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A protective tin oxide layer formed by atomic-layer deposition limits surface recombination at n-Si surfaces and produces ∼620 mV of photovoltage on planar n-Si photoanodes. The layer conformally coats structures such as Si microcone arrays.
A hard x-ray, high-speed, high dynamic range scientific x-ray imager is described. The imager is based on the mixed-mode pixel array detector (MM-PAD) readout chip coupled to a 750 µm thick cadmium telluride (CdTe) sensor. The full imager is a 2 × 3 tiled array of MM-PAD sensor/readout chip hybrids. CdTe improves detection for high energy x-rays as compared to silicon sensors, enabling efficient x-ray imaging to extend to >100 keV. The detector is capable of 1 kHz imaging and in-pixel circuitry has been designed to allow for well depths of greater than 4 × 10 6 80 keV x-rays. Here, a charge integrating front-end allows for quantitative measurement of high flux x-ray images beyond the capabilities of photon counting detectors. Detector performance is summarized and experimental measurements are presented.
We develop a methodology to separate continuous seismic waveforms into random noise (RN), not random noise (NRN) produced by earthquakes, wind, traffic and other sources of ground motions, and an undetermined mixture of signals. The analysis is applied to continuous records from a dense seismic array on the San Jacinto fault zone. To detect RN signals, we cut hourly waveforms into non-overlapping 1 s time windows and apply cross-correlations to separate RN candidates from outliers. The cross-correlation coefficients between different RN candidates fall into a tight range (i.e. 0.09–0.35), while cross-correlation coefficients of RN candidates with NRN signals (e.g. seismic or air-traffic events) are lower. The amplitude spectra of RN candidates have a well-defined level, while the amplitude spectra of other signals deviate from that level. Using these properties, we examine the amplitude spectra of moving time windows and cross-correlation coefficients with RN templates in each hour. The hourly RN is quasi-stationary and the results cluster tightly in the parameter space of cross-correlation coefficients and L2 norm deviations from the mean spectra of RN candidates. Time windows with parameters in this tight cluster are identified as RN, windows that deviate significantly from the RN cluster are identified as NRN and windows with values in between are identified as mixed signals. Several iterations on each hourly data are used to update and stabilize the selection of RN templates and mean noise spectra. For the days examined, the relative fractions of RN, NRN and mixed signals in local day (night) times are about 26 (42 percent), 40 (33 percent) and 34 percent (25 percent), respectively.
Processing In Memory (PIM), the concept of integrating processing directly with memory, has been attracting a lot of attention since PIM can assist in overcoming the throughput limitation caused by data movement between CPU and memory. The challenge, however, is that it requires the programmers to have a deep understanding of the PIM architecture to maximize the benefits such as data locality and parallel thread execution on multiple PIM devices. In this study, we present AnalyzeThat, a programmable shared-memory system for parallel data processing with PIM devices. Thematic to AnalyzeThat is a rich PIM-Aware Data Structure (PADS), which is an encapsulation that integrally ties together the data, the analysis tasks and the runtime needed to interface with the PIM device array. The PADS abstraction provides (i) a key-value data container that allows programmers to easily store data on multiple PIMs, (ii) a suite of parallel operations with which users can easily implement data analysis applications, and (iii) a runtime, hidden to programmers, which provides the mechanisms needed to overlay both the data and the tasks on the PIM device array in an intelligent fashion, based on PIM-specific information collected from the hardware. We have developed a PIM emulation framework called AnalyzeThat. Our experimental evaluation with representative data analytics applications suggests that the proposed system can significantly reduce the PIM programming effort without losing its technology benefits.
The design, fabrication, and characterization of a 16-element optical phased array (OPA) using a high index (n = 3.1) silicon-rich silicon nitride (SRN) is demonstrated. Here, we present one-dimensional beam steering with end-fire facet antennas over a wide steering range of >115° at a fixed wavelength of 1525 nm. A beam width of 6.3° has been measured at boresight, consistent with theory. We demonstrate SRN as a viable material choice for chip-scale OPA applications due to its high thermo-optic coefficient, high optical power handling capacity [negligible two-photon absorption (TPA)], wide transparency window, and CMOS compatibility.
We conducted four geophone array experiments in southwestern Utah between July 2016 and October 2017 to characterize regional levels of microseismicity. Each experiment comprised between 48 and 93 geophones and recorded continuously for ~30 days. Using a new frequency domain array-based detector and a traditional STA/LTA detector, we identified 242 earthquakes. We were able to precisely locate 203 of these earthquakes using HYPOINVERSE. During the same time period the University of Utah Seismograph Stations (UUSS) catalog contained 68 earthquakes in the study area, therefore, we were able to locate 135 additional events. We estimate local magnitudes for events using maximum peak-to-trough amplitude and epicentral distance. As expected, our detection threshold becomes lower with higher station density. We find sparse seismicity across the study area apart from two areas that contain clustered and more frequent activity (Dog Valley and Cove Fort), which together account for roughly 70% of the recorded seismicity. A third prominent cluster in the central Mineral Mountains contains about 15% of the recorded seismicity. Lastly, the remainder of the detected seismicity is spread across the study area.
We perform simulations for future Cherenkov Telescope Array (CTA) observations of RX J1713.7−3946, a young supernova remnant (SNR) and one of the brightest sources ever discovered in very high energy (VHE) gamma rays. Special attention is paid to exploring possible spatial (anti)correlations of gamma rays with emission at other wavelengths, in particular X-rays and CO/H i emission. We present a series of simulated images of RX J1713.7−3946 for CTA based on a set of observationally motivated models for the gamma-ray emission. In these models, VHE gamma rays produced by high-energy electrons are assumed to trace the nonthermal X-ray emission observed by XMM-Newton , whereas those originating from relativistic protons delineate the local gas distributions. The local atomic and molecular gas distributions are deduced by the NANTEN team from CO and H i observations. Our primary goal is to show how one can distinguish the emission mechanism(s) of the gamma rays (i.e., hadronic versus leptonic, or a mixture of the two) through information provided by their spatial distribution, spectra, and time variation. This work is the first attempt to quantitatively evaluate the capabilities of CTA to achieve various proposed scientific goals by observing this important cosmic particle accelerator.
Large-scale, clean, efficient and sustainable hydrogen production from water is one of the major goal in solar-to-fuel conversion as the sun and water represent the two most abundant and geographically-balanced free resources available on Earth. Considering that most of the liquid water available on Earth’s surface is present in the form of seawater, H 2 generation from seawater splitting is highly desirable for large-scale practical and economical application. Herein, we report on the first demonstration of direct efficient overall solar-driven seawater splitting on p-GaN-based nanowire-arrays without any external bias or sacrificial agents from various types of simulated seawater solutions. As a result, a stable solar-to-hydrogen (STH) conversion efficiency of 1.9% was obtained under concentrated irradiation, demonstrating its possible utilization for large-scale, environmental-friendly and sustainable generation of clean solar fuel.
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The equations to calculate equal-power reflectivity of a sequential series of beam splitters is presented. Non-sequential optical design examples are offered for uniform illumination using diode lasers. Objects created using Boolean operators and Swept Surfaces can create objects capable of reflecting light into predefined elevation and azimuth angles. Analysis of the illumination patterns for the array are also presented.
Sensor (or Data) fusion is the process of integrating multiple data sources to produce more consistent, accurate and comprehensive information than is provided by a single data source. Sensor fusion may also be used to combine multiple signals from a single modality to improve the performance of a particular inspection technique. Industrial nondestructive testing may utilize multiple sensors to acquire inspection data depending upon the object under inspection and the anticipated types of defects that can be identified. Sensor fusion can be performed at various levels of signal abstraction with each having its strengths and weaknesses. A multimodal data fusion strategy first proposed by Heideklang and Shokouhi that combines spatially scattered detection locations to improve detection performance of surface-breaking and near-surface cracks in ferromagnetic metals is shown using a surface inspection example and is then extended for volumetric inspections. Utilizing data acquired from an Olympus Omniscan MX2 from both phased array eddy current and ultrasound probes on test phantoms, single and multilevel fusion techniques are employed to integrate signals from the two modalities. Preliminary results demonstrate how confidence in defect identification and interpretation benefit from sensor fusion techniques. Lastly, techniques for integrating data into radiographic and volumetric imagery from computed tomography are described and results are presented.
Despite many potential applications, miniature mass spectrometers have had limited adoption in the field due to the tradeoff between throughput and resolution that limits their performance relative to laboratory instruments. Recently, a solution to this tradeoff has been demonstrated by using spatially coded apertures in magnetic sector mass spectrometers, enabling throughput and signal-to-background improvements of greater than an order of magnitude with no loss of resolution. This paper describes a proof of concept demonstration of a cycloidal coded aperture miniature mass spectrometer (C-CAMMS) demonstrating use of spatially coded apertures in a cycloidal sector mass analyzer for the first time. C-CAMMS also incorporates a miniature carbon nanotube (CNT) field emission electron ionization source and a capacitive transimpedance amplifier (CTIA) ion array detector. Results confirm the cycloidal mass analyzer’s compatibility with aperture coding. A >10× increase in throughput was achieved without loss of resolution compared with a single slit instrument. Several areas where additional improvement can be realized are identified. .
Molten mixtures of MgCl2-KCl-NaCl are promising heat transfer fluid (HTF) candidates for the next generation of concentrating solar power systems (Gen3 CSP), because of their low cost, high decomposition temperature, and favorable heat transfer characteristics. However, if the chemistry of the salts is not properly controlled, molten chlorides can cause corrosion of high-temperature metal alloys, such as Inconel (R), in CSP systems. Here we demonstrate a corrosion monitoring system that can be used to monitor the health of the structural alloys over long durations. The monitoring system includes a multielectrode array voltammetry sensor that measures the redox potential of molten MgCl2-KCl-NaCl mixture as well as the presence of corrosion products and impurities. The sensors have been successfully operated in static molten salt environments for long durations, and are capable of detecting corrosion products, such as Cr2+ and Fe2+, quantitatively at low concentrations (i.e., approximately 100 ppm) with less than 4% error.