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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Functionally Designed Ultra-Lightweight Carbon Fiber Reinforced Thermoplastic Composites Door Assembly (Final Scientific/Technical Report)

This is the final technical report for a project that developed an ultra-lightweight carbon fiber reinforced thermoplastics composite door assembly for a mid-size SUV. The project was able achieve a weight reduction of at least 42.5% while maintaining or exceeding the fit, function, safety, stiffness, and crash performance requirements of the current baseline door. Additionally, the target cost was set at less than $5 per pound of weight saved. To accomplish these goals, the project team employed a systems approach that involves redesigning the door assembly, exploring novel materials, optimizing the design, and utilizing advanced manufacturing technologies.

42 ENGINEERING↗
DOE OSTI2023

Spread Spectrum Time Domain Reflectometry (SSTDR) and Frequency Domain Reflectometry (FDR) for Detection of Cable Anomalies Using Machine Learning

Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

SSTDR and FDR Detection of Un-Energized and Energized Cable Anomalies Including Thermal Degradation Using Machine Learning

Historically, cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages. A related technology that has found some acceptance in the aircraft and rail industry is spread spectrum time domain reflectometry (SSTDR). This technology has been implemented with a custom commercial instrument by LiveWire Innovation that is designed to operate on live cables up to 1000 volts and with a bandwidth of 48 MHz. Initial evaluation by the Pacific Northwest National Laboratory (PNNL) of the Live Wire system indicated that a broader bandwidth (BW) SSTDR may be better for many kinds of flaws. This led PNNL to develop an SSTDR laboratory instrument suitable for tests up to 500 MHz bandwidth. Testing on energized cables is also desirable for online monitoring systems so an inductive clamshell coupler was developed that allows energized cables to be tested up to at least 5 kV and likely higher voltage levels. Dielectric spectroscopy and tan delta testing plus various laboratory destructive tests were included in this data acquisition campaign directed to feed a machine learning (ML) study. With these kinds of developments, online energized cable tests may be possible with industrial adoption of such hardware advances but it will be completely impractical to have highly skilled data analysts continually examine these complex signals for indications of damage or compromised conditions. If online testing is to be implemented in new test hardware, it must be accompanied by software that can interpret the signals and alert plant operators of changing or degraded conditions. The thermally aged, shielded cable investigated here was separately treated for ML analysis. Visual analysis of electrical data showed generally increasing peaks where the cable entered and exited the oven. These peaks were not exactly aligned with expected locations, but these differences were attributed to velocity of propagation calibration errors. Only supervised ML was applied to the thermally aged data as this data was only available shortly before the committed publication date of this report. The supervised ML was structured to divide the 0 to 70-day responses as ‘normal’ from 0 to 35 days or ‘anomalous’ from 36 to 70 days, based on cable tensile elongation at break (EAB) insulation characterization. Using 80% of the data for training and 20% for testing, the supervised ML predicted normal versus anomalous was 70% accurate. Important conclusions include: • Accuracy to predict the presence of cable damage is improved from the 2023 effort by more training data. Weighted accuracies for comparisons among the instruments ranged from 67 to 89 % for unsupervised ML and 71 to 99% for supervised ML. • Based on the synthetic data tests, the unsupervised models are more generalizable to unseen anomalies. The Multi-Layer Perceptron classifier (MLP) model reported as high as 99.7% accuracy on the test data, but this dropped to 58.3% when tested on the synthetic data. In contrast, the unsupervised Pointwise model only achieved 89.7% accuracy on the experimental data but reported 78.3% accuracy on the synthetic data. • The best anomaly indicators are higher frequency (400 MHz BW) FDR data. Other tests may be interesting but for this study, this was the best predicter.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

SSTDR and FDR Detection of Un-Energized and Energized Cable Anomalies Including Thermal Degradation Using Machine Learning

Historically, cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Spread Spectrum Time Domain Reflectometry (SSTDR) and Frequency Domain Reflectometry (FDR) for Detection of Cable Anomalies Using Machine Learning

Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages. A related technology that has found some acceptance in the aircraft and rail industry is spread spectrum time domain reflectometry (SSTDR). This technology has been implemented with a custom commercial instrument by LiveWire Innovation Inc. that is designed to operate on live cables up to 1000 volts. One of the main conclusions of a previous effort was that cable reflectometry plots can be difficult for humans to analyze due to baseline noise, low or noisy anomaly response peaks, or large responses from cable ends. Detection of cable anomalies for many of these frequencies and test conditions was challenging for manual analysis. This presented an ideal opportunity for ML analysis to distinguish undamaged cable indications from anomalous cable indications. This research discusses application of machine learning (ML) to reflectometry cable test methods. The goal was to assess feasibility to distinguish undamaged cable reflectometry responses from damaged or anomalous cable reflectometry responses. The assessment considered the 3 instruments, multiple frequency bandwidths from each instrument, multiple cable anomalies and test conditions, and both supervised and unsupervised ML approaches. Although approaches and analysis methods were not identical or directly comparable, both outputs were encouraging. The unsupervised prediction weighted accuracy was assessed by instrument and by frequency. It performed better at high frequencies with the highest prediction accuracy of 0.84 for the higher frequency FDR, 0.79 for the 48-MHz LiveWire SSTDR, and 0.77 for 300-MHz PNNL SSTDR. The initial weighted accuracy average across all frequencies for using supervised ML was 0.56 to 0.68. The supervised analysis was repeated with noisier training data removed resulting in weighted accuracies of 0.69 to 0.87. These weighted accuracies are not directly comparable due to differences in the supervised and unsupervised analysis details but do indicate an encouraging trend. Even with limited and unbalanced data, strong prediction accuracies seem encouraging for further work including more data under a wider range of conditions.

42 ENGINEERING↗
DOE OSTI2024

Comparison of Intact Fish Skin Graft and Allograft as Temporary Coverage for Full-Thickness Burns: A Non-Inferiority Study

The extent and depth of burn injury may mandate temporary use of cadaver skin (allograft) to protect the wound and allow the formation of granulation tissue while split-thickness skin grafts (STSGs) are serially harvested from the same donor areas. However, allografts are not always available and have a high cost, hence the interest in identifying more economical, readily available products that serve the same function. This study evaluated intact fish skin graft (IFSG) as a temporary cover to prepare the wound bed for STSG application. Thirty-six full-thickness (FT) 5 × 5 cm burn wounds were created on the dorsum of six anesthetized Yorkshire pigs on day -1. To mimic the two-stage clinical situation, on day 0, wounds were excised down to a bleeding wound bed and a temporary cover (either IFSG or cadaver porcine skin) was applied; then, on day 7, wounds were debrided to a viable wound bed prior to the application of autologous 1.5:1 meshed STSG (mSTSG). Rechecks were performed on days 14, 21, 28, 45, and 60 with digital images, non-invasive measurements, and punch biopsies. The IFSG created a granulated wound bed receptive to the application of an mSTSG. FT burn wounds treated with an IFSG had similar outcome measures, including contraction rates, trans-epidermal water loss (TEWL) measurements, hydration, and blood perfusion levels, compared to cadaver skin-treated burn wounds. Pathology scoring indicated significant differences between the allograft- and IFSG-treated wounds on day 7, with the IFSG having increased angiogenesis, granulation tissue formation, and immune cells. Pathology scoring indicated no significant differences once mSTSGs were applied to wounds. The IFSG performed as well as cadaver skin as a temporary cover and was not inferior to the standard of care, suggesting the potential to transition IFSGs into clinical use for burns.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition

Multi-fidelity Bayesian optimization (MFBO) is a powerful approach that utilizes lowfidelity, cost-effective sources to expedite the exploration and exploitation of a high-fidelity objective function. Existing MFBO methods with theoretical foundations either lack justification for performance improvements over single-fidelity optimization or rely on strong assumptions about the relationships between fidelity sources to construct surrogate models and direct queries to low-fidelity sources. To mitigate the dependency on cross-fidelity assumptions while maintaining the advantages of low-fidelity queries, we introduce a random sampling and partition-based MFBO framework with deep kernel learning. This framework is robust to cross-fidelity model misspecification and explicitly illustrates the benefits of low-fidelity queries. Our results demonstrate that the proposed algorithm effectively manages complex cross-fidelity relationships and efficiently optimizes the target fidelity function.

Zhang, Fengxue [University of Chicago, Illinois, U↗
DOE OSTI2020

Temperature dependence of nuclear quantum effects on liquid water via artificial neural network model based on SCAN meta-GGA functional

We investigate the temperature dependence of nuclear quantum effects (NQEs) on structural and dynamic properties of liquid water by training a neural network force field using first-principles molecular dynamics (FPMD) based on the strongly constrained and appropriately normed meta-generalized gradient approximation exchange-correlation approximation. The FPMD simulation based on density functional theory has become a powerful computational approach for studying a wide range of condensed phase systems. However, its large computational cost makes it difficult to incorporate NQEs in the simulation and investigate temperature dependence of various properties. To circumvent this difficulty, we use an artificial neural network model and employ the thermostatted ring polymer MD approach for studying the temperature dependence of NQEs on various properties. The NQEs generally bring the radial distribution functions closer to the experimental measurements. Translational diffusivity and rotational dynamics of water molecules are both slowed down by the NQEs. The competing inter-molecular and intra-molecular quantum effects on hydrogen bonds, as discussed by Habershon, Markland, and Manolopoulos [J. Chem. Phys. 131(2), 024501 (2019)], can explain the observed temperature dependence of the NQEs on the dynamical properties in our simulation.

Yao, Yi (ORCID:0000000299576979)↗
DOE OSTI2022

Additive Manufacture of Multi-Functional, Large-Scale Components

The poster summarizes the main results for a LDRD focused on developing a arc-based additive manufacturing (AM) system capable of fabricating large components with a composition gradient otherwise known as functionally graded materials (FGMs). FGMs are capable of addressing local extreme conditions in an optimal and cost-effective manner. The AM system (referred to as multi-Wire Arc Additive Manufacturing or m-WAAM) consisted of modifying a standard automated gas tungsten arc welding system to deliver multiple welding wires to the weld pool at controlled rates to deposit desired alloy compositions at critical locations. Numerous shapes and composition gradients were fabricated with the system and the effect of various deposition parameters on the nature of the composition profile were evaluated. Additionally, heat treatment of the FGM was performed to develop graded microstructures which should enhance dynamic impact performance of inexpensive steels. The project highlights the potential of m-WAAM to efficiently fabricate FGMs. However, this project only explored the science behind the technique and additional development work will be required to develop a fully-functional system that can build more complex components.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi-functional Sorbent Technology (MUST) for the Recovery/Removal of Critical/Heavy Metals from Fossil-Related Wastewater

A collection of remediation and recovery technologies like those predicated on photocatalytic-, electric-, chemical-, membrane-, and adsorptive-based processes currently exists in different stages of development or deployment between these methods of water management. Among these technologies, solid sorbent processes embody an optimal balance between cost-effectiveness, environmental friendliness, and technological maturity. The U.S. Department of Energy’s National Energy Technology Laboratory developed a suit of cross-linked, functionalized silica sorbents tailored to eliminate the most toxic metals regulated by the U.S. Environmental Protection Agency (EPA); critical metals identified by the U.S. Geological Survey (USGS); and harmful organics like the infamous perfluoroalkyl and polyfluoroalkyl substances (PFAS). An array of laboratory and field tests proved that the MUST sorbents removed ppb-level lead from drinking water, selenium from FGD wastewater below EPA limits, aqueous dyes and PFAS; and fractionated low ppm-level critical metals (CM) from AMD and simulated produced water. Proof-of-concept for commercial CM recovery was verified through obtaining milligram-quantities of purified Al solids from a sorbent multi-bed AMD field-site test. Commercial efficacy was further supported by achieving purified fractions of adsorbed Mn upon treating both the AMD and synthetic produced water with the laboratory multi-bed test unit.

Wilfong, Walter C.↗
DOE OSTI2023

Toward Quantum Chemical Free Energy Simulations of Platinum Nanoparticles on Titania Support

Platinum nanoparticles (Pt-NPs) supported on titania surfaces are costly but indispensable heterogeneous catalysts because of their highly effective and selective catalytic properties. Therefore, it is vital to understand their physicochemical processes during catalysis to optimize their use and to further develop better catalysts. However, simulating these dynamic processes is challenging due to the need for a reliable quantum chemical method to describe chemical bond breaking and bond formation during the processes but, at the same time, fast enough to sample a large number of configurations required to compute the corresponding free energy surfaces. Density functional theory (DFT) is often used to explore Pt-NPs; nonetheless, it is usually limited to some minimum-energy reaction pathways on static potential energy surfaces because of its high computational cost. In this work, we report a combination of the density functional tight binding (DFTB) method as a fast but reliable approximation to DFT, the steered molecular dynamics (SMD) technique, and the Jarzynski equality to construct free energy surfaces of the temperature-dependent diffusion and growth of platinum particles on a titania surface. In particular, we present the parametrization for Pt-X (X = Pt, Ti, or O) interactions in the framework of the second-order DFTB method, using a previous parametrization for titania as a basis. The optimized parameter set was used to simulate the surface diffusion of a single platinum atom (Pt 1 ) and the growth of Pt 6 from Pt 5 and Pt 1 on the rutile (110) surface at three different temperatures (T = 400, 600, 800 K). The free energy profile was constructed by using over a hundred SMD trajectories for each process. We found that increasing the temperature has a minimal effect on the formation free energy; nevertheless, it significantly reduces the free energy barrier of Pt atom migration on the TiO 2 surface and the transition state (TS) of its deposition. In a concluding remark, the methodology opens the pathway to quantum chemical free energy simulations of Pt-NPs’ temperature-dependent growth and other transformation processes on the titania support.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Asymmetric side-chain engineering in semiconducting polymers: a platform for greener processing and post-functionalization of organic electronics

Organic semiconducting polymers are a powerful platform for the design of next-generation technologies due to their excellent optoelectronic properties and solution processability, allowing access to low-cost and scalable manufacturing techniques such as spin-coating, slot-die coating and roll-to-roll printing. However, their extended π-conjugation results in low solubility, requiring the use of toxic halogenated solvents to generate thin films and devices. Furthermore, accessible post-functionalization of semiconductors toward the development of multifunctional devices and sensors remains a challenge due to limited solid-state chemistry for alkyl side chains. In this work, an asymmetric side-chain engineering approach was used to introduce terminal hydroxyl moieties alongside traditional solubilizing branched alkyl chains into an isoindigo-based polymer. The hydroxyl moieties led to significantly improved processability in alcohol-based solvents without sacrificing electronic performance in thin film organic field-effect transistors. Solid state morphologies of the thin films processed from both alcohol-based and traditional halogenated solvents were further characterized using atomic force microscopy and grazing incidence wide angle X-ray scattering. Additionally, Cryo-EM was utilized in order to characterize the role of asymmetric side-chain functionality in solution state aggregation. The versatility of this design was further probed using fluorescein isothiocyanate to directly functionalize the asymmetric polymer in thin film. Furthermore, this facile solid-state post-functionalization further demonstrates asymmetric side-chain engineering to be a viable approach toward the development of sustainably manufactured multifunctional electronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Biomass-Derived Carbon and Their Composites for Supercapacitor Applications: Sources, Functions, and Mechanisms

Biomass-derived carbons are eco-friendly and sustainable materials, making them ideal for supercapacitors due to their high surface area, excellent conductivity, cost-effectiveness, and environmental benefits. This review provides valuable insights into biomass-derived carbon and modified carbon for supercapacitors, integrating both experimental results and theoretical calculations. This review begins by discussing the origins of biomass-derived carbon in supercapacitors, including plant-based, food waste-derived, animal-origin, and microorganism-generated sources. Then, this review presents strategies to improve the performance of biomass-derived carbon in supercapacitors, including heteroatom doping, surface functionalization, and hybrid composite construction. Furthermore, this review analyzes the functions of biomass-derived carbon in supercapacitors both in its pure form and as modified materials. The review also explores composites derived from biomass-based carbon, including carbon/MXenes, carbon/MOFs, carbon/graphene, carbon/conductive polymers, carbon/transition metal oxides, and carbon/hydroxides, providing a thorough investigation. Most importantly, this review offers an innovative summary and analysis of the role of biomass-derived carbon in supercapacitors through theoretical calculations, concentrating on four key aspects: energy band structure, density of states, electron cloud density, and adsorption energy. Finally, the review concludes the future research directions for biomass carbon-based supercapacitors, including the discovery of novel biomass materials, tailoring surface functional groups, fabricating high-performance composite materials, exploring ion transfer mechanisms, and enhancing practical applications. In summary, this review offers a thorough exploration of the sources, functions, and mechanisms of biomass-derived carbon in supercapacitors, providing valuable insights for future research.

biomass-derived carbon↗
DOE OSTI2021

Functionalized Embedded Monometallic Nickel Catalysts for Enhanced Hydrogen Evolution: Performance and Stability

Viable anionic exchange membrane (AEM) electrolysis for H 2 production requires highly active hydrogen evolution reaction (HER) catalysts that are also robust and cost-effective. Traditionally juxtaposition of two transition metals with one being more oxophilic is necessary for easier access to water in the inner Helmholtz plane as a source of protons in the crucial Volmer step for enhanced HER activity. However, they are prone to passivation at anodic potentials or deactivation via the formation of sub-surface metal hydrides at high cathodic overpotentials. Here we report a method of enabling close juxtaposition of Ni-Ni-oxide surfaces by functionalizing Ni-based catalysts via the careful thermal treatment of carbon-supported Ni nano-particles chelated with a select organic chelating compound referred to as cupferron. This functionalization results in an embedded Ni surface with a clear graphitic coating engendering long-term stability for passivation and subsurface hydride formation. The stability and activity of the functionalized Ni catalyst are demonstrated in half-cell RDE and full-cell AEM membrane-based hydrogen pump experiments. In the case of the latter, the functionalized Ni outperforms carbon-supported Pt.

25 ENERGY STORAGE↗
DOE OSTI2025

Influence of functional additives, fillers, and pigments on thermal and catalytic pyrolysis of polyethylene for waste plastic upcycling

Pyrolysis offers a relatively green and economical method to convert waste plastics into valuable chemicals and fuels without the need for harmful solvents, toxic chemicals, or costly high-pressure reactors. Despite its popularity among chemical upcycling technologies, industrial adoption suffers from feedstock heterogeneity, low-quality products, and catalyst deactivation. Most plastics in our daily lives are formulated with functional additives, fillers, and colorants. These additives remaining in end-of-life waste streams increase feedstock heterogeneity, creating a challenging issue in recycling plastics. Still, the potential impacts of additives on the chemical upcycling of plastics have been poorly understood. In this study, polyethylene compounded with a range of widely used additives (antioxidants, stabilizers, pigments, fillers, slip agents, and flame retardants) was subjected to both thermal pyrolysis and catalytic pyrolysis in different catalyst-to-feedstock contact modes. It showed that many inorganic additives, such as talc, kaolin, CaCO 3 , TiO 2 , carbon black, and zinc stearate, facilitated polymer decomposition during pyrolysis, increasing light hydrocarbons while also promoting aromatic and carbon residue formation. Conversely, antioxidants and stabilizers inhibited depolymerization, favoring heavier hydrocarbons. During catalytic pyrolysis with HZSM-5 zeolite, additives strongly enhanced aromatic and catalytic coke formation, especially when there was direct contact between plastics and catalysts. Although certain additives seem beneficial in the short term by promoting polymer cracking and improving the selectivity of aromatics, the transport of the additives and their degradation products and increased carbon coking can contaminate products, deactivate or modify catalysts, and foul reactors. These findings address a critical knowledge gap in effectively converting waste plastics via a greener route.

42 ENGINEERING↗
DOE OSTI2025

Stacking polymorphism of PtSe 2 : its implication to layer-dependent metal-insulator transitions

Using diffusion Monte Carlo (DMC) and density functional theory (DFT) calculations, we examine the structural stability and interlayer binding properties of PtSe 2 . Our DMC study reveals that AA and AB-r bilayer stacking modes are nearly degenerate, highlighting the significant role of interlayer hybridization in offsetting the energy cost due to larger interlayer separations in the AB-r mode. Additionally, our DMC-benchmarked DFT calculations with the r 2 SCAN+rVV10 functional uncover pronounced stacking polymorphism in few-layer PtSe 2 , driven by degenerate AA and AB-r interfaces, which leads to substantial band gap variations across different stacking configurations. This polymorphism, along with selenium vacancies, influences a layer-dependent metal-insulator transition observed in few-layer PtSe 2 . Our findings emphasize the importance of both van der Waals interactions and interlayer hybridization in determining the phase stability and electronic properties of PtSe 2 , advancing our understanding of its fundamental properties and refining theoretical models for practical applications in nanoelectronic devices.

Electronic materials↗