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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fractional Quantum Anomalous Hall Effect

The realization of the fractional quantum anomalous Hall effect (FQAHE) in a zero-field fractional Chern insulator is a new advancement in condensed matter physics, resulting from the interplay among strong correlations, topology, and spontaneous time-reversal symmetry breaking in lattice systems. In this review, we highlight the experimental and theoretical progress toward achieving FQAHE in two material platforms: twisted bilayer MoTe 2 and rhombohedral-stacked multilayer graphene. These systems host narrow topological bands with nontrivial Chern numbers, enabling interaction-driven fractionalized states analogous to the fractional quantum Hall effect, but without external magnetic fields. We discuss how spontaneous ferromagnetism, moiré lattice reconstruction, and band topological effects underpin the emergence of FQAHE in twisted MoTe 2 . We describe experimental discoveries of zero-field fractional Chern insulators in both transport and optical experiments, as well as signatures of composite Fermi liquids and higher-energy Chern band, which may shed light on engineering nonabelian states. In rhombohedral graphene/hexagonal boron nitride moiré superlattices, we review the recent observations of fractionally quantized Hall resistance, connections between FQAHE and extended quantum anomalous Hall phases, and the coexistence of superconductivity and FQAHE. Furthermore, these discoveries not only deepen our understanding of strongly correlated topological matter but also open new frontiers for exploring nonabelian anyons, fault-tolerant quantum computation, and topological opto-spintronics free of magnetic fields.

bilayer↗
DOE OSTI2020

Single-frame far-field diffractive imaging with randomized illumination

Contains two transmission ptychography datasets, one collected from a Siemens star, and the second from an Fe/Gd multilayer. Both samples are under illumination from the same randomized zone plate. Each dataset has a single diffraction pattern removed, and these patterns are reported in a separate cxi file. Either ptychography dataset can be used for calibration of the probe function, and the remaining diffraction patterns can be reconstructed via randomized probe imaging.

BESSY II↗
DOE OSTI2021

Component Importance and Interdependence Analysis for Transmission, Distribution and Communication Systems

For critical infrastructure restoration planning, the real-time scheduling and coordination of system restoration efforts, the key in decision-making is to prioritize those critical components that are out of service during the restoration. For this purpose, there is a need for component importance analysis. While it has been investigated extensively for individual systems, component importance considering interdependence among transmission, distribution and communication (T&D&C) systems has not been systematically analyzed and widely adopted. In this study, we propose a component importance assessment method in the context of interdependence between T&D&C networks. Analytic methods for multilayer networks and a set of metrics have been applied for assessing the component importance and interdependence between T&D&C networks based on their physical characteristics. The proposed methodology is further validated with integrated synthetic Illinois regional transmission, distribution, and communication (T&D&C) systems, the results reveal the unique characteristics of component/node importance, which are strongly affected by the network topologies and cross-domain node mapping.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

High Power Density Cost-Effective MVDC Aircraft Cable

To make the power density of electric aircraft closer to conventional aircraft, an electric power system (EPS) with high power delivery and low system mass is necessary. As an essential component of aircraft EPS, cables are necessary to transmit power from one node to another. Virginia Tech will design high-power density, cost-effective ±5 kV cables for twin-aisle all-electric aircraft. Innovations include conductors with increased current-carrying capacity and a multilayer, multifunctional insulation system made of exceptionally high thermal conductivity materials. Designed for DC voltage, the new insulator will allow fewer partial discharge events and provide improved electromagnetic interference protection.

42 ENGINEERING↗
DOE OSTI2024

Orienting Strained Interfaces designed to Direct Energy Flow (Final Technical Report)

This proposal aims to establish a transformative paradigm for oxide heterostructures with an exceptionally large number of interfaces designed to direct energy flow through controlled interface orientation, enabling fast ion transport. A primary focus is on unveiling the key role of interfacial strain in oxygen ion migration at low temperatures. The central challenge is to create, understand, utilize self-assembled vertical heteroepitaxial nanostructures with the goal of obtaining and understanding fast ion transport properties by modulating interfacial strain. The specific objectives are: (1) to synthesize multilayer thin films and vertical heteroepitaxial nanostructures with fluorite Gd-doped CeO 2 (GDC) and bixbyite RE 2 O 3 (RE = Y and Sm), (2) to evaluate the interfacial strain states under various temperatures and ambient conditions, (3) to understand the effect of interfacial strain on ionic conductivity of the proposed nanostructures, (4) to understand the three-dimensional (3D) atomic structure of the proposed material design and interface phenomena at the atomic scale.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigation of new superconducting materials for the next generation high-performance RF superconducting cavities for particle accelerators

In this DOE-funded project DE-SC0010081-020 Old Dominion University (ODU) in collaboration with University of Wisconsin (UW) and Jefferson Laboratory have investigated both experimentally and theoretically electromagnetic response and losses in multilayered superconducting structures made of new SRF materials which can push the field and Q performance limits of accelerating cavities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

BOTTLE: Hybrid Chemical-Mechanical Separation and Upcycling of Mixed Plastic Waste

The main objective of this project is to develop a hybrid mechanical-chemical recycling technology for multilayered and laminated plastics. We aimed to separate and upcycle up to more than 80% of the two main constituents of such structures, polyolefins and polyesters, for a significantly lower cost and at higher energetic efficiency and much larger throughputs than chemical recycling. At the end of the project, the team was able to: a) Develop an extrusion-based separation technology that resorts to polyester depolymerizaton and extraction and allows for more than 90% of the polyester to be separated in the melt from the main polyolefin stream. b) Depolymerize the separated PET to more than 90%, which facilitates its posterior repolymerization and guarantees its retention in the polymer value-chain. c) Develop a zeolite-induced extrusion-based technology able to conduct continuous catalytic cracking of polyolefins, including highly contaminated PCR streams, at temperatures as low as 350 OC. d) Show, using LCA/TEA analysis that the two technologies are much more advantageous techno-economically and over the material’s life cycle than existing recycling technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Dynamic Probabilistic Risk Assessment Based Response Surface Approach for FLEX and Accident Tolerant Fuels for Medium Break LOCA Spectrum

After the Fukushima Daiichi Accident, the safety features such as accident tolerant fuel (ATF) and diverse and flexible coping strategies (FLEX) for existing nuclear fleets are being investigated by the US Department of Energy under the Light Water Reactor Sustainability Program. This research is being conducted to quantify the risk-benefit of these safety features. Dynamic probabilistic risk assessment (DPRA)-based response-surface approach has been presented to quantify the FLEX and ATF benefits by estimating the risk associated with each option. ATFs with multilayered silicon carbide (SiC), iron-chromium-aluminum, and chromium-coated zirconium cladding were considered in this study. While these ATF candidates perform better than the current zirconium cladding (Zr), they may introduce additional failure modes in some operating conditions. The fuel failure analysis modules (FAMs) were developed to investigate ATF performance. The dynamic risk assessments were performed using RAVEN, a DPRA tool, coupled with RELAP5 and FAMs. A cumulative distribution function-based index provided a mean of comparing the benefits of safety enhancements. For medium break loss of coolant accidents, FLEX operational timing window for each fuel type was estimated. Among these ATF candidates, SiC-type ATF was the most beneficial candidate for an increased safety margin than Zr-based fuel and was found to complement FLEX strategies in terms of risk and coping time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)

The characterization of wind speed distribution and the optimal assessment of wind energy potential are critical factors in selecting a suitable site for wind power plants (WPP). The Weibull distribution law has been used extensively to analyze the wind characteristics of candidate WPP sites, and to estimate the available and deliverable energy. This paper presents a comparative study of five wind energy resource assessment methods as they applied to the context of wind sites in West Sub-Saharan Africa. We investigated three numerical approaches, namely, the adaptive neuro-fuzzy inference system (ANFIS), the multilayer perceptron method (MLP), and support vector regression (SVR), to derive the distribution law of wind speeds and to optimally quantify the corresponding wind energy potential. Next, we compared these three approaches to two well-known Weibull distribution law-based methods: the empirical method of Justus (EMJ) and the maximum likelihood method (MLM). Case study results indicated that the neural network-based methods, ANFIS and MLP, yielded the most accurate distribution fits and wind energy potential estimates, and consequently, are the most recommended methods for the wind sites in Togo and Benin. The orders of magnitude of the root mean squared error (RMSE) in estimating the recoverable energy using ANFIS were, respectively, 10-4 and 10-5 for Lomé and Cotonou, while MLP achieved an RMSE order of magnitude of 10-3 for both sites.

17 WIND ENERGY↗
DOE OSTI2021

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗