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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses of fuels: A review

Gas chromatography (GC) is undoubtedly the analytical technique of choice for analyzing the composition of petroleum-based fuels. Over the past twenty years, as comprehensive two-dimensional gas chromatography (GC×GC) has evolved, fuel analysis has often been highlighted in scientific reports, as their complexity allows for illustration of the impressive peak capacity gains afforded by GC×GC. Indeed, several research groups in recent years have applied GC×GC and chemometrics to demonstrate the potential of these analytical tools to address important compliance (tax evasion, tax credits, physical quality standards) and forensic (arson investigations, oil spills) applications involving fuels. None the less, routine use of GC×GC in forensic laboratories has been limited largely by (1) legal and regulatory guidelines, (2) lack of chemometrics training, and (3) concerns about the reproducibility of GC×GC. The goal of this review is to highlight recent advances in one-dimensional GC (1D-GC) and GC×GC analyses of fuels for compliance and forensic applications, in an effort to assist scientists in overcoming the aforementioned hindrances. An introduction to 1D-GC principles, GC×GC technology (column stationary phases and modulators) and several chemometric techniques will be provided. More specifically, chemometric techniques will be broken down into (1) signal pre-processing, (2) peak decomposition, identification and quantification, and (3) classification and pattern recognition. Examples of compliance and forensic applications will be discussed, with particular emphasis on the demonstrated success of the employed chemometric techniques. Overall, this review will hopefully make 1D-GC and GC×GC coupled with chemometric data analysis tools more accessible to the larger scientific community, and aid in eventual widespread standardization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Energy prediction using spatiotemporal pattern networks

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shown to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Towards COP27: Decarbonization patterns of residential building in China and India

As the two largest emerging emitters with the highest growth in operational carbon emissions from residential buildings, the historical emission patterns and decarbonization efforts of China and India warrant further exploration. This study aims to be the first to present a carbon intensity model considering end-use performances, assessing the operational decarbonization progress of residential building in India and China over the past two decades using the newest decomposing structural decomposition approach. Results indicate (1) the annual operational carbon intensity increased by 1.4% and 2.5% in China and India, respectively, between 2000 and 2020. Household expenditure-related energy intensity and emission factors were crucial in decarbonizing residential buildings. (2) Building electrification played a significant role in decarbonizing space cooling (–87.7 in China and – 130.2 kg of carbon dioxide (kgCO 2 ) per household in India) and appliances (~ –169.7 in China and ~ –43.4 kgCO 2 per household in India). (3) China and India collectively decarbonized 1498.3 and 399.7 M-tons of CO 2 in residential building operations, respectively. In terms of decarbonization intensity, India (164.8 kgCO 2 per household) nearly caught up with China (182.5 kgCO 2 per household) in 2020 and is expected to surpass China in the upcoming years, given the country's robust annual growth rate of 7.3%. Overall, this study provides an effective data-driven tool for investigating the building decarbonization potential in China and India, and offers valuable insights for other emerging economies seeking to decarbonize residential buildings in the forthcoming COP28 1 age.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A multi-phase-field model of topological pattern formation during electrochemical dealloying of binary alloys

Porous metal structures produced by electrochemical dealloying have been extensively studied for use in various applications that require high surface areas. A new comprehensive multi-phase-field (MPF) model is proposed to study topological porous patterns formed by spontaneously etching a bulk binary alloy that involves electrochemical reactions, bulk and surface diffusion, ion transport, applied electrode potential, and charge conservation. The governing equations for the alloy-porous cluster-electrolyte system account for a generalized Butler-Volmer electrochemical reaction and are in accordance with the classical nucleation theory. Based on a quantitative examination of the effects of electrode potential and precursor composition, the simulation results reproduce typical phenomena including passive surface dealloying, active porosity evolution, critical potential, and characteristic length scale in two-dimension (2-D) and three-dimension (3-D). To consider more complicated dealloying systems, the evolutions of a bimodal porous metal structure, nanocomposite, and nested porous network with a structured hierarchy are investigated. Finally, the proposed model can be a useful tool for understanding and predicting the morphology evolution of diverse porous structures during electrochemical dealloying.

Hierarchical porous structure↗
DOE OSTI2020

Vertical meteorological patterns and their impact on the energy demand of tall buildings

In accordance with the developing economy and growing population, an increasing number of tall buildings have been constructed over the last 20 years. In 2017, there were 144 new buildings worldwide that were 200 m or greater in height; 50% of these buildings were in China. Due to the vertical gradient impact of meteorological parameters, the energy performance of tall buildings differs from that of general buildings. Few studies exist on vertical meteorological changes using measured data at different heights. Most studies on dynamic energy simulation simulate meteorological parameters using models. This study explores vertical meteorological patterns using hourly dry-bulb temperature, relative humidity, and wind speed data from 2007 to 2017 for a 325 m meteorological tower in Beijing. The temperature decreased significantly with increasing altitude (~0.9 °C per 100 m), while the daily temperature difference decreased with increasing altitude. The absolute humidity did not change significantly with height. The wind speed increased with altitude at approximately 2 m/s per 100 m. The building simulation showed that the annual heating load at a height of 320 m increased by 85% from that at 8 m; the annual cooling load decreased by 20%. Such significant differences in thermal loads for 300-m-tall buildings confirm the need to consider vertical meteorological differences in building performance simulations for tall buildings. A greater number of measurement points at different heights improve the simulation accuracy. Finally, guidance on selecting the heights for vertical metrological measurements is provided based on the influences of building thermal loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns

The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extraction method and the problem of multicollinearity and interpretability for regression and machine learning models), this paper proposes an association rule mining based quantitative analysis approach of household characteristics impact on residential ECPs trying to address them together. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to create seasonal TECP of each individual customer only for weekdays. K-means is then adopted to group all the TECPs into several clusters. An enhanced Apriori algorithm is proposed to reveal the relationships between TECPs and thirty five factors covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Results of the case study using 3326 records containing smart metering data and survey information in Ireland suggest that socio-demographic and cooking related factors such as employment status, occupants and whether cook by electricity have strong significant associations with TECPs, while attitudes related factors almost have no effect on TECPs. The results also indicate that those households with more than one person are more likely to change ECP across seasons. The proposed approach and the findings of this study can help to support decisions about how to reduce electricity consumption and CO2 emissions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Multilevel peel-off patterning of a prototype semitransparent organic photovoltaic module

Semitransparent organic photovoltaics (ST-OPVs) with applications to power generating windows have shown substantial increases in power conversion efficiency (PCE) and average photopic transmission (APT) at the laboratory scale. Here, the demonstration of similarly efficient, large-scale ST-OPV modules with geometric fill factors (GFF) approaching 100%, however, remains a challenge. Here, we employ a multilevel peel-off patterning method that can achieve micron-scale resolution without exposing chemically sensitive organic materials to solvents. Eight, 4 cm × 0.4 cm cells are connected in series to realize a prototype ST-OPV module with GFF = 95.8%, with PCE = 7.3 ± 0.2% under simulated AM 1.5G illumination at 1 sun intensity, APT = 41.8 ± 1.4%, and a light utilization efficiency of LUE = 3.1 ± 0.1%. A neutral color STOPV module is also demonstrated with 1.7 ± 0.1% and International Commission on Illumination (CIE) LAB coordinates of (L*, a*, b*) = (53.7, -1.9, -3.9).

14 SOLAR ENERGY↗
DOE OSTI2016

Using the automata processor for fast pattern recognition in high energy physics experiments. A proof of concept

Here, we explore the Micron Automata Processor (AP) as a suitable commodity technology that can address the growing computational needs of pattern recognition in High Energy Physics (HEP) experiments. A toy detector model is developed for which an electron track confirmation trigger based on the Micron AP serves as a test case. Although primarily meant for high speed text-based searches, we demonstrate a proof of concept for the use of the Micron AP in a HEP trigger application.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Surface-plasmon enhanced photoemission of a silver nano-patterned photocathode

Here, nano-patterned photocathodes (NPC) take advantage of plasmonic effects to resonantly increase absorption of light and localize electromagnetic field intensity on metal surfaces leading to surface-plasmon enhanced photoemission. In this paper, we report the status of NPC research at UCLA including in particular the optimization of the dimensions of a nanohole array on a silver wafer to enhance plasmonic response at 800 nm light, the development of a spectrally-resolved reflectivity measurement setup for quick nanopattern validation, and of a novel cathode plug to enable high power tests of NPCs on single crystal substrates in a high gradient radiofrequency gun.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

GaAs Thermophotovoltaic Patterned Dielectric Back Contact Devices with Improved Sub-Bandgap Reflectance

We demonstrate GaAs thermophotovoltaic (TPV) devices with a patterned dielectric back contact (PDBC) architecture, featuring a dielectric spacer between the semiconductor and back metal contact over most of the back surface for high reflectance, and metal point contacts over a smaller area for electrical conduction. In the TPV application, high sub-bandgap reflectance is needed to reflect unused sub-bandgap photons to the thermal emitter to minimize energy losses in this portion of the thermal spectrum. We explore different PDBC fabrication processes with SU-8 and SiO2 dielectric spacer layers to maximize sub-bandgap reflectance while minimizing series resistance to increase TPV conversion efficiency. We successfully demonstrate GaAs SU-8 PDBC TPV devices with 2200 degrees C blackbody-weighted sub-bandgap reflectance of 94.9% and 96.5% with and without a front metal grid, respectively. This is 0.7% and 2.3% (absolute) higher than the mean sub-bandgap reflectance of 94.2% for GaAs baseline TPV devices with 100% Au back contact with front metal grid. Lower sub-bandgap reflectance in TPV devices with front grids indicates the front grid induces light scattering leading to additional parasitic absorption in the TPV device. We also show that for higher contact coverage fractions, the PDBC reflectance cannot in general be treated by a linear interpolation using simple 1D transfer matrix method modeling and should be treated instead as a diffraction grating by solving Maxwell's equations in 3D.

energy storage↗
DOE OSTI2016

Measurement of Heavy Ion Irradiation Induced In-Plane Strain in Patterned Face-Centered-Cubic Metal Films: An in Situ Study

Nanocrystalline Ag, Cu, and Ni thin films and their coarse grained counterparts are patterned in this paper using focused ion beam and then irradiated by Kr ions within an electron microscope at room temperature. Irradiation induced in-plane strain of the films is measured by tracking the location of nanosized holes. The magnitude of the strain in all specimens is linearly dose-dependent and the strain rates of nanocrystalline metals are significantly greater as compared to that of the coarse grained metals. Finally, real-time microscopic observation suggests that substantial grain boundary migration and grain rotation are responsible for the significant in-plane strain.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reversible Electron-Beam Patterning of Colloidal Nanoparticles at Fluid Interfaces

The directed self-assembly of colloidal nanoparticles (NPs) using external fields guides the formation of sophisticated hierarchical materials but becomes less effective with decreasing particle size. As an alternative, electron-beam-driven assembly offers a potential avenue for targeted nanoscale manipulation, yet remains poorly controlled due to the variety and complexity of beam interaction mechanisms. Here, we investigate the beam–particle interaction of silica NPs pinned to the fluid–vacuum interface of ionic liquid droplets. In these experiments, scanning electron microscopy of the droplet surface resolves NP trajectories over space and time while simultaneously driving their reorganization. With this platform, we demonstrate the ability to direct particle transport and create transient, reversible colloidal patterns on the droplet surface. By tuning the beam voltage, we achieve precise control over both the strength and sign of the beam–particle interaction, with low voltages repelling particles and high voltages attracting them. This response stems from the formation of well-defined solvent flow fields generated from trace radiolysis of the ionic liquid, as determined through statistical analysis of single-particle trajectories under varying solvent composition. Altogether, electron-beam-guided assembly introduces a versatile strategy for nanoscale colloidal manipulation, offering new possibilities for the design of dynamic, reconfigurable systems with applications in adaptive photonics and catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Patterning and Reversible Actuation of Liquid Gallium Alloys by Preventing Adhesion on Rough Surfaces

This work reports here a simple approach to form, study, and utilize rough coatings that prevent the adhesion of gallium-based liquid metal alloys. Typically, liquids with large interfacial tension do not wet nonreactive surfaces, regardless of surface topography. However, these alloys form a surface oxide “skin” that adheres to many substrates, even those with low surface energy. This work reports a simple approach to render closed channels and surfaces, including soft materials, to be “oxide-phobic” via spray-coating (NeverWet, which is commercially available and inexpensive). Surface spectroscopic techniques and metrology tools elucidate the coatings to comprise silica nanoparticles grafted with silicones that exhibit dual length scales of roughness. Although prior work shows the importance of surface roughness in preventing adhesion, the present work confirms that both hydrophobic and hydrophilic rough surfaces prevent oxide adhesion. Furthermore, the coating enables reversible actuation through submillimeter closed channels to form a reconfigurable antenna in the gigahertz range without the need for corrosive acids or bases that remove the oxide. In addition, the coating enables open surface patterning of conductive traces of liquid metal. This shows it is possible to actuate liquid metals in air without leaving neither metal nor oxide residue on surfaces to enable reconfigurable electronics, microfluidics, and soft electrodes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Liquid–Solid Mixtures of Ga Metal Infused with Cu Microparticles and Nanoparticles for Microscale and Nanoscale Patterning of Solid Metals at Room Temperature

This paper demonstrates and characterizes a simple ink for nanopatterning of solid metallic structures under ambient conditions by taking advantage of the low melting point of gallium and its affinity to form intermetallics with other metals. Bare copper micro- and nanoparticles readily mix with liquid gallium near room temperature to form a paste that ultimately amalgamates into a dense solid when mixed at the appropriate concentration ratios. The paste has sufficient shelf life that can be extruded from a nozzle at modest temperatures to form solid three-dimensional (3D) shapes. Additionally, the paste can be molded at room temperature to replicate feature sizes ranging from a few millimeters down to hundreds of nanometers. In situ X-ray diffraction (XRD) and thermo-mechanical analysis (TMA) data show gallium and copper readily interdiffuse to form the thermodynamically expected intermetallic phase. We describe the capabilities and limitations of a simple way to pattern solid metals in an additive fashion (syringe-based extrusion) and with high resolution (molding) at or near room temperature. Here, the use of a paste that solidifies provides a novel route for 3D printing of solid metals at ambient temperatures as well as the creation of micro- and nanostructured metallic surfaces that may be useful for optics, non-wetting surfaces, or electronic microcomponents.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Strain-Induced Lateral Heterostructures in Patterned Semiconductor Nanomembranes for Micro- and Optoelectronics

The ability to tailor the energy band lineup of semiconductor materials plays a key role in the development of many electronic and optoelectronic devices and normally relies on heteroepitaxy. In this work, we report a different method, based on strain engineering, for the controlled introduction of variations in bandgap energy with lateral position in thin films. External stress is applied on Ge nanomembranes stacked with an array of amorphous-Si pillars in order to create a non-uniform strain (and therefore bandgap energy) distribution commensurate with the sample thickness variations. The resulting strain profiles are mapped using Bragg diffraction with a hard X-ray probe featuring nanoscale spatial resolution. Compared with traditional heterostructures grown by epitaxial techniques, these strain-engineered samples involve a single chemical composition and are not limited in the choice of compatible materials by any restriction imposed by lattice-matching requirements. Furthermore, their energy band lineups can be patterned in nearly arbitrary shapes using nanolithography to control the thickness profile and can be tuned actively by varying the applied stress. As a result, these structures are attractive for a wide range of device applications (including lasers, LEDs, solar cells, and thermoelectrics) that require complex heterostructure lineups with multiple bandgap energies.

36 MATERIALS SCIENCE↗
DOE OSTI2019

An Externally Forced Decadal Rainfall Seesaw Pattern Over the Sahel and Southeast Amazon

Abstract By analyzing observations and model simulations, here we show that there exists a significant anticorrelation on interannual to multidecadal time scales between the Sahel and southeast Amazon rainfall during July‐August‐September. This rainfall seesaw, which is strongest on decadal to multidecadal scales, is due to an anomalous meridional gradient of sea surface temperatures across the tropical Atlantic that pushes the Intertropical Convergence Zone and its associated rain belt toward the anomalously warm hemisphere. Large ensemble model simulations suggest that the seesaw pattern is likely caused by decadal changes in anthropogenic and volcanic aerosols, rather than internal climate variability. Our results suggest that the recent decadal to multidecadal climate variations in and around the North Atlantic basin are largely externally forced and that projected large North Atlantic warming could lead to a wetter Sahel but drier Amazon in the future.

58 GEOSCIENCES↗
DOE OSTI2016

Wettability control on multiphase flow in patterned microfluidics

Significance The simultaneous flow of multiple fluid phases through a porous solid occurs in many natural and industrial processes—for example, rainwater infiltrates into soil by displacing air, and carbon dioxide is stored in deep saline aquifers by displacing brine. It has been known for decades that wetting—the affinity of the solid to one of the fluids—can have a strong impact on the flow, but the microscale physics and macroscopic consequences remain poorly understood. Here, we study this in detail by systematically varying the wetting properties of a microfluidic porous medium. Our high-resolution images reveal the fundamental control of wetting on multiphase flow, elucidate the inherently 3D pore-scale mechanisms, and help explain the striking macroscopic displacement patterns that emerge.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗