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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Reflection Optical Imaging to Study Oxygen Evolution Reactions

Discovery of high-efficiency water-splitting catalysts is critical for clean hydrogen fuel generation. Quantitative and reliable chemical imaging methods can play a crucial role in providing critical catalyst efficiency information for materials development and screening. Here, we developed a reflection optical imaging method to map the performance of transition metal oxide catalysts for oxygen evolution reaction (OER). The generated high concentration of oxygen in the electrolyte will decrease the refractive index of the solutions near the electrode and change the reflectivity of optical images. We successfully imaged and measured the OER on cobalt oxide, and the localized current density and overpotential distribution maps have been extracted. The oxidation and reduction of cobalt oxide can be imaged with our reflection optical imaging method as well. These maps showed that the catalytic activities are quite different at different locations, and some regions showed much higher activities. This is due to the non-uniformity created by the catalyst synthesis process and demonstrates the importance of using the optical imaging method to map the localized catalytic efficiency. In addition, the NiFe alloy’s OER catalytic activities have been studied as well. The proposed reflection optical imaging method could find many applications in catalyst activity measurement and screening.

Electrochemistry↗
DOE OSTI2021

MOA-2007-BLG-400 A Super-Jupiter-mass Planet Orbiting a Galactic Bulge K-dwarf Revealed by Keck Adaptive Optics Imaging

We present Keck/NIRC2 adaptive optics imaging of planetary microlensing event MOA-2007-BLG-400 that resolves the lens star system from the source. We find that the MOA-2007-BLG-400L planetary system consists of a 1.71 ± 0.27M {sub Jup} planet orbiting a 0.69 ± 0.04M {sub ⊙} K-dwarf host star at a distance of 6.89 ± 0.77 kpc from the Sun. So, this planetary system probably resides in the Galactic bulge. The planet–host star projected separation is only weakly constrained due to the close-wide light-curve degeneracy; the 2σ projected separation ranges are 0.6–1.0 au and 4.7–7.7 au for close and wide solutions, respectively. This host mass is at the top end of the range of masses predicted by a standard Bayesian analysis. Our Keck follow-up program has now measured lens-source separations for six planetary microlensing events, and five of these six events have host star masses above the median prediction under the assumption that assumes that all stars have an equal chance of hosting planets detectable by microlensing. This suggests that more massive stars may be more likely to host planets of a fixed mass ratio that orbit near or beyond the snow line. These results also indicate the importance of host star mass measurements for exoplanets found by microlensing. The microlensing survey imaging data from NASA’s Nancy Grace Roman Space Telescope (formerly WFIRST) mission will be doing mass measurements like this for a huge number of planetary events.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Analysis of an optical imaging system prototype for autonomously monitoring zooplankton in an aquaculture facility

Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms. Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate. Furthermore, our comparison shows that advancement in both optical imaging and ML are needed in order for the SAO prototype to yield comparable results to manual community monitoring in an aquaculture facility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High level of integration of front-end imaging optics system for electron cyclotron emission imaging diagnostics on the DIII-D tokamak

The optics system of the Electron Cyclotron Emission Imaging (ECEI) diagnostics on the DIII-D tokamak has been developed and installed for high-resolution temperature fluctuations measurement in the previous decade. The System-on-Chip approach upgrade was finished in June 2019 with high integrated receiver array with 20 individual modules. In this paper, we design new optics with high-level integration. In the new optics system, there will be two mirrors from plasma to receiver array, including one replaceable curved mirror out of the vacuum and one fixed curved mirror in the vessel. There are two options with 1:1 and 1:2 magnifications for high poloidal resolution and larger poloidal window modes. Overall, the new optics system has multiple improvements, including lighter weight, smaller size, more flexible installation and alignment. The synthetic ECEI module is used to evaluate the new optics system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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

Simultaneous measurements of volume, pressure, optical images, and crystal structure with a dynamic diamond anvil cell: A real-time event monitoring system

The dynamic diamond anvil cell (dDAC) technique has attracted great interest because it possibly provides a bridge between static and dynamic compression studies with fast, repeatable, and controllable compression rates. The dDAC can be a particularly useful tool to study the pathways and kinetics of phase transitions under dynamic pressurization if simultaneous measurements of physical quantities are possible as a function of time. We report the development of a real-time event monitoring (RTEM) system with dDAC, which can simultaneously record the volume, pressure, optical image, and structure of materials during dynamic compression runs. In particular, the volume measurement using both Fabry–Pérot interferogram and optical images facilitates the construction of an equation of state (EoS) using the dDAC in a home-laboratory. We also developed an in-line ruby pressure measurement (IRPM) system to be deployed at a synchrotron x-ray facility. This system provides simultaneous measurements of pressure and x-ray diffraction in low and narrow pressure ranges. The EoSs of ice VI obtained from the RTEM and the x-ray diffraction data with the IRPM are consistent with each other. The complementarity of both RTEM and IRPM systems will provide a great opportunity to scrutinize the detailed kinetic pathways of phase transitions using dDAC.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
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 OSTI2022

Post maximum light and late time optical imaging polarimetry of type I superluminous supernova 2020znr

ABSTRACT Optical imaging polarimetry was conducted on the hydrogen poor superluminous supernova 2020znr during three phases after maximum light (≈ +34 d, +288 d, and +289 d). After instrumental and interstellar polarization correction, all measurements are consistent with null-polarization detection. Modelling the light curve with a magnetar spin-down model shows that SN2020znr has similar magnetar and ejecta parameters to other SLSNe. A comparison of the best-fitting values discussed in the literature on SN 2017egm and SN 2015bn, two hydrogen poor SLSNe showing an increase of polarization after maximum light, suggests that SN 2020znr has higher mass ejecta that may prevent access to the geometry of the inner ejecta with optical polarimetry. The combined information provided by spectroscopy and light-curve analysis of type I SLSNe may be an interesting avenue to categorize the polarization properties of this class of transients. This approach would require to expand the sample of SLSNe polarimetry data currently available with early and late time epochs new measurements.

2015bn↗
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 OSTI2023

Multimodal nonlinear optical imaging via evanescent wave excitation

A system for multimodal nonlinear optical imaging is provided. Each mode uses a high NA objective to cause total internal reflection excitation at a sample-substrate interface. The system has a femtosecond oscillator to generate pulses used for two beams. The objective receives at least one beam, redirects the received at least one beam through a dielectric substrate to cause the TIR and produces corresponding evanescent waves in a portion of the sample adjacent to the sample-substrate interface, and collects a backward-propagating beam of pulses of responsive light. The portion of the sample illuminated by the evanescent waves emits responsive light. Different modes or combinations of the distinct modalities may be selected to access complementary chemical and structural information for various chemical species near the sample-substrate interface. Each mode may have mode-specific control such as selective beam blocking, power ratios and filtering.

Doughty, Benjamin L.↗
DOE OSTI2024

All-optical image denoising using a diffractive visual processor

Abstract Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images – implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30–40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Plasmonics-Enhanced Optical Imaging Systems for Bioenergy Research

This final report summarizes the key accomplishments of the project entitled “Plasmonics-Enhanced Optical Imaging Systems for Bioenergy Research”. The goal of this project is aimed at developing innovative and improved imaging instrumentation for visualization and quantitative characterization of biomarkers related to molecular processes and cellular function in living plants. This project focused on three specific aims: 1) Development of Advanced Multimodal Optical Sensing And Imaging Combinatory (MOSAIC) System for SERS biosensing and bioimaging. 2) Evaluation of MOSAIC technologies for sensing and imaging gene expression in plant systems using plasmonic nanoprobes. 3) Application of MOSAIC techniques to bioenergy research.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Accelerated Nano-Optical Imaging through Sparse Sampling

The integration time and signal-to-noise ratio are inextricably linked when performing scanning probe microscopy based on raster scanning. This often yields a large lower bound on the measurement time, for example, in nano-optical imaging experiments performed using a scanning near-field optical microscope (SNOM). Here, in this study, we utilize sparse scanning augmented with Gaussian process regression to bypass the time constraint. We apply this approach to image charge-transfer polaritons in graphene residing on ruthenium trichloride (α-RuCl 3 ) and obtain key features such as polariton damping and dispersion. Critically, nano-optical SNOM imaging data obtained via sparse sampling are in good agreement with those extracted from traditional raster scans but require 11 times fewer sampled points. As a result, Gaussian process-aided sparse spiral scans offer a major decrease in scanning time.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optical Imaging on Z LDRD: Design and Development of Self-Emission and Debris Imagers

We present an overview of the design and development of optical self-emission and debris imaging diagnostics for the Z Machine at Sandia National Laboratories. These diagnostics were designed and implemented to address several gaps in our understanding of visibly emitting phenomenon on Z and the post-shot debris environment. Optical emission arises from plasmas that form on the transmission line that delivers energy to Z loads and on the Z targets themselves; however, the dynamics of these plasmas are difficult to assess without imaging data. Addressing this, we developed a new optical imager called SEGOI (Self-Emission Gated Optical Imager) that leverages the eight gated optical imagers and two streak cameras of the Z Line VISAR system. SEGOI is a low cost, side-on imager with a 1 cm field of view and 30-50 µm spatial resolution, sensitive to green light (540-600 nm). This report outlines the design considerations and development of this diagnostic and presents an overview of the first diagnostic data acquired from four experimental campaigns. SEGOI was fielded on power flow experiments to image plasmas forming on and between transmission lines, on an inertial confinement fusion experiment called the Dynamic Screw Pinch to image low density plasmas forming on return current posts, on an experiment designed to measure the magneto Rayleigh-Taylor instability to image the instability bubble trajectory and self-emission structures, and finally on a Magnetized Liner Inertial Fusion (MagLIF) experiment to image the emission from the target. The second diagnostic developed, called DINGOZ (Debris ImagiNG on Z), was designed to improve our understanding of the post-shot debris environment. DINGOZ is an airtight enclosure that houses electronics and batteries to operate a high-speed (10-400 kfps) camera in the Z Machine center section. We report on the design considerations of this new diagnostic and present the first high-speed imaging data of the post-shot debris environment on Z.

42 ENGINEERING↗
DOE OSTI2023

Optical Imaging Methods for Volumetric Additive Manufacturing

Tomographic volumetric additive manufacturing (VAM) is a technique that enables light-induced curing of photoresins into complex 3D end use objects within a single step. This is made possible through projecting tomographically patterned light energy into a photo-curable resin volume within a rotating container. In order to monitor, quantify, and control curing during tomographic VAM, researchers need to visualize the curing parts in real-time. This may enable advancements toward dynamic, controlled and closedloop VAM methods in the future, as some researchers have shown. Herein we briefly review various optical imaging methods used to monitor printing in real-time, as well as provide some perspectives for future needs and considerations for imaging methods in VAM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ultrafast Metal Electrodeposition Revealed by In Situ Optical Imaging and Theoretical Modeling towards Fast–Charging Zn Battery Chemistry

Metallic Zn is a preferred anode material for rechargeable aqueous batteries towards a smart grid and renewable energy storage. Importantly, understanding how the metal nucleates and grows at the aqueous Zn anode is a critical and challenging step to achieve full reversibility of Zn battery chemistry, especially under fast-charging conditions. Here, by combining in situ optical imaging and theoretical modeling, we uncover the critical parameters governing the electrodeposition stability of the metallic Zn electrode, that is, the competition among crystallographic thermodynamics, kinetics, and Zn 2+ -ion diffusion. Moreover, steady-state Zn metal plating/ stripping with Coulombic efficiency above 99 % is achieved at 10-100 mA cm -2 in a reasonably high concentration (3 M) ZnSO 4 electrolyte. Significantly, a long-term cycling-stable Zn metal electrode is realized with a depth of discharge of 66.7% under 50 mA cm -2 in both Zn || Zn symmetrical cells and MnO 2 || Zn full cells.

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

Molecular homing and retention of muscle membrane stabilizing copolymers by non-invasive optical imaging in vivo

First-in-class membrane stabilizer Poloxamer 188 (P188) has been shown to confer membrane protection in an extensive range of clinical conditions; however, elements of the systemic distribution and localization of P188 at the organ, tissue, and muscle fiber levels in vivo have not yet been elucidated. Here we used non-invasive fluorescence imaging to directly visualize and track the distribution and localization of P188 in vivo. The results demonstrated that the Alx647 probe did not alter the fundamental properties of P188 to protect biological membranes. Distribution kinetics in mdx mice demonstrated that Alx647 did not interface with muscle membranes and had fast clearance kinetics. In contrast, the distribution kinetics for P188-Alx647 was significantly slower, indicating a dramatic depot and retention effect of P188. Results further demonstrated the significant retention of P188-Alx647 in the skeletal muscle of mdx mice, showing a significant genotype effect with a higher fluorescence signal in the mdx muscles over BL10 mice. High-resolution optical imaging provided direct evidence of P188 surrounding the sarcolemma of skeletal and cardiac muscle cells. Taken together, these findings provide direct evidence of muscle-disease-dependent molecular homing and retention of synthetic copolymers in striated muscles thereby facilitating advanced studies of copolymer-membrane association in health and disease.

60 APPLIED LIFE SCIENCES↗