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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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537 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Water Uptake by Silica Nanopores: Impacts of Surface Hydrophilicity and Pore Size

Water uptake at 298 K by two mesoporous silicas with different pore sizes was studied using volumetric vapor sorption. Here, through variation of sample pretreatment temperature and time, the number of surface hydroxyl groups was varied, leading to marked changes in the water sorption behavior. With increasing surface hydroxyl density, the adsorption of water to the pore surfaces increases, leading to formation of a thicker and denser water sorption layer. The BET model was used to measure surface hydroxyl density from low pressure parts of water adsorption isotherms. Adsorption of water molecules scales with the number density of surface hydroxyl groups, and monolayer coverage is reached at reduced pressure of ca. 0.3. The onset of adsorption pore condensation of water shifts to lower reduced pressure with increasing surface hydroxyl density, indicating growing thickness of adsorption layers. However, the water pore condensation step of the desorption branch shifts to smaller reduced pressure with increasing surface hydroxylation, reducing the adsorption-desorption hysteresis width. An adsorbed phase model is utilized to calculate the distribution of pore water molecules between adsorbed and pore condensation phases, and characterize the density and thickness of the water sorption phase. In the smaller pores, the adsorbed phase forms a sparse monolayer, while approximately a bilayer is formed in the wider pores. The previously reported silica nanopore underfilling by water is confirmed and rationalized by a significantly reduced water density in the adsorbed phase with respect to bulk, while the pore core is filled with water of approximately bulk density.

36 MATERIALS SCIENCE↗
DOE OSTI2022

To Fail or not to Fail: An Exploration of Machine Learning Techniques for Predictive Maintenance

Predictive maintenance refers to the ability to predict when machinery or systems need to be maintained. Making an accurate prediction is quite challenging given the costs for both over-estimating (unnecessary maintenance and reduction in availability of assets) and under-estimating (untimely breakdowns and possible loss of equipment or lives). To address these challenges researchers were able to develop new approaches for analyzing oil samples taken extracting samples from oil-wetted machinery that may provide information critical to developing predictive capabilities. We consider the problem from both supervised (though data limited) and unsupervised approaches and provide a first look into a data driven approach for identification of condition indicators. Through this work we identify a collection of candidate features that can form the basis of condition indicators for both a high level discrimination of failure vs. normal operation as well as a set for potential failure mode identification. Finally, we present an anomaly detection framework for detecting failures which can be a viable solution for an onboard analysis tool in deployed systems.

predictive maintenance, anomaly detection, Laserne↗
DOE OSTI2016

Photon Counting Data Analysis: Application of the Maximum Likelihood and Related Methods for the Determination of Lifetimes in Mixtures of Rose Bengal and Rhodamine B

It is often convenient to know the minimum amount of data needed in order to obtain a result of desired accuracy and precision. It is a necessity in the case of subdiffraction-limited microscopies, such as stimulated emission depletion (STED) microscopy, owing to the limited sample volumes and the extreme sensitivity of the samples to photobleaching and photodamage. We present a detailed comparison of probability-based techniques (the maximum likelihood method and methods based on the binomial and the Poisson distributions) with residual minimization-based techniques for retrieving the fluorescence decay parameters for various two-fluorophore mixtures, as a function of the total number of photon counts, in time-correlated, single-photon counting experiments. The probability-based techniques proved to be the most robust (insensitive to initial values) in retrieving the target parameters and, in fact, performed equivalently to 2-3 significant figures. This is to be expected, as we demonstrate that the three methods are fundamentally related. Furthermore, methods based on the Poisson and binomial distributions have the desirable feature of providing a bin-by-bin analysis of a single fluorescence decay trace, which thus permits statistics to be acquired using only the one trace for not only the mean and median values of the fluorescence decay parameters but also for the associated standard deviations. Lastly, these probability-based methods lend themselves well to the analysis of the sparse data sets that are encountered in subdiffraction-limited microscopies.

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

Deployment of ADTimePix3 areaDetector Driver at Neutron and X-ray User Facilities

TimePix3 is a 65k hybrid pixel readout chip with simultaneous Time-of-Arrival (ToA) and Time-over-Threshold (ToT) recording in each pixel*. The chip operates without a trigger signal with a sparse readout where only pixels containing events are read out. The flexible architecture allows 40 MHits/s/cm² readout throughput, using simultaneous readout and acquisition by sharing readout logic with transport logic of superpixel matrix formed using 2x4 structure. The chip ToA records 1.5625 ns time resolution. The X-ray and charged particle events are counted directly. However, indirect neutron counts use 6Li fission in a scintillator matrix, such as ZnS(Ag). The fission space-charge region is limited to 5-9 um. A photon from scintillator material excites a photocathode electron, which is further multiplied in dual-stack MCP. The neutron count event is a cluster of electron events at the chip. We report on the EPICS areaDetector** ADTimePix3 driver that controls Serval*** using json commands. The driver directs data to storage and to a real-time processing pipeline and configures the chip. The time-stamped data are stored in raw .tpx3 file format and passed through a socket where the clustering software identifies individual neutron events. The conventional 2D images are available as images for each exposure frame, and a preview is useful for sample alignment. The areaDetector driver allows integration of time-enhanced capabilities of this detector into SNS beamlines controls and unprecedented time resolution.

Gofron, Kaz↗
DOE OSTI2026

Evaluating the Limits of QAOA Parameter Transfer at High-Rounds on Sparse Ising Models With Geometrically Local Cubic Terms

The emergent practical applicability of the Quantum Approximate Optimization Algorithm (QAOA) for approximate combinatorial optimization is a subject of considerable interest. One of the primary limitations of QAOA is the task of finding a set of good parameters, which is usually done using a variational optimization loop. Parameter transfer, or parameter concentration, is a phenomenon where QAOA angles trained on problem instances that are self-similar tend to perform well for other problem instances from that similar class. This suggests a potentially highly efficient and scalable non-variational learning method for QAOA angle finding. In this work, we systematically study QAOA parameter transferability from small problem sizes (16 and 27 decision variables) onto large problem instances (up to 156 qubits) for heavy-hex graph Ising models with geometrically local higher order terms using the Julia based QAOA simulation tool \texttt{JuliQAOA} to perform classical angle finding for up to $49$ QAOA layers ($p$). Parameter transfer of the fixed angles is validated using a combination of full statevector, Projected Entangled Pair States (PEPS), Matrix Product State (MPS), and LOWESA numerical simulations. We find that the QAOA parameter transfer from single instances applied to other (unseen) problem instances does not in general provide monotonically improving performance as a function of $p$ - there are many cases where the performance temporarily decreases as a function of $p$ - but despite this the transferred angles have a general trend of improved expectation value as the QAOA depth increases, in many cases converging close to the true ground-state energy of the $100+$ qubit instances. We also sample the hardware-compatible Ising models using the ensemble of transfer-learned QAOA parameters on several superconducting qubit IBM Quantum processors with 127, 133, and 156 qubits. We find continuous solution quality improvement of the hardware-compatible QAOA circuits run on the IBM NISQ processors up to $p=5$ on \texttt{ibm\_fez}, up to $p=9$ on \texttt{ibm\_torino}, and up to $p=10$ on \texttt{ibm\_pittsburgh}.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Enhancing Neutrino Event Reconstruction with Pixel-Based 3D Readout for Liquid Argon Time Projection Chambers

In this paper we explore the potential improvements in neutrino event reconstruction that a 3D pixelated readout could offer over a 2D projective wire readout for liquid argon time projection chambers. We simulate and study events in two generic, idealized detector configurations for these two designs, classifying events in each sample with deep convolutional neural networks to compare the best 2D results to the best 3D results. In almost all cases we find that the 3D readout provides better reconstruction efficiency and purity than the 2D projective wire readout, with the advantages of 3D being particularly evident in more complex topologies, such as electron neutrino charged current events. We conclude that the use of a 3D pixelated detector could significantly enhance the reach and impact of future liquid argon TPC experiments physics program, such as DUNE.

Adams, C↗
DOE OSTI2021

Increasing freshwater and dissolved organic carbon flows to Northwest Alaska’s Elson lagoon

Manifestations of climate change in the Arctic are numerous and include hydrological cycle intensification and permafrost thaw, both expected as a result of atmospheric and surface warming. Across the terrestrial Arctic dissolved organic carbon (DOC) entrained in arctic rivers may be providing a carbon subsidy to coastal food webs. Yet, data from field sampling is too often of limited duration to confidently ascertain impacts of climate change on freshwater and DOC flows to coastal waters. This study applies numerical modeling to investigate trends in freshwater and DOC exports from land to Elson Lagoon in Northwest Alaska over the period 1981-2020. While the modeling approach has limitations, the results point to significant increases in freshwater and DOC exports to the lagoon over the past four decades. The model simulation reveals significant increases in surface, subsurface (suprapermafrost), and total freshwater exports. Significant increases are also noted in surface and subsurface DOC production and export, influenced by warming soils and associated active-layer thickening. The largest changes in subsurface components are noted in September, which has experienced a ~50% increase in DOC export emanating from suprapermafrost flow. Direct coastal suprapermafrost freshwater and DOC exports in late summer more than doubled between the first and last five years of the simulation period, with a large anomaly in September 2019 representing a more than fourfold increase over September direct coastal export during the early 1980s. These trends highlight the need for dedicated measurement programs that will enable improved understanding of climate change impacts on coastal zone processes in this data sparse region of Northwest Alaska.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

3D Characterization and Time-Lapse Imaging of the Desiccation Treatability Test at the Hanford BC-Cribs and Trenches Site using High Performance Computing applied to Electrical Resistivity Imaging - 12271

Contaminated vadose zone materials are a potential source of long-term groundwater contamination at many sites across the Department of Energy (DOE) complex. Deep vadose zone contamination presents a particularly challenging remedial problem due to the difficultly of locating contaminants and the expense of access and ex situ treatment. In situ remediation techniques, whereby remedial amendments must be delivered to contaminated soils, have been identified as a potential alternative. However, amendment delivery is typically uncertain and post delivery remedial performance is often not well understood due to limited information available from sparsely spaced boreholes. Recent advancements in electrical resistivity tomography (ERT) are being used to address these challenges at the Hanford site by providing remote, three-dimensional images of contaminant distribution and four-dimensional (three spatial dimensions plus the time dimension) images of in-situ vadose zone remediation processes. These capabilities were recently demonstrated with a large scale surface characterization effort and during a smaller scale desiccation treatability test at the Hanford BC-Cribs area. The results of these efforts demonstrate the utility of leveraging high-performance computing resources to process ERT data for 3D reconnaissance mapping of subsurface contaminants and for detailed 3D monitoring of vadose zone remediation efforts. Two examples of ERT imaging at a former waste disposal facility have been demonstrated at different scales; a large scale reconnaissance survey spanning many hundreds of meters and a detailed monitoring application spanning a few tens of meters. Although ERT is applicable at many scales, resolution is generally governed by proximity to electrodes. For instance, the BCCT reconnaissance survey demonstrated herein lost resolution with depth (i.e. away from surface electrodes), and was unable to detect a high conductivity anomaly at approximately 75 m below ground surface in the cribs area; the deep anomalies were detected during highly focused borehole sampling. Conversely, the desiccation site characterization and monitoring demonstrated high resolution at a small scale, but only because of close proximity to survey electrodes. Although proximity to electrodes is not the only factor governing ERT resolution, it is often the primary factor for determining how an ERT system should be effectively deployed for a given application. Regardless of resolution limitations, the utility of ERT for remotely detecting wastes and monitoring subsurface processes is evident. ERT imaging is not a final detection or monitoring solution alone, but provides a necessary capability to remotely detect subsurface conditions and understand subsurface processes. Such a capability can significantly increase the efficacy and reduce the cost of remediation by guiding sampling efforts and informing remedial system design and operation. However, given the computational demands of 3D ERT characterization and monitoring inversions, the full imaging capability enabled by contemporary ERT survey systems can generally only be realized with high performance computing resources, both hardware and software. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

CURRENT SHEET REGULATION OF SOLAR NEAR-RELATIVISTIC ELECTRON INJECTION HISTORIES

We present a sample of three large near-relativistic (>50 keV) electron events observed in 2001 by both the ACE and the Ulysses spacecraft, when Ulysses was at high-northern latitudes (>60 Degree-Sign ) and close to 2 AU. Despite the large latitudinal distance between the two spacecraft, electrons injected near the Sun reached both heliospheric locations. All three events were associated with large solar flares, strong decametric type II radio bursts and accompanied by wide (>212 Degree-Sign ) and fast (>1400 km s{sup -1}) coronal mass ejections (CMEs). We use advanced interplanetary transport simulations and make use of the directional intensities observed in situ by the spacecraft to infer the electron injection profile close to the Sun and the interplanetary transport conditions at both low and high latitudes. For the three selected events, we find similar interplanetary transport conditions at different heliolatitudes for a given event, with values of the mean free path ranging from 0.04 AU to 0.27 AU. We find differences in the injection profiles inferred for each spacecraft. We investigate the role that sector boundaries of the heliospheric current sheet (HCS) have on determining the characteristics of the electron injection profiles. Extended injection profiles, associated with coronal shocks, are found if the magnetic footpoints of the spacecraft lay in the same magnetic sector as the associated flare, while intermittent sparse injection episodes appear when the spacecraft footpoints are in the opposite sector or a wrap in the HCS bounded the CME structure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Aerosol detection efficiency in inductively coupled plasma mass spectrometry

We used an electrostatic size classification technique to segregate particles of known composition prior to being injected into an inductively coupled plasma mass spectrometer (ICP-MS). Moreover, we counted size-segregated particles with a condensation nuclei counter as well as sampled with an ICP-MS. By injecting particles of known size, composition, and aerosol concentration into the ICP-MS, efficiencies of the order of magnitude aerosol detection were calculated, and the particle size dependencies for volatile and refractory species were quantified. Similar to laser ablation ICP-MS, aerosol detection efficiency was defined as the rate at which atoms were detected in the ICP-MS normalized by the rate at which atoms were injected in the form of particles. This method adds valuable insight into the development of technologies like laser ablation ICP-MS where aerosol particles (of relatively unknown size and gas concentration) are generated during ablation and then transported into the plasma of an ICP-MS. In this study, we characterized aerosol detection efficiencies of volatile species gold and silver along with refractory species aluminum oxide, cerium oxide, and yttrium oxide. Aerosols were generated with electrical mobility diameters ranging from 100 to 1000 nm. In general, it was observed that refractory species had lower aerosol detection efficiencies than volatile species, and there were strong dependencies on particle size and plasma torch residence time. Volatile species showed a distinct transition point at which aerosol detection efficiency began decreasing with increasing particle size. This critical diameter indicated the largest particle size for which complete particle detection should be expected and agreed with theories published in other works. Aerosol detection efficiencies also displayed power law dependencies on particle size. Aerosol detection efficiencies ranged from 10 -5 to 10 -11 . Free molecular heat and mass transfer theory was applied, but evaporative phenomena were not sufficient to explain the dependence of aerosol detection on particle diameter. Additional work is needed to correlate experimental data with theory for metal-oxides where thermodynamic property data are sparse relative to pure elements. Finally, when matrix effects and the diffusion of ions inside the plasma were considered, mass loading was concluded to have had an effect on the dependence of detection efficiency on particle diameter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Experimental demonstration of direct L-shell x-ray fluorescence imaging of gold nanoparticles using a benchtop x-ray source

Purpose: To develop a proof-of-principle L-shell x-ray fluorescence (XRF) imaging system that locates and quantifies sparse concentrations of gold nanoparticles (GNPs) using a benchtop polychromatic x-ray source and a silicon (Si)-PIN diode x-ray detector system.Methods: 12-mm-diameter water-filled cylindrical tubes with GNP concentrations of 20, 10, 5, 0.5, 0.05, 0.005, and 0 mg/cm{sup 3} served as calibration phantoms. An imaging phantom was created using the same cylindrical tube but filled with tissue-equivalent gel containing structures mimicking a GNP-loaded blood vessel and approximately 1 cm{sup 3} tumor. Phantoms were irradiated by a 3-mm-diameter pencil-beam of 62 kVp x-rays filtered by 1 mm aluminum. Fluorescence/scatter photons from phantoms were detected at 90° with respect to the beam direction using a Si-PIN detector placed behind a 2.5-mm-diameter lead collimator. The imaging phantom was translated horizontally and vertically in 0.3-mm steps to image a 6 mm × 15 mm region of interest (ROI). For each phantom, the net L-shell XRF signal from GNPs was extracted from background, and then corrected for detection efficiency and in-phantom attenuation using a fluorescence-to-scatter normalization algorithm.Results: XRF measurements with calibration phantoms provided a calibration curve showing a linear relationship between corrected XRF signal and GNP mass per imaged voxel. Using the calibration curve, the detection limit (at the 95% confidence level) of the current experimental setup was estimated to be a GNP mass of 0.35 μg per imaged voxel (1.73 × 10{sup −2} cm{sup 3}). A 2D XRF map of the ROI was also successfully generated, reasonably matching the known spatial distribution as well as showing the local variation of GNP concentrations.Conclusions: L-shell XRF imaging can be a highly sensitive tool that has the capability of simultaneously imaging the spatial distribution and determining the local concentration of GNPs presented on the order of parts-per-million level within subcentimeter-sized ex vivo samples and superficial tumors during preclinical animal studies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Joint Inversion of Surface Electrical Resistivity Tomography and Seismic Refraction Data between the 200 Areas

Geologic stratigraphy on the Hanford Site influences groundwater and contaminant migration through the aquifer system and the vadose zone. The current geologic framework model (GFM) relies heavily on a sparse distribution of borehole data in some locations to map geologic contacts and hydrologic properties in the subsurface. Non-invasive geophysical methods such as electrical resistivity tomography (ERT), transient electromagnetic surveying, and seismic imaging are being used at Hanford to map subsurface structure in areas with limited well observations. This is to develop and mature the capability of geophysical methods to aid in GFM refinement, to identify regions of subsurface complexity, and for optimal well siting. A joint inversion of co-located seismic refraction and ERT data was carried out for data collected on a ~2.3-km profile between the 200 Areas on the Hanford Site. While ERT and seismic refraction images have sensitivity to overlapping physical properties (porosity, moisture content, lithology), the resolution and physics used to acquire each of these datasets are different and therefore information can be different or mutually complementary. Performing a joint inversion provides a reasonable option for a coherent, coupled interpretation for mutually complementary datasets. Between the 200 Areas, there are few boreholes to interpret the geologic framework model, and these data sets were obtained to provide a first line of evidence toward identifying stratigraphic structure. The seismic refraction and ERT data were independently inverted during fiscal year 2022 and broadly showed a two-layer structure with a trough-like feature that is ~1 km wide and upwards of 150 m deep. The depth of the trough feature was greater in the ERT image compared to the seismic image, which indicated a maximum depth of approximately 110 m. The objective of the joint inversion described in this report was to invert the seismic refraction and ERT data together while constraining the ERT image to be structurally similar to the seismic refraction image. The approach was applied using the geophysical inverse modeling program E4D, which has the capability to invert first-arrival times from seismic refraction data and ERT resistances using a “cross-gradient” constraint. The application of cross-gradient constraints with different weights produces ERT models that show a high degree of similarity within the upper 100 m (above ~120 m elevation). None of the ERT models show an improved structural similarity to the seismic result; therefore, it is recommended that further attempts to jointly interpret these models focus on petrophysics and image resolution. Petrophysical measurements of core samples would improve knowledge of what drives the ERT response in this region and, along with downhole geophysical measurements, could be used to “ground truth” the surface-based geophysical results. Image resolution studies would provide insight into which regions of the inverted images are reliable and which regions are poorly constrained.

58 GEOSCIENCES↗
DOE OSTI2016

Effects of EMF Emissions from Cables and Junction Boxes on Marine Species

Studies have shown that diverse aquatic species are electrosensitive. Many fishes, and marine mammals, can either detect, navigate by, or are affected by electromagnetic fields (EMF) with various sensitivities, and their behavior may be impacted by unnatural EMF emissions in the water column. Sharks, rays and skates are known to have the highest sensitivity to electric fields. Electric field emissions in the range 0.5–100 micro volt/m appear to attract them, and emissions over 100 micro volt/m to repulse them. A marine hydrokinetic MHK device will have multiple components and associated multiple submarine cables on the seafloor and running through the water column and would potentially increase the level of EMF emissions to which the marine species at the site may be exposed to. There are therefore concerns amongst stakeholders that EMF emissions associated with MHK devices and their components may act as barriers to species migration, cause disorientation, change community compositions and ecosystems, and that they may attract sharks, leading to a local increase in the risk of shark attacks. However, field data to validate and model potential relationships between observed responses and the EMF emissions in situ are sparse. A program of experimental field surveys were conducted off the coast of South Florida, USA to characterize the electromagnetic field (EMF) emissions in the water column from a submarine cable, and to monitor for responses of local aquatic species. The field surveys were conducted at the South Florida Ocean Measurement Facility (SFOMF) off Fort Lauderdale, which is a cabled offshore in-water navy range. It consists of multiple active submarine power cables and a number of junction boxes, with the capability to transmit AC/DC power at a range of strength and frequencies. The site includes significant marine life activities and community structure, including highly mobile species, such as sharks, stingrays, mammals and turtles. SFOMF therefore typifies a setting representative of an offshore location where a MHK device may be sited. Background electromagnetic field (EMF) levels and EMF emissions due to submarine cables were measured using a custom E-field sensor and a commercial magnetometer deployed from an autonomous underwater vehicle (AUV) at various fixed altitudes above the seafloor. EMF signatures detected from power cables and junction boxes are contrasted against ambient background levels and other EMF sources. The potential responses of local marine species were observed at selected representative locations using divers on SCUBA, complemented with fixed cameras on the sea bottom and by a set of video cameras mounted on the AUV. The objectives of the study were: 1) to characterize the EMF emission levels associated with submarine cables 2) to monitor potential responses of aquatic animals to the emissions and 3) to develop an associated database of field observations. As control, observations of EMF levels and in situ marine species were conducted with power in the cable turned off. Good quality measurements of EMF emissions were obtained using the mobile AUV platform and the data from the surveys were used to develop contour maps of the EMF levels in the water column above the live cable as well as to provide information about how the field decays away from the cable. The measurements show good agreement with theoretical models of the how EMF levels decay away from the cable in deep and shallow water environment. Electric fields in excess of 200µV/m were measured in the vicinity of the cable during the power on state. Quarterly surveys by SCUBA divers were conducted, using point and transect count methods, over a period of one year at three locations, one at a shallow site where the water depth is approximately 5m, and the other at the Barracuda Reef where the water depth is approximately 10m. The sampling results were analyzed to determine if the presence of an SFOMF generated EMF alters: (1) abundance, species richness, and assemblage structure of coral reef fishes, (2) the behavior of fishes including elasmobranchs, and (3) the distribution of marine turtles and mammals. Diver observations were also used in attempt to discern if there were any noticeable organismal responses during the transitional period between ambient OFF to energized AC or DC power states, and video footage was intended to augment the in-situ visual survey data and aid in interpretation of the results. Comparisons are provided between observation datasets between the three sites and between the point and transect count methods. Presence of several individual elasmobranch species, including sharks and stingrays, were recorded during the surveys. No apparent effect on richness could be discerned between the power on and off states. No apparent sudden animal movements were observed during transitions between power states.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Evaluation of an event-driven 3FI ASIC for spectroscopic X-ray detection with synchrotron radiation

The novel design and evaluation on the NSLS-II beamline of the 3FI application-specific integrated circuit (ASIC) bump-bonded to a simple, planar, 2D segmented silicon sensor are presented. The ASIC was developed for full-field fluorescence spectral X-ray imaging (3FI). It is a small-scale prototype that features a square array of 32 × 32 pixels, and the size of the pixels is 100 µm × 100 µm. The ASIC was implemented in a 65 nm CMOS integrated circuit fabrication process. Each pixel incorporates a charge-sensitive amplifier, a shaping filter, a discriminator, a peak detector and a sample-and-hold circuit, allowing detection of events and storage of signal amplitudes. The system operates in a frameless event-driven readout mode, outputting analog values for threshold-triggered events, allowing high-speed multi-element X-ray fluorescence data acquisition. The 3FI ASIC achieves per-channel spectrometric performance at a power consumption of only 200 µW per pixel, with nearly all dissipation confined to the analog front-end. An energy resolution is measured at the level of 308 eV full width at half-maximum (FWHM) at 8.04 keV (Cu Kα), and 138 eV FWHM at 3.69 keV (Ca Kα). This per-pixel capability makes the prototype suitable for in situ trace element microanalysis in biological and environmental studies. Moreover, the frameless architecture of the detector is designed to address limitations of conventional X-ray fluorescence microscopy, which typically requires mechanical scanning, by enabling continuous high-throughput data acquisition in future full-field implementations.

47 OTHER INSTRUMENTATION↗