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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
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 OSTI2018

Ionically Paired Layer-by-Layer Hydrogels: Water and Polyelectrolyte Uptake Controlled by Deposition Time

Despite intense recent interest in weakly bound nonlinear (“exponential”) multilayers, the underlying structure-property relationships of these films are still poorly understood. This study explores the effect of time used for deposition of individual layers of nonlinearly growing layer-by-layer (LbL) films composed of poly(methacrylic acid) (PMAA) and quaternized poly-2-(dimethylamino)ethyl methacrylate (QPC) on film internal structure, swelling, and stability in salt solution, as well as the rate of penetration of invading polyelectrolyte chains. Thicknesses of dry and swollen films were measured by spectroscopic ellipsometry, film internal structure—by neutron reflectometry (NR), and degree of PMAA ionization—by Fourier-transform infrared spectroscopy (FTIR). The results suggest that longer deposition times resulted in thicker films with higher degrees of swelling (up to swelling ratio as high as 4 compared to dry film thickness) and stronger film intermixing. The stronger intermixed films were more swollen in water, exhibited lower stability in salt solutions, and supported a faster penetration rate of invading polyelectrolyte chains. These results can be useful in designing polyelectrolyte nanoassemblies for biomedical applications, such as drug delivery coatings for medical implants or tissue engineering matrices.

36 MATERIALS SCIENCE↗
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↗
DOE OSTI2018

2018 Technology Innovation Program Carbon Nanomaterial Enabled Ultra Conductive Metal Composites

Carbon nanomaterial enabled UCC metal composites combine ORNL’s strength in nanomaterial research, electron microscopy, and scalable material processing together with novel methods to produce a scalable assembly of Cu-CNT multilayer composites. These composites enable higher electrical conductivities than that of pure Cu. Using scalable, cost-effective, and commercially viable solution-based processing methods, our technological platform achieves a high degree of CNT alignment and coating stability, which demonstrates a novel technological platform to reproducibly produce carbon nanomaterial enabled ultra conductive conductors for a broad range of electrical systems and industrial applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Performance of Low-Complexity Spectrally Selective One-Dimensional Mirrors for Photovoltaic Thermal Management: Preprint

Operation at elevated temperatures is detrimental to the performance of crystalline Si solar modules. One method of reducing module operating temperature is selective reflection of sub-band gap photons, which can otherwise only be absorbed parasitically. We numerically optimize the design of a series of multilayer photonic mirrors based on real materials using a previously developed optimization routine. Combined ray tracing and finite element simulations reveal the ability of each mirror to increase energy yield and decrease operating temperature. The best design outperforms a conventional glass antireflection coating, contains only nine layers, and maintains performance regardless of geographic location.

14 SOLAR ENERGY↗
DOE OSTI2025

Evaluating Direct and Indirect Influence on EV Charging Stations Across the US

The adoption of new technology for electric vehicles (EV) and mobility applications can bring underappreciated vulnerabilities to the power grid. One area of potential fraud and adversarial influence is through the business ecosystem of startups that own and deploy EV technology. Yet, there are no models or analyses that map the network of organizations and people that have direct and indirect influence over technologies currently deployed in the grid. To fill this gap, we develop a multilayer network model to measure direct and indirect influence on EV charging stations. First, we create and adversarial socio-technical network (ASTN) model via a data fusion pipeline for different US regions of interest (ROI). Then, we develop an integrated ASTN for Chicago, Los Angeles, New York, and Philadelphia. We rank EV charging companies direct influence within each geographic region as well as indirect influence via social network analysis. While some companies have strong direct and indirect influence (i.e., ChargePoint) others show a mismatch between their influence over charging stations and their position within the social network. For example, Tesla has strong direct influence on stations and weak indirect influence over competitors. In contrast, 7Charge has weak direct influence over stations, but strong indirect influence over competitors.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2017

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. As such, in this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.

58 GEOSCIENCES↗
DOE OSTI2018

A Porphyrin Spin Qubit and Its 2D Framework Nanosheets

Abstract Molecular spin qubits have been shown to reach sufficiently long quantum coherence times to envision their use as hardware in quantum processors. These will however require their implementation in hybrid solid‐state devices for which the controlled localization and homogeneous orientation of the molecular qubits will be necessary. An alternative to isolated molecules that can ensure these key aspects is 2D framework in which the qubit would act as node. In this work, it is demonstrated that the isolated metalloporphyrin [Cu(H 4 TCPP)] molecule is a potential spin qubit, and maintains similar quantum coherence as node in a 2D [{CuTCPP}Zn 2 (H 2 O) 2 ] metal–organic framework. Mono‐ and multilayer deposits of nanosheets of a similar 2D framework are then successfully formed following a modular method based on Langmuir–Schaefer conditions. The orientation of the {CuTCPP} qubit nodes in these nanosheets is homogeneous parallel to the substrate. These nanosheets are also formed with a control over the qubit concentration, i.e., by dilution with the unmetallated porphyrin. Eventually, 2D nanosheets are formed in situ directly on a substrate, through a simple protocol devised to reproduce the Langmuir–Schaefer conditions locally. Altogether these studies show that 2D spin qubit frameworks are ideal components to develop a hybrid quantum computing architecture.

Urtizberea, Ainhoa↗
DOE OSTI2018

Competition between Exceptionally Long‐Range Alkyl Sidechain Ordering and Backbone Ordering in Semiconducting Polymers and Its Impact on Electronic and Optoelectronic Properties

Abstract Intra‐ and intermolecular ordering greatly impacts the electronic and optoelectronic properties of semiconducting polymers. The interrelationship between ordering of alkyl sidechains and conjugated backbones has yet to be fully detailed, despite much prior effort. Here, the discovery of a highly ordered alkyl sidechain phase in six representative semiconducting polymers, determined from distinct spectroscopic and diffraction signatures, is reported. The sidechain ordering exhibits unusually large coherence lengths (≥70 nm), induces torsional/twisting backbone disorder, and results in a vertically multilayered nanostructure with ordered sidechain layers alternating with disordered backbone layers. Calorimetry and in situ variable temperature scattering measurements in a model system poly{4‐(5‐(4,8‐bis(3‐butylnonyl)‐6‐methylbenzo[1,2‐b:4,5‐b′]dithiophen‐2‐yl)thiophen‐2‐yl)‐2‐(2‐butyloctyl)‐5,6‐difluoro‐7‐(5‐methylthiophen‐2‐yl)‐2H‐benzo[d][1,2,3]triazole} (PBnDT‐FTAZ) clearly delineate this competition of ordering that prevents simultaneous long‐range order of both moieties. The long‐range sidechain ordering can be exploited as a transient state to fabricate PBnDT‐FTAZ films with an atypical edge‐on texture and 2.5× improved field‐effect transistor mobility. The observed influence of ordering between the moieties implies that improved molecular design can produce synergistic rather than destructive ordering effects. Given the large sidechain coherence lengths observed, such synergistic ordering should greatly improve the coherence length of backbone ordering and thereby improve electronic and optoelectronic properties such as charge transport and exciton diffusion lengths.

Carpenter, Joshua H.↗
DOE OSTI2019

Metallo‐Hydrogel‐Assisted Synthesis and Direct Writing of Transition Metal Dichalcogenides

Abstract Two dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted interest for their compelling nanoscale new properties and numerous potential applications including fast optoelectronic devices, ultrathin photovoltaics, and high‐performance catalysts. Large‐scale growth of uniform TMDC materials is essential for investigating their physics and for their integration into devices. However, the wafer scale deposition of TMDCs on arbitrary nonselective substrates is still beyond the current state‐of‐the‐art. In this article, a method to synthesize layered TMDCs (MoS 2 and WS 2 ) at the wafer‐scale by sulfurization of transition metal ions (Mo 5+ and W 6+ ) in a gelatin template (metallo‐hydrogel) is reported. This process is adaptable to versatile substrates, including amorphous silicon oxide, high‐temperature quartz, and silicon. Although the products are nominally few layer materials, direct band photoluminescent (≈1.8 eV), similar to single‐ or decoupled multilayer MoS 2 is observed. Finally, the solution‐based deposition enables contact printing of TMDC channels to be useable for device applications including thin film transistors with printed silver contacts using the same process.

Zang, Xining↗
DOE OSTI2019

Atomic Insight into Thermolysis-Driven Growth of 2D MoS 2

Understanding and controlling the transformations of transition metal dichalcogenides (TMDs) from amorphous precursors into two-dimensional (2D) materials is important for guiding synthesis, directing fabrication, and tailoring functional properties. Here, the combined effects of thermal energy and electron beam irradiation are explored on the structural evolution of 2D MoS 2 flakes through the thermal decomposition of a (NH 4 ) 2 MoS 4 precursor inside an ultrahigh vacuum (10 -9 Torr) scanning transmission electron microscope (STEM). The influence of reaction temperature, growth substrate, and the initial precursor morphology on the resulting 2D MoS 2 flake morphology, edge structures, and point defects are explored. Although thermal decomposition occurs extremely fast at elevated temperatures and is difficult to capture using current STEM techniques, electron beam irradiation can induce local transformations at lower temperatures, enabling direct observation and interpretation of critical growth steps including oriented attachment and transition from single- to multilayer structures at atomic resolution. An increase in the number of layers of the MoS 2 flakes from island growth is investigated using electron beam irradiation. These findings provide insight into the growth mechanisms and factors that control the synthesis of few-layer MoS 2 flakes through thermolysis and toward the prospect of atomically precise control and growth of 2D TMDs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Printing 2D Conjugated Polymer Monolayers and Their Distinct Electronic Properties

Abstract Recently, 2D monolayer films of conjugated polymers have gained increasing attention owing to the preeminence of 2D inorganic films that exhibit unique optoelectronic and mechanical properties compared to their bulk analogs. Despite numerous efforts, crystallization of semiconducting polymers into highly ordered 2D monolayer films still remains challenging. Herein, a dynamic‐template‐assisted meniscus‐guided coating is utilized to fabricate continuous, highly ordered 2D monolayer films of conjugated polymers over a centimeter scale with enhanced backbone π–π stacking. In contrast, monolayer films printed on solid substrates confer upon the 1D fiber networks strong alkyl side‐chain stacking at the expense of backbone packing. From single‐layers to multilayers, the polymer π‐stacks change from edge‐on to bimodal orientation as the film thickness reaches ≈20 nm. Spectroscopic and cyclic voltammetry analysis reveals an abrupt increase in J‐aggregation and absorption coefficient and a decrease in bandgap and highest occupied molecular orbital level until critical thickness, possibly arising from the straightened polymer backbone. This is corroborated by an abrupt increase in hole mobility with film thickness, reaching a maximum of 0.7 cm 2 V −1 s −1 near the critical thickness. Finally, fabrication of chemical sensors incorporating polymer films of various thicknesses is demonstrated, and an ultrahigh sensitivity of the ≈7 nm thick ultrathin film (bilayers) to 1 ppb ammonia is shown.

Kafle, Prapti↗
DOE OSTI2021

RKKY Exchange Bias Mediated Ultrafast All-Optical Switching of a Ferromagnet

The discovery of ultrafast helicity-independent all-optical switching (HI-AOS), as well as picosecond all-electrical switching of a ferrimagnet, has inspired the ultrafast spintronics community to explore ultrafast switching of a ferromagnet to achieve practical ultrafast storage and memory devices. Two explored mechanisms of HI-AOS of a ferromagnet in ferromagnet-ferrimagnet heterostructure are: a) exploiting the indirect exchange coupling with and b) injection of non-local spin current originated from a switching ferrimagnet. Here, in this manuscript, exchange mediated HI-AOS of a Ruderman–Kittel–Kasuya–Yosida (RKKY) exchange coupled “[Co/Pt]-multilayers/Pt spacer/CoGd” heterostructure is demonstrated. The authors have measured layer-resolved static magnetic properties, single-shot HI-AOS, and magnetization dynamics of the ferromagnetic Co/Pt multilayers (MLs), that are ferromagnetically or antiferromagnetically coupled with ferrimagnetic CoGd layers. Time-resolved magnetization dynamics reveal a 3.5 ps switching time of the Co/Pt MLs, which is the fastest switching of a ferromagnet reported to date. Employing an extended microscopic three-temperature model, the temporal dynamics of the exchange coupled ferromagnet–ferrimagnet heterostructure are simulated, qualitatively and quantitatively explaining the experimental switching phenomena. This work experimentally as well as theoretically establishes the mechanism of exchange mediated all-optical switching of ferromagnet-ferrimagnet heterostructures, which can be integrated with a magnetic tunnel junction for efficient reading after ultrafast energy-efficient switching.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Spontaneous Asymmetry of Chiral Magnetic Domains Within a Magnetic Field

Abstract Chiral magnetic domains are topological spin textures in which the Dzyaloshinskii–Moriya interaction assigns a given chirality to the domain walls. Notably, despite rapid progress in chiral magnetic research, one fundamental issue that remains unclear is how the chirality of chiral magnetic domains change as a magnetic field deforms the spin texture. Using spin‐polarized low energy electron microscopy, the evolution of Fe/Ni chiral magnetic stripe domains are investigated in single‐crystalline Fe/Ni/Cu/Co/Cu(001) multilayers in which the interlayer magnetic coupling between the Co and Fe/Ni films serves as an in‐plane magnetic field. Contrary to theoretical works, it is found that the chirality of the Néel walls results in a parallel alignment of the magnetic stripes with the in‐plane magnetic field direction. The transformation of chiral Néel walls into achiral Bloch walls can be precisely controlled by tuning the Cu spacer layer thickness. In addition, the domain wall exhibits a spontaneous asymmetry within the in‐plane magnetic field, leading to an unbalanced chirality between the left‐handed and right‐handed Bloch walls. These new results foster a better understanding of the chiral domain properties within a magnetic field.

Li, Qian↗
DOE OSTI2023

Deterministic Current–Induced Perpendicular Switching in Epitaxial Co/Pt Layers without an External Field

Current–induced spin–orbit torques (SOTs) have emerged as a powerful tool to control magnetic elements and non–uniform magnetic textures such as domain walls and skyrmions. SOT–induced switching of perpendicular magnetization generally requires an external field to break the rotational symmetry of the spin–orbit effective fields responsible for the deterministic reversal. The proposed mechanisms to eliminate this requirement often rely on complex multilayer structures that necessitate laborious optimization in the material and spin transport properties, making them less attractive for applications. Herein, current–induced, external field–free switching of an epitaxial MgO/Pt/Co trilayer with an extremely large perpendicular anisotropy in excess of 3 Tesla is reported. It is found that switching occurs due to the interplay of strong SOTs, local anisotropy fluctuations, and the Dzyaloshinkii–Moriya interaction inherent to this epitaxial system. Finally, given that these layers constitute the base stack of a magnetic tunnel junction, this switching mechanism offers the most technologically viable path toward devices such as field–free SOT–based magnetic random–access memories.

36 MATERIALS SCIENCE↗