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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 451 records · Page 25

Shaping Peptide Assemblies Using Multifaceted Cyclic Tectons

Constructing distinct biomacromolecular assemblies typically necessitates target-specific selection and engineering of building blocks alongside optimization of assembly conditions. The challenge lies in achieving diverse morphological outcomes using simple, shared modules under identical conditions, a hallmark of natural systems that remains elusive in synthetic approaches. Here, in this work, we present a molecular scaffold-based strategy to instruct the coassembly of the same set of peptides into a variety of nanostructures across multiple dimensions. We create trifaceted cyclic scaffolds to manipulate two pairs of dimeric coiled-coil peptides prior to coassembly. These scaffolds, with addressable and orthogonal modules, allow controlled exposure of their cohesive faces, directing the formation of nanotriangles and fibrillar and lamellar assemblies. By tuning interpeptide arrangements that dictate scaffold geometry, we construct nonstraight fibrils with tunable curvature, which are rarely observed before. Notably, these scaffolds exhibit plasticity in adapting the sizes and orientations of cohesive faces to different assembly morphologies. The resultant nanostructures are consistent with the design and simulation results, demonstrating the reliability and predictability of this approach. Multifaceted cyclic scaffolds bridge the intellectual and physical gaps between building peptides and assemblies, holding promise for endowing various existing assembly systems with high tunability and versatility.

assembly morphology↗

Machine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering

Atomic vibrations govern many macroscopic properties of materials, but experiments to comprehensively probe them remain challenging. Inelastic neutron scattering (INS) is a powerful technique to map phonon dispersions in crystals, especially when leveraging modern time-of-flight (ToF) spectrometers with large detectors. However, efficiently and robustly extracting interatomic force constants (FCs) parameterizing phonon dynamics from experimental spectra remains a bottleneck due to the complexity and high dimensionality of ToF INS datasets. Here, we present a machine learning approach for the direct inversion of FCs from single-crystal INS measurements. The framework leverages synthetic training data generated using universal machine-learned force fields and an efficient physics-based forward model. We benchmark two neural architectures–one emphasizing structured latent representation learning and the other direct, supervised spectral regression–across simulated datasets for two materials under idealized and noisy conditions. The latent-representation model is subsequently applied to experimental single-crystal INS data on germanium. The model is shown to reproduce FCs derived from both first-principles simulations and from iterative optimization, and furthermore achieves reliable inference even from sparse, single-orientation measurements representing short data acquisitions. Analysis of the learned latent space reveals semantically continuous and physically interpretable encodings that support strong cross-domain generalization. By bridging theoretical and experimental domains, we establish a path toward rapid inversion of experimental spectra and data-driven interpretation of temperature-dependent lattice dynamics.

42 ENGINEERING↗

Hierarchical Composites Patterned via 3D Printed Cellular Fluidics

Additive manufacturing of freeform structures containing multiple materials with deterministic spatial arrangement and interactions remains a challenge for most 3D printing processes, due to complex fabrication tool requirements and limitations in printability of some material classes. Here, in this paper, a versatile method is reported to produce architected composites using the concept of cellular fluidics, in which lattices of unit cells are used as templating scaffolds to guide flowable infill materials in a programmed spatial pattern, upon which they are cured in place to produce a deterministically ordered multimaterial solid. The lattice design relies on the unit cell size, type, strut diameter, surface wetting, and distribution of cellular structures to control liquid flow and retention. Individual unit cells are tuned to achieve reliable infilling and combined into higher-order architectures to achieve multiscale composite materials with disparate mechanical properties, including those considered non-printable. Lattice design considerations for leveraging capillary phenomena and demonstrate several methods of patterning polymers in 3D-printed cellular fluidic structures are presented. The concept of tuning the compressive response of an architected composite using a flexible-elastomer as the lattice and a stiff-epoxy as the infill material is illustrated.

36 MATERIALS SCIENCE↗

Exploring the Performance and Reliability of Screen-Printable Fire-Through Copper Paste on PERC Solar Cells

For 40 TW of PV required to transition our planet to 100% renewables, the silver (Ag) should disappear from PV production. Advantages of copper (Cu) over silver (Ag) include 1) bulk Cu has a similar conductivity to Ag (1.7 mu O-cm for Cu, 1.6 mu O-cm for Ag) and 2) Cu is approximately 100 times cheaper than Ag, making it an excellent potential replacement. Problems associated with copper (Cu) contacts include 1) easy oxidation and 2) diffusion into the Si cell and recombination activity.

copper paste↗

Effect of stress and temperature on zero group velocity Lamb modes

Zero group velocity (ZGV) Lamb wave resonances are highly localized and sensitive to changes in material properties, making them a viable option for probing residual stresses and elastic nonlinearity through acoustoelastic effects. Temperature changes also influence ZGV modes and need to be accounted for, particularly when measuring the small frequency shifts associated with acoustoelastic effects. We use a laser-based ultrasonic system to measure the third-order elastic constants of several materials. A temperature compensation scheme is devised to remove the effects of temperature fluctuations from the measurements. Furthermore, the measured third-order elastic constants are used to predict the temperature dependence of the ZGV resonances using thermo-acoustoelasticity theory, and these predictions are compared to experimental measurements. We find that thermo-acoustoelasticity theory was unable to reliably predict the shift in ZGV resonances with temperature. These results could have important implications in understanding the limitations of thermo-acoustoelasticity theory and in developing precision ZGV resonance-based systems to detect and quantify residual stress in parts, a particularly relevant problem in additive manufacturing techniques including powder bed fusion and directed energy deposition.

Engineering↗

CONUS-wide Projected Flood Frequency and Uncertainty Estimates, Version 1.0

This dataset presents a large-ensemble of CONUS-wide projected flood frequency and uncertainty estimates across ~2.7 million NHDPlusV2 river reaches over the CONUS. The framework producing this dataset leverages a multi-model, uncertainty-aware modeling framework that allows evaluating shifts in flood frequences at the stream reach level across the CONUS. CONUS-wide ensemble streamflow projections generated from hydrologic simulations driven by downscaled and bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs are used to derive these flood frequency and uncertainty estimates over the period 1980 - 2099. A spatially consistent regional L-moment algorithm is applied across clusters defined by the US Hydrologic Unit Code Subregions (HUC4s and HUC8s) and NHDPlusV2 stream orders to estimate flood frequencies. The dataset also includes at-site based flood estimates that allow for the comparison between local and regional approach-based estimates, assess projected changes, and characterize their uncertainties. For more reliable estimation of rare flood frequencies such as 500 and 1000-year return periods, super-ensemble based estimates are also included in the dataset. This dataset is derived to support the "Impact-Informed Dam Safety Risk Assessment for Securing Hydropower Assests" project for the US Department of Energy (DOE) Hydropower and Hydrokinetic Office (H2O). For further details, refer to Kao et al. (2022), Ghimire et al. (2023), Ghimire et al. (2025), and Hosking and Wallis (1997).

Ghimire, Ganesh [ORNL] (ORCID:0000000242843941)↗

Techno-economic feasibility of borehole thermal energy storage system connected to geothermal heat pumps for seasonal heating load of two buildings in Fairbanks, Alaska

Borehole thermal energy storage (BTES) is an effective solution for managing imbalanced heating and cooling loads in cold regions. This study evaluated the long-term feasibility of a BTES system in the Fairbanks area, Alaska, through building energy modeling, resource characterization, and numerical modeling. The system was designed to store waste heat from a nearby coal power plant during summer and provide thermal energy during winter to geothermal heat pumps supplying heating loads in two buildings. Heating load profiles were modeled for the buildings using EnergyPlus, and the results indicated the annual heating load was 5.6 times greater than the cooling load. 40 borehole heat exchangers were pre-designed approximately 100 m away from the two buildings in terms of land availability and regulatorily optimized depth. The 20-year performance of the designed BTES system under two operational scenarios—one with a 5-year preheating period and one without—was numerically modeled using subsurface temperature and properties characterized through the literature review and thermal response tests. Both scenarios demonstrated that the BTES has the capacity to fully cover the heating loads in the two buildings throughout the 20-year lifetime. Production temperatures at central wells were 33 % higher on average than at outer wells in both scenarios. The 5-year preheating period increased subsurface and extraction temperatures, and correspondingly annual average and total thermal energy production was higher for 8 years than in the scenario without the preheating period. These results highlight the long-term reliability and sustainability of the BTES system in meeting heating demands over its lifetime, with the preheating period offering potential performance improvements. Implementing the BTES system in cold regions with high heating demand, such as Fairbanks, Alaska, could provide a long-term, sustainable energy solution for managing imbalanced heating and cooling loads.

15 GEOTHERMAL ENERGY↗

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗

Electronic Trap-State Modulation in Sm-Doped SnO 2 Nanofibers Enables Ultrasensitive Hydrogen Sensing

The demand for sub-ppm hydrogen (H 2 ) sensing is growing across emerging applications such as environmental monitoring, breath-based disease diagnostics, and early-stage battery failure detection. However, achieving reliable ppb-level detection with chemiresistive metal oxide sensors remains challenging. At trace gas concentrations, resistance modulation is often insufficient, particularly in the absence of noble metal catalysts. Here, we report samarium-doped tin dioxide (Sm-SnO 2 ) nanofibers in which electronic trap-state modulation is exploited to enable ultrasensitive hydrogen sensing. The 2 at% Sm-doped SnO 2 nanofibers exhibited markedly enhanced H 2 sensitivity, achieving clear detection down to 25 ppb H 2 at 200 °C, with a theoretical limit of detection of 4.5 ppb, placing this material among the most sensitive noble-metal-free SnO 2 -based H 2 sensors reported to date. Mechanistic investigations through X-ray photoelectron spectroscopy and electron energy loss spectroscopy revealed that Sm 3+ doping introduces deep trap states associated with charge-compensating defect complexes. These states reduce free carrier density, increase baseline resistance, and enable trap-assisted charge release during H 2 exposure, thereby amplifying the sensing response. Trap-state engineering via rare-earth doping, exemplified by Sm-SnO 2 , provides an effective pathway for achieving ppb-level hydrogen detection in noble-metal-free chemiresistive sensors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Feature Based Qualification of 17-4PH Stainless Steel to Evaluate Location-Specific Variability in Wire Arc Additive Manufacturing

Qualifying large-scale metal additive manufacturing (M-AM) technologies such as wire arc additive manufacturing (WAAM) can be challenging. This is especially significant in precipitation hardened martensitic stainless steels like SS 17-4PH, where thermal histories induce location-specific microstructural variability and property anisotropy. The Department of Defense (DOD) and the United States Army Combat Capabilities Development Command Ground Vehicle Systems Center (GVSC) Ground Vehicle Materials Engineering (GVME) aim to build robust and qualified large-scale M-AM workflows that could reduce the time and cost through quick and informed evaluation, testing, and development of feedstock, processes, and parts. The report presents the findings from the collaborative efforts between Oak Ridge National Laboratory (ORNL) and the U.S. Army GVSC GVME. The aim of this project was to develop a geometric feature-based qualification framework for WAAM of SS 17-4PH components. This report outlines selection methodology of representative build geometries, optimization of WAAM process parameters, in-situ monitoring, microstructure-property evaluation, thermal simulations, as well as data visualization techniques incorporated in this project. The results from this project demonstrate a clear understanding of thermal history dependent phase evolution and consequent location-specific property variations in WAAM of SS 17-4PH. These results in conjunction with the data-driven methodologies used in this project are expected to reduce qualification timelines, improve predictability, and accelerate the development of reliable feature-based qualification strategies for part production via large-scale M-AM technologies.

36 MATERIALS SCIENCE↗

Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.

42 ENGINEERING↗

Zinc batteries for grid-scale energy storage: Challenges, opportunities, and future directions

The global electricity sector is undergoing rapid transformation, increasing demand for reliable stationary energy storage and intensifying the need for safe, cost-effective, and scalable technologies for grid-scale applications. While lithium-ion batteries currently dominate the market, concerns over cost, safety, and resource availability motivate the exploration of alternative chemistries. Zinc-based batteries have emerged as a promising option due to the abundance, low cost, and wide geographic distribution of zinc, combined with the inherent safety of aqueous electrolytes. This perspective examines the potential of zinc batteries for stationary energy storage, with particular focus on rechargeable zinc-air systems. We discuss the evolution of zinc battery technologies and compare zinc-air, zinc-bromine, nickel-zinc, and aqueous zinc-ion chemistries, highlighting their advantages for grid applications. Key challenges limiting rechargeable zinc battery performance are analyzed, including dendrite formation and corrosion at the zinc anode, electrolyte degradation from carbonation and evaporation, and sluggish oxygen electrocatalysis at the air cathode. Emerging strategies to address these limitations are reviewed, including advanced electrode architectures, electrolyte engineering, catalyst development, and system-level design improvements. Lastly, we outline future research directions and opportunities for developing durable, efficient, and economically viable zinc-based energy storage systems for grid-scale applications.

Aqueous electrolytes↗

Utility-Scale Operational Consequences for Solar Grid Services

This report delves into the critical aspects of grid services provided by solar inverter-based resources (IBRs), with an emphasis on the evolving landscape of microgrids, virtual power plants (VPPs), aggregators, and distributed energy resource management systems (DERMS). As the energy sector undergoes a transformative shift towards more decentralized and resilient grid architectures, understanding the multifaceted risks associated with these technologies becomes paramount. The report categorizes these risks into organizational, technical, and procedural domains, providing a thorough risk assessment framework that stakeholders can utilize to anticipate and mitigate potential issues. In addressing the increasing complexity of grid interconnections, the report highlights the importance of Cyber-Informed Engineering (CIE). By embedding engineering controls and cybersecurity measures into the early stages of system design, this approach aims to fortify grid infrastructure against emerging cyber threats. The analysis includes an exploration of best practices and strategies for integrating CIE principles to enhance grid security and resilience. To provide practical insights, the report conducts a detailed consequence analysis of various grid services and cyber mitigations that can be applied through the interconnection process. This analysis evaluates the potential impacts of different failure modes and vulnerabilities, offering a clear understanding of the consequences that could arise from disruptions within the energy grid. The findings are further enriched by a series of case studies that illustrate real-world scenarios and lessons learned from past incidents. Through this comprehensive examination of grid services and their criticality, the report aims to prepare industry professionals with the knowledge and tools necessary to navigate the complexities of modern energy systems. By providing a comprehensive approach that includes risk assessment, cybersecurity, and consequence analysis, solar stakeholders can more effectively guarantee the reliability, efficiency, and security of the energy grid.

14 SOLAR ENERGY↗

Analysis of a Disturbance Event with Inverter-Based Resources Using EMT Simulations

Increasing penetration of inverter-based resources (IBRs) necessitates newer methods of planning and analysis of disturbances. The existing phasor-domain transient stability (TS) analysis may not capture the dynamics of IBRs during fault events. Here, in this paper, electromagnetic transient (EMT) simulations using high-fidelity detailed model of power grid and one of the affected photovoltaic (PV) plants during the Angeles Forest disturbance in 2018 are performed. In these simulations, the processes to develop EMT models of power grid from traditional phasor-domain TS data and PV plant from collected data are described. Thereafter, using these simulations, the response of the PV plant during the fault event in 2018 is replicated and a sensitivity analysis is performed. The sensitivity analysis consists of making changes to the components within the PV plant and in the power grid to evaluate the impact they have on the response observed by the PV plant during the fault event. This analysis provides an understanding of the components that impact the operation of a PV plant during fault events and provide guidance to system planners on the studies that need to be performed to maintain a reliable power grid as new IBR plants are integrated.

42 ENGINEERING↗

A Secondary Control Framework for Microgrid Interoperability With Vendor-Agnostic Grid-Forming Units: Design, Implementation, and Demonstration via Large-Scale Hardware Setup

The reliable operation of islanded microgrids increasingly depends on secondary controls that restore voltage and frequency to nominal values and ensure accurate active and reactive power sharing. Centralized secondary control architectures achieve high accuracy through global coordination at the cost of single-point failures and limited scalability compared with decentralized/distributed approaches. But a critical gap remains in addressing the interoperability and vendor-agnostic operation of secondary controls in real-world microgrids where heterogeneous diesel generator(s) and grid-forming (GFM) inverter(s) from multiple manufacturers always coexist. Practical and vendor-agnostic interoperability guidelines for the secondary control architecture of microgrids with multiple GFM units have not yet been developed; therefore, this paper proposes an interoperable and vendor-agnostic secondary control framework that operates seamlessly across GFM units from different vendors without relying on proprietary controls and protocols, hardware, or lock-ins. The framework leverages existing communication infrastructures (e.g., Modbus TCP/IP) to enable cost-effective deployment while addressing practical challenges, such as packet loss and quantization errors. Mitigation strategies-including data averaging, situational event-triggered control, and finite-iteration execution-are introduced to enhance reliability under real-world conditions. A generalized modeling and design framework is also presented, supported by robustness analysis to demonstrate independence from vendor-specific implementations. The proposed framework is validated through a large-scale hardware demonstration using a 3-$\phi$, 480-V, 60-Hz, 713-kVA laboratory hardware microgrid involving a heterogeneous diesel generator and multiple GFM inverters, showcasing its effectiveness in achieving stable voltage and frequency restoration and accurate power sharing under practical constraints. The results highlight the framework's potential as a scalable and practical solution for next-generation microgrids requiring openness, standard framework, and interoperability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Surrogate-assisted optimization under uncertainty for design for remanufacturing considering material price volatility

Remanufacturing is a well-established end-of-life (EOL) strategy that promises significant savings in energy and carbon emissions. However, the current design practices are not remanufacturing-inclusive, i.e., the majority of products are designed for a single life cycle. As a result, potential products that can sustain multiple life cycles are deprived of additional benefits of being designed for remanufacturing, such as reduced material usage, lower cost, and improved environmental impact. Moreover, the uncertainty in design, material selection, and economics are not considered to produce remanufacturable designs. Accordingly, this research proposes a design for remanufacturing (DfRem) framework that accounts for design uncertainty and material price volatility. The framework systematically explores the design space, performs design optimization under uncertainty, followed by topology optimization to provide additional mass savings, and finally, a price volatility analysis for plausible design material choices. The candidate designs are evaluated based on their design mass, material price volatility, failure mode characteristics, carbon footprint, and embodied energy impacts. The proposed framework's utility is demonstrated via the use of an engine cylinder head case study subjected to thermo-mechanical loads along with fatigue and wear failure. Considering grey cast iron and aluminum alloy as the design material choices, it was found that the cast iron design reduced the initial design mass by 6% as opposed to a 5% decrease for aluminum. On the other hand, about 8% area of the cast iron design failed due to fatigue, compared to 3% for aluminum. Here, we further observed that although the aluminum design provided better mechanical performance than the cast iron design, this material was more expensive and volatile in price.

36 MATERIALS SCIENCE↗

Versatile & Intelligent Biodetection via Environmental Sensing (VIBES)

Reactive health monitoring strategies during events like the COVID-19 pandemic highlighted the need for predictive, threat-agnostic diagnostics that can detect both known diseases and novel chemical or biological threats. To address this, we investigated an optical biosensor as a breath volatile organic compound (VOC) analyzer, aiming to emulate biological olfaction. We assembled and validated the device with thin film metal coated substrate-based sensors. We immobilized small biological recognition elements on the substrates and delivered controlled concentrations of target VOCs. The sensor was irradiated with a visible laser and the sensor signal was recorded. We characterized the laser performance and tested 3 recognition elements for 2 VOCs with varying concentrations (1-100 ppm). We also evaluated enhancement of the signal using nanostructures on the metal film in comparison with planar film substrate. We demonstrated detecting ethanol reliably at concentrations as low as ~2 ppm along with preliminary detection of acetone (<100 ppm). We also found several unexpected factors that influence the sensor behavior that should be addressed to further refine the device’s performance. The nanostructures were, as expected, found to amplify the sensor signals. These findings demonstrate the feasibility of the optical bio-sensing modality for breath VOC monitoring at physiologically relevant levels. This positions LLNL to develop a low-cost, scalable, broad-spectrum health monitoring capability aligned with the Early Detection thrust of the Bioresilience Mission Focus Area and attract external funding.

47 OTHER INSTRUMENTATION↗

Enhanced Component Performance Study: Motor-Operated Valves 1998-2024

This report presents an enhanced performance evaluation of motor-operated valves (MOVs) at U.S. commercial nuclear power plants. The data used in this study are based on the operating experience failure reports from calendar year 1998 through 2024 as reported in the Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS). The MOV failure modes considered are fail to open or close (FTOC), fail to operate or control (FTOP), and spurious operation (SO). The component reliability estimates and the reliability data are trended for the most recent 10-year period while yearly estimates for reliability are provided for the entire study period. The following increasing trend was identified for MOVs for the most recent 10-year period: • Low-demand MOV frequency of FTOC demands (demands per reactor year). The following decreasing trends were identified for MOVs for the most recent 10-year period: • Low-demand MOV FTOC failure probability • High-demand MOV SO failure rate • Low-demand MOV frequency of FTOC events (failures per reactor year) • High-demand MOV frequency of SO events (failures per reactor year).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗