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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An Adaptive Reference Scheme to Extend the Functional Range of Optical Backscatter Reflectometry in Extreme Environments

Optical frequency domain reflectometry (OFDR) measurements are performed by recording the interference pattern of light backscattered by density fluctuations or Bragg gratings along the length of an optical fiber. Changes in local temperature or strain in the fiber cause shifts in the backscattered light spectrum, which can be calibrated to the applied temperature or strain. Comparing the backscattered optical spectra from each reflection site with a reference (unstrained) spectrum allows for quantification of local spectral shifts. While mature OFDR-based technologies can provide high-precision spatially distributed measurements over kilometer lengths, the post-processing approaches used to recover spectral shifts from raw optical intensity data can limit the use of OFDR in harsh environments. This paper presents a novel approach to post-processing OFDR data which extends the usability of optical fibers exposed to harsh environments. This approach is the first to use the spectral shift quality, calculated for two OFDR measurements, to identify an appropriate reference scan from which the spectral shift can be determined. Here, three experimental data sets are used to test the algorithm: (1) an optical fiber heated to >950°C, (2) an aluminum-embedded optical fiber under strain from differential thermal expansion, and (3) an optical fiber exposed to a total neutron flux of 2×10 13 n/cm 2 /s over 40 hours in a nuclear test reactor. Results show that using a metric of quality to select an appropriate reference measurement extends the functional range of OFDR strain and temperature sensors. Furthermore, this algorithm can be applied to existing OFDR data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Advanced SOFC quality control and the role of manufacturing defects on stack reliability

Atrex Energy rolled out first commercial SOFC generator products in 2013. Following that we embarked on implementation of mass production in order to ramp up productivity and lower the costs in the meantime. To ensure the goal of mass production, an accurate, cost effective and consistent quality control methodology needs to be in place. The aim of this DOE funded project is to devise a machine and methodology for cell quality control using modern non-destructive testing methods and cell rejection criterion based on the empirical determination of the role of manufacturing flaws on stack degradation and reliability. The work scope encompasses two goals aimed at 1) developing automatable techniques for objective flaw detection and 2) developing an understanding of the ramifications of flaws on stack and cell reliability. By accomplishing these two goals it is likely that an advanced stage Quality Control (QC) device can be developed. This project scope encompasses: 1) Invention of a high speed automated instrument for spatial mapping of surface and sub-surface imperfections in production cells (ex situ) using modern imaging techniques. 2) Identification of rejection criteria upon which to screen cells from production by: a. Magnification of the effect of production defects on cell degradation through fabrication and testing of micro-cells which encompass these imperfections. b. The long term testing of full size production cells both as single cells and in small stacks, in order to correlate the (in situ) performance and degradation with the imperfection type (identified ex-situ) and hence be able to classify imperfections as actual defects. We first built a defect library with the cells containing imperfections classified and documented. The cells with various imperfections were tested to evaluate the imperfection potential impact on cell electrochemical performance. Evaluations were conducted on minicell, single cell and stack levels. On minicell level, pinhole, scratch, crack, contamination, and pop-out were investigated. We found: (1) none of the imperfections causes calamity type failure, (2) pinholes do not show measurable effect on minicell performance, (3) imperfection such as electrolyte abrasion, scratch, and pop out cause minicell potential to fluctuate by different degrees; the cells with such imperfections exhibit sensitivity of potential to high fuel utilization and “self healing” behavior. On single cell level we evaluated the effect of long crack and high population pinholes. The cell with a long crack does not show performance compromise at start. There is no potential fluctuation or sensitivity to high fuel utilization or catastrophic failure. The voltage degradation due to thermal cycle is slightly higher than normal cells. The cells with high population pinholes perform surprisingly as well as normal cells over a period of 3,500h. Even when subjected to cyclic load condition, the cells 4 show negligible degradation. Our cells appear to have “self immunization” feature which fends off the adversary effects of certain imperfections. Defects were also evaluated on stack level. Two stacks were made each with ten defect cells. The first stack was tested for 13,2000h under steady load condition while the second was tested for 9,000h under transient load conditions. We found: (1) voltage fluctuation and sensitivity to thermal cycle were not observed in contrast to minicell test, (2) of the various cracks examined, including longitudinal cracks, circumferential crack, crack near closed and open ends, none exhibited distinguishable impact on performance, (3) defect cells exhibited higher degradation rates on average under steady load condition; Individually some defect cells performed similarly or better than reference cells, (4) under steady load condition all the cells degrade monotonically with degradation rate increasing with time, (5) under cyclic (transient) load condition, both defect and reference cells exhibit complex dynamic behaviors, (6) fuel loss (leak) was measured occasionally for stack 1, the cells with crack showed the highest fuel loss increase, nevertheless, no correlation with performance degradation could be established with certainty, and (7) no catastrophic failure ever happened other than incremental degradation. Posttest inspection and microanalysis were carried out; the observations are summarized in what follows: (1) Electrolyte pinholes, some scratches and IC porosity became undetectable after testing. (2) The cathode covering the popout defects is intact without any cracking or delamination. (3) The scratch was covered by cathode in pristine state; Ni oxide or NiO-YSZ filled the space where electrolyte was missing. After >10,000h operation, the cathode that used to cover the scratch was missing but the size of the scratch remains unchanged; there is a fairly dense Ni oxide layer on top of the scratch. (4) All of the cracks remain their original dimensions. Current collector kept longitudinal cracks closed which mitigated the adversary effect of crack on cell performance/durability. (5) The crack at open end showed some deterioration manifested by a small oxidation zone in the vicinity of the crack. Wide spectrum of imaging techniques were surveyed and evaluated to detect tubular SOFC defects. Optical methods, with and without assistance of expression technique, were applied to detect all kinds of defects. Thermograph method in combination of CO2 leak, ultra-sound and electric potential was also extensively evaluated to detect surface and under-surface defects such as short circuit. Based on these works, laser camera has been selected to build the final QC machine vision inspection system. The Gocator laser profile sensor was setup to generate 2D surface intensity images of tubular SOFC surface which were analyzed with Sherlock machine vision software supplied by Teledyne Dalsa. The Gocator output was integrated with the Sherlock software and enabled live images to be imported by a Common Vision Blox driver. The Sherlock software was programmed to detect various surface defects from the surface intensity images generated by the Gocator. These measurements are subsequently used to make pass or fail decisions.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Development of an Energy Efficient and Cost Effective Autonomous Vehicle Research Platform

Commercialization of autonomous vehicle technology is a major goal of the automotive industry, thus research in this space is rapidly expanding across the world. However, despite this high level of research activity, literature detailing a straightforward and cost-effective approach to the development of an AV research platform is sparse. To address this need, we present the methodology and results regarding the AV instrumentation and controls of a 2019 Kia Niro which was developed for a local AV pilot program. This platform includes a drive-by-wire actuation kit, Aptiv electronically scanning radar, stereo camera, MobilEye computer vision system, LiDAR, inertial measurement unit, two global positioning system receivers to provide heading information, and an in-vehicle computer for driving environment perception and path planning. Robotic Operating System software is used as the system middleware between the instruments and the autonomous application algorithms. After selection, installation, and integration of these components, our results show successful utilization of all sensors, drive-by-wire functionality, a total additional power* consumption of 242.8 Watts (*Typical), and an overall cost of $118,189 USD, which is a significant saving compared to other commercially available systems with similar functionality. This vehicle continues to serve as our primary AV research and development platform.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Natural bacterial communities serve as quantitative geochemical biosensors

Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive. Here we show that DNA from natural bacterial communities can be used as a quantitative biosensor to accurately distinguish unpolluted sites from those contaminated with uranium, nitrate, or oil. These results indicate that bacterial communities can be used as environmental sensors that respond to and capture perturbations caused by human impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Review—Electrolytic Metal Atoms Enabled Manufacturing of Nanostructured Sensor Electrodes

Sensing materials play a key role in the successful implementation of electrochemical sensors, and nanotechnology has emerged as an important and rapidly growing field for stimulating the innovation of high-performance sensors. The fabrication, characterization, and evaluation of the nanostructured electrodes are therefore a focus of this field. Compared to a variety of dry and wet technologies which have been extensively developed for this purpose, electrochemical methods are typically convenient, highly effective, and potentially low-cost for the production of different nanostructures. This minireview is designed to introduce a unique electrochemical method - electrolytic metal-atom enabled manufacturing (EM 2 ) and its application in electrochemical sensors. The EM 2 technique employs electrolytic metal atoms generated from their corresponding salt precursor as a tool to nanostructure a wide range of substrate electrodes used in electrochemical sensors, based on a one-pot electrochemical deposition and dissolution of the metal atoms in the same electrolyte bath. Briefly, the metal atoms are electrodeposited on a substrate electrode during the cathode reduction, and they are selectively removed from the substrate during the subsequent anode oxidation. Because of the interactions between the electrolytic metal atoms and the substrate atoms, the repetitive electrodeposition and dissolution of the former on the substrate enable the nanostructuration of the substrate, particularly within its surface layers. The nanostructured electrodes have demonstrated very attractive performance for the determination of numerous analytes, such as high sensitivity and selectivity, high interference tolerance, and low detection limits. However, the EM 2 technique and the application of the resulting nanostructured electrodes in electrochemical sensors and beyond have still been very limitedly investigated. In order to bring the community from academic, industries, agencies, and customers together to develop the EM 2 technique and advance electrochemical sensor systems, this minireview will introduce the thermodynamic and kinetic fundamentals of this technique, the characterization of resulting nanostructures, the analysis of their electrochemical behavior, and the implementation of this technique for the development of advanced sensor electrodes. Finally, an outlook with a focus on further research areas is provided.

Jiang, Junhua (ORCID:0000000324379812)↗
DOE OSTI2018

Pixel Detector Developments for Tracker Upgrades of the High Luminosity LHC

This paper reports on the INFN (Istituto Nazionale di Fisica Nucleare, Italy) research activity in collaboration with FBK foundry, which is aiming at the development of new pixel detectors for the LHC Phase-2 upgrades. The R&D covers both planar pixel devices and 3D detectors built using columnar technology. All sensors are low thickness n-in-p type, as this is the general direction envisaged for the High Luminosity LHC pixel detector upgrades. Hybrid modules with 100 $\upmu $ m and 130 $\upmu $ m active thickness, connected to the PSI46dig readout chip, have been tested on beam test experiments. Selected preliminary results from test beams are described for both planar and 3D devices. The results on the 3D pixel sensors before irradiation are very satisfactory and support the conclusion that columnar devices are very good candidates for the inner layers of the upgrade pixel detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Self-powered Wireless Dual-mode Langasite Sensor for Pressure/Temperature Monitoring of Nuclear Reactors

Safety issues in nuclear power plants are of extreme importance. Numerous sensors are used within nuclear power plants for sensing and monitoring of its critical operation parameters. As currently implemented, during massive losses of power across the plant in accidents, the backup batteries for the monitoring sensors will ultimately be drained and can cease to operate. The overall objective of this NEUP project is to develop a novel self-powered wireless hybrid sensor which can accurately monitor both pressure and temperature using a single device, even in the extreme harsh environments of severe nuclear accidents, without the requirement of external electricity. At Virginia Tech side, a heat pipe assisted thermoelectric generator (TEG) energy harvester and associated power management and data transmission circuits were developed. The energy harvester can produce electrical power continuously, even in loss of plant and external power situations, providing power for important data transmission for accident diagnosis. The self-powered sensing system could be integrated directly onto key nuclear components including pipes, pump housings, heat exchangers, reactor vessels, and shielding structures inside and outside of the reactor core. Two TEG based energy harvesters which can work in relatively low (50-250 ºC) and high temperature ranges (250-350 ºC) were established and tested. In the prototypes, heat pipes were introduced to reduce the heat resistances of the device at the hot and cold ends. The power output of the heat pipe assisted TEG module is 2.22 W, which is 6 times more than one using an aluminum rod. In-lab tests showed that these devices canmeet the requirement to power the sensors and circuit during normal and off-normal conditions.Amodel was then built to analyze the thermal network in the energy harvester, and was used to optimize its performance and reduce its geometry size. The design and testing of the vibrational energy harvester were completed. The harvester, has shown to effectively increase the vibrational potential of the main coolant pump, though it has been shown that thermal energy has a much higher potential for the powering of sensors. In this project, a self-start charger circuit for the energy harvester was designed to regulate and manage the power. An energy storage element was incorporated into the energy harvesting module for sensors and wireless transceivers in an off-normal situation. The signal conditioning and wireless communication electronic circuits were developed and tested in the lab. An important consideration for an energy harvester within a nuclear power plant is the level of radiation it will encounter during operation. The two main sources of radiation in a nuclear power plant are gamma and neutron radiation. Aradiation experiment was conducted in the Westinghouse Electric Company during which the prototypes were placed in a 106 rad radiation environment to test their ability to operate in the extreme environment. The results showed that this level of radiation had little effect on the performance of the TEGmaterial and heat pipe heat exchanging system. Its influence on the circuit was still under research, however the need for more development on radiation hardened electronics is needed. Pressure measurement based on piezoelectric crystal resonators has advantages over the commercially available methods like capacitive, electromagnetic, optical, potentiometric, and piezo resistive strain gauges. Since the output of piezoelectric based sensors are digital in format, it has a very high resolution, high accuracy, and long-term stability. Quartz is one of the commonly used piezoelectric material in digital pressure sensors design. This material is elastic in nature, providing high stability, high repeatability, high elasticity, repeatable mechanical behavior for many cycles of operation, and is free of hysteresis. The quartz resonator has a high Q, which means that very high vibration can be driven with the very low electric power. Langasite (LGS) is a new promising piezoelectric material that combines the advantageous properties of quartz with a better performance at high temperatures. LGS will not undergo any phase transitions up to its melting temperature of 1473 C, and hence, the application of Langasite in measuring pressure at high temperatures is promising. In this project, the design and fabrication of the piezo oscillator crystal for temperature and pressure measurement has been completed. The entire sensor has been fabricated and tested to see its sensitivity to temperature and pressure changes. The design and optimization of the radiation shielding systems that can effective protect the duel-mode Langasite sensor inside reactor core were demonstrated. To maximize the radiation shielding effectiveness, different types of materials are investigated based upon recent developments of radiation shielding systems including aluminum + Boron carbide and bismuth borosilicate glass B2O3 in addition to tungsten/B4C. New materials based upon metatheses is included as potential shielding to mitigate neutron fluencies. The radiation shielding effectiveness of selected composites are evaluated within a broader neutron spectrum from thermal, epithermal, fast (1 MeV) and 15 MeV neutrons. Performance analysis was also conducted to evaluate the effects of selected composite systems.Aconceptual design was also proposed for the packaging and integration of the radiation shielding system with the sensor, and its applicability within the reactor core was discussed. At end of the project, all components necessary for a complete sensing and monitoring system, including a langasite sensor, electrodes, the TEG harvester, an energy storage device, and power management and transmission electronics were integrated to demonstrate feasibility in powering the sensor. Finally, integration of each component into a full package, and a full demonstration were done.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Integrated Hydrogen Sensor and Separator for Parabolic Trough Expansion Tanks

The National Renewable Energy Laboratory (NREL) and Acciona Energy USA Global are developing and implementing a process that addresses the issue of hydrogen buildup in parabolic trough power plants. Our method selectively removes hydrogen from the expansion tanks of the power plant to control hydrogen levels in the circulating heat-transfer fluid (HTF). As part of this effort, we developed a sensor that measures hydrogen partial pressure in the expansion-tank headspace gas. During previous work, we demonstrated that our sensor measures hydrogen levels over a wide range of partial pressure from 1.33 mbar down to 0.003 mbar. In this paper, we report our most recent work, in which we conceived and developed an integrated process module that performs both hydrogen sensing and separating functions. The sensor/separator measures hydrogen partial pressure in the headspace gas in the same way as our original sensor design. Additionally, the integrated module separates hydrogen from the headspace gas to reduce hydrogen to the level needed to maintain the performance of receivers in the collector field. Sensor data from the integrated module also provide an estimate of the rate at which hydrogen is being extracted from the headspace gas. We designed and fabricated a laboratory-scale integrated module and determined its performance in our laboratory at NREL. Testing showed that the module performed as expected in both sensing and separating modes. The accuracy of the sensing function was +/-10%. The hydrogen extraction rate for the separating mode was consistent with our modeling predictions. The primary benefit of this module is its simple design, both in terms of function and incorporation into the HTF subsystem of the power plant. The integrated module can be installed close to the HTF expansion tanks, where it draws headspace gas from the tanks, removes hydrogen, and returns the treated gas back to the tanks. We are currently installing a test-scale process at the Nevada Solar One (NSO) power plant to demonstrate this process and determine its operational performance. Results from the test-scale process will be used to specify and design the full-scale process that will be installed at NSO.

14 SOLAR ENERGY↗
DOE OSTI2024

Chemical detection system with at least one electronic nose

A system for predicting one or more analytes based on outputs from thin film gas sensors is provided. The system may comprise an electronic nose (e-nose). The e-nose may comprise the gas sensors and a first processor. The system may further comprise a second processor. The second processor may be configured to receive the output from each of the gas sensors, evaluate a prediction accuracy using an evaluation parameter of each of a plurality of models which are trained and tested and select a model from among the plurality of models to deploy based on a comparison of the evaluation parameter for each of the plurality of models and use the same. The second processor may also receive, an output of each of the gas sensors caused by unknown one or more analytes; and predict, using the deployed model, the one or more analytes that causes the output.

Ivanov, Ilia N.↗
DOE OSTI2018

Hydrogen Sensor for Parabolic Trough Expansion Tanks

The National Renewable Energy Laboratory and Acciona Energy North America are developing and implementing a process that addresses the issue of hydrogen buildup in parabolic trough power plants. We are pursuing a method that selectively removes hydrogen from the expansion tanks of the power plant to control hydrogen levels in the circulating heat-transfer fluid. As part of this effort, we are developing a sensor that measures hydrogen partial pressure in the expansion-tank headspace gas. During previous work, we demonstrated that our sensor design measures hydrogen levels over a wide partial pressure range from 1.33 mbar down to 0.003 mbar with an accuracy of +/-20%. We acquired these results under limited conditions because we measured hydrogen partial pressure in gas mixtures of hydrogen and nitrogen only. In this paper, we report our most recent results in which we measured hydrogen partial pressures in gas mixtures that contain hydrogen, nitrogen, biphenyl, and diphenylether. Our results for this gas mixture were essentially the same as those obtained for the hydrogen/nitrogen mixture only, and they also had the same accuracy of +/-20%. This level of accuracy is sufficient to evaluate the performance of a hydrogen mitigation process because hydrogen partial pressure in the expansion tank decreases more than an order of magnitude when the mitigation process is operating. We examined the palladium/silver membrane after completing these measurements and did not observe any signs of membrane deterioration or contamination due to exposure to biphenyl and diphenylether at 405 degrees C. The membrane for these tests was 25 um thick. For the next sensor design that we are developing in 2018, we are using a membrane that is 127 um thick. This thickness is required for the membrane to withstand the 150 psi pressure drop across the membrane when exposed to actual headspace gas from the expansion tank. We performed initial tests in which we exposed a 127-um-thick palladium alloy membrane to 400 degrees C and 150 psi pressure drop and observed no signs of failure or gas leakage. Finally, we developed a transient model that accounts for hydrogen transport mechanisms within the sensor when measurements are made. Our model accounted for hydrogen diffusion through the boundary layer that is adjacent to the membrane and for hydrogen permeation across the membrane and into the permeate volume. Our modeling results agreed very closely with our experimental data when we assumed a boundary-layer thickness of 0.2 cm. This thickness is within the range of expected thicknesses for this gas composition, temperature, and pressure at minimum flowrate. Our model results showed that hydrogen diffusion through the boundary layer adjacent to the membrane is the rate-determining step for the transient pressure response when measuring hydrogen partial pressure.

14 SOLAR ENERGY↗
DOE OSTI2024

Boundary-Aware Adversarial Learning Domain Adaption and Active Learning for Cross-Sensor Building Extraction

The use of convolutional neural networks (CNNs) for building extraction from remote sensing images has been widely studied and many public datasets have been made available for accelerating development of these CNN models. Yet adapting pretrained models at scale in real-world scenarios remains a challenging task. The main barrier is that certain new labels are still needed to compensate for domain shifting between the labeled data and new images that potentially cover new geographic locations or that are from a different sensor. In this article, we propose to add informatively labeled samples from a new image pool under the paradigm of active learning. To select the most useful samples based on model uncertainty, we first tackle the problem of uncalibrated uncertainty estimation due to distribution shifting by adapting feature extractors with boundary-based adversarial learning. Calibrated uncertainty is used as the query criterion in the active learning process, where the most uncertain samples are selected for annotation and included for model retraining. The proposed workflow was tested with three data pairs in which each workflow represents a scenario often encountered in real-world applications, including adapting pretrained models to new images collected with different sensors or to new geographic areas where appearances and types of buildings are very different. Compared to several baselines, including random sampling, temperature scaling (a well-known uncertainty calibration technique), different query strategies, and active domain adaptation methods, the proposed workflow shows that strategically querying a smaller set of samples for labeling achieves comparable or better building extraction performance. The proposed method reduces the number of labeled samples required to achieve sufficient model accuracy, thus significantly reducing hundreds of person-hours for labeled data creation. In addition, we include a few considerations when deploying this workflow in a GPU cluster that can be easily adapted to achieve operational building extraction model retraining.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗
DOE OSTI2019

Integrating Carbon Nanomaterials with Metals for Bio-sensing Applications

Age structure in most developed countries is changing fast as the average lifespan is increasing significantly, calling for solutions to provide improved treatments for age-related neurological diseases and disorders. In order to address these problems, a reliable way of recording information about neurotransmitters from in vitro and in vivo applications is needed to better understand neurological diseases and disorders as well as currently used treatments. Likewise, recent developments in medicine, especially with the opioid crisis, are demanding a swift move to personalized medicine to administer patient needs rather than population-wide averages. In order to enable the so-called personalized medicine, it is necessary to be able to do measurements in vivo and in real time. These actions require sensitive and selective detection of different analytes from very demanding environments. Current state-of-the-art materials are unable to provide sensitive and selective detection of neurotransmitters as well as the required time resolution needed for drug molecules at a reasonable cost. To meet these challenges, we have utilized different metals to grow carbon nanomaterials and applied them for sensing applications showing that there are clear differences in their electrochemical properties based on the selected catalyst metal. Additionally, we have combined atomistic simulations to support optimizing materials for experiments and to gain further understanding of the atomistic level reactions between different analytes and the sensor surface. With carbon nanostructures grown from Ni and Al + Co + Fe hybrid, we can detect dopamine, ascorbic acid, and uric acid simultaneously. On the other hand, nanostructures grown from platinum provide a feasible platform for detection of H 2 O 2 making them suitable candidates for enzymatic biosensors for detection of glutamate, for example. Tetrahedral amorphous carbon electrodes have an ability to detect morphine, paracetamol, tramadol, and O-desmethyltramadol. With carbon nanomaterial-based sensors, it is possible to reach metal-like properties in sensing applications using only a fraction of the metal as seed for the material growth. We have also seen that by using nanodiamonds as growth catalyst for carbon nanofibers, it is not possible to detect dopamine and ascorbic acid simultaneously, although the morphology of the resulting nanofibers is similar to the ones grown using Ni. This further indicates the importance of the metal selection for specific applications. However, Ni as a continuous layer or as separate islands does not provide adequate performance. Thus, it appears that metal nanoparticles combined with fiber-like morphology are needed for optimized sensor performance for neurotransmitter detection. This opens up a new research approach of application-specific nanomaterials, where carefully selected metals are integrated with carbon nanomaterials to match the needs of the sensing application in question.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Fully Distributed Multi-Material Magnetic Sensing Structures for Multiparameter DAS Applications

This dissertation demonstrates the first of its kind distributed magnetic field sensor based on a fiber optic distributed acoustic sensing (DAS) scheme. Ferromagnetic nickel and Metglas® were dispersed internally within a fiber optic preform and then drawn on an in-house fiber optic draw tower to lengths in the kilometers. Due to the close proximity of the ferromagnetic metals and fiber optic core, the magnetostrictive strain response of the ferromagnetic materials when exposed to a magnetic field would perturbate within the fiber cladding and transfer that strain, internally, to the fiber optic core. Strain resulting from the magnetostrictive effect allows the DAS based sensor to accurately translate strain into readable magnetic field data. Due to the high sensitivity seen in this sensor design, multiparameter sources, acoustic and magnetic fields, were tested and validated and a three dimensional magnetic-field vector sensor was proposed. Numerical analysis of the novel sensor design was first implemented using COMSOL Multiphysics, where inputs such as magnetostrictive element shape, size, distance, and number were first investigated. Upon optimizing system constraints, the sensor design was further modified such that single mode operation was consistent across multiple fiber draws while retaining high strain transfer from the ferromagnetic elements to the fiber optic core. Ferromagnetic material selection was evaluated as a function of the saturation magnetostriction constants and a total of 4 modules were used to fully characterize the complex physics involved in this sensor design. All fabrication and testing were performed in-house using a full scale 3-story fiber draw tower and custom environmental testing stations to imitate naturally occurring events such as magnetic or acoustic point sources. A unique stacking method was used to embed ferromagnetic nickel and Metglas® into a fiber optic preform which when combined with a custom fiber draw process resulted in consistent multi-material fibers drawn to lengths of 1-km. In-house testing facilities included different types of electromagnetic generators, in addition to a soil test bed, and an outdoor test bed which allowed 100 meters of fiber to be tested simultaneously. All tested sensors demonstrated high strain transfer capabilities on the order of 0.01-10 μϵ depending on the materials used, ferromagnetic rod number, and core to metal spacing. Due to the sensitivity of the system the difference between AC and DC was distinct, and directional magnetostriction was studied. Transverse and longitudinal magnetic wave propagation was controlled through a solenoid and rectangular Helmholtz coil, both built in-house. A three-dimensional magnetic field vector sensor was proposed due to the success of the magnetic field sensor, and a design was proposed and initially tested to validate direction as a function of field strength and distance. To summarize, this dissertation explores the first fully distributed magnetic field sensor using DAS based techniques and one of the first multi-material fiber draw processes which can produce consistent single mode fiber up to 1-km. Due to extensive FEA modeling, multiple iterations of the magnetic sensor were fully characterized and an equation describing the relationship between sensor design and strain transfer has been created and validated experimentally. Multi-parameter tests including acoustic and magnetic fields were implemented and an algorithm was developed to separate the mixed signals. Finally, a test was performed to demonstrate the feasibility of sensing magnetic fields directionally. Cumulative results demonstrate a high-quality sensor alternative to current designs which may surpass other magnetic sensors due to innate multi-parameter capabilities, in addition to the inexpensive production cost and extremely long operating lengths.

02 PETROLEUM↗
DOE OSTI2015

Facile synthesis of α-Fe{sub 2}O{sub 3} nanoparticles for high-performance CO gas sensor

Highlights: • We have demonstrated a facile method to prepare Fe{sub 2}O{sub 3} nanoparticles. • The gas sensing properties of α-Fe{sub 2}O{sub 3} have been invested. • The results show potential application of α-Fe{sub 2}O{sub 3} NPs for CO sensors in environmental monitoring. - Abstract: Iron oxide nanoparticles (NPs) were prepared via a simple hydrothermal method for high performance CO gas sensor. The synthesized α-Fe{sub 2}O{sub 3} NPs were characterized by X-ray diffraction, nitrogen adsorption/desorption isotherm, scanning electron microscopy (SEM), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), and selected area electron diffraction (SAED). The SEM, TEM results revealed that obtained α-Fe{sub 2}O{sub 3} particles had a peanut-like geometry with hemispherical ends. The response of the α-Fe{sub 2}O{sub 3} NPs based sensor to carbon monoxide (CO) and various concentrations of other gases were measured at different temperatures. It found that the sensor based on the peanut-like α-Fe{sub 2}O{sub 3} NPs exhibited high response, fast response–recovery, and good selectivity to CO at 300 °C. The experimental results clearly demonstrated the potential application of α-Fe{sub 2}O{sub 3} NPs as a good sensing material in the fabrication of CO sensor.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Investigations of Association Among Atmospheric Radionuclide Measurements

Large networks producing frequent atmospheric radionuclide measurements have additional power in characterizing and localizing radionuclide release events over the analysis done with four or fewer samples. However, adding unrelated samples to an analysis dilutes that advantage, unless models are extended to account for this complexity. A key steppingstone to obtaining network power is to select a group of related sample measurements that are associated with a release event. Such collections of measurements can be assembled by an analyst, or perhaps they can be selected by algorithm. The authors explore, using a year of atmospheric transport calculations and realistic sensor sensitivities, the potential for a computed radionuclide association tool.

Eslinger, Paul W.↗
DOE OSTI2024

Novel 4x4 SiPM array readout with integrated preamplification stage, optimized for the PWO detectors of the EIC EEEMCal

Here we are reporting on a new readout circuit developed for the lead tungstate (PbWO4) scintillation detectors for the Electron Ion Collider (EIC) Electron Endcap Electromagnetic Calorimeter EEEMCal. The high magnetic field region precludes the use of photomultiplier tubes while the detector requirements specify good spectral resolution performance and fast pulse integration over a large dynamic range. We selected a silicon photomultiplier (SiPM) from Hamamatsu and produced a matrix of 4 x 4 sensors to cover the 20 mm x 20 mm scintillator coupling surface. Signal acquisition and amplification electronics boards were designed and integrated with the sensor board to produce a compact high performance readout package. A prototype detector was built and tested at Jefferson Lab with encouraging resolution and timing performance results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Sterol-dependent nuclear import of ORP1S promotes LXR regulated trans-activation of apoE

Oxysterol binding protein related protein 1S (ORP1S) is a member of a family of sterol transport proteins. Here we present evidence that ORP1S translocates from the cytoplasm to the nucleus in response to sterol binding. The sterols that best promote nuclear import of ORP1S also activate the liver X receptor (LXR) transcription factors and we show that ORP1S binds to LXRs, promotes binding of LXRs to LXR response elements (LXREs) and specifically enhances LXR-dependent transcription via the ME.1 and ME.2 enhancer elements of the apoE gene. We propose that ORP1S is a cytoplasmic sterol sensor, which transports sterols to the nucleus and promotes LXR-dependent gene transcription through select enhancer elements. -- Highlights: Black-Right-Pointing-Pointer ORP1S translocates to the nucleus in response to sterol binding. Black-Right-Pointing-Pointer The sterols that best promote nuclear import of ORP1S are LXR agonists. Black-Right-Pointing-Pointer ORP1S binds to LXRs, enhances binding of LXRs to LXREs and promotes LXR-dependent transcription of apoE.

60 APPLIED LIFE SCIENCES↗