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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

One–Step Fabrication of Nanocrystalline Nanonetwork SnO 2 Gas Sensors by Integrated Multilaser Processing

An integrated multilaser process is developed to fabricate nanocrystalline nanonetwork SnO 2 gas sensors in one integrated procedure, which combines electrodes fabrication, nanomaterials deposition, and postannealing. Interdigit electrodes are fabricated on an Au-coated fused silica substrate using a picosecond (ps) laser, which ablates the Au coating from the back of the substrate to pattern the electrodes. A novel transmitted Ps laser deposition (TPLD) process is designed to deposit SnO 2 nanonetwork on the interdigit electrodes with precise deposition area control under a close target-to-substrate distance. The obtained SnO 2 nanonetwork is in situ postannealed by a CO 2 laser to improve the crystallinity, while the nano morphology and grain size keep intact. To investigate the morphology and formation process of the nanonetwork, the microstructure of the laser-deposited SnO 2 layer is characterized. As a result, the crystallization control of CO 2 laser annealing is investigated through analyzing the Raman spectrum, X-ray diffraction (XRD) patterns, and lattice structures of the samples. By exposed to H 2 atmosphere, the fabricated gas sensor is demonstrated for H 2 monitoring.

08 HYDROGEN↗
DOE OSTI2024

RuBisCO activity assays: a simplified biochemical redox approach for in vitro quantification and an RNA sensor approach for in vivo monitoring

Abstract Background Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is the most abundant soluble protein in nature. Extensive studies have been conducted for improving its activity in photosynthesis through approaches like protein engineering. Concurrently, multiple biochemical and radiolabeling assays have been developed for determining its activity. Although these existing assays yield reliable results, they require addition of multiple external components, rendering them less convenient and expensive. Therefore, in this study, we have developed two relatively cheaper, convenient, and easily reproducible assays for quantitative and qualitative estimation of RuBisCO activity. Results We simplified a contemporary NADH based spectrophotometric RuBisCO assay by using cyanobacterial cell lysate as the source for Calvin cycle enzymes. We analyzed the influence of inorganic carbon substrates, CO 2 and NaHCO 3 , and varying protein concentrations on RuBisCO activity. Ribulose-1,5-bisphosphate (RuBP) consumption rates for the cultures grown under 5% CO 2 were 5–7 times higher than the ones grown with 20 mM NaHCO 3 , at different protein concentrations. The difference could be due to the impaired activity of carbonic anhydrase in the cell lysate, which is required for the conversion of HCO 3 − to CO 2 . The highest RuBisCO activity of 2.13 nmol of NAD + / µg of Chl-a/ min was observed with 50 µg of protein and 5% CO 2 . Additionally, we developed a novel RNA-sensor based fluorescence assay that is based on the principle of tracking the kinetics of ATP hydrolysis to ADP during the conversion of 3-phosphoglycerate (3-PG) to 1,3-bisphosphoglycerate (1,3-BPG) in the Calvin cycle. Under in vitro conditions, the fluorometric assay exhibited ~ 3.4-fold slower reaction rate (0.37 min −1 ) than the biochemical assay when using 5% CO 2 . We also confirmed the in vivo application of this assay, where increase in the fluorescence was observed with the recombinant strain of Synechocystis sp. PCC 6803 (SSL142) expressing the ADP-specific RNA sensor, compared to the WT. In addition, SSL142 exhibited three-fold higher fluorescence when supplemented with 20 mM NaHCO 3 as compared to the cells that were grown without NaHCO 3 supplementation. Conclusions Overall, we have developed a simplified biochemical assay for monitoring RuBisCO activity and demonstrated that it can provide reliable results as compared to the prior literature. Furthermore, the biochemical assay using 5% CO 2 (100% relative activity) provided faster RuBP consumption rate compared to the biochemical assay utilizing 20 mM NaHCO 3 (30.70% relative activity) and the in vitro fluorometric assay using 5% CO 2 (29.64% relative activity). Therefore, the absorbance-based biochemical assay using 5% CO 2 or higher would be suitable for in vitro quantification of the RuBisCO activity. On the other hand, the RNA-sensor based in vivo fluorometric assay can be applied for qualitative analysis and be used for high-throughput screening of RuBisCO variants. As RuBisCO is an enzyme shared amongst all the photoautotrophs, the assays developed in this study can easily be extended for analyzing the RuBisCO activities even in microalgae and higher plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Data collected using small uncrewed aircraft systems during the TRacking Aerosol Convection interactions ExpeRiment (TRACER)

Abstract. The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas, area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower-atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UASs). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado Boulder RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 d was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. The quality-controlled TRACER-UAS observations from the CopterSonde and RAAVEN can be found at https://doi.org/10.5439/1969004 (Lappin, 2023) and https://doi.org/10.5439/1985470 (de Boer, 2023), respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Object Detection and Recognition with PointPillars in LiDAR Point Clouds – Comparisions

In the field of autonomous systems, neural networks have been leveraged for object detection and recognition in 2-dimensional images captured by cameras. Other types of sensors are available for sensing surroundings, including LiDAR sensors, and corresponding networks have been developed to perform detection and recognition in the point clouds generated by these sensors. The approaches are similar, both perform convolutions, but have distinct characteristics and challenges. In designing and configuring autonomous systems, a variety of LiDAR sensors are available, along with configurable deep neural networks to leverage their data. This work presents a review of the PointPillars network, an evolution of the seminal PointNet, comparing accuracy and training time relative to different LiDAR sensors, network and training parameters, CPU and GPU hardware, and the criticality of the use of reflective intensity as a feature. The value of using reflectivity as a predictive feature is explored and quantified to determine if it makes a significant difference in accuracy of the PointPillars network. Two separate LiDAR sensors are utilized, a 16-plane and a 32-plane, and corresponding accuracies and training times with the PointPillars network are evaluated.

LiDAR, machine learning, neural network, object re↗
DOE OSTI2021

Feature Extraction: Improving Remote Sensor Classification of Non-Proliferation

This research focuses on developing algorithms for nuclear non-proliferation detection using remote sensor modeling. To improve the performance of classification models, we implemented a data pipeline with feature extraction. This pipeline takes raw data and transforms it into smaller data points called features that still describe the model. Improving this classification works towards the departments of energy’s missions of ensuring American’s security and prosperity by creating technology that addresses nuclear challenges. To conduct this analysis, we used the Python programming language and some key packages, including tsfresh and TSFEL. Originally tsfresh was selected because it has the most statistical features out of all the packages. Later TSFEL was incorporated due to the additional features it can extract from data, such as temporal and spectral. However, feature extraction becomes challenging in the presence of missing values. In this case, two additional Python packages were added to our workflow, NumPy and pandas, allowing for the feature extraction process to handle unknown values. Our data pipeline was tested on data collected from a simulation that describes the process state of a physical example. The results show the pipeline’s capability to consume and extract a total 17 features from tabular data. Future work includes producing classifications using decision tree-based models such as XGBoost and improving data collection by analyzing feature importance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Digital Engineering Sensor Architectures for Future Microreactor Builds

The development of new nuclear reactors continues to be high risk during the construction phase, with many programs exceeding their budget by 2–3×. This work entails an investigation of sensors that can be utilized during the construction of microreactors and their incorporation into a digital twin supporting the construction process. These sensors can be used to monitor construction progress, inform of variances from the expected design, and verify that the reactor is being built to the planned building information management model. This will ensure reactor equipment and components accurately interact through welding or additive manufacturing technologies. This study illustrates the importance of the digital engineering architecture for microreactor deployment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Old Woman Creek Wetland Sediment and Electrochemical Sensor Microbial Community, 2023

We are developing a technique to monitor microbiological activities referred to as zero resistance ammetry, which entails the deployment of graphite electrodes in sediments. Measurement of current between electrodes of contrasting redox regimes and/or predominant terminal electron accepting processes can be used as an indicator of the extents of microbiological activity. We deployed an electrode array at depths of 2 mm, 4 mm, 76 mm, 78 mm, 152 mm, 154 mm, 227 mm, and 229 mm below the wetland sediment water interface in the Old Woman Creek National Estuarine Research Center, Huron, OH, USA (Lat. = 41.380833, Long. = -82.508889). A core was collected from adjacent sediment and subsamples were collected from depth intervals of 0 – 25 mm, 25 – 127 mm, 127 – 128 mm, and below 178 mm. To determine if the microbial communities attached to the electrodes were reflective of the adjacent sediment-associated microbial community, we conducted a 16S rRNA gene-based (V4 region) survey of these respective materials. This data package contains the results of these surveys, including metadata on the depths from which samples were collected (samples.csv), DNA extraction and sequencing information (OWC_DEPTH_AMPLICON_SEQUENCING_METADATA), sequence processing information (OWC_DEPTH_BIOINFORMATIC_METADATA.csv), an operational taxonomic unit (OTU) table (OWC_DEPTH_97OTUS_TABLE.csv), and nucleotide sequences of OTUs (OWC_DEPTH_97OTUS_SEQS.fasta). All files can be opened using a text-editing application. The fasta file is compatible with bioinformatics applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Flow Enhanced Electrochemical Sensor Performance in Complex Salt Systems

To advance MSR MC&A practices, Argonne National Laboratory developed and optimized robust flow enhanced electrochemical sensors (FEES) and multielectrode array voltammetry sensors (MAVS) which provide accurate near-real time measurements of actinides in molten salts. Flow-enhanced electrochemical sensors are installed directly into MSR flow conduits for inline salt chemistry measurement, while MAVS are deployed in quiescent salt conditions enabling online species concentration determination in stationary salt vessels. This report summarizes efforts to improve electrochemical sensor technological readiness through (1) sensor testing in complex, high concentration molten salt systems to demonstrate low uncertainty actinide measurements in MSR-representative solutions and (2) demonstrations of sensor performance in challenging real-world conditions in collaboration with partner MSR institutions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Development of ASTM International D8405—Standard Test Method for Evaluating PM 2.5 Sensors or Sensor Systems Used in Indoor Applications

Sensors and sensor systems for monitoring fine particles with aerodynamic diameters smaller than 2.5 µm can provide real-time feedback on indoor air quality and thus can help guide actions to manage indoor air pollutant concentrations. Standardized verification of the performance and accuracy of sensors and sensor systems is crucial for predicting the efficacy of such monitoring. A new ASTM International standard test method (ASTM D8405) was created for this need and is the most exacting laboratory protocol published to date for evaluating indoor air quality sensors and sensor systems measuring particles smaller than 2.5 µm in diameter. ASTM D8405 subjects sensors and sensor systems to five test phases: (1) an initial particle concentration ramp; (2) exposure to various temperature and humidity conditions; (3) exposure to interfering particles; (4) temperature cycling; and (5) a final particle concentration ramp to assess drift. This paper discusses the development of the standard test method, key aspects of the testing process, example evaluation results, and a comparison of this standard test method against peer evaluation protocols.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Performance of low-cost indoor air quality monitors for PM 2.5 and PM 10 from residential sources

Advances in particle sensor design and manufacturing have enabled the development of low-cost air quality monitors (LCMs). Here, the sensors use light scattering to estimate mass concentration and thus require evaluation for aerosols of varied composition and size distribution. We tested the performance of six LCMs designed for home use and having a retail price under US$300 in October 2018. We assessed their performance by comparing their output to reference PM 2.5 and PM 10 measurements from 21 common residential sources and from infiltrated outdoor PM 2.5 . Reference data were obtained by using gravimetric measurements to adjust time-resolved output from an aerosol spectrometer with both electrical mobility and optical particle sensors. Compared by linear regression to reference measurements, LCMs had negative intercepts and slopes of 1–2 for infiltrated outdoor PM 2.5 . Semi-quantitative responses (~50–200% of actual PM 2.5 ) were obtained for varied aerosols including minerals (ultrasonic humidifier, vacuuming, test dust); combustion products (incense, mosquito coil, extinguished candles); microwave popcorn; and cooking involving frying or grilling. LCMs had low or no response to sources for which all mass was in particles smaller than 0.25 μm, including steady candle flames and cooking without frying or grilling. PM 10 data from LCMs was more variable than PM 2.5 .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Optimize the Autonomous Sensor Fish Device for Understanding Interactions of Aquatic Animals with Marine and Hydrokinetic and Hydro Systems (CRADA 476) Final Report

This project at the Pacific Northwest National Laboratory (PNNL) enhances our understanding of how aquatic life interacts with marine renewable energy (MRE) and hydropower systems through the development and testing of advanced sensor technology. The Marine Sensor Fish (MSF) and its smaller variants, the Sensor Fish Mini (SF Mini) and Flexible Sensor Fish (FSF), measure critical environmental stressors, such as shear forces, pressure changes, and strike impacts, that are essential for assessing the risks posed by energy devices, including tidal turbines, hydropower installations, and other MRE infrastructure. Rigorous field testing has shown that these tools are effective in capturing detailed data on turbulence, pressure variations, strike impacts, and other stressors near these devices, providing valuable insights into the conditions faced by aquatic species. Economically, these tools offer renewable energy developers a practical solution for streamlining environmental assessments and ensuring regulatory compliance, ultimately reducing costs associated with evaluating and mitigating ecological impacts. This research also benefits the public by promoting the growth of renewable energy sources that protect aquatic ecosystems, advancing both sustainable energy production and environmental stewardship.

13 HYDRO ENERGY↗
DOE OSTI2020

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Data analytic methods are being developed to address the problem of how to assign a sensor set in a nuclear facility such that a requisite level of process monitoring capability is realized and that the sensor set is sufficiently rich to determine the status of the individual sensors with respect to need for calibration. There is an awareness in the nuclear industry that data analytics combined with rich sensor sets represent a means to improve operations and reduce costs. In the industry the calibration problem has been previously approached as an empirical data-driven problem with several methods having been developed. However, the experience of the utilities over the past ten years with these methods indicates that the absence of physics-based information renders the data-driven approach less reliable. Complicating factors such as the inherent variability of operation (both equipment alignment and operating condition) can confound a pure data-driven approach while there are no rigorous guidelines for determining what constitutes an adequate sensor set. The solution under development to overcome these shortcomings supplements the data analytic method with process information in a so-called process-constrained data-analytic approach. Simple balance equations are written for generic components (e.g., mechanical pump, valve, and heat exchanger). These do not require a priori knowledge of process parameters, such as heat transfer coefficients or friction factors. All that is needed on the part of the utility user is to identify the components and how they are connected. This report describes the development of a parallel computing capability for determining the optimal sensor set. The optimal sensor set problem suffers from the curse of dimensionality. Computation time increases exponentially as the size of the system grows. To overcome this difficulty a pre-conditioner algorithm is developed to find an approximate solution close the actual solution. This serves as a seed for the full-blown algorithm and acts to constrain the space that must searched. The optimization algorithms are described and the implementation on a parallel computing platform is described. The application of the method to a use case we are solving in collaboration with our utility partner served to illustrate how the default sensor set in a nuclear plant may not provide sufficient coverage to infer sensor calibration status.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Sensor Impacts Evaluation and Verification: Expert Interview Responses

The sensor configuration/deployment method has critical impacts on energy efficient building control and thermal comfort. However, traditional sensor techniques for building operation and fault detection and diagnostics (FDD) are not necessarily optimal in terms of energy efficiency and thermal comfort, and their global effects are not thoroughly investigated. In an effort to address and overcome this limitation, a multilaboratory and multiyear project, “Sensor Impact Evaluation and Verification,” was proposed. Its purpose is to develop a framework to investigate the impacts of sensor deployment and configuration on building energy optimization, FDD, occupant thermal comfort, and potentially grid efficiency. The first project task was a literature review to establish a solid knowledge of and a background related to sensor technologies and placement. To accomplish this task, an extensive review of previous research literature was performed. A series of expert interviews were conducted to augment the findings of the literature review. This report summarizes the interview design and interview results and findings. The interview was designed and performed to (1) investigate the current status and limitations of sensor configuration, (2) identify the research gaps and expectations for potential improvements in sensor configuration and deployment, and (3) integrate expert (e.g., researcher, building operation practitioner) knowledge and experience to develop use-case scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Development of an autonomous solvent extraction system to isolate astatine-211 from dissolved cyclotron bombarded bismuth targets

Abstract Cyclotron-produced astatine-211 ( 211 At) shows tremendous promise in targeted alpha therapy (TAT) applications due to its attractive half-life and its 100% α-emission from nearly simultaneous branched alpha decay. Astatine-211 is produced by alpha beam bombardment of naturally monoisotopic bismuth metal ( 209 Bi) via the ( α, 2n ) reaction. In order to isolate the small mass of 211 At (specific activity = 76 GBq·µg −1 ) from several grams of acid-dissolved Bi metal, a manual milliliter-scale solvent extraction process using diisopropyl ether (DIPE) is routinely performed at the University of Washington. As this process is complex and time consuming, we have developed a fluidic workstation that can perform the method autonomously. The workstation employs two pumps to concurrently deliver the aqueous and organic phases to a mixing tee and in-line phase mixer. The mixed phases are routed to a phase settling reservoir, where they gravity settle. Finally, each respective phase is withdrawn into its respective pump. However, development of a phase boundary sensor, placed in tandem with the phase settling reservoir, was necessary to communicate to the system when withdrawal of the denser aqueous phase was complete (i.e., the intersection of the two phases was located). The development and optimization of the autonomous solvent extraction system is described, and the 211 At yields from several ~1.1 GBq-level 211 At processing runs are reported.

211At↗
DOE OSTI2025

Advanced Manufactured Strain Sensors for Extreme Environments

Experimentation at irradiation test facilities are essential for reducing the innovation time of developmental fuels, fuel cladding, and structural materials employed in next-generation nuclear reactors. However, due to the harsh conditions generated in such reactors and limited instrumentation space, the evaluation of a material’s mechanical properties is often limited to characterization after the materials have been removed from reactor conditions, or post-irradiation examination. These experiments are costly, time-consuming, and fail to capture the critical time-evolving phenomena that occur during the irradiation experiments. Advanced manufactured digital image correlation patterns and strain sensor devices serve as two promising technologies that can be deployed in the confined and challenging orientations of these irradiation experiments while also providing key insight on salient materials phenomena (i.e., mechanical properties). To guide the development of the printed strain sensors prior to their deployment in critical experiments, the adhesion strength between the substrate and printed film interface is measured via tensile testing and a non-contact laser-induced spallation technique. The establishment of these process control steps helped guide the successful fabrication and testing of direct-write strain sensing devices discussed in this work. The fabrication process controls are necessary for enabling the sustained operation of these strain sensing device through experimentation and minimize the potential for premature failure.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Advanced Manufactured Strain Sensors for Extreme Environments

Experimentation at irradiation test facilities are essential for reducing the innovation time of developmental fuels, fuel cladding, and structural materials employed in next-generation nuclear reactors. However, due to the harsh conditions generated in such reactors and limited instrumentation space, the evaluation of a material’s mechanical properties is often limited to characterization after the materials have been removed from reactor conditions, or post-irradiation examination. These experiments are costly, time-consuming, and fail to capture the critical time-evolving phenomena that occur during the irradiation experiments. Advanced manufactured digital image correlation patterns and strain sensor devices serve as two promising technologies that can be deployed in the confined and challenging orientations of these irradiation experiments while also providing key insight on salient materials phenomena (i.e., mechanical properties).To guide the development of the printed strain sensors prior to their deployment in critical experiments, the adhesion strength between the substrate and printed film interface is measured via tensile testing and a non-contact laser-induced spallation technique. The establishment of these process control steps helped guide the successful fabrication and testing of direct-write strain sensing devices discussed in this work. The fabrication process controls are necessary for enabling the sustained operation of these strain sensing device through experimentation and minimize the potential for premature failure.

36 - MATERIALS SCIENCE↗
DOE OSTI2021

Surface Acoustic Wave Sensors for Refrigerant Leak Detection: Compact, Inexpensive, Selective and Sensitive Sensors

Pacific Northwest National Laboratory (PNNL), in collaboration with Parker and Hannifin, is developing a compact, inexpensive, and highly sensitive and selective surface acoustic wave sensor coated with fluorophillic sorbent for detecting fluorocarbon leaks from HVAC systems. Having a highly effective sorbent sensitive to fluorocarbon refrigerant vapors provides a means to develop a sensing device for leak detection. Surface acoustic wave (SAW) sensors with a gas sensing film deposited between the delay lines or on the interdigital transducer have been used to detect gas and vapor molecules in harsh environments with high sensitivity. As part of this project, PNNL screened several sorbent materials that are shown to be selective towards fluorocarbon refrigerant (R32) molecule. The identified sorbent materials were synthesized, characterized, and tested towards R32 using various spectroscopic technique. Next, the sorbent material was coated on a SAW sensor as a thin film using vapor deposition and drop coating methods. The coated thin film was further characterized and tested towards the detection of pure R32 and R32 in ambient air at room temperature to demonstrate the SAW response towards R32 in presence of other competing gases and vapors in air.

42 ENGINEERING↗
DOE OSTI2024

Laboratory Testing of the Temperature Sensor Qualification Device

The Temperature sensor Qualification Device (TQD) has been developed and tested under laboratory conditions to evaluate its thermal performance and the reliability of its retractable sensor system. The TQD is designed to provide an isothermal environment for temperature sensors in both test and reference zones during irradiation experiments. Initial testing revealed minimal radial and azimuthal temperature variations but identified a significant axial gradient. To address this, design modifications were implemented, including enhanced insulation and the addition of a small heater at the device's top. These changes are expected to produce a more uniform axial temperature profile, though further testing is recommended prior to irradiation. The retractable sensor mechanism was rigorously tested, achieving 1,350 cycles at room temperature and 332 cycles at 400°C before failure. The primary failure mechanism was the thermocouple becoming stuck in the wire guide or capillary tube. Based on these results, design improvements were proposed, such as incorporating a load cell for force monitoring and a stepper motor for precise sensor positioning.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗