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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Direct Multimodal Nanoscale Visualization of Early Phosphorus-Based Antiwear Tribofilm Formation

Understanding the mechanism of antiwear (AW) tribofilm formation and how to tune surface chemistry to control functionality is essential for the development of the next generation of oil lubricants. In particular, understanding and optimizing early AW tribofilm formation can increase the energy efficiency of mechanical systems. However, the mechanism for how these films form is not well understood. The majority of prior work has focused on analyzing only end-of-test surfaces long after the film has formed. In this work, we develop an in situ multimodal chemical imaging methodology to directly visualize the early formation of AW films on steel surfaces. We investigate an oil formulation containing a phosphorus-based additive commonly used to protect surfaces from wear and fatigue processes in machine elements, such as gears, bearings, and sliding contacts. Using nanoscale multimodal chemical imaging on combined platforms of atomic force microscopy (AFM) coupled directly with in situ nano-infrared (nano-IR) spectroscopy, and further combined ex situ with time-of-flight secondary ion mass spectrometry (ToF-SIMS), we demonstrate a direct correlation between changes in friction and local surface chemistry. In these experiments, the AFM probe acts as a single asperity contact to generate the tribofilm as well as a tool to analyze it in situ as it is forming. To verify our in situ measurements, we compare these results to the ex situ ToF-SIMS of macroscale block-on-ring tribometer-formed samples. We report the understanding gained here on how AW films form and how film properties can be modified by tuning the chemistry of the additives will facilitate developing transmission fluids to meet increasing demands for vehicle performance and efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Revealing key structures for reversible sulfur redox in amorphous polymeric sulfur

Amorphous polymeric sulfur cathodes, such as sulfurized polyacrylonitrile (SPAN), enable high-energy lithium-sulfur batteries without cobalt or nickel, leveraging abundant sulfur. However, the limited in situ understanding of their synthesis and electrochemistry has impeded targeted optimization. Here, in this study, we integrate operando high-energy total scattering with sulfur K-edge X-ray absorption spectroscopy to monitor SPAN's formation and cycling in real time. Our results show that S-C bond formation halts further fusion of cyclized polyacrylonitrile, fostering π-π stacking and a transition from long-chain to short-chain sulfur-critical for reversible sulfur redox. These features synergistically minimize polysulfide dissolution and charge-transfer resistance, enabling optimized SPAN to achieve high capacity retention over 1,000 cycles. Operando X-ray absorption spectroscopy reveals that residual protons drive thiol-thione tautomerism, with lithium replacement during the first discharge causing ~20% irreversible capacity loss. To enhance performance, minimizing -NH groups and expanding pyridine networks are key. These findings transform SPAN optimization from empirical tuning to mechanism‑guided engineering and point the way towards sulfur loadings and energy densities competitive with state‑of‑the‑art Li‑ion cathodes.

25 ENERGY STORAGE↗
DOE OSTI2025

Bifunctionality of supported metal hydrodeoxygenation catalysts

The transition to sustainable energy relies on innovative methods to convert biomass-derived compounds into viable biofuels. In this study, the hydrodeoxygenation (HDO) of 6-undecanone is used as a model reaction to screen bifunctional catalysts, where metal sites facilitate hydrogenation and the support promotes deoxygenation, enabling high conversion and selectivity toward desirable alkanes for biofuel production. This reaction is particularly relevant as it represents a critical step in upgrading volatile fatty acids, derived from biomass, into long-chain hydrocarbons suitable for fuel applications. By examining a range of metals (nickel, cobalt, and tin) on different supports, it is revealed that the choice of metal–support combination is critical to catalyst performance. Zeolite beta's 3D microporous structure and adjustable acidity provide an ideal environment for fine-tuning metal–support interactions (MSIs), which are essential for balancing deoxygenation with alkane isomerization, a desirable trait for biofuels.

09 BIOMASS FUELS↗
DOE OSTI2025

Charge regulation effects on colloidal mixture nanoparticles

Changes in pH within a system containing dissociable sites affect the protonation and deprotonation of these groups, thereby influencing their physical properties. In response, the system modifies their surface charge, affecting electrostatic interactions, aggregation, stability, and structural behavior. Although the pH can be tuned in experiments, it is difficult to model this phenomenon using simulations or theoretical approaches. Here, we perform hybrid Monte Carlo-molecular dynamics simulations to model charge regulation effects in an equimolar colloidal charged system. We compare charge regulation effects with those of a system in which the charges of colloidal nanoparticles are not dissociable. The comparison between the two cases modifies the phase diagram, and it changes the volume fraction where a percolation network of nanoparticles is found. Charge regulation is found to destroy network formation, as the charge in the nanoparticles is modified because of the cooperativity dependency of the degree of charge dissociation sites among the nanoparticles favoring cluster formation. Furthermore, our work suggests that the ionic and/or electronic conductivity in functionalized nanoparticles can be modified by changing pH values. It also guides the experimental design of oppositely charged nanoparticles as inks for 3D printing processes.

Classical statistical mechanics↗
DOE OSTI2026

Superconducting low-beta Nb 3 Sn cavity for ATLAS and future ion accelerators

We report on a Nb 3 Sn-coated low-beta superconducting radio frequency (RF) cavity intended for accelerating ions. We aim to apply the cavity in ATLAS, our Argonne National Laboratory user facility for nuclear physics studies with ion beams in the energy range of 5–20 MeV u −1 . The Nb 3 Sn-coated cavity, a 145 MHz quarter-wave optimized for ions moving with velocity β = $v/c$ = 0.08 exhibits an order-of-magnitude reduction in RF losses into helium at 4.4 K compared to a superconducting niobium (Nb) cavity at the same frequency and temperature. Experimentally measured fields are among the highest to date for any Nb 3 Sn-coated cavity, reaching a peak surface magnetic field of 105 mT. We also present a practical solution to the problem of cavity frequency tuning. Tuning by mechanical deformation has been a challenge with Nb 3 Sn due to its brittle nature, however, using a set of techniques tailored to the properties of thin-film Nb 3 Sn on Nb, we can repeatably tune the cavity to the ATLAS master clock frequency after it is cooled, while maintaining the excellent performance characteristics. The same Nb 3 Sn cavity technology offers broad benefits for future ion accelerators.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Automatic Extraction of Network Configurations for Realistic Simulation and Validation

Popular HPC network interconnection simulators such as SST Macro provide a variety of configurable parameters to explore the design space of hardware components such as network links and switches. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or are intentionally omitted. In this work, we provide a methodology to automatically tune the simulation configuration of the multiple network models running within SST Macro using Bayesian optimization. We perform this optimization in the context of multiple messaging regimes (i.e., small to large and latency to bandwidth-bound messages) and provide a detailed analysis of the simulation error for four systems. With our automated framework, we achieve a 5x improvement in accuracy over best-effort configurations based on available hardware specifications.

Suetterlein, Joshua D.↗
DOE OSTI2022

Surrogate Modeling of Nonlinear Dynamic Systems: A Comparative Study

Surrogate models play a vital role in overcoming the computational challenge in designing and analyzing nonlinear dynamic systems, especially in the presence of uncertainty. This paper presents a comparative study of different surrogate modeling techniques for nonlinear dynamic systems. Four surrogate modeling methods, namely, Gaussian process (GP) regression, a long short-term memory (LSTM) network, a convolutional neural network (CNN) with LSTM (CNN-LSTM), and a CNN with bidirectional LSTM (CNN-BLSTM), are studied and compared. All these model types can predict the future behavior of dynamic systems over long periods based on training data from relatively short periods. The multi-dimensional inputs of surrogate models are organized in a nonlinear autoregressive exogenous model (NARX) scheme to enable recursive prediction over long periods, where current predictions replace inputs from the previous time window. Three numerical examples, including one mathematical example and two nonlinear engineering analysis models, are used to compare the performance of the four surrogate modeling techniques. The results show that the GP-NARX surrogate model tends to have more stable performance than the other three deep learning (DL)-based methods for the three particular examples studied. The tuning effort of GP-NARX is also much lower than its deep learning-based counterparts.

42 ENGINEERING↗
DOE OSTI2026

Multifunctional electrochemical memory stabilized by phase coexistence

Our growing computing needs, especially in applications that heavily rely on artificial intelligence (AI), motivate a search for new components that could substantially augment the performance of general-purpose digital computers. Beyond ON/OFF switching, new components with linear multistate analog resistive tuning, nonlinear volatile switching, spiking, oscillatory, stochastic and other complex functionalities could enable highly efficient neuromorphic computing schemes for AI information processing. Compared to the extreme multifunctionality of biological neurons, realizing all the above characteristics in a single, scalable analog component remains a grand challenge. Here we investigate electrochemical gating combined with localized thermal activation to program and switch a single, vertically integrated and dimensionally scaled electrothermal chemical random access memory (ETCRAM) with a channel and reservoir composed of phase-separated vanadium oxide. Closely related to electrochemical RAM (ECRAM), ETCRAM uses an integrated gate-heater electrode to overcome kinetic barriers that help retain states at ambient temperatures. In addition to synapse-like stable and programmable analog resistance states arising from redox-tunable phase coexistence, a single component exhibits neuron-like nonlinear conductance switching with a tunable threshold and self-driven dynamics owing to the thermally driven metal-insulator phase transition in vanadium dioxide. More broadly, we demonstrate that electrochemically stabilized phase coexistence could unlock analog electronics with novel functionality, stability, reconfigurability, and scalability.

Oh, Sangheon [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2021

Verification and validation of developed short-term forecasting models

Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Method for the preparation of uniform triaminotrinitrobenzene microparticles

A new, rapid and inexpensive synthesis method for monodispersed triaminotrinitrobenzene (TATB) microparticles based on micelle-confined precipitation that enables control of microscopic morphology. The morphology of the TATB microparticles can be tuned between quasi-spherical and faceted by controlling the speed of recrystallization. The method enables improved performance and production consistency of TATB explosives for military grade explosives and propellants

36 MATERIALS SCIENCE↗
DOE OSTI2023

Performance Versus Maintainability: A Case Study of Scream on Frontier

The Simple Cloud-Resolving E3SM Atmosphere Model (Scream) won the inaugural ACM Gordon Bell Prize for Climate Modeling. While most of Scream is portable Kokkos code, the Gordon-Bell runs did include tuning specifically for Frontier, the exascale computer at Oak Ridge National Laboratory. Production science runs use the same high-level configuration of Scream, but the tuned kernels do not meet the software standards necessary to merge into the production code base. This work describes experiments to refactor these kernels to meet the maintainability requirements of the production Scream code base while preserving high performance.

White, Trey↗
DOE OSTI2024

Multilayer film coating for laser ion source target for increase of low charge state production

The use of Laser Ion Source for accelerator facilities has the advantage to tune the characteristic of the produced beams by changing the laser parameters using the same primary target. The advantageous and innovative opportunity to manipulate the characteristic of charge state distributions by the use of composed target, may open new possibilities for the ion sources. In this experiment we characterize and study the plasma produced by the laser ablation of coated targets at constant laser parameters. The performed investigation has the double purpose to have a better understanding of penetration depth of laser in composed materials and understand how to tune the charge states by adding coating films. Finally, the obtained results showed that for particular thickness of coating, the low charge states were produced with higher yield than in the case of pure material.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Experimental Validation of Models of a Hull-Based Tuned Mass Damper System for a Semisubmersible Floating Offshore Wind Turbine Platform

Floating offshore wind turbine designs can be further optimized if the controller and remaining systems are designed together, known as control co-design. To effectively perform control co-design, modeling tools predicting the influence of the control systems on the response of a system must be validated. This article presents an experimental validation that utilizes a scale model of a semisubmersible platform for an offshore wind turbine that is fitted with adjustable tuned mass dampers. These dampers can be tuned to attenuate either the hull-pitch resonance or the tower-bending resonance. The data from the experiment are used to validate state-of-the-art modeling tools. It is shown that the models capture the overall effects of the tuned mass dampers; however, some models overpredict the reduction in platform pitch motion when the dampers are tuned to the pitch resonance. The relative reduction in the tower-base bending moment is more consistently captured by the models when the dampers are tuned to the tower-bending resonance. However, there are significant differences in the absolute level of tower-base bending moment between the models and the experiment. Much of the differences observed are a consequence of the challenge with accurately predicting the baseline platform resonance motion and the tower-bending moment, which should be addressed in future modeling efforts.

17 WIND ENERGY↗
DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Adaptive model tuning studies for non-invasive diagnostics and feedback control of plasma wakefield acceleration at FACET-II

Beam-driven plasma wakefield acceleration (PWFA) achieves the same energy gain in a single meter, for which conventional accelerators require several kilometers, however much work is still required to match the beam quality of conventional accelerators. The PWFA processes to be studied at the FACET-II facility will utilize extremely short (down to a few fs) high energy (10 GeV), high charge (few nC) electron and eventually positron bunches. The PWFA process is extremely sensitive to the detailed longitudinal current profiles of these bunches and it would be of great benefit to have precise measurement and control of these profiles. We present an adaptive model tuning technique for FACET-II to adaptively tune online models based on real time accelerator and beam data to continuously provide a non-invasive diagnostic which predicts the longitudinal phase space (LPS) of extremely short and highly compressed electron beams, which otherwise require destructive X-band transverse deflecting cavity (XTCAV)-based measurements. Based on simulation studies, our method has the potential for: (1). The development of a non-invasive longitudinal phase space diagnostic by adaptively tuning models based on non-invasive measurements such as energy spread spectra which could be recorded at all of the bunch compressors in the facility. (2). Utilize these diagnostic to perform model-independent feedback-based to achieve desired longitudinal phase space distributions. (3). Utilize the model-independent feedback approach to maximize energy gain while minimizing emittance growth and energy gain variance of the PWFA process by tuning accelerator parameters, while monitoring what longitudinal phase space the algorithm has found, information that will be useful for further analytical and simulation studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Tuning Thermal Stability through Dopant Size in Chemically Doped DPP–Thiophene Polymers

Molecular doping of conjugated polymers (CPs) is a key strategy for improving the performance of organic electronics devices, particularly thermoelectrics. Doped donor–acceptor (D–A) conjugated polymers, characterized by a tunable energy gap between the Fermi level and the transport band, show great promise in achieving high electrical conductivity (σ) while preserving a favorable Seebeck coefficient (S). Despite the promising performance enhancement of chemically doped D–A polymers, their thermal stability remains largely underexplored, a crucial consideration for the long-term operation of organic thermoelectric devices. In this study, we investigated the dopant size-dependent thermal stability of a diketopyrrolopyrrole-thiophene (DPP-T) D–A copolymer, utilizing two p-dopants: 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F 4 TCNQ) and Mo(tfd-CO 2 Me) 3 . Temperature-dependent UV–vis–NIR spectroscopy revealed that DPP-T/F 4 TCNQ is more prone to dedoping under a high temperature thermal stress than DPP-T/Mo(tfd-CO 2 Me) 3 . Although the F 4 TCNQ doped polymer shows higher initial in-plane conductivity than its Mo(tfd-CO 2 Me) 3 counterpart, it undergoes a conductivity loss of more than an order of magnitude after annealing at 120 °C for 30 min. In contrast, the in-plane conductivity of DPP-T/Mo(tfd-CO 2 Me) 3 remains stable under the same thermal conditions. Thermogravimetric analysis ruled out dopant sublimation as a primary contributor to dedoping, leading us to attribute the conductivity loss in F 4 TCNQ-doped DPP-T to dopant phase separation and migration. This observation was further confirmed by X-ray scattering studies and nanoscale infrared microscopy and spectroscopy studies. This work could provide further insights into the thermal stability of doped conjugated polymers and suggests that incorporating bulkier dopants is an effective strategy to enhance the thermal robustness of doped DPP-type systems.

Conjugated polymers↗
DOE OSTI2022

Active and Durable PGM-free Cathodic Electrocatalysts for Fuel Cell Application

Platinum Group Metal-free (PGM-free) Oxygen Reduction Reaction (ORR) electrocatalysts possess high intrinsic activity measured by a Rotating Disk Electrode (RDE) method. However, under fuel cell operating conditions, PGM-free ORR electrocatalysts have underperformed compared to platinum catalysts. PGM-free ORR electrocatalyst’s fuel cell performance can be improved by designing and optimizing the cathodic catalyst layer (CL) and Membrane Electrode Assembly (MEA) construction such that: 1) it efficiently provides oxygen access to ORR active sites (through catalyst morphology control); 2) it removes water from the CL (by tuning the hydrophobicity of the PGM-free catalysts and the catalyst layer structure); and 3) it increases proton conductivity (by homogeneous mixing of catalysts and ionomer). Studying the CL is complex due to the absence of well-established protocols of MEA activation, especially compared to platinum-containing catalysts. PGM-free fuel cell testing protocols would need to optimize potentiostatic vs galvanostatic measurements, scan rates, parameters of Electrochemical Impedance Spectroscopy (EIS) and Beginning of Experiment (BOE) criteria. To make the PGM-free catalyst’s fuel cell performance comparable to platinum, the synergistic effort of materials design, fine tuning of the catalyst layer and comprehensive electrochemical analysis is required.

08 HYDROGEN↗
DOE OSTI2022

Zero Magnetic Field Plateau Phase Transition in Higher Chern Number Quantum Anomalous Hall Insulators

The plateau-to-plateau transition in quantum Hall effect under high magnetic fields is a celebrated quantum phase transition between two topological states. It can be achieved by either sweeping the magnetic field or tuning the carrier density. The recent realization of the quantum anomalous Hall (QAH) insulators with tunable Chern numbers introduces the channel degree of freedom to the dissipation-free chiral edge transport and makes the study of the quantum phase transition between two topological states under zero magnetic field possible. Here, we synthesized the magnetic topological insulator (TI)/TI pentalayer heterostructures with different Cr doping concentrations in the middle magnetic TI layers using molecular beam epitaxy. By performing transport measurements, we found a potential plateau phase transition between C=1 and C=2 QAH states under zero magnetic field. In tuning the transition, the Hall resistance monotonically decreases from h/e2 to h/2e 2 , concurrently, the longitudinal resistance exhibits a maximum at the critical point. Our results show that the ratio between the Hall resistance and the longitudinal resistance is greater than 1 at the critical point, which indicates that the original chiral edge channel from the C=1 QAH state coexists with the dissipative bulk conduction channels. Subsequently, these bulk conduction channels appear to self-organize and form the second chiral edge channel in completing the plateau phase transition. Our study will motivate further investigations of this novel Chern number change-induced quantum phase transition and advance the development of the QAH chiral edge current-based electronic and spintronic devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗