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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Noninvasive Medical Diagnostics & Treatment Using Ultrasonics

In parallel to the industrial application of NDE to flaw detection and material property determination, the medical community has succesfully adapted such methods to the noninvasaive diagnostics and treatment of many conditions and disorders of the human body.

NDE noninvasive diagnostics destruction of blood c↗
DOE OSTI2022

Unique Role of GeO 2 as a Noninvasive Promoter of Nano‐Sized Zeolite Crystals

Abstract The synthesis of zeolites with nano‐sized dimensions is often limited to a narrow design space that conventionally relies upon the design of organics to direct hierarchical materials. Here, it is demonstrated that the addition of an inorganic modifier, germanium oxide (GeO 2 ), to a zeolite growth mixture directs the formation of crystals with ultrasmall dimensions. This effect is observed for zeolites ZSM‐11 and ZSM‐5 over a range of synthesis conditions wherein the role of GeO 2 in zeolite crystallization deviates from its typical function as a heteroatom. Notably, the final products contain trace amounts of Ge, which indicates the inorganic modifier does not compete for sites in the zeolite framework based on its formation of a discrete phase that enables GeO 2 recovery. Catalytic tests using the methanol‐to‐hydrocarbons reaction reveal significant enhancement in the performance of zeolite catalysts prepared with GeO 2 compared to reported examples of nano‐sized zeolites. These findings highlight a potentially generalizable and commercially viable synthesis method to reduce mass‐transport limitations in zeolites for diverse applications.

Chemistry↗
DOE OSTI2020

Noninvasive Near‐Field Spectroscopy of Single Subwavelength Complementary Resonators

Abstract Subwavelength metallic resonators provide a route to achieving strong light–matter coupling by means of tight confinement of resonant electromagnetic fields. Investigation of such resonators however often presents experimental difficulties, particularly at terahertz (THz) frequencies. A single subwavelength resonator interacts weakly with THz beams, making it difficult to probe it using far‐field methods, whereas arrays of resonators exhibit inter‐resonator coupling, which affect the resonator spectral signature and field confinement. Here, traditional far‐field THz spectroscopy is systematically compared with aperture‐type THz near‐field microscopy for investigating complementary THz resonators. While the far‐field method proves impractical for measuring single resonators, the near‐field technique gives high signal‐to‐noise spectral information, only achievable in the far‐field with resonator arrays. At the same time, the near‐field technique allows to analyze single resonators without significant interaction with the near‐field probe. Furthermore, the near‐field technique allows highly confined fields and surface waves to be mapped in space and time. This information gives invaluable insight into spectral response in resonator arrays. This near‐field microscopy and spectroscopy method enables investigations of strong light–matter coupling at THz frequencies in the single‐resonator regime.

Hale, Lucy L.↗
DOE OSTI2023

Balanced Fast-SpaciMS capillary configurations provide practically noninvasive channel-average measurements in catalytic monoliths

Spatially resolved capillary-inlet mass spectrometry (SpaciMS) provides a detailed picture of the spatiotemporal evolution of reaction network in catalytic monoliths. In the present work, we combine the SpaciMS experiments with a newly developed non-isothermal 3D CFD model for heterogeneously catalyzed reactive flows, including diffusion and permeation through the coated catalyst and channel wall. We explore how the capillary size and sampling rate can be balanced to minimize the impact on probed-channel conversions and provide species concentration measurements representative of the free channel average. In all studied configurations, the balanced sampling-rate fraction is noticeably higher than the corresponding capillary occlusion fraction. For a typical 350-micron capillary that occupies 11% of the channel cross- section, the balanced sampling rate represents 43% of the channel flow. Furthermore, the present work shows that together, a balanced SpaciMS configuration and numerical simulation provide accurate channel-averaged information of a monolithic catalyst under realistic operation conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Depth Thermography: Noninvasive 3D Temperature Profiling Using Infrared Thermal Emission

Here, we introduce a technique based on infrared thermal emission, termed depth thermography, that can remotely measure the temperature distribution beneath the surface of certain objects. Depth thermography utilizes the thermal-emission spectrum in the semitransparent spectral region of the target object to extract its temperature as a function of depth, in contrast with conventional thermography, which uses the spectrally integrated thermally emitted power to measure the surface temperature. Coupled with two-dimensional imaging, for example using an infrared hyperspectral camera or scanning a single-pixel spectrometer, this technique can yield volumetric temperature distributions. Furthermore, we carried out a proof-of-concept experiment on an asymmetrically heated fused-silica window, extracting the temperature distribution throughout the sample. Depth thermography may enable noncontact volumetric temperature measurements of microscopic objects such as multilayer electronic devices or macroscopic volumes of liquids and gases, as well as simultaneous all-optical measurements of optical and thermal properties of materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Noninvasive Thermal Mass Flow Meter for Safeguards

This project addressed the long-standing nuclear safeguards problem of detecting in a timely manner the unauthorized production of significant quantities of highly enriched uranium in gas centrifuge enrichment plants (GCEPs).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Noninvasive Detection, Tracking, and Characterization of Aerogel Implants Using Diagnostic Ultrasound

Medical implants are routinely tracked and monitored using different techniques, such as MRI, X-ray, and ultrasound. Due to the need for ionizing radiation, the two former methods pose a significant risk to tissue. Ultrasound imaging, however, is non-invasive and presents no known risk to human tissue. Aerogels are an emerging material with great potential in biomedical implants. While qualitative observation of ultrasound images by experts can already provide a lot of information about the implants and the surrounding structures, this paper describes the development and study of two simple B-Mode image analysis techniques based on attenuation measurements and echogenicity comparisons, which can further enhance the study of the biological tissues and implants, especially of different types of biocompatible aerogels.

36 MATERIALS SCIENCE↗
DOE OSTI2024

On the Generalizability of Time-of-Flight Convolutional Neural Networks for Noninvasive Acoustic Measurements

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.

47 OTHER INSTRUMENTATION↗