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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Indicator Patterns of Forced Change Learned by an Artificial Neural Network

Abstract Many problems in climate science require the identification of signals obscured by both the “noise” of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to predict the year of a given map of annual‐mean temperature (or precipitation) from forced climate model simulations. This prediction task requires the ANN to learn forced patterns of change amidst a background of climate noise and model differences. We then apply a neural network visualization technique (layerwise relevance propagation) to visualize the spatial patterns that lead the ANN to successfully predict the year. These spatial patterns thus serve as “reliable indicators” of the forced change. The architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change. Results are compared to those of more standard approaches like signal‐to‐noise ratios and multilinear regression in order to gain intuition about the reliable indicators identified by the ANN. We then apply an additional visualization tool (backward optimization) to highlight where disagreements in simulated and observed patterns of change are most important for the prediction of the year. This work demonstrates that ANNs and their visualization tools make a powerful pair for extracting climate patterns of forced change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Understanding the Influence of Urban Form on the Spatial Pattern of Precipitation

Urban areas are known to modify the spatial pattern of precipitation climatology. Existing observational evidence suggests that precipitation can be enhanced downwind of a city. Among the proposed mechanisms, the thermodynamic and aerodynamic processes in the urban lower atmosphere interact with the meteorological conditions and can play a key role in determining the resulting precipitation patterns. In addition, these processes are influenced by urban form, such as the impervious surface extent. This study aims to unravel how different urban forms impact the spatial patterns of precipitation climatology under different meteorological conditions. We use the Multi-Radar Multi-Sensor quantitative precipitation estimation data products and analyze the hourly precipitation maps for 27 selected cities across the continental United States from the years 2015–2021 summer months. Results show that about 80% of the studied cities exhibit a statistically significant downwind enhancement of precipitation. Additionally, we find that the precipitation pattern tends to be more spatially clustered in intensity under higher wind speed; the location of radial precipitation maxima is located closer to the city center under low background winds but shifts downwind under high wind conditions. The magnitude of downwind precipitation enhancement is highly dependent on wind directions and is positively correlated with the city size for the south, southwest, and west directions. This study presents observational evidence through a cross-city analysis that the urban precipitation pattern can be influenced by the urban modification of atmospheric processes, providing insight into the mechanistic link between future urban land-use change and hydroclimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Optimizing HAARP Beam Pattern for Generation of Strong F‐Region Field‐Aligned Irregularities

Field-aligned irregularities (FAIs) are the signatures of plasma turbulence and convection in the mid- and high-latitude F-region ionosphere, and also provide coherent backscatter targets for HF radars. To serve irregularity generation and characterization studies, we conducted experiments at the High-frequency Active Auroral Research Program (HAARP) in August 2023 with the goal of identifying the optimal HAARP beam pattern for reliable generation of intense FAIs over a large geographic region. The HAARP beam patterns we tested were the commonly-used narrow beam, also known as L0, as well as the wider L1 and L2 “twisted” beam patterns. The size and intensity of the FAI region generated by each HAARP beam pattern was quantified using the Kodiak Island Super Dual Auroral Radar Network (SuperDARN) radar. Stimulated electromagnetic emissions (SEE) from heater wave-FAI scattering were also recorded using a receiver located near HAARP. The L1 beam pattern was found to produce the strongest SuperDARN backscatter over the largest region. Although the heater frequency was intended to be tuned a few hundred kHz below the F-region critical frequency (foF2) during each experiment, difficulty in estimating foF2 during the campaign likely resulted in HAARP heating at significantly different frequency ranges around foF2 during each experiment. Although this additional free parameter complicated data analysis for this study, the SuperDARN and SEE measurements have led to further inquiry into the role heater frequency plays in the artificial generation of FAIs.

58 GEOSCIENCES↗
DOE OSTI2016

Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China

Soil microbiota play a critical role in soil biogeochemical processes and have a profound effect on soil functions. Recent studies have revealed microbial co-occurrence patterns in soil microbial communities, yet the geographic pattern of topological features in soil microbial co-occurrence networks at the continental scale are largely unknown. Here, we investigated the shifts of topological features in co-occurrence networks inferred from soil microbiota along a continental scale in eastern China. Integrating archaeal, bacterial and fungal community datasets, we inferred a meta-community co-occurrence network and analyzed node-level and network-level topological shifts associated with five climatic regions. Both node-level and network-level topological features revealed geographic patterns wherein microorganisms in the northern regions had closer relationships but had a lower interaction influence than those in the southern regions. We further identified topological differences associated with taxonomic groups and demonstrated that co-occurrence patterns were random for archaea and non-random for bacteria and fungi. Given that microbial interactions may contribute to soil functions more than species diversity, this geographic shift of topological features provides new insight into studying microbial biogeographic patterns, their organization and impacts on soil-associated function.

Environmental Sciences & Ecology↗
DOE OSTI2021

Foundry-compatible high-resolution patterning of vertically phase-separated semiconducting films for ultraflexible organic electronics

Solution processability of polymer semiconductors becomes an unfavorable factor during the fabrication of pixelated films since the underlying layer is vulnerable to subsequent solvent exposure. A foundry-compatible patterning process must meet requirements including high-throughput and high-resolution patternability, broad generality, ambient processability, environmentally benign solvents, and, minimal device performance degradation. However, known methodologies can only meet very few of these requirements. Here, a facile photolithographic approach is demonstrated for foundry-compatible high-resolution patterning of known p- and n-type semiconducting polymers. This process involves crosslinking a vertically phase-separated blend of the semiconducting polymer and a UV photocurable additive, and enables ambient processable photopatterning at resolutions as high as 0.5 μm in only three steps with environmentally benign solvents. The patterned semiconducting films can be integrated into thin-film transistors having excellent transport characteristics, low off-currents, and high thermal (up to 175 °C) and chemical (24 h immersion in chloroform) stability. Moreover, these patterned organic structures can also be integrated on 1.5 μm-thick parylene substrates to yield highly flexible (1 mm radius) and mechanically robust (5,000 bending cycles) thin-film transistors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Nanoscale Turing patterns in a bismuth monolayer

Turing’s reaction–diffusion theory of morphogenesis has been very successful for understanding macroscopic patterns within complex objects ranging from biological systems to sand dunes. However, Turing patterns on microscopic length scales are extremely rare. Here we show that a strained atomic bismuth monolayer assembled on the surface of NbSe 2 —and subject to interatomic interactions and kinetics—displays Turing patterns. Our reaction–diffusion model produces stripe patterns with a period of five atoms (approximately 2 nm) and domain walls with Y-shaped junctions that bear a striking resemblance to what has been experimentally observed. Our work establishes that Turing patterns can occur at the atomic scale in a hard condensed-matter setting.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Patterning at the 10 nanometer length scale using a strongly segregating block copolymer thin film and vapor phase infiltration of inorganic precursors

Here in this work, we demonstrate the use of self-assembled thin films of the cylinder-forming block copolymer poly(4-tert-butylstyrene-block-2-vinylpyridine) to pattern high density features at the 10 nm length scale. This material's large interaction parameter facilitates pattern formation in single-digit nanometer dimensions. This block copolymer's accessible order–disorder transition temperature allows thermal annealing to drive the assembly of ordered 2-vinylpyridine cylinders that can be selectively complexed with the organometallic precursor trimethylaluminum. This unique chemistry converts organic 2-vinylpyridine cylinders into alumina nanowires with diameters ranging from 8 to 11 nm, depending on the copolymer molecular weight. Graphoepitaxy of this block copolymer aligns and registers sub-12 nm diameter nanowires to larger-scale rectangular, curved, and circular features patterned by optical lithography. The alumina nanowires function as a robust hard mask to withstand the conditions required for patterning the underlying silicon by plasma etching. Lastly, we conclude with a discussion of some of the challenges that arise with using block copolymers for patterning at sub-10 nm feature sizes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

DFT simulation of the X-ray diffraction pattern of aluminum-ion-intercalated graphite used as the cathode material of the aluminum-ion battery

Many breakthroughs have been achieved in rechargeable aluminum-ion battery technologies in recent years. Most recently, operando X-ray diffraction (XRD) combined with density functional theory (DFT) calculations was reported to study the chloroaluminate anion (AlCl 4 -)-intercalated graphite cathode of the battery. However, there are quite a few discrepancies between the measured and simulated XRD patterns. This work is focused on the simulation of XRD patterns of graphite intercalation compounds (GICs) with DFT calculations. Our results reveal that both the geometry of AlCl 4 - in graphite and the gallery height of GICs are dependent on the intercalant density. At low intercalant density, the gallery height keeps constant, but at high intercalant densities, the gallery height is linearly related to the intercalant density. Our simulated XRD patterns are highly consistent with the measured operando XRD patterns. Not only do the angles of the peaks match very well, but also the relative intensities and the corresponding electrode capacities show reasonable agreement with the experimental results. The DFT simulation of the XRD pattern provides significant information on the stage index and the charge capacity of the GIC electrode.

25 ENERGY STORAGE↗
DOE OSTI2022

The fate of liquid crystal topological defects on chemically patterned surfaces during phase transitions

Controlling topological defects in liquid crystals (LCs) is an essential element in the development of areas such as directed self-assembly and micropatterning materials. However, during the phase transition on confined patterned surfaces, how the morphologies in one liquid crystalline phase change from deformations or defects into another phase is much less known. Here, we examine the fate of defects in a LC confined on a patterned surface during smectic-A–nematic and nematic–isotropic phase transitions, using experiments and simulation analyses. Upon heating from smectic-A to nematic, a Toric focal conical domain (TFCD) melts into a +1 converging boojum defect, which then transitioned into a concentric configuration as temperature increases, attributed to a steeper decrease of the bend and twist modulus compared to splay modulus. During cooling, TFCDs are developed from two distinct pathways depending on the cooling rates. Our continuum simulation recapitulates these transformations and provides elastic constant-based explanations for the two pathways. Although the phase transition pathways of defects are independent of the geometry of the confined patterns, the arrangement of FCDs is highly dependent on the size and shape of the patterns. Finally, taken together, this simple approach offers promising opportunities for tuning the micro- or nano-patterning of topological defects in liquid crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

On the limits of uniaxial magnetic anisotropy tuning by a ripple surface pattern

Ion beam patterning of a nanoscale ripple surface has emerged as a versatile method of imprinting uniaxial magnetic anisotropy (UMA) on a desired in-plane direction in magnetic films. In the case of ripple patterned thick films, dipolar interactions around the top and/or bottom interfaces are generally assumed to drive this effect following Schlömann's calculations for demagnetizing fields of an ideally sinusoidal surface [E. Schlömann, J. Appl. Phys. 41, 1617 (1970)]. We have explored the validity of his predictions and the limits of ion beam sputtering to induce UMA in a ferromagnetic system where other relevant sources of magnetic anisotropy are neglected: ripple films not displaying any evidence of volume uniaxial anisotropy and where magnetocrystalline contributions average out in a fine grain polycrystal structure. To this purpose, the surface of 100 nm cobalt films grown on flat substrates has been irradiated at fixed ion energy, fixed ion fluency but different ion densities to make the ripple pattern at the top surface with wavelength Λ and selected, large amplitudes (ω) up to 20 nm so that stray dipolar fields are enhanced, while the residual film thickness t = 35–50 nm is sufficiently large to preserve the continuous morphology in most cases. The film-substrate interface has been studied with X-ray photoemission spectroscopy depth profiles and is found that there is a graded silicon-rich cobalt silicide, presumably formed during the film growth. This graded interface is of uncertain small thickness but the range of compositions clearly makes it a magnetically dead layer. On the other hand, the ripple surface rules both the magnetic coercivity and the uniaxial anisotropy as these are found to correlate with the pattern dimensions. Remarkably, the saturation fields in the hard axis of uniaxial continuous films are measured up to values as high as 0.80 kG and obey a linear dependence on the parameter ω{sup 2}/Λ/t in quantitative agreement with Schlömann's prediction for a surface anisotropy entirely ruled by dipolar interaction. The limits of UMA tuning by a ripple pattern are discussed in terms of the surface local angle with respect to the mean surface and of the onset of ripple detachment.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Dynamics of fast pattern formation in porous silicon by laser interference

Patterns are fabricated on 290 nm thick nanostructured porous silicon layers by phase-mask laser interference using single pulses of an excimer laser (193 nm, 20 ns pulse duration). The dynamics of pattern formation is studied by measuring in real time the intensity of the diffraction orders 0 and 1 at 633 nm. The results show that a transient pattern is formed upon melting at intensity maxima sites within a time <30 ns leading to a permanent pattern in a time <100 ns upon solidification at these sites. This fast process is compared to the longer one (>1 μs) upon melting induced by homogeneous beam exposure and related to the different scenario for releasing the heat from hot regions. The diffraction efficiency of the pattern is finally controlled by a combination of laser fluence and initial thickness of the nanostructured porous silicon layer and the present results open perspectives on heat release management upon laser exposure as well as have potential for alternative routes for switching applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Tilted dipole model for bias-dependent photoluminescence pattern

In a guest-host system containing elongated dyes and a nematic liquid crystal, both molecules are aligned to each other. An external bias tilts these molecules and the radiation pattern of the system is altered. A model is proposed to describe this bias-dependent photoluminescence patterns. It divides the liquid crystal/dye layer into sub-layers that contain electric dipoles with specific tilt angles. Each sub-layer emits linearly polarized light. Its radiation pattern is toroidal and is determined by the tilt angle. Its intensity is assumed to be proportional to the power of excitation light absorbed by the sub-layer. This is calculated by the Lambert-Beer's Law. The absorption coefficient is assumed to be proportional to the cross-section of the tilted dipole moment, in analogy to the ellipsoid of refractive index, to evaluate the cross-section for each polarized component of the excitation light. Contributions from all the sub-layers are added to give a final expression for the radiation pattern. Self-absorption is neglected. The model is simplified by reducing the number of sub-layers. Analytical expressions are derived for a simple case that consists of a single layer with tilted dipoles sandwiched by two layers with horizontally-aligned dipoles. All the parameters except for the tilt angle can be determined by measuring transmittance of the excitation light. The model roughly reproduces the bias-dependent photoluminescence patterns of a cell containing 0.5 wt. % coumarin 6. It breaks down at large emission angles. Measured spectral changes suggest that the discrepancy is due to self-absorption and re-emission.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Direct nano-patterning of graphene with helium ion beams

Helium ion microscopy (HIM) was used for direct nano-patterning of single-layer graphene (SLG) on SiO{sub 2}/Si substrates. This technique involves irradiation of the sample with accelerated helium ions (He{sup +}). Doses of 2.0 × 10{sup 16 }He{sup + }cm{sup −2} from a 30 kV beam induced a metal-insulator transition in the SLG. The resolution of HIM patterning on SLG was investigated by fabricating nanoribbons and nanostructures. Analysis of scanning capacitance microscopy measurements revealed that the spatial resolution of HIM patterning depended on the dosage of He{sup +} in a non-monotonic fashion. Increasing the dose from 2.0 × 10{sup 16} to 5.0 × 10{sup 16 }He{sup + }cm{sup −2} improved the spatial resolution to several tens of nanometers. However, doses greater than 1.0 × 10{sup 17 }He{sup + }cm{sup −2} degraded the patterning characteristics. Direct patterning using HIM is a versatile approach to graphene fabrication and can be applied to graphene-based devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Domain patterning by electron beam of MgO doped lithium niobate covered by resist

Periodical domain structuring by focused electron beam irradiation of MgO-doped lithium niobate (MgOCLN) single crystalline plate covered by resist layer was studied both experimentally and by computer simulation. The dependences of domain size on the charge dose and distance between isolated domains were measured. It has been shown that the quality of periodical domain pattern depends on the thickness of resist layer and electron energy. The experimentally obtained periodic domain structures have been divided into four types. The irradiation parameters for the most uniform patterning were obtained experimentally. It was shown by computer simulation that the space charge slightly touching the crystal surface produced the maximum value of electric field at the resist/LN interface thus resulting in the best pattern quality. The obtained knowledge allowed us to optimize the poling process and to make the periodical domain patterns in 1-mm-thick wafers with an area up to 1 × 5 mm{sup 2} and a period of 6.89 μm for green light second harmonic generation. Spatial distribution of the efficiency of light frequency conversion confirmed the high homogeneity of the tailored domain patterns.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Photoluminescence of patterned CdSe quantum dot for anti-counterfeiting label on paper

We successfully developed a method utilizing colloidal CdSe nanocrystalline quantum dot for anti-counterfeiting label on a piece of glossy paper. We deposited numbers and lines patterns of toluene soluble CdSe quantum dot using rubber stamper on a glossy paper. The width of line pattern was about 1-2 mm with 1-2 mm separation between lines. It required less than one minute for deposited CdSe quantum dot on glossy paper to dry and become invisible by naked eyes. However, patterned quantum dot become visible using long-pass filter glasses upon excitation of UV lamp or blue laser. We characterized photoluminescence of line patterns of quantum dot, and we found that emission boundaries of line patterns were clearly observed. The error of line size and shape were mainly due to defect of the original stamper. The emission peak wavelength of CdSe quantum dot was 629 nm. The emission spectrum of deposited quantum dot has full width at half maximum (FWHM) of 30-40 nm. The spectra similarity between deposited quantum dot and the original quantum dot in solution proved that our stamping method can be simply applied on glossy paper without changing basic optical property of the quantum dot. Further development of this technique is potential for anti-counterfeiting label on very important documents or objects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗