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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 379 records · Page 21

Microscopic Object Classification through Passive Motion Observations with Holographic Microscopy

Digital holographic microscopy provides the ability to observe throughout a volume that is large compared to its resolution element without the need to refocus through the volume. This capability enables simultaneous observations of large numbers of small objects within such a volume. We have constructed a microscope that can observe a volume 0.4 x 0.4 x 1.0 µm with submicrometer resolution for observation of microorganisms and minerals in liquid environments in earth and on potential planetary missions. Because environmental samples are likely to contain mixtures of inorganics and microorganisms that are of comparable sizes near the resolution limit of the instrument, discrimination of living and non-living objects may be difficult. The motion of motile organisms can be used to readily distinguish them from non-motile objects (live or inorganic), but additional methods are required to distinguish non-motile organisms and inorganic objects that are of comparable size, but different composition and structure. In this paper we evaluate the use of passive motion to make this discrimination by evaluating diffusion and buoyancy characteristics of objects in the field of view.

Lindensmith, Christian↗

DHMx (Digital Holographic Microscope Experience) Software Tool

The DHMx (Digital Holographic Microscope Experience) is an open-source software suite designed to perform reconstructions from off-axis holograms obtained from either a real-time stream or from disk with a graphical interface for commanding and display

Fregoso, S. Felipe↗

A Cosmic Microscope for the Preheating Era

Light fields with spatially varying backgrounds can modulate cosmic preheating, and imprint the nonlinear effects of preheating dynamics at tiny scales on large scale fluctuations. This provides us a unique probe into the preheating era which we dub the “cosmic microscope”. We identify a distinctive effect of preheating on scalar perturbations that turns the Gaussian primordial fluctuations of a light scalar field into square waves, like a diode. The effect manifests itself as local non-Gaussianity. We present a model, “modulated partial preheating”, where this nonlinear effect is consistent with current observations and can be reached by near future cosmic probes.

Cosmology of Theories beyond the SM↗

Going to Extremes: Architecting Holographic Microscopes for Extreme Environments

Bacterial life exists on earth in extreme environments. These are environments described by large temperature excur- sions, large pressure excursions, and large radiation excursions from the nominal conditions near sea level which are largely populated by humans. These extreme locales represent such places as the ocean ice, the briny pools of Death Valley and deep mines, the acidic hot springs of the High Sierra or Yellowstone, or even the clouds of our upper atmosphere. The preponderance of bacterial life in these extreme environments here on earth suggests that bacterial life might likely exist in the extreme environments of our own solar system, such as the icy moons of Europa or Enceladus. Thus, architecting instruments for detect- ing life in these extreme environments on earth builds confidence that we can architect such instruments for flight missions. In this paper, we discuss our experience with designing digital holo- graphic microscope instruments to enable detection of bacteria in several extreme environments. In particular we discuss three different instruments. The first is our field instrument which is a small, portable instrument for examination of remote sites. The second is submersible instrument which enables exploration of deep aquatic environments and is deployed on a ocean-going drone. The third is a balloon-borne instrument to examine the bacterial content of the upper atmosphere. We will provide a review of each instrument and discuss aspects of instrument engineering for each particular application.

Ramirez, Alex↗

Design of a Low-Cost, Submersible, Digital Holographic Microscope for in Situ Microbial Imaging

The methodologies for studying marine microbiology typically consist of utilizing instrumentation within a laboratory. This typically requires extracting a sample from its place of origin prior to examination, which may be days after collection. Oftentimes, the solution is to bring the lab to the ocean, which may be costly and provide further limitations for a sterile and stable laboratory environment. Here we present a low-cost, submersible, digital holographic microscope (DHM) designed to image marine microorganisms (such as bacteria and plankton) in their natural underwater environment. Our instrument eliminates the need to transport samples and allows for instantaneous data collection of microbes in-situ. The DHM achieves sub-micron spatial resolution and is paired with artificial intelligence for the detection and tracking of specimens to reduce the overall collected data. This instrument also aims to reduce the cost of manufacturing and field expenses relative to marine microbiological research. “Off the shelf” components were selected in the design process of this instrument which allows us to achieve precise results without sacrificing data quality. The DHM itself costs under one thousand dollars and features a low-cost high-resolution camera, the Arducam MT9J001. Included in the design were five main subsystems: optical, mechanical, electrical, power, and machine learning. Our on board computer and artificial intelligence consist of a Raspberry Pi 4 (8 Gb) and Google Coral USB Tensorflow accelerator. Instrument testing has successfully proven our abilities of data acquisition for at least two hours in depths of at least forty meters below sea level. Additionally, our artificial intelligence system is currently capable of tracking up to ten areas of interest in a fraction of a second with over ninety percent confidence via the neural net driven by the tensor cores on the Google Coral. Furthermore, we have demonstrated the versatility of our instrument by mounting it on an ocean-going ROV, the BlueRov2 by BlueRobotics. Our tests on the BlueRov2 exemplified the cost-effective nature of a submersible and reusable instrument that can be implemented in moderate environments and on most vessels.

Wallace, James Kent↗

In-Situ Scanning Electron Microscope Experiments for Microscale Mechanical Testing and Validated Modeling of Fiber Reinforced Thermoplastics

A novel, in-situ, scanning electron microscope (SEM) mechanical testing capability for materials at the microscale which provides experimental validation to a machine learning (ML) toolset for full-field validation of physics-based micromechanics models is being developed by researchers at NASA Glenn Research Center. These are enabling technologies for the integration of multiscale digital twins for materials into system level models which will result in the improved performance, material discovery, reduced production cost and time, rapid characterization, and prognostic structural health monitoring (SHM) for materials and structures for extreme environments in support of NASA space exploration missions. In order to bridge the material structure-to-system gap for digital twins, physics-based models must be experimentally validated at multiple length scales. Seminal microscale experiments, conducted at the Air Force Research Laboratory (AFRL), were limited to transverse compression of single-layer, unidirectional thermoset polymer matrix composite (PMC) micropillar specimens [1]. The early phases of the current project followed those initial results and setup to reproduce the compression testing of PMC material on the custom-built piezoelectric actuated micromechanical testing rig built by MicroTesting Solutions LLC. In this work, samples of thermoplastic PMC material were first machined into 3 mm cubes, and then further machining and final milling was done using a Focused Ion Beam (FIB). The initial experiment was done on a pillar roughly 20 µm x 20 µm x 40 µm tall. Additional pillars were milled with final sizes ranging from 20 µm x 20 µm x 40 µm tall to 40 µm x 40 µm x 65 µm tall. A speckle pattern for in-situ full-field measurements using Digital Image Correlation (DIC) was applied with platinum, which was coated on the surface, and then the FIB was used to mill away some of the coating to produce an irregular pattern of Pt on the pillar surface. The samples were loaded into the custom testing rig and placed into the SEM and loaded under compression until failure. Images were collected in the SEM during testing. Post-processing of the images was conducted using DIC to obtain full-field displacement and strain measurements elucidating the role of the matrix as well as fiber-fiber interaction at the microscale within the composite subjected to compression loading well into the non-linear regime of the material. Moreover, the evolution of fiber-matrix debonding and matrix cracking is observed in-situ at the microscale. This data, along with images segmented with a newly developed ML toolset [2], was used to create and validate physics-based micromechanics models. An image of the failed micropillar is shown in Figure 1. The techniques developed in the initial compression experiment was tailored to the validation needs of the models and expanded to include different sized samples as well as possibly tension and fatigue.

Laura Wilson↗

Microscopic Understanding of the Effects of Impurities in Low RRR SRF Cavities

The SRF community has shown that introducing certain impurities into high-purity niobium can improve quality factors and accelerating gradients. We question why some impurities improve RF performance while others hinder it. The purpose of this study is to characterize the impurities of niobium coupons with a low residual resistance ratio (RRR) and correlate these impurities with the RF performance of low RRR cavities so that the mechanism of impurity-based improvements can be better understood and improved upon. The combination of RF testing, temperature mapping, frequency vs temperature analysis, and materials studies reveals a microscopic picture of why low RRR cavities experience low BCS resistance behavior more prominently than their high RRR counterparts. We evaluate how differences in the mean free path, grain structure, and impurity profile affect RF performance. The results of this study have the potential to unlock a new understanding on SRF materials and enable the next generation of high Q/high gradient surface treatments.

Howard, K.↗

Microscopic understanding of the effects of impurities in low RRR SRF cavities

The SRF community has shown that introducing certain impurities into high-purity niobium can improve quality factors and accelerating gradients. We question why some impurities improve RF performance while others hinder it. The purpose of this study is to characterize the impurities of niobium coupons with a low residual resistance ratio (RRR) and correlate these impurities with the RF performance of low RRR cavities so that the mechanism of impurity-based improvements can be better understood and improved upon. The combination of RF testing, temperature mapping, frequency vs temperature analysis, and materials studies reveals a microscopic picture of why low RRR cavities experience low BCS resistance behavior more prominently than their high RRR counterparts. We evaluate how differences in the mean free path, grain structure, and impurity profile affect RF performance. The results of this study have the potential to unlock a new understanding on SRF materials and enable the next generation of high Q/high gradient surface treatments.

Howard, K.↗

Using machine learning to jointly harness the strength of microscopic, fundamental-science driven and macroscopic, application-driven experiments

The PARADIGM project aims at accelerating progress in science by quantitatively answering the following question: What is the optimal combination of fundamental-science and application driven experiments to maximally reduce pertinent data uncertainties? Hence, we are bridging between microscopic experiments and data, and macroscopic simulations and experiments. Answering this question entails solving a high-dimensional and complex optimization problem which we solve with machine learning techniques.

LANSCE↗

Application of Machine Learning to Multigroup Microscopic Cross Sections

Presentation discussing the research and development of deep neural network models for modeling microscopic neutron cross-section data in the Griffin reactor physics application for pebble-bed reactors. This work details advancements made between the last review meeting in July 2024 until July 2025.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Automated vehicle microscopic energy consumption study (AV-Micro): Data collection and model development

While the Adaptive Cruise Control (ACC) system in automated vehicles (AVs) is expected to impact transportation energy significantly, existing AV energy consumption models only directly adopt those developed with Human-driven Vehicle (HV) data without even slight adaptation or calibration to accommodate unique AV energy consumption features. This study will investigate how accurately HV data-based models can predict the energy consumption of AVs. Empirical trajectory data and corresponding instantaneous energy consumption rates from both AVs and HVs were collected. We adopted two classical HV data-based models to fit these data. The calibration results indicated that these models yield around 20 30% prediction errors for AVs. To further improve the prediction accuracy, this study designed an AV-Micro model by incorporating components of multiple classic energy consumption models that better capture ACC energy consumption features, including piecewise driving behavior. With this, the AV-Micro model achieves lower than 10% prediction errors. The AV-Micro model’s high consistency across different test runs was verified with statistical significance tests, demonstrating its adaptability in different driving profiles. To confirm the discrepancies between the energy consumption features of AVs and HVs, more statistical significance tests were conducted to show that the AV-Micro model cannot be directly applied to HV data. The findings by calibrated AV-Micro models revealed that AVs consume approximately 80.5–146.4 J more energy than HVs for each meter traveled. Furthermore, the frequency analysis of energy consumption indicates that there is still some room for AVs to improve energy efficiency, particularly given their larger amplitude high-frequency fluctuations.

33 ADVANCED PROPULSION SYSTEMS↗