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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Patterned anodes with sub-millimeter spatial resolution for large-area MCP-based photodetector systems

Micro-channel-plate-based photo-detectors are unique in being capable of covering very large areas such as those required in elementary particle and nuclear physics, while providing sub-millimeter space resolution, time resolutions of less than 10 picoseconds for charged particles, and time resolutions of 30 psec–50 psec for single photons. In such systems the electronic channel count is a major cost driver. Incorporating a capacitively-coupled anode allows the use of external pickup electrodes with patterns of individual channels optimized for occupancy, rate, and time/space resolution. The signal pickup antenna can be economically implemented as a printed circuit card with a 2-dimensional array of pads for high-occupancy/high-rate applications such as in particle colliders and medical imaging, or a 1-dimensional array of strips for a lower channel count in low-occupancy/low-rate applications such as large neutrino detectors. In this paper we present pad patterns that enhance signal-sharing between pads to lower the channel count per unit area in large-area systems by factors up to 4, while maintaining spatial resolutions of approximately 100 to 200 μ m for charged particles and 400 to 1000 μ m for single photons. Patterns that use multiple signal layers in the signal-pickup board can lower the channel count even further, moving the scaling behavior in the number of pads versus total area from quadratic to linear.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

The role of geographical spreaders in infectious pattern formation and front propagation speeds

The pattern formation and spatial spread of infectious populations are investigated using a kernel-based Susceptible–Infectious–Recovered (SIR) model applicable across a wide range of basic reproduction numbers R O . The goal is to examine the role of geographical spreaders on transient spatial pattern formation of infectious populations and the associated maximum invasive front speeds c max . In the simulations conducted here, geographical spreaders are defined as a portion of the infected population Φ experiencing high mobility between identical communities. The spatial organization of the infected population and c max are determined when the infections are randomly initiated in space within multiple communities. For small but finite , scaling analysis and numerical simulations in 1-dimension suggest that when the spreading kernel is Gaussian-shaped, where is the inverse of the infectious duration. This finding for agrees with a diffusion-based representation of mobility in 1-D. Numerical simulations in 2-D across wide-ranging suggest that , the variance of the spatial kernel describing mobility of long-distance geographical spreaders across communities, determines the spatial organization of infections across communities. When (long-distance mobility, where is the minimum spatial extent defining adjacent communities), the infectious population will experience a transient but spatially coherent pattern with a wavelength that can be derived from the spreading kernel properties. Moreover, the 2-D simulations for the bounded kernel suggest that attainment of is also dictated by but the magnitude is not sensitive to unlike diffusion-based models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Modeling electric vehicle adoption considering a latent travel pattern construct and charging infrastructure

This paper presents a behavioral model of public, revealed preferences (RP) for various types of electric vehicles (EVs) while accounting for a latent (green) travel pattern construct and charging infrastructure characteristics. Specifically, a two-level nested logit (NL) model is estimated to explain households' fuel type choice among five alternatives and three nests: (1) battery electric vehicles (BEVs); (2) hybrid vehicles including hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs); and (3) conventional vehicles including gasoline and diesel vehicles. Further, a latent travel pattern construct which captures a week-long number of trips by non-vehicle travel modes as well as daily vehicle and tollway use is estimated in a structural equation setting and subsequently fed into the NL model. Using a recent RP dataset from the California Household Travel Survey, we identify market segments for alternative fuel types based on households' socio-economic characteristics, built environment factors concerning public plug-in EV (PEV) charging infrastructure characteristics, latent and observable travel behavior factors of a household vehicle's principal driver, and vehicle attributes. The results highlight that the number of public PEV charging stations is only significant for households choosing PHEVs and interestingly insignificant in the BEV utility. Furthermore, the sensitivity analysis of the findings reveals that PHEV users are elastic with respect to household vehicle ownership ratio and the latent green travel pattern construct, while BEV users are inelastic to any explanatory variable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Discovering and Demonstrating a Novel High-Performing 2D-Patterned Electrode for Proton-Exchange Membrane Water Electrolysis Devices

Proton-exchange membrane water electrolysis (PEMWE) produces hydrogen with high efficiency and purity but uses high-loading platinum-group metal (PGM) catalysts. Such concerns call for the development of novel electrode architectures to improve catalyst utilization and mass activity, thus promoting PEMWE cost competitiveness for large-scale implementation. In this study, we demonstrated, for the first time, a novel two-dimensional (2D)-patterned electrode with edge effects to address these challenges. The edge effect was induced by membrane properties, potential distribution, and counter electrode coverage and could be optimized by tuning the catalyst layer dimensions. To achieve identical PEMWE performance, the optimal pattern saved the 21% anode PGM catalyst compared with the conventional catalyst fully covered electrode. The PGM catalyst could be further reduced by 61% to boost mass activity with no significant performance loss. The results also indicated that the electrode uniformity in PEMWE cells might not be as critical as that in PEM fuel cells. Finally, the novel 2D-patterned electrode could effectively reduce PGM catalyst loading, accelerating affordable and large-scale production of hydrogen and other value-added chemicals via electrolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Comparing Synoptic Pattern Evolution for Flash‐Flood‐Producing and Non‐Flash‐Flood‐Producing Mesoscale Convective Systems in the United States

Understanding how the short-term evolution of synoptic weather patterns influence Mesoscale Convective Systems (MCSs) is essential, as these systems are responsible for over half of central U.S. flash floods, leading to substantial socioeconomic and water resource management impacts. This study analyzes long-term MCS data, flash flood reports, and atmospheric reanalyses from 2007 to 2017 using a machine learning clustering algorithm to examine how the synoptic weather patterns evolve prior to MCS initiation. While the clusters reflect seasonal and regional differences in MCS occurrence, they do not consistently distinguish between MCSs that do and do not produce flash floods. Systems in the southern Great Plains are more flood-prone when a synoptic-scale forcing, located near the system, drives strong water vapor transport from the nearby moisture source. More generally under different synoptic weather patterns, a broader precipitating area is the most dominant factor governing MCS flash flood potential.

atmospheric dynamics↗
DOE OSTI2022

Solvent-free bottom-up patterning of zeolitic imidazolate frameworks

Patterning metal-organic frameworks (MOFs) at submicrometer scale is a crucial yet challenging task for their integration in miniaturized devices. Here we report an electron beam (e-beam) assisted, bottom-up approach for patterning of two MOFs, zeolitic imidazolate frameworks (ZIF), ZIF-8 and ZIF-67. A mild pretreatment of metal oxide precursors with linker vapor leads to the sensitization of the oxide surface to e-beam irradiation, effectively inhibiting subsequent conversion of the oxide to ZIFs in irradiated areas, while ZIF growth in non-irradiated areas is not affected. Well-resolved patterns with features down to the scale of 100 nm can be achieved. This developer-free, all-vapor phase technique will facilitate the incorporation of MOFs in micro- and nanofabrication processes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

A novel hardmask-to-substrate pattern transfer method for creating 3D, multi-level, hierarchical, high aspect-ratio structures for applications in microfluidics and cooling technologies

Abstract This letter solves a major hurdle that mars photolithography-based fabrication of micro-mesoscale structures in silicon. Conventional photolithography is usually performed on smooth, flat wafer surfaces to lay a 2D design and subsequently etch it to create single-level features. It is, however, unable to process non-flat surfaces or already etched wafers and create more than one level in the structure. In this study, we have described a novel cleanroom-based process flow that allows for easy creation of such multi-level, hierarchical 3D structures in a substrate. This is achieved by introducing an ultra-thin sacrificial silicon dioxide hardmask layer on the substrate which is first 3D patterned via multiple rounds of lithography. This 3D pattern is then scaled vertically by a factor of 200–300 and transferred to the substrate underneath via a single shot deep etching step. The proposed method is also easily characterizable—using features of different topographies and dimensions, the etch rates and selectivities were quantified; this characterization information was later used while fabricating specific target structures. Furthermore, this study comprehensively compares the novel pattern transfer technique to already existing methods of creating multi-level structures, like grayscale lithography and chip stacking. The proposed process was found to be cheaper, faster, and easier to standardize compared to other methods—this made the overall process more reliable and repeatable. We hope it will encourage more research into hybrid structures that hold the key to dramatic performance improvements in several micro-mesoscale devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Equilibrium-gated pattern formation: How molecular dissociation thermodynamics drive emergent behavior in dissipative polymeric systems

Emergent patterns in biological systems arise through dissipative processes that balance reaction and transport phenomena, producing highly functional properties from self-regulating mechanisms. Synthetic fabrication, by contrast, often relies on user-controlled, multistep methods that lack the self-organizing capabilities of natural systems. Inspired by nature, we sought chemical systems that integrate strongly coupled reaction and transport phenomena, identifying frontal ring-opening metathesis polymerization (FROMP) as a method capable of creating diverse forms and functions through reactive processing. By employing discrete molecular initiators, FROMP allows precise control of key reaction steps—inhibition, initiation, and propagation. Using an integrated computational and experimental framework, we uncover how near-equilibrium inhibition dynamics, coupled with far-from-equilibrium reaction kinetics, drive pattern formation in frontally polymerized synthetic materials. We propose the concept of equilibrium-gated pattern formation, demonstrating how initiator chemistry can be tuned to achieve programmable macroscale properties. Our study reveals a surprising insight: Emergent behavior in FROMP systems arises from the inhibition-dominated regime of resin composition, expanding prior observations that such behavior is confined to a narrow compositional space near the boundary between front quenching and uniform front propagation. We identify a broader compositional window, far from the quenching regime, where emergent behavior reliably manifests. This expanded design space significantly enhances the operational flexibility of reactive systems and their capacity for self-organization. Furthermore, these insights provide a roadmap for designing bioinspired materials with self-organizing capabilities, unlocking possibilities in synthetic manufacturing.

Architected Polymers↗
DOE OSTI2014

Searching for pulsars using image pattern recognition

In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ∼9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The performance of this system can be improved over time as more training data are accumulated. This AI system has been integrated into the PALFA survey pipeline and has discovered six new pulsars to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Patterned OLEDs: effect of substrate corrugation pitch and height

Abstract An ongoing OLED challenge is cost-effective enhancement of light extraction, i.e., increasing the external quantum efficiency ( EQE ∼20% in conventional devices). OLEDs on corrugated substrates often show enhanced EQE s providing insight into light emission processes. In particular, patterned plastic substrates directly imprinted easily at room temperature and amenable to low-cost R2R production are ideal for studying/optimizing various structures, further elucidating the extraction process. We show new semi-quantitative data of the effect of the pitch ( a ) and height/depth ( h ) of plastic substrate patterns on the OLEDs’ stack and EQE , focusing on new designs, interestingly, some showing surprisingly enhanced EQE s that were neither reported nor discussed before. These includ e : ( i ) shallow ( h < 200 nm) convex polycarbonate with a ∼ 750 versus ∼400 nm, where the h gradually decreases as the OLED stack is built and ( ii ) concave PET/CAB with large a (∼2.8 and ∼7.8 μ m), where the EQE enhancement of conformal OLEDs may be due largely to scattering. EQE s of green, blue, and white phosphorescent OLEDs were measured. OLEDs on substrates with narrow a ∼ 400 nm and low h < 200 nm s h owed no enhancement, resembling flat devices. In contrast, OLEDs on substrates with comparable or smaller h , but larger a ∼ 750 nm show signific a nt EQE enhancement despite h reduction across the stack. Green OLEDs with a ∼ 750 nm and h ∼ 160 to ∼180 nm, showed EQE s ∼30%, reaching ∼58% with substrate mode extraction. Surprisingly, fully conformal OLEDs on a PET/CAB substrate with a ∼ 7.8 μ m showed blue a nd white EQE s reaching ∼33%, without substrate mode extraction. The enhancing patterns increase the OLEDs’ EQE by reducing surface plasmon excitation and internal waveguiding. The experimental results for OLEDs on substrates with a < 2 μ m are supported by scattering matrix simulations that assume conformal stacks, incorporating diffraction for internal losses reduction. EQE enhancement not predicted by simulations may be due additionally to scattering mostly for substrates with a signific a ntly larger than the emitting wavelength.

42 ENGINEERING↗
DOE OSTI2022

Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and dQ/dx (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30% for charged-current ν e interactions. Therefore, this pattern recognition achieves 80–90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current ν e (ν μ ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Scale-dependent effects of climate and geographic distance on bacterial diversity patterns across northern China's grasslands

Patterns of variation in plant and animal diversity along precipitation gradients have been extensively studied, but much less is known about how and to what extent precipitation affects the biogeographic distribution of microbial diversity in arid areas across large spatial scales. Here we collected soils from 54 sites along a 3700 km transect covering a wide range of grassland ecosystems with distinct aridity gradients. We quantified the bacterial community diversity and the effects of climate, edaphic parameter and geographic distance on the bacterial community structure using high-throughput 16S rRNA gene sequencing. Of the 35 phyla detected, 6 were dominant: Actinobacteria, Acidobacteria, Alphaproteobacteria, Deltaproteobacteria, Bacteroidetes and Planctomycetes . Aridity was a major factor influencing bacterial diversity, community composition and taxon abundance. Although the pattern of bacterial species richness is markedly different from that of plant species richness, most soil bacteria were endemic to particular bioregions like macro-organisms. Community similarity significantly declined with environmental distance and geographic distance (r = -0.579 and -0.773, respectively). Geographic distance (historical contingencies) contributed more to bacterial community variation (36.02%) than combined environmental factors (24.06%). Finally, our results showed that geographic distance and climatic factors concurrently govern bacterial biogeographic patterns in arid and semi-arid grassland.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Fast calculation of diffraction patterns from an ensemble of aligned molecules

We report an algorithm to calculate electron diffraction patterns for molecules with anisotropic angular distribution, which is significantly faster than existing methods. The algorithm uses a transform to convert the molecular orientation distribution, which is a function of three Euler angles, to the atom-pair distribution functions which depend on the polar and azimuthal angles. The diffraction signal can then be calculated from the atom-pair distributions. We demonstrate the computation method numerically by calculating electron diffraction patterns for a symmetric top molecule (trifluoroiodomethane) and an asymmetric top molecule (formaldehyde) and show that it reduces the calculation time by approximately two orders of magnitude compared to the standard brute-force method. Here, the method can also be applied to the calculation of x-ray diffraction patterns.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Compositional patterning in irradiated alloys: Effective potentials and effective interfacial energy

Compositional patterning (CP) in binary alloys during energetic particle irradiation is studied using a kinetic model that considers two competing kinetic processes, a thermally activated one promoting macroscopic phase separation (MPS) of the concentration field c(r,t) and a forced one resulting in finite-range random atomic mixing. The forced mixing is modeled by a Gaussian relocation distribution with a characteristic distance R. A series of approximate kinetic models are introduced by expanding the mixing function into a series of n terms, thus replacing the non-local evaluations of the concentration field c(r'-r,t) by local derivatives of c(r,t). This approach makes it possible to obtain exact effective potentials and build steady-state diagrams for each order-n model. Phase field (PF) simulations using these order-n models reveal that near the onset of patterning, phase evolution is accurately described using an order-3 model, which changes smoothly from an extended Cahn-Hilliard free energy in the MPS regime to a one-mode Swift-Hohenberg functional in the CP regime. Deeper into the patterning regime, higher-order models are required to achieve convergence, yielding square-like concentration profiles characteristic of a strong segregation regime. These higher-order effective free energies are analogous to multimodal Swift-Hohenberg functionals. Here, a new definition for the effective interfacial energy is proposed in the CP regime, since the interfacial area is no longer an excess quantity in that regime, precluding the use of the standard thermodynamic definition of interfacial energy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Pattern-matching indexing of Laue and monochromatic serial crystallography data for applications in materials science

Serial crystallography data can be challenging to index, as each frame is processed individually, rather than being processed as a whole like in conventional X-ray single-crystal crystallography. An algorithm has been developed to index still diffraction patterns arising from small-unit-cell samples. The algorithm is based on the matching of reciprocal-lattice vector pairs, as developed for Laue microdiffraction data indexing, combined with three-dimensional pattern matching using a nearest-neighbors approach. As a result, large-bandpass data (e.g. 5–24 keV energy range) and monochromatic data can be processed, the main requirement being prior knowledge of the unit cell. Furthermore, angles calculated in the vicinity of a few theoretical and experimental reciprocal-lattice vectors are compared, and only vectors with the highest number of common angles are selected as candidates to obtain the orientation matrix. Global matching on the entire pattern is then checked. Four indexing options are available, two for the ranking of the theoretical reciprocal-lattice vectors and two for reducing the number of possible candidates. Furthermore, the algorithm has been used to index several data sets collected under different experimental conditions on a series of model samples. Additionally, knowing the crystallographic structure of the sample and using this information to rank the theoretical reflections based on the structure factors helps the indexing of large-bandpass data for the largest-unit-cell samples. For small-bandpass data, shortening the candidate list to determine the orientation matrix should be based on matching pairs of reciprocal-lattice vectors instead of triplet matching.

36 MATERIALS SCIENCE↗
DOE OSTI2023

SpeckleNN: a unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples

With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or `speckles', to extract single-hits that are needed for real-time vetoing and three-dimensional reconstruction poses a challenge for high-data-rate facilities like the European XFEL and LCLS-II-HE. Here, we introduce SpeckleNN, a unified embedding model for real-time speckle pattern classification with limited labeled examples that can scale linearly with dataset size. Trained with twin neural networks, SpeckleNN maps speckle patterns to a unified embedding vector space, where similarity is measured by Euclidean distance. We highlight its few-shot classification capability on new never-seen samples and its robust performance despite having only tens of labels per classification category even in the presence of substantial missing detector areas. Without the need for excessive manual labeling or even a full detector image, our classification method offers a great solution for real-time high-throughput SPI experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Short-Term Global Horizontal Irradiance Forecasting Based on Sky Imaging and Pattern Recognition

Accurate short-term forecasting is crucial for solar integration in the power grid. In this paper, a classification forecasting framework based on pattern recognition is developed for 1-hour-ahead global horizontal irradiance (GHI) forecasting. Three sets of models in the forecasting framework are trained by the data partitioned from the preprocessing analysis. The first two sets of models forecast GHI for the first four daylight hours of each day. Then the GHI values in the remaining hours are forecasted by an optimal machine learning model determined based on a weather pattern classification model in the third model set. The weather pattern is determined by a support vector machine (SVM) classifier. The developed framework is validated by the GHI and sky imaging data from the National Renewable Energy Laboratory (NREL). Results show that the developed short-term forecasting framework outperforms the persistence benchmark by 16% in terms of the normalized mean absolute error and 25% in terms of the normalized root mean square error.

14 SOLAR ENERGY↗
DOE OSTI2020

Flexible Machine Learning-Based Cyberattack Detection Using Spatiotemporal Patterns for Distribution Systems

This letter develops a flexible machine learning detection method for cyberattacks in distribution systems considering spatiotemporal patterns. Spatiotemporal patterns are recognized by the graph Laplacian based on system-wide measurements. A flexible Bayes classifier (BC) is used to train spatiotemporal patterns which could be violated when cyberattacks occur. Cyberattacks are detected by using flexible BCs online. The effectiveness of the developed method is demonstrated through standard IEEE 13- and 123-node test feeders.

97 MATHEMATICS AND COMPUTING↗