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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optical image analysis for graphene layer detection: Enhanced green channel methodology

Graphene, a material of increasing research interest, requires accurate layer identification due to its sensitivity to layer count. Existing methods for graphene layer number identification are either time-consuming or of low accuracy, with high-accuracy methods often requiring expensive processes. This paper aims to address this challenge by proposing a cost-effective and efficient approach. Specifically, the current work highlights only the green channel—one of the three primary color channels (red, green, blue) that make up an optical image—from images of exfoliated graphene flakes for layer count identification. A linear regression is performed between pixel position and substrate green channel value, and this effect is subtracted from the entire optical image to mitigate background effects. By storing the range of green channel values for each type of flake (monolayer, bilayer, or tri-layer) based on a few images, we establish thresholds for identifying different types of layers in a particular setup. Additionally, our methodology allows for flexible threshold tuning using a single reference image, enabling adjustment to changes in detection setup such as illumination level, magnification, or microscope used. Finally, demonstrating high accuracy and flexibility, this methodology presents a suitable technique for graphene layer number identification without the need for large datasets or expensive instruments.

2D materials↗
DOE OSTI2023

Versatile recognition of graphene layers from optical images under controlled illumination through green channel correlation method

In this study, a simple yet versatile method is proposed for identifying the number of exfoliated graphene layers transferred on an oxide substrate from optical images, utilizing a limited number of input images for training, paired with a more traditional number of a few thousand well-published Github images for testing and predicting. Two thresholding approaches, namely the standard deviation-based approach and the linear regression-based approach, were employed in this study. The method specifically leverages the red, green, and blue color channels of image pixels and creates a correlation between the green channel of the background and the green channel of the various layers of graphene. This method proves to be a feasible alternative to deep learning-based graphene recognition and traditional microscopic analysis. The proposed methodology performs well under conditions where the effect of surrounding light on the graphene-on-oxide sample is minimum and allows rapid identification of the various graphene layers. Here, the study additionally addresses the functionality of the proposed methodology with nonhomogeneous lighting conditions, showcasing successful prediction of graphene layers from images that are lower in quality compared to typically published in literature. In all, the proposed methodology opens up the possibility for the non-destructive identification of graphene layers from optical images by utilizing a new and versatile method that is quick, inexpensive, and works well with fewer images that are not necessarily of high quality.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Towards AI Based Data Classification for Decision Making During Testing

During the development of high-consequence items, test systems should be capable of differentiating between test failures resulting from narrowly missing requirements versus those indicating potentially catastrophic faults. In many instances, classifying the data corresponds to simply identifying whether measured waveforms have approximately the anticipated shape. Cast in this light, the problem reduces to converting raw data into a form optimal for use with neural network classifiers. This manuscript investigates different means of representing raw data for image classification. Raw data plots and Short Time Fourier Transform (STFT) spectrograms are classified by both custom built, small-scale, Convolution Neural Networks (CNN) and open-source, multi-million parameter, pre-trained deep CNNs. In the case of time varying frequency content, the STFTs provide images with greater detail and can be accurately classified with simpler networks. This requires less memory and runs faster than classifying the raw data using the more sophisticated options—making STFTs optimal for applications with memory constraints. STFTs are not a panacea. In some cases the time-domain signal contains useful information that should not be discarded. Rather than using raw data or STFTs, the images can be constructed from both by using red and green channels of an RGB image to visualize the real and imaginary components of the transform, with the raw data occupying the blue channel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Optical image analysis of WSe 2 − thresholding for layer detection

The fast and reliable layer identification of two-dimensional transition metal dichalcogenide (TMD), such as WSe 2 , is essential to investigating their thickness-dependent electronic and optical properties. This article presents efficient optical image thresholding methodology designed to segment the mono, bi, and tri-layer regions of WSe 2 flakes mechanically exfoliated onto a SiO 2 /Si substrate. The optical images were first preprocessed to exclude the background effect and analyzed using the pixel medians and interquartile ranges for fundamental color channels—red, green, and blue (RGB). The analysis of red channel pixel intensities yielded three distinct ranges, serving as thresholds for layer segmentation: monolayer (111.0–118.0), bilayer (103.0–110.0), and tri-layer (93.0–103.0). Similarly, thresholds were established for each color channel, facilitating a comparative study of the segmentation performances. Further, the intersection-over-union ($IoU$) calculations revealed that the red and green channels demonstrated greater than 99 % and 90 % accuracy in differentiating each layer, respectively. This approach yields remarkable results without substantial data calibration that utilizes time-intensive heuristic techniques. Moreover, the proposed methodology offers the flexibility to compare performances across different color channels, expanding the applicability for other 2D material systems.

2D Materials↗
DOE OSTI2022

Estimating Channel Width for the Middle Green River Using Remote Sensing

Channel geometry can affect the abundance and quality of fish habitats along rivers in the Colorado River basin. However, previous analyses of sediment transport and channel narrowing in the middle Green River have focused on very limited reaches of the river and have used techniques with varying degrees of resolution, leading to potential problems with interpretation and generalization to the overall reach. The goal of this study is to develop comprehensive baseline information for channel width estimation along the Green River by combining a high-resolution digital elevation model (DEM) derived from Light Detection and Ranging (LiDAR) data and Sentinel-2 multispectral satellite imagery. The objectives are to (1) estimate channel width using Sentinel-2 satellite imagery alone, (2) estimate channel width using the high-resolution DEM with ancillary information extracted from the multispectral imagery, and (3) examine the agreement in channel width estimates between the products derived from the two methods to gain insights for cost effective channel-width monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Hydrate-bearing sands in the Terrebonne Basin record the transition from ponded deposition to bypass in the deep-water Gulf of Mexico

Here, we integrated well and seismic data to interpret the stratigraphic architecture and the paleogeographic evolution of two hydrate-bearing sand reservoirs in the Terrebonne Basin in the deep-water Gulf of Mexico. These sands were deposited during the final stage of ponded fill within the Terrebonne Basin. The Green sand filled the final accommodation in the basin as a perched apron; it was formed coincidentally with downstream erosion and sediment bypass which cut through a canyon formed on the southeastern edge of the basin. Subsequently, a low energy turbidite channel continued to bypass sediment through the basin and aggrade. The Orange sand was then deposited as a second perched apron and it was once again incised by the low energy turbidite channel. The Green and Orange sands are regional sheet sands that extend and thicken deep into the Terrebonne Basin. They provide a connected regional aquifer that captured and focused gas migration to the crest of the structure where hydrate was formed. These sands were formed by large, unconfined, turbidity flows. We envision that they will be composed of coarser grained deposits relative to previously explored hydrate reservoirs within levee deposits. As such, they have the potential to be relatively coarse grained and hence have higher permeability: a condition favorable to production of hydrate deposits.

3D seismic interpretation↗
DOE OSTI2020

Utilizing a Database of Simulated Geometric Albedo Spectra for Photometric Characterization of Rocky Exoplanet Atmospheres

In anticipation of future flagship missions focused on the goal of achieving direct imaging of rocky exoplanets, we have developed a database of models to help the community examine the potential spectral characteristics of a broad range of rocky planet atmospheres. Using the publicly available Planetary Spectrum Generator, we have computed a grid of 141,600 rocky exoplanet geometric albedo spectra across a seven-dimensional parameter space. Using this grid, we have performed a color–color analysis seeking to identify the most useful near-UV and red or near-infrared photometric follow-up channels to combine with a green-optical (discovery) spectral channel. We found that a combination of filters at 0.4, 0.58, and ∼0.8 μm were able to distinguish between atmospheres with moderate-to-high concentrations of four different dominant absorbing constituents, given at least 10 hr of observation on a star at 10 parsec with a 15 m class space telescope; however, more moderate abundances similar to those of solar system rocky bodies would be more challenging to detect. We recommend that future missions seeking to characterize directly imaged rocky exoplanets by colors alone further consider multiband photometry as a first discriminator for planetary characteristics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm

The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. Here this study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Shape- and Orientation-Dependent Scattering of Isolated Gold Nanostructures Using Polarized Dark-Field Microscopy

In this work, we demonstrate a simple and robust method for measuring the shape- and orientation-dependent optical scattering of various plasmonic nanostructures adsorbed onto a solid surface using polarized dark-field microscopy. By analyzing the dark-field scattering images of gold nanostructures using grazing incidence polarized light with a bench-top microscope, we are able to correlate optical scattering from the individual nanostructures with their shape and orientation. Depending on the size, shape, and orientation of the plasmonic nanostructures, they exhibit characteristic angle- and polarization-dependent scattering signals. Extracting the red, green, and blue channels of the scattering signals from a color-imaging detector as a function of azimuthal angle provides a polar plot of color intensity values that is characteristic of the underlying nanostructure. Examples are presented for various nanostructures, including spherical nanoparticles, nanotriangles, and nanorods. Experimental results are complemented by numerical calculations of the scattering spectra of representative model nanostructures. We demonstrate that the polarization- and orientation-dependent scattering behavior is a consequence of various localized surface plasmon modes existing in the nanostructures and can be used to verify the shape of individual objects as well as quickly identify the orientation of numerous objects on a densely populated substrate. We anticipate that this method will provide a rapid and efficient complement to the typical structural analysis of nanoparticles that is achieved by electron microscopy as well as providing a simple method for generating detailed information of the optical scattering of various types of individual plasmonic nanostructures.

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

RadVolViz: An Information Display-Inspired Transfer Function Editor for Multivariate Volume Visualization

In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. Here, we devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deep learning for in situ data compression of large turbulent flow simulations

As the size of turbulent flow simulations continues to grow, in situ data compression is becoming increasingly important for visualization, analysis, and restart checkpointing. For these applications, single-pass compression techniques with low computational and communication overhead are crucial. In this paper we present a deep-learning approach to in situ compression using an autoencoder architecture that is customized for three-dimensional turbulent flows and is well suited for contemporary heterogeneous computing resources. The autoencoder is compared against a recently introduced randomized single-pass singular value decomposition (SVD) for three different canonical turbulent flows: decaying homogeneous isotropic turbulence, a Taylor-Green vortex, and turbulent channel flow. Our proposed fully convolutional autoencoder architecture compresses turbulent flow snapshots by a factor of 64 with a single pass, allows for arbitrarily sized input fields, is cheaper to compute than the randomized single-pass SVD for typical simulation sizes, performs well on unseen flow configurations, and has been made publicly available. The results reported here show that the autoencoder dramatically outperforms a randomized single-pass SVD with similar compression ratio and yields comparable performance to a higher-rank decomposition with an order of magnitude less compression in regard to preserving a number of important statistical quantities such as turbulent kinetic energy, enstrophy, and Reynolds stresses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

AEflow (Autoencoder fluid flow compression network) [SWR-22-29]

As the size of turbulent flow simulations continues to grow, in situ data compression is becoming increasingly important for visualization, analysis, and restart checkpointing. For these applications, single-pass compression techniques with low computational and communication overhead are crucial. In this paper we present a deep-learning approach to in situ compression using an autoencoder architecture that is customized for three-dimensional turbulent flows and is well suited for contemporary heterogeneous computing resources. The autoencoder is compared against a recently introduced randomized single-pass singular value decomposition (SVD) for three different canonical turbulent flows: decaying homogeneous isotropic turbulence, a Taylor-Green vortex, and turbulent channel flow. Our proposed fully convolutional autoencoder architecture compresses turbulent flow snapshots by a factor of 64 with a single pass, allows for arbitrarily sized input fields, is cheaper to compute than the randomized single-pass SVD for typical simulation sizes, performs well on unseen flow configurations, and has been made publicly available. The results reported here show that the autoencoder dramatically outperforms a randomized single-pass SVD with similar compression ratio and yields comparable performance to a higher-rank decomposition with an order of magnitude less compression in regard to preserving a number of important statistical quantities such as turbulent kinetic energy, enstrophy, and Reynolds stresses.

King, Ryan↗
DOE OSTI2023

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Water, Protons, and the Gating of Voltage-Gated Potassium Channels

Ion channels are ubiquitous throughout all forms of life. Potassium channels are even found in viruses. Every cell must communicate with its surroundings, so all cells have them, and excitable cells, in particular, especially nerve cells, depend on the behavior of these channels. Every channel must be open at the appropriate time, and only then, so that each channel opens in response to the stimulus that tells that channel to open. One set of channels, including those in nerve cells, responds to voltage. There is a standard model for the gating of these channels that has a section of the protein moving in response to the voltage. However, there is evidence that protons are moving, rather than protein. Water is critical as part of the gating process, although it is hard to see how this works in the standard model. Here, we review the extensive evidence of the importance of the role of water and protons in gating these channels. Our principal example, but by no means the only example, will be the Kv1.2 channel. Evidence comes from the effects of D2O, from mutations in the voltage sensing domain, as well as in the linker between that domain and the gate, and at the gate itself. There is additional evidence from computations, especially quantum calculations. Structural evidence comes from X-ray studies. The hydration of ions is critical in the transfer of ions in constricted spaces, such as the gate region and the pore of a channel; we will see how the structure of the hydrated ion fits with the structure of the channel. In addition, there is macroscopic evidence from osmotic experiments and streaming current measurements. The combined evidence is discussed in the context of a model that emphasizes the role of protons and water in gating these channels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Evolutionary diversity of proton and water channels on the oxidizing side of photosystem II and their relevance to function

One of the reasons for the high efficiency and selectivity of biological catalysts arise from their ability to control the pathways of substrates and products using protein channels, and by modulating the transport in the channels using the interaction with the protein residues and the water/hydrogen-bonding network. Here, this process is clearly demonstrated in Photosystem II (PS II), where its light-driven water oxidation reaction catalyzed by the Mn 4 CaO 5 cluster occurs deep inside the protein complex and thus requires the transport of two water molecules to and four protons from the metal center to the bulk water. Based on the recent advances in structural studies of PS II from X-ray crystallography and cryo-electron microscopy, in this review we compare the channels that have been proposed to facilitate this mass transport in cyanobacteria, red and green algae, diatoms, and higher plants. The three major channels (O1, O4, and Cl1 channels) are present in all species investigated; however, some differences exist in the reported structures that arise from the different composition and arrangement of membrane extrinsic subunits between the species. Among the three channels, the Cl1 channel, including the proton gate, is the most conserved among all photosynthetic species. We also found at least one branch for the O1 channel in all organisms, extending all the way from Ca/O1 via the ‘water wheel’ to the lumen. However, the extending path after the water wheel varies between most species. The O4 channel is, like the Cl1 channel, highly conserved among all species while having different orientations at the end of the path near the bulk. The comparison suggests that the previously proposed functionality of the channels in T. vestitus (Ibrahim et al., Proc Natl Acad Sci USA 117:12624–12635, 2020; Hussein et al., Nat Commun 12:6531, 2021) is conserved through the species, i.e. the O1-like channel is used for substrate water intake, and the tighter Cl1 and O4 channels for proton release. The comparison does not eliminate the potential role of O4 channel as a water intake channel. However, the highly ordered hydrogen-bonded water wire connected to the Mn 4 CaO 5 cluster via the O4 may strongly suggest that it functions in proton release, especially during the S 0 → S 1 transition (Saito et al., Nat Commun 6:8488, 2015; Kern et al., Nature 563:421–425, 2018; Ibrahim et al., Proc Natl Acad Sci USA 117:12624–12635, 2020; Sakashita et al., Phys Chem Chem Phys 22:15831–15841, 2020; Hussein et al., Nat Commun 12:6531, 2021).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hydrologic Evaluation of Smallmouth Bass Spawning Sites in the Upper Green River Using LiDAR

The smallmouth bass has been considered one of the most problematic invasive species in the upper Colorado River basin. As a predator of larval fish and juveniles, this nonnative species is an impediment to conservation efforts for endangered fish species of the Colorado River basin including Colorado pikeminnow (Ptychocheilus lucius), razorback sucker (Xyrauchen texanus), humpback chub (Gila cypha), and bonytail chub (Gila elegans). Several measures have been recommended to control smallmouth bass populations to conserve the endangered fish species. Among these measures are efforts to control flows during the spawning season to disrupt smallmouth bass reproduction. Previous work has suggested that smallmouth bass spawning in areas of the Green River could be inhibited during higher flows from the Flaming Gorge Dam due to lack of suitable habitat. Little research has been conducted to evaluate an optimal range of flow increases (“flow spikes”) needed to disrupt smallmouth bass reproduction in the Green River. Current research proposals to evaluate the effectiveness of flow spikes on controlling smallmouth bass reproduction target post-spawning flows of 4,500 cfs, maintained for three days, to inundate side-channel spawning habitats. In this report, we present results of a topographic study of smallmouth bass spawning sites in the upper Green River to characterize flow levels needed to inundate spawning habitats. This study used remotely sensed Light Detection and Ranging (LiDAR) data collected along the entire Green River, including channel margins and floodplain areas, to model water surface elevations and flow spikes needed to connect and inundate smallmouth bass spawning sites.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Chemical control of excited-state reactivity of the anionic green fluorescent protein chromophore

Controlling excited-state reactivity is a long-standing challenge in photochemistry, as a desired pathway may be inaccessible or compete with other unwanted channels. An important example is internal conversion of the anionic green fluorescent protein (GFP) chromophore where non-selective progress along two competing torsional modes (P: phenolate and I: imidazolinone) impairs and enables Z-to-E photoisomerization, respectively. Developing strategies to promote photoisomerization could drive new areas of applications of GFP-like proteins. Motivated by the charge-transfer dichotomy of the torsional modes, we explore chemical substitution on the P-ring of the chromophore as a way to control excited-state pathways and improve photoisomerization. As demonstrated by methoxylation, selective P-twisting appears difficult to achieve because the electron-donating potential effects of the substituents are counteracted by inertial effects that directly retard the motion. Conversely, these effects act in concert to promote I-twisting when introducing electron-withdrawing groups. Specifically, 2,3,5-trifluorination leads to both pathway selectivity and a more direct approach to the I-twisted intersection which, in turn, doubles the photoisomerization quantum yield. Our results suggest P-ring engineering as an effective approach to boost photoisomerization of the anionic GFP chromophore.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗