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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

AMPPED LDRD: Advanced Models of the Physics & Phenomena of Electrical Discharge.

This Laboratory Directed Research and Development (LDRD) project aimed at addressing an unmet need for physics-based models that enable prediction of electrical breakdown in the presence of high voltage gradients and real-world environments. This project focused on understanding coupled plasma-surface feedback phenomena critical to electrical discharge processes and design of breakdown voltages, breakdown times, and breakdown paths. New secondary electron emission and coupled plasma-surface measurement methods and models were developed. Highly reproducible discharge experiments were developed to provide current-voltage data as a function of time, and methods were developed to condition electrodes for predictable discharge. To assess plasma-surface feedback mechanisms, new surface physics capabilities, including measurement systems for local and global photoemission yield and ion-induced secondary electron emission yield were developed. Discharge models were developed to incorporate these yields for predicting discharge behavior to understand influence of surface physics on discharge current and time evolution, and methods to accelerate these simulations were demonstrated. A key finding was that the initiation process of a non-vacuum discharge appears highly dependent on photoemission at the cathode. Advanced diagnostics were developed, including spatial electrical field imaging, spectroscopy of plasmas, and high-speed imaging of discharge event s in metal and dielectric environments. These diagnostics and improved electrical models are a step toward enabling quantitative design margins, assurance of safety architectures, and implementation of novel materials and technologies for wide ranging energy and electronics applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Laser-induced digital oxidation for copper-based flexible photodetectors

Copper oxide compounds (Cu x O) with bandgaps of 1.3–2.1 eV (CuO) and 2.1–2.6 eV (Cu 2 O) have been investigated as promising p-type semiconducting materials. Cu x O is generally obtained by deposition or thermal oxidation, but those methods are not optimal for flexible substrates. Furthermore, additional patterning steps are required to fabricate devices. In this work, we present an easy, controllable method to fabricate a metal-semiconductor-metal (MSM) photodetector using laser-induced oxidation of a thin Cu film. After laser irradiation, the Cu film is heated under ambient conditions, and this leads to a thermal oxidation reaction, in which Cu oxide is monolithically formed in the Cu film and a Cu-Cu x O-Cu MSM structure is produced. Since the laser offers localized heating, an arbitrary CuxO pattern can be written in the Cu film by spatially controlled heating. In addition, by optimizing the heating time, the laser-induced oxidation can be successfully performed even on a flexible substrate. To study the laser-induced oxidation, we examined the correlation between laser parameters and the oxidation pattern and analyzed the composition using scanning electron microscopy, Raman spectroscopy, and X-ray photoemission spectroscopy. Furthermore, we measured the transient photoresponse and employed scanning photocurrent microscopy to investigate the mechanism of carrier transport behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Why Seeding Works When Nucleation Barriers Vanish

This paper explains why seeds can speed up crystallization even when the formation of new crystal nuclei is not the slow step. In zeolite synthesis, local crystalline order can form relatively early, but the material may still appear poorly crystalline by X-ray diffraction because many small crystallites are misoriented and lack long-range coherence. Using coarse-grained molecular simulations, the study showed that seeds help solve this coherence problem. Rather than primarily lowering a nucleation barrier, seeds impose a common orientation on nearby growing crystallites, allowing them to merge into larger, better aligned domains. This accelerates the development of X-ray-detectable crystallinity even if the total amount of locally ordered material has not changed much. The work changes how induction times in zeolite synthesis should be interpreted: the delay before X-ray crystallinity appears can reflect the time needed to build long-range structural coherence, not simply the time needed to form crystalline material. This provides a new explanation for why seeding is effective in growth-limited crystallization and offers design principles for controlling crystallization rate and crystal quality.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Organic Microbial Electrochemical Transistor Monitoring Extracellular Electron Transfer

Abstract Extracellular electron transfer (EET) denotes the process of microbial respiration with electron transfer to extracellular acceptors and has been exploited in a range of microbial electrochemical systems (MESs). To further understand EET and to optimize the performance of MESs, a better understanding of the dynamics at the microscale is needed. However, the real‐time monitoring of EET at high spatiotemporal resolution would require sophisticated signal amplification. To amplify local EET signals, a miniaturized bioelectronic device, the so‐called organic microbial electrochemical transistor (OMECT), is developed, which includes Shewanella oneidensis MR‐1 integrated onto organic electrochemical transistors comprising poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) combined with poly(vinyl alcohol) (PVA). Bacteria are attached to the gate of the transistor by a chronoamperometric method and the successful attachment is confirmed by fluorescence microscopy. Monitoring EET with the OMECT configuration is achieved due to the inherent amplification of the transistor, revealing fast time‐responses to lactate. The limits of detection when using microfabricated gates as charge collectors are also investigated. The work is a first step toward understanding and monitoring EET in highly confined spaces via microfabricated organic electronic devices, and it can be of importance to study exoelectrogens in microenvironments, such as those of the human microbiome.

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

The exact exchange–correlation potential in time-dependent density functional theory: Choreographing electrons with steps and peaks

The time-dependent exchange–correlation potential has the unusual task of directing fictitious non-interacting electrons to move with exactly the same probability density as true interacting electrons. This has intriguing implications for its structure, especially in the non-perturbative regime, leading to step and peak features that cannot be captured by bootstrapping any ground-state functional approximation. Here, we review what has been learned about these features in the exact exchange–correlation potential of time-dependent density functional theory in the past decade or so and implications for the performance of simulations when electrons are driven far from any ground state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Interfacial pressure improves calendar aging of lithium metal anodes

Lithium (Li) metal is a promising anode because its theoretical specific capacity is approximately ten times larger than graphite. However, Li anodes suffer from long-term capacity fade due to Li stranding (becoming electronically disconnected) and electrolyte decomposition. Applied interfacial pressure has been shown to improve Li anode cycling, likely due to reincorporating stranded or “dead” Li into the anode. Calendar aging can also lead to Li capacity loss due to electrolyte decomposition/Li corrosion and the formation of stranded Li. Some research suggests that calendar aging during cycling results in reversible capacity losses due to Li stranding and reconnection. We here investigate the effect of applied interfacial pressure on Li anode calendar aging during cycling with incorporated rest steps in a localized high-concentration electrolyte (LHCE) to understand if pressure can mitigate stranded Li formation during rest by manipulating the Li morphology. Pouch cells exhibit more stable cycling and denser Li deposits between 10 kPa and 1,000 kPa of applied pressure compared to no applied pressure. Despite drops in CE during periodic rest cycles, the average cumulative lost capacity and average coulombic efficiency (CE) of cells over 50 cycles show that cells aged with incorporated rest steps perform similarly to cells cycled without added rests. This similar average CE suggests that dead Li is largely responsible for drops in CE during rest rather than irreversible Li corrosion and that the dead Li can be reconnected in subsequent cycling. The addition of a lithiophilic ZnO coating to the Cu working electrode increases the adhesion and coverage of Li deposits at low pressures and improves CE during the first cycle.

25 ENERGY STORAGE↗
DOE OSTI2021

Constrained Block Nonlinear Neural Dynamical Models

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aerodynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.

Skomski, Elliott↗
DOE OSTI2024

PMU Data Quality and Sensor Health Monitoring

Phasor Measurement Units (PMUs) play a critical role in the evolution of the electric power industry by providing high-precision, real-time monitoring of essential power system metrics. However, effectively detecting abnormalities and critical events from PMU data is a complex task, complicated by intricate temporal patterns, a scarcity of labeled data for training algo- rithms, and constraints on online computational power. In this study, we apply TranAD, an innovative algorithm that combines transformer architectures with the refinement of adversarial learning, to both synthetic and real-world PMU datasets for developing a data quality and sensor online health monitoring platform for utilities. Our findings reveal that TranAD not only provides efficient detection and localization but also enhances the detail with which abnormalities are detected, marking a a significant step forward in the field of clean data acquisition processes for power system monitoring

deep neural network, machine learning (ML)↗
DOE OSTI2023

The Impacts of Developing a Port Network for Floating Offshore Wind Energy on the West Coast of the United States

Floating offshore wind is a pre-commercial industry with the potential for significant market growth on the U.S. West Coast in the near future; however, significant investment in port infrastructure will be required to enable the industry to progress from demonstration projects to efficient and cost effective commercial deployment. Developing a system of ports that can enable commercial-scale floating wind development on the West Coast of the United States will require significant levels of funding and coordination between governments, industry, ports, and local communities. A critical first step to strategically planning these resources is understanding the number of ports (and associated investment) that would be required to support different phases of offshore wind projects, including manufacturing, installation, and operation. But simply tallying up these costs is not sufficient to understand how a robust network of ports could impact local communities, the environment, workforce development, the offshore wind industry, and the West Coast region as a whole. In this report, the authors present analyses and perspectives related to port development in California, Oregon, and Washington. We describe the requirements for floating offshore wind ports that conduct manufacturing, installation, and/or service activities, and estimate the investment and time frames required to construct these ports at suitable locations in West Coast states. We develop indicators for the vulnerability and workforce accessibility of coastal communities and consider the potential risks and benefits associated with port development in these locations. We model how the proximity of an offshore wind project to installation and operations ports can impact the levelized cost of energy of the project, and then consider how these costs could be affected by local or foreign supply chains. We build upon these analyses to develop scenarios with increasing levels of offshore wind deployment and port assets on the West Coast and show how these ports could enable deployment goals to be achieved. Finally, we draw upon outreach with key floating wind stakeholders to summarize five key challenges that will need to be overcome to develop a comprehensive port network, and present potential approaches that could help to address these obstacles.

17 WIND ENERGY↗
DOE OSTI2024

Electric-Field-Driven Localization of Molecular Nanowires in Wafer-Scale Nanogap Electrodes

As integrated circuits continue to scale toward the atomic limit, bottom-up processes, such as epitaxial growth, have come to feature prominently in their fabrication. At the same time, chemistry has developed highly tunable molecular semiconductors that can perform the functions of ultimately scaled circuit components. Hybrid techniques that integrate programmable structures comprising molecular components into devices however are sorely lacking. Here we demonstrate a wafer-scale process that directs the localization of a conductive polymer, M w = 20 kg mol –1 polyaniline, from dilute solutions into 50 nm vertical nanogap device architectures using electric-field-driven self-assembly. The resulting metal–polymer–metal junctions were characterized by electron microscopy, Raman spectroscopy and transport measurements demonstrating that our technique is highly selective, assembling conductive polymers only in electrically activated nanogaps. Our results represent a step toward scalable hybrid nanoelectronics that seamlessly integrate established lithographic top-down fabrication with bottom-up synthesized molecular functional circuit components.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017

Abstract. Data on irrigation patterns and trends at field-level detail across broad extents are vital for assessing and managing limited water resources. Until recently, there has been a scarcity of comprehensive, consistent, and frequent irrigation maps for the US. Here we present the new Landsat-based Irrigation Dataset (LANID), which is comprised of 30 m resolution annual irrigation maps covering the conterminous US (CONUS) for the period of 1997–2017. The main dataset identifies the annual extent of irrigated croplands, pastureland, and hay for each year in the study period. Derivative maps include layers on maximum irrigated extent, irrigation frequency and trends, and identification of formerly irrigated areas and intermittently irrigated lands. Temporal analysis reveals that 38.5×106 ha of croplands and pasture–hay has been irrigated, among which the yearly active area ranged from ∼22.6 to 24.7×106 ha. The LANID products provide several improvements over other irrigation data including field-level details on irrigation change and frequency, an annual time step, and a collection of ∼10 000 visually interpreted ground reference locations for the eastern US where such data have been lacking. Our maps demonstrated overall accuracy above 90 % across all years and regions, including in the more humid and challenging-to-map eastern US, marking a significant advancement over other products, whose accuracies ranged from 50 % to 80 %. In terms of change detection, our maps yield per-pixel transition accuracy of 81 % and show good agreement with US Department of Agriculture reports at both county and state levels. The described annual maps, derivative layers, and ground reference data provide users with unique opportunities to study local to nationwide trends, driving forces, and consequences of irrigation and encourage the further development and assessment of new approaches for improved mapping of irrigation, especially in challenging areas like the eastern US. The annual LANID maps, derivative products, and ground reference data are available through https://doi.org/10.5281/zenodo.5548555 (Xie and Lark, 2021a).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Monitoring water quality in the lower Kansas River using remote sensing

Abstract We demonstrate how to combine remote sensing data from satellite imagery (Sentinel‐2) with in situ water quality gauging (USGS Super Gages and the Gybe hyperspectral radiometer) to create spatially dense maps of water quality parameters (chlorophyll‐a concentration, turbidity, and nitrate plus nitrite concentration) along the lower Kansas River. The water quality maps are created using locally tuned models of the target water quality parameters, and this study describes the steps used to design, calibrate, and validate the empirical correlations. Water quality parameters such as chlorophyll‐a concentration are correlated with well‐studied absorption and scattering features in the visible spectrum (roughly 400–700 nm). Nutrients (such as nitrate plus nitrite concentration) lack strong absorption features in the visible spectrum, and in those cases we describe a novel surrogate data modeling approach that identifies overlapping water parcels between the in situ gauging and the remote sensing imagery. Measurements from the overlapping water parcels yield excellent correlations () for the target water quality parameters for limited windows of time (or limited sections of river reaches). Examples are provided illustrating how the water quality maps can be used to track river inputs from ungauged sources (such as creeks), or reveal the mixing patterns at the confluences.

Tufillaro, Nicholas↗
DOE OSTI2024

Generation and Life Cycle of Solar Spicules

The physical mechanism for the creation of solar spicules is proposed with three stages of their life cycle. It is assumed that at stage I the density hump is formed locally in the x-y plane in the lower chromosphere in the presence of temperature gradients of electrons and ions along the z-axis (the vertical direction). In this region, the density structure of quasi-neutral (n i ≃ n e = n) plasma after taking birth is accelerated in the vertical direction owing to the thermal force F th ∝ ∇n(x, y, t) × (∇T e + ∇T i ). The exact time-dependent analytical solution of two-fluid plasma equations is presented assuming that density is maximum at the center of the density structure and decays away from it gradually. The 2D density structure is created as a step function H(t) in time at the bottom of the chromosphere, and consequently, the vertical plasma velocity turns out to be the ramp function of time R(t) = tH(t), whereas the source term S(x, y, t) for the density follows the delta function δ(t) form. The upward acceleration a = a(x, y) $\hat{z}$ produced in this density structure is greater than the downward constant solar acceleration −g ⊙ in the chromosphere. In the transition region, the temperature gradients are steeper; therefore, the upward acceleration increases in magnitude g ⊙ ≪ a and the density hump spends less time there. This is stage II of its life cycle. In stage III, the density structure enters into the corona, where the gradients of temperatures vanish and the structure decelerates to zero velocity under the action of the solar gravitational force.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

One dimensional wormhole corrosion in metals

Corrosion is a ubiquitous failure mode of materials. Often, the progression of localized corrosion is accompanied by the evolution of porosity in materials previously reported to be either three-dimensional or two-dimensional. However, using new tools and analysis techniques, we have realized that a more localized form of corrosion, which we call 1D wormhole corrosion, has previously been miscategorized in some situations. Using electron tomography, we show multiple examples of this 1D and percolating morphology. To understand the origin of this mechanism in a Ni-Cr alloy corroded by molten salt, we combined energy-filtered four-dimensional scanning transmission electron microscopy and ab initio density functional theory calculations to develop a vacancy mapping method with nanometer-resolution, identifying a remarkably high vacancy concentration in the diffusion-induced grain boundary migration zone, up to 100 times the equilibrium value at the melting point. Deciphering the origins of 1D corrosion is an important step towards designing structural materials with enhanced corrosion resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Control of Quantized Spontaneous Emission from Single GaAs Quantum Dots Embedded in Huygens’ Metasurfaces

Advancements in photonic quantum information systems (QIS) have driven the development of high-brightness, on-demand, and indistinguishable semiconductor epitaxial quantum dots (QDs) as single photon sources. Strain-free, monodisperse, and spatially sparse local-droplet-etched (LDE) QDs have recently been demonstrated as a superior alternative to traditional Stranski–Krastanov QDs. However, integration of LDE QDs into nanophotonic architectures with the ability to scale to many interacting QDs is yet to be demonstrated. Here, we present a potential solution by embedding isolated LDE GaAs QDs within an Al 0.4 Ga 0.6 As Huygens’ metasurface with spectrally overlapping fundamental electric and magnetic dipolar resonances. We demonstrate for the first time a position- and size-independent, 1 order of magnitude increase in the collection efficiency and emission lifetime control for single-photon emission from LDE QDs embedded within the Huygens’ metasurfaces. Our results represent a significant step toward leveraging the advantages of LDE QDs within nanophotonic architectures to meet the scalability demands of photonic QIS.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Plasma performance and operational space with an RMP-ELM suppressed edge

Abstract The operational space and global performance of plasmas with edge-localized modes (ELMs) suppressed by resonant magnetic perturbations (RMPs) are surveyed by comparing AUG, DIII-D, EAST, and KSTAR stationary operating points. RMP-ELM suppression is achieved over a range of plasma currents, toroidal fields, and RMP toroidal mode numbers. Consistent operational windows in edge safety factor are found across devices, while windows in plasma shaping parameters are distinct. Accessed pedestal parameters reveal a quantitatively similar pedestal-top density limit for RMP-ELM suppression in all devices of just over 3 × 10 19 m −3 . This is surprising given the wide variance of many engineering parameters and edge collisionalities, and poses a challenge to extrapolation of the regime. Wide ranges in input power, confinement time, and stored energy are observed, with the achieved triple product found to scale like the product of current, field, and radius. Observed energy confinement scaling with engineering parameters for RMP-ELM suppressed plasmas are presented and compared with expectations from established H and L-mode scalings, including treatment of uncertainty analysis. Different scaling exponents for individual engineering parameters are found as compared to the established scalings. However, extrapolation to next-step tokamaks ITER and SPARC find overall consistency within uncertainties with the established scalings, finding no obvious performance penalty when extrapolating from the assembled multi-device RMP-ELM suppressed database. Overall this work identifies common physics for RMP-ELM suppression and highlights the need to pursue this no-ELM regime at higher magnetic field and different plasma physical size.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗