protection of natural and built environments. The roadmap’s two main technical sections describe the components of a linked physical model, followed by a description of models of lightning signals and sensors that are driven by outputs from the physical model. The goal is to predict the time-varying physical properties of lightning that are self-consistent with the thunderstorm’s structure and dynamics. These lightning signals then propagate through the storm, with realistic dispersion and attenuation, to receivers on the ground or in space. At a high level, the model begins with weather (cloud) model output, including explicit prediction of the electrification of cloud particles. The cloud’s electrical structure drives a model of lightning physics, from initiation, through channel development, and discharges along those channels. Key lightning parameters, such as the temperature and currents in the channel, and their space and time distribution, are then used to produce optical and electromagnetic signal sources that propagate to modeled receivers. This architecture therefore generates a dataset suitable for comparison to existing and envisioned observing systems. The need for additional measurements and field campaigns to support model development is described. In each model sub-component, inputs, outputs, uncertainties, evaluation methods, and next steps are summarized, interleaved with references to the scientific literature. Identifying boundaries between the model sub-components aids in segmenting an integrated, complex model into practical work packages and system sub-components, allowing a diverse team to contribute and maintain the system. We estimate that at least five years of effort and a $\$$10M initial investment is necessary to make a significant step forward. Mechanisms to facilitate community coordination, including annual workshops and open-source code repositories, are described.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data-driven reduced-order models for port-Hamiltonian systems with operator inference

Hamiltonian operator inference has been developed in Sharma et al. (2022) to learn structure-preserving reduced-order models (ROMs) for Hamiltonian systems. The method constructs a low-dimensional model using only data and knowledge of the functional form of the Hamiltonian. The resulting ROMs preserve the intrinsic structure of the system, ensuring that the mechanical and physical properties of the system are maintained. In this work, we extend this approach to port-Hamiltonian systems, which generalize Hamiltonian systems by including energy dissipation, external input, and output. Based on snapshots of the system’s state and output, together with the information about the functional form of the Hamiltonian, reduced operators are inferred through optimization and are then used to construct data-driven ROMs. To further alleviate the complexity of evaluating nonlinear terms in the ROMs, a hyper-reduction method via discrete empirical interpolation is applied. Accordingly, we derive error estimates for the ROM approximations of the state and output. Lastly, we demonstrate the structure preservation, as well as the accuracy of the proposed port-Hamiltonian operator inference framework, through numerical experiments on a linear mass–spring-damper problem and a nonlinear Toda lattice problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ICALEPCS 2025: Managing Technical Debt Across Large-Scale Control Systems

This presentation provides an overview of technical debt in the context of control systems for large-scale physics facilities. We explore various forms, common causes, and potential consequences on system reliability, maintainability, and extensibility. Drawing on experiences from multiple projects, including the ACORN control system modernization at Fermilab, we present a range of strategies for proactively managing technical debt, including best practices in design, development, testing, and documentation, as well as reactive approaches for identifying and mitigating existing issues.

Watts, Adam [Fermilab]↗
DOE OSTI2024

Tackling the Giants: Applying Smart Labs Principles to Constant Air Volume Lab Buildings

Laboratories typically consume 3 to 10 times more energy than similarly sized commercial buildings, and as much as 50% of that energy is wasted by inefficient and poorly operating fume hoods and ventilation systems. One challenge faced by older laboratory buildings is the heating, ventilation, and air-conditioning systems serving many of these buildings. The older systems are usually constant air volume (CAV) systems that maintain constant ventilation rates that cause excess airflow and inefficient energy use. Variable air volume systems can be more efficient systems with sensors to detect the need for a change in volumetric flow rate; however; renovation of ventilation systems can create disruption to ongoing research and operations along with considerable up-front costs. When a Smart Labs program is implemented, an organization has a systems-based management approach that yields a high-performing laboratory building. As decarbonization continues as a priority for sites, buildings with CAV systems are difficult to address. This work centers around practical guidance for improving lab buildings with CAV. In conjunction with industry input on top technology solutions and best practices, recommendations will include performing a laboratory ventilation risk assessment in conjunction with robust retro-commissioning work, which is a crucial step in the Smart Lab process. By applying Smart Labs principles, the aging laboratory building stock of 153,343 (Lawrence Berkeley National Laboratory [LBNL] 2017), comprising roughly 500,000 lab spaces in the United States, can be brought to safe and high-performance operations.

building↗
DOE OSTI2024

Average and Marginal Capacity Credit Values of Renewable Energy and Battery Storage in the United States Power System

As deployment of renewable resources and storage continue to significantly grow in the coming decades, these technologies will play increasingly important roles in maintaining power systems' resource adequacy. Few analyses so far offer comprehensive comparisons of forward-looking average and marginal capacity credits of variable renewable energy and storage in the U.S. interconnections across a wide range of possible futures. To fill this research gap, we quantify the average and marginal capacity credits of solar PV, onshore and offshore wind, and batteries between 2026 and 2050 across the U.S power systems to examine the temporal trends, spatial patterns, and trade-offs between these two capacity accreditation approaches. Across technologies, capacity credits of solar PV most clearly follow downward trends over time, reflecting the significant rise in solar PV generation share as the grid decarbonizes. While battery storages' generation shares also rise significantly over time, their capacity credits always remain stably high due to their capabilities to be dispatched strategically during critical periods to maintain reliability. On the other hand, capacity credits of wind technologies in general follow slight upward trends as their generation shares level off. There are strong spatial variabilities of both average and marginal capacity credits across technologies, but capacity credits of solar PV displaying the most obvious spatial patterns with high capacity credits concentrating in wind-rich, solar-poor regions in SPP, PJM, and MISO, suggesting potential reliability benefits of interconnection-wide planning for renewable energy deployments. Additionally, except for offshore wind, average capacity credits of all other renewable technologies tend to be higher than their marginal capacity credits, indicating that existing renewable resources tend to be accredited higher than new resources at almost any time.

25 ENERGY STORAGE↗
DOE OSTI2024

Lyapunov-based nonlinear control of nonautonomous systems with individual input constraints

A control algorithm that can locally stabilize a specific class of multi-input multi-output nonautonomous nonlinear dynamical systems while satisfying individual input constraints is developed. The proposed Lyapunov-based state-feedback control law inherently accounts for the actuator amplitude saturation limits without the need for computationally expensive real-time optimization techniques. In addition to the control law, a formal definition for the local “controllable region” within which the controller can asymptotically drive the system states to the origin and satisfy the input saturation limits is also presented. The nonautonomous nature of the system dynamics implies that the “controllable region” continuously evolves with time. Therefore, a sufficient condition to maintain the system states within the “controllable region” is proposed in this work to make practical implementation feasible. The effectiveness of the controller is tested for a specific control problem arising in tokamaks, which are toroidal devices that use strong magnetic fields to confine a plasma (hot ionized gas). Here, the primary emphasis of tokamak research is to regulate the plasma properties around predetermined values to achieve stable plasma confinement. Nonlinear simulations show that the proposed controller can achieve the desired plasma control objectives in a DIII-D tokamak scenario.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators

Abstract Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations . These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems.

Hossain, Raisa↗
DOE OSTI2026

Deployable UHV Pump

This project presents an external motorized actuation system for a deployable ultra-high vacuum (UHV) pump, enabling internal motion without compromising vacuum conditions. All active components remain external due to environmental and space constraints. A motor-driven mechanical feedthrough transfers motion into the pump, supported by a modular, adjustable mounting system that maintains alignment and integrates with existing hardware. CAD modeling and iterative design were used to refine geometry and ensure proper fit. The final design reliably transfers motion while maintaining alignment and structural integrity. Its adjustability improves installation and maintenance, demonstrating a practical solution for actuation in UHV systems.

Remington, Austin [Northern Illinois U.]↗