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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Wireless Sensor Node Power Profiling Based on IEEE 802.11 and IEEE 802.15.4 Communication Protocols. Modeling and Simulation

Most wireless sensor network (comprising of thousands of WSNs) applications require operation over extended periods of time beginning with their deployment. Network lifetime is extremely critical for most applications and is one of the limiting factors for energy-constrained networks. Based on applications, there are wide ranges of different energy sources suitable for powering WSNs. A battery is traditionally used to power WSNs. The deployed WSN is required to last for long time. Due to finite amount of energy present in batteries, it is not feasible to replace batteries. Recently there has been a new surge in the area of energy harvesting were ambient energy in the environment can be utilized to prolong the lifetime of WSNs. Some of the sources of ambient energies are solar power, thermal gradient, human motion and body heat, vibrations, and ambient RF energy. The design and development of TEGs to power WSNs that would remain active for a long period of time requires comprehensive understanding of WSN operational. This motivates the research in modeling the lifetime, i.e., power consumption, of a WSN by taking into consideration various node and network level activities. A WSN must perform three essential tasks: sense events, perform quick local information processing of sensed events, and wirelessly exchange locally processed data with the base station or with other WSNs in the network. Each task has a power cost per unit tine and an additional cost when switching between tasks. There are number of other considerations that must also be taken into account when computing the power consumption associated with each task. The considerations includes: number of events occurring in a fixed active time period and the duration of each event, event-information processing time, total communication time, number of retransmission, etc. Additionally, at the network level the communication of information data packets between WSNs involves collisions, latency, and retransmission, which result in unanticipated power losses. This report focuses rigorous stochastic modeling of power demand for a schedule-driven WSN utilizing Institute of Electrical and Electronics Engineers 802.11 and 802.15.4 communication protocols. The model captures the generic operation of a schedule-driven WSN when an external event occurs, i.e., sensing, following by processing, and followed by communication. The report will present development of an expression to compute the expected energy consumption per operational cycle of a schedule-driven WSN by taking into consideration the node level activities, i.e., sensing and processing, and the network level activities, i.e., channel access, packet collision, retransmission attempts, and transmission of a data packet.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Cloud Properties under Different Synoptic Circulations: Comparison of Radiosonde and Ground-Based Active Remote Sensing Measurements

In this study, long-term (10 years) radiosonde-based cloud data are compared with the ground-based active remote sensing product under six prevailing large-scale synoptic patterns, i.e., cyclonic center (CC), weak pressure pattern (WP), the southeast bottom of cyclonic center (CB), cold front (CF), anticyclone edge (AE) and anticyclone center (AC) over the Southern Great Plains (SGP) site. The synoptic patterns are generated by applying the self-organizing map weather classification method to the daily National Centers for Environmental Protection mean sea level pressure records from the North American Regional Reanalysis. It reveals that the large-scale synoptic circulations can strongly influence the regional cloud formation, and thereby have impact on the consistency of cloud retrievals from the radiosonde and ground-based cloud product. The total cloud cover at the SGP site is characterized by the least in AC and the most in CF. The minimum and maximum differences between the two cloud methods are 10.3% for CC and 13.3% for WP. Compared to the synoptic patterns characterized by scattered cloudy and clear skies (AE and AC), the agreement of collocated cloud boundaries between the two cloud approaches tends to be better under the synoptic patterns dominated by overcast and cloudy skies (CC, WP and CB). The rainy and windy weather conditions in CF synoptic pattern influence the consistency of the two cloud retrieval methods associated with the limited capabilities inherent to the instruments. As a result, the cloud thickness distribution from the two cloud datasets compares favorably with each other in all synoptic patterns, with relative discrepancy of ≤0.3 km.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Broadband enhanced transmission of acoustic waves through serrated metal gratings

In this letter, we have demonstrated that serrated metal gratings, which introduce gradient coatings, can give rise to broadband transmission enhancement of acoustic waves. Here, we have experimentally and theoretically studied the acoustic transmission properties of metal gratings with or without serrated boundaries. The average transmission is obviously enhanced for serrated metal gratings within a wide frequency range, while the Fabry-Perot resonance is significantly suppressed. An effective medium hypothesis with varying acoustic impedance is proposed to analyze the mechanism, which was verified through comparison with finite-element simulation. We report the serrated boundary supplies gradient mass distribution and gradient normal acoustic impedance, which could efficiently reduce the boundary reflection. Further, by increasing the region of the serrated boundary, we present a broadband high-transmission grating for wide range of incident angle. Our results may have potential applications to broadband acoustic imaging, acoustic sensing, and acoustic devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Quantum Hamiltonian for the surface charge density on a ring torus and radiative decay of plasmons

Photon scattering and the ensuing excitation of surface plasmons in single rings, single composite rings, and many-ring systems was recently shown to provide dispersion and field distributions [K. V. Garapati et al., J. Phys. Commun. 2, 015031 (2018)] of potential for specific applications in particle and molecular trapping [R. Alaee et al., Appl. Phys. Lett. 109, 141102 (2016); M. Salhi et al., Phys. Rev. A 92, 033416 (2015)], in addition to metamaterials and sensing. Furthermore, following photon or electron interactions with metallic nanorings, both radiative and nonradiative decay channels are important in the consideration of the nanoparticle as a photon or phonon source, and in related applications. In this work, we quantize the electronic normal modes of the ring torus and obtain the radiative decay of plasmons. Due to a geometry-related complexity, we employ a perturbation approach to obtain analytical expressions for the radiative decay channel for a vacuum bounded single solid nanoring. In quantizing the fields, the frequency spectrum of the charge density normal modes of the nanoring is obtained and shown to agree with the exact quasistatic plasmon dispersion relations. Higher-order corrections beyond the presented zero- and first-order calculations may be obtained following the presented results. The results are of potential interest in quantum sensing such as entangling photons and plasmons, or plasmons and trapped molecules.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Foundations of Molecular 'Isotomics'

The naturally occurring rare isotopes are versions of common elements, such as hydrogen, carbon and oxygen, that contain a larger than usual number of neutrons in their atomic nuclei and therefore are higher in mass than the common atoms of that element. Isotopes exist for most elements and are found in most natural and synthetic materials, but are uneven in their distribution because chemical and physical processes are isotope-selective (e.g., a chemical reaction may proceed more rapidly for one isotope than for another). For this reason, abundances of isotopes in a material of interest can provide a record, or ‘signature’ of various features of that material’s origin and history. These signatures have been used in the geo, life, chemical and physical sciences in a wide variety of ways over close to 8 decades. However, many such applications struggle to reach unique interpretations of isotopic data because multiple factors combine to control a given sample’s overall isotopic content. That is, the factors controlling isotopic content are too numerous and complex to fully constrain from a simple measurement of a material’s isotope abundances. However, the distribution of isotopes within materials, at molecular scales potentially provides a vastly larger number and diversity of constraints on the chemical and physical processes that comprise a material’s history. The rare isotopes may be concentrated into one atomic position in a molecule relative to another, some proportion of molecules in a sample may contain two or more rare isotopes, and those multiply-isotope-substituted forms of molecules may also have uneven distributions of those isotopes across individual atomic sites. For these reasons, even small, seemingly simple molecules, such as sugars, amino acids or drug compounds, actually exist in a vast number of isotopically unique forms (often millions or more), and each one of those forms is in some sense an independent ‘vote’ on that sample’s history. This project has focused on opening this rich archive of information by enabling the creation of routinely and widely applicable ways of measuring and interpreting isotopic structures of molecules. This work has included the development of core technologies and analytical methods, advancing fundamental understanding of the physical and chemical properties of isotopic versions of molecules, and conducting proof of concept studies of illustrative geochemical, cosmochemical and forensic problems in order to show how these technologies, methods and principles come together to solve problems in new ways. A key to the success of this project was the adaptation of ‘Fourier transform mass spectrometry’ (FTMS) to the task of precisely measuring proportions of the rare, naturally occurring isotopic forms of molecules. FTMS is a highly specialized form of mass spectrometry that traps ions within magnetic or electrostatic cavities and, effectively, ‘listens’ (through registering of subtle electrical signals) to the harmonic signals they make while rapidly orbiting within those cavities. These signals have periods that are a function of their mass and strength (or ‘loudness’) that is proportional to their abundances. Thus, these signals constrain relative amounts of molecules that differ in their mass due to various isotopic substitutions. This technology has been essential to the identification of organic molecules in the life, chemical and environmental sciences for over 4 decades, but generally has lacked the control, stability and precision to meaningfully measure rare isotope forms of molecules. This project’s most fundamental contribution has been to modify FTMS, both in terms of hardware and methods, to enable such measurements. The raw data of molecular isotopic structure is tremendously voluminous and complex, so another important activity of this project has been developing the theoretical and data-science tools needed to interpret the data generated by this new form of isotopic measurement. A particularly challenging part of this task has been predicting molecular isotopic structure, as only through the comparison of measurements with predictions can we make progress on hypothesis driven research questions. We have attacked this this prediction task through a combination of first-principles chemical-physics models of the effects of isotope substitution on molecule properties and data-science models that permit us to generalize that chemical physics to cases that have not yet been studied by detailed chemical physics theory. The proof of concept applications we have pursued over the course of this study include biological reactions of amino acids and other biomolecules, non-biological synthesis of organic molecules in extra-terrestrial settings such as meteorites, petroleum geoscience questions concerning the origin and evolution of natural gas, oil and kerogen compounds, and forensic questions such as the sourcing of chemical weapons. The successes of these applications have laid the groundwork for the next phase of this field’s development, which will include larger scale and more ambitious studies of molecular isotopic structure as a means of diagnosing human diseases, such as cancer, and reconstructing detailed interpretations of the origin and evolution of organic molecules in modern and geological environments.

Cesar, Jaime↗
DOE OSTI2013

The simulation of decontamination works in premises of the research reactor in NRC 'Kurchatov institute'

Application of remote sensing methods using a spectrometric collimated system allows obtaining information about features of a formation of radiation fields in contaminated premises. This information helps in a preparation of a phased plan for dismantling of contaminated equipment. When the survey of technological premises of the research reactor at the Russian Research Centre 'Kurchatov institute' was conducted the remote controlled collimated spectrometric system was used. With its help the scanning of surveyed premises were carried out. As a result of this work, the distribution pattern of radionuclides activity was restored. The simulation of decontamination works was carried out and maps of the distribution of activity and dose rate for surveyed premises were plotted and superimposed on its photo for situations before and after decontamination. The use of obtained results will allow significantly reduce radiation dose for staff at work on dismantling. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Deep Vadose Zone Monitoring Test Bed (FY23 Status Report)

A subsurface air injection at the Hanford Site’s Deep Vadose Zone Monitoring Test Bed was completed to realize a change in subsurface hydrologic conditions in accordance with a soil desiccation remedy. The injection mimicked a previous injection at the site that was performed in accordance with a vadose zone treatability study. Unlike the previous test which relied on electrical methods only, the change in hydrologic conditions during the recent test was also monitored using cross-hole seismic sensing methods to assess the ability of the seismic methods to evaluate changes in moisture conditions of desiccated sediment at the field scale. Data from in situ neutron probes indicates a reduction of soil moisture in the vicinity of the injection well due to the air injection. Similar changes were observed in the time-lapse electrical resistivity and seismic data, which indicates a loss of soil moisture over time. Tomographic inversions of the time-lapse geophysical data illustrate the 2D and 3D features of the soil moisture distribution over time. Time-lapse electrical resistivity tomography (ERT) results show a reduction in the electrical conductivity of the subsurface in the vicinity of the injection well, with most changes occurring within the screened interval. Similar patterns are observed in the seismic tomography results, with both methods illustrating two lobe-shaped features of reduced soil moisture. Use of seismic and ERT technologies in tandem takes advantage of two complementary geophysical monitoring technologies, providing increased sensitivity to specific hydrologic conditions. The multiphysics approach, therefore, has the potential to improve the ability to estimate subsurface moisture conditions from sensor-based and remotely sensed geophysical data that will ultimately improve the ability of remediation contractors to evaluate remedy performance.

58 GEOSCIENCES↗
DOE OSTI2025

Evaluating spectral cloud effective radius retrievals from the Enhanced MODIS Airborne Simulator (eMAS) during ORACLES

Satellite remote sensing retrievals of cloud effective radius (CER) are widely used for studies of aerosol–cloud interactions. Such retrievals, however, rely on forward radiative transfer (RT) calculations using simplified assumptions that can lead to retrieval errors when the real atmosphere deviates from the forward model. Here, coincident airborne remote sensing and in situ observations obtained during NASA's ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign are used to evaluate retrievals of CER for marine boundary layer stratocumulus clouds and to explore impacts of forward RT model assumptions and other confounding factors. Specifically, spectral CER retrievals from the Enhanced MODIS Airborne Simulator (eMAS) and the Research Scanning Polarimeter (RSP) are compared with polarimetric retrievals from RSP and with CER derived from droplet size distributions (DSDs) observed by the Phase Doppler Interferometer (PDI) and a combination of the Cloud and Aerosol Spectrometer (CAS) and the Two-Dimensional Stereo Probe (2D-S). The sensitivities of the eMAS and RSP spectral retrievals to assumptions about the DSD effective variance (CEV) and liquid water complex index of refraction are explored. CER and CEV inferred from eMAS spectral reflectance observations of the backscatter glory provide additional context for the spectral CER retrievals. The spectral and polarimetric CER retrieval agreement is case dependent, and updating the retrieval RT assumptions, including using RSP polarimetric CEV retrievals as a constraint, yields mixed results that are tied to differing sensitivities to vertical heterogeneity. Moreover, the in situ cloud probes, often used as the benchmark for remote sensing CER retrieval assessments, themselves do not agree, with PDI DSDs yielding CER values 1.3–1.6 µm larger than CAS and with CEV roughly 50 %–60 % smaller than CAS. Implications for the interpretation of spectral and polarimetric CER retrievals and their agreement are discussed.

Meyer, Kerry [NASA Goddard Space Flight Center (GS↗
DOE OSTI2022

Cyber Infrastructure for the Smart Electric Grid

As electric power systems undergo a transformative upgrade with the integration of advanced technologies to enable the smarter electric grid, professionals who work in the area require a new understanding of the evolving complexity of the grid. Cyber Infrastructure for the Smart Electric Grid delivers a comprehensive overview of the fundamental principles of smart grid operation and control, smart grid technologies, including sensors, communication networks, computation, data management, and cyber security, and the interdependencies between the component technologies on which a smart grid's security depends. The book offers readers the opportunity to critically analyze the smart grid infrastructure needed to sense, communicate, compute, and control in a secure way.

communication networks↗
DOE OSTI2016

Sequence-Dependent Structure/Function Relationships of Catalytic Peptide-Enabled Gold Nanoparticles Generated under Ambient Synthetic Conditions

Peptide-enabled nanoparticle (NP) synthesis routes can create and/or assemble functional nanomaterials under environmentally friendly conditions, with properties dictated by complex interactions at the biotic/abiotic interface. Manipulation of this interface through sequence modification can provide the capability for material properties to be tailored to create enhanced materials for energy, catalysis, and sensing applications. Fully realizing the potential of these materials requires a comprehensive understanding of sequence-dependent structure/function relationships that is presently lacking. In this work, the atomic-scale structures of a series of peptide-capped Au NPs are determined using a combination of atomic pair distribution function analysis of high-energy X-ray diffraction data and advanced molecular dynamics (MD) simulations. The Au NPs produced with different peptide sequences exhibit varying degrees of catalytic activity for the exemplar reaction 4-nitrophenol reduction. The experimentally derived atomic-scale NP configurations reveal sequence-dependent differences in structural order at the NP surface. Replica exchange with solute-tempering MD simulations are then used to predict the morphology of the peptide overlayer on these Au NPs and identify factors determining the structure/catalytic properties relationship. We show that the amount of exposed Au surface, the underlying surface structural disorder, and the interaction strength of the peptide with the Au surface all influence catalytic performance. A simplified computational prediction of catalytic performance is developed that can potentially serve as a screening tool for future studies. Our approach provides a platform for broadening the analysis of catalytic peptide-enabled metallic NP systems, potentially allowing for the development of rational design rules for property enhancement.

Bedford, Nicholas M.↗
DOE OSTI2014

Transition from thermal to turbulent equilibrium with a resulting electromagnetic spectrum

A recent paper [Ziebell et al., Phys. Plasmas 21, 010701 (2014)] discusses a new type of radiation emission process for plasmas in a state of quasi-equilibrium between the particles and enhanced Langmuir turbulence. Such a system may be an example of the so-called “turbulent quasi-equilibrium.” In the present paper, it is shown on the basis of electromagnetic weak turbulence theory that an initial thermal equilibrium state (i.e., only electrostatic fluctuations and Maxwellian particle distributions) transitions toward the turbulent quasi-equilibrium state with enhanced electromagnetic radiation spectrum, thus demonstrating that the turbulent quasi-equilibrium discussed in the above paper correctly describes the weakly turbulent plasma dynamically interacting with electromagnetic fluctuations, while maintaining a dynamical steady-state in the average sense.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Room temperature multiplexed gas sensing using chemical-sensitive 3.5-nm-thin silicon transistors

There is great interest in developing a low-power gas sensing technology that can sensitively and selectively quantify the chemical composition of a target atmosphere. Nanomaterials have emerged as extremely promising candidates for this technology due to their inherent low-dimensional nature and high surface-to-volume ratio. Among these, nanoscale silicon is of great interest because pristine silicon is largely inert on its own in the context of gas sensing, unless functionalized with an appropriate gas-sensitive material. We report a chemical-sensitive field-effect transistor (CS-FET) platform based on 3.5-nm-thin silicon channel transistors. Using industry-compatible processing techniques, the conventional electrically active gate stack is replaced by an ultrathin chemical-sensitive layer that is electrically nonconducting and coupled to the 3.5-nm-thin silicon channel. We demonstrate a low-power, sensitive, and selective multiplexed gas sensing technology using this platform by detecting H 2 S, H 2 , and NO 2 at room temperature for environment, health, and safety in the oil and gas industry, offering significant advantages over existing technology. Moreover, the system described here can be readily integrated with mobile electronics for distributed sensor networks in environmental pollution mapping and personal air-quality monitors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advanced and functional composite materials via additive manufacturing: Trends and perspectives

Additive manufacturing (AM) has many advantages over conventional subtractive manufacturing methods. The cost-effective AM allows for precise fabrication of complex structures with less material waste, making it a popular manufacturing process in many applications. The recent development of AM materials has advanced significantly. Further, by precisely controlling material distribution and microstructural features, AM facilitates the creation of new composites with specific requirements. AM techniques have contributed considerably to integrating unique functionalities for various applications. This review emphasizes multiple categories of materials, including metal alloys, polymer-based composites, and sustainable composites, as well as applications of sensing materials and strategies and emerging artificial intelligence and machine learning.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Reduction of Carbon Footprint and Energy Efficiency Improvement in Aluminum Production by Use of Novel Wireless Instrumentation Integrated with Mathematical Modeling

The work addressed the greenhouse gas emission and electrical energy consumption of the aluminum industry. The objective was to provide a means for reducing both through the application of wireless instrumentation, coupled to mathematical modeling. Worldwide the aluminum industry consumes more electrical energy than all activities in many major countries (e.g. the UK) and emits more greenhouse gasses (e.g. than France). Most of these excesses are in the 'primary production' of aluminum; that is the conversion of aluminum oxide to metal in large electrolytic cells operating at hundreds of thousands of amps. An industry-specific GHG emission has been the focus of the work. The electrolytic cells periodically, but at irregular intervals, experience an upset condition known as an 'anode effect'. During such anode effects the cells emit fluorinated hydrocarbons (PFCs, which have a high global warming potential) at a rate far greater than in normal operation. Therefore curbing anode effects will reduce GHG emissions. Prior work had indicated that the distribution of electrical current within the cell experiences significant shifts in the minutes before an anode effect. The thrust of the present work was to develop technology that could detect and report this early warning of an anode effect so that the control computer could minimize GHG emissions. A system was developed to achieve this goal and, in collaboration with Alcoa, was tested on two cells at an Alcoa plant in Malaga, Washington. The project has also pointed to the possibility of additional improvements that could result from the work. Notable among these is an improvement in efficiency that could result in an increase in cell output at little extra operating cost. Prospects for commercialization have emerged in the form of purchase orders for further installations. The work has demonstrated that a system for monitoring the current of individual anodes in an aluminum cell is practical. Furthermore the system has been installed twice on a smelter in the US without exposing workers to hazards usually associated with running signal wires in aluminum plants. The results display the early warning of an anode effect that potentially can be used to minimize such anode effects with their excessive GHG emissions. They also point to a possible, but substantial, economic benefit that could result in improved current efficiency by anode adjustment based on individual anode current measurements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Resistance transition assisted geometry enhanced magnetoresistance in semiconductors

Magnetoresistance (MR) reported in some non-magnetic semiconductors (particularly silicon) has triggered considerable interest owing to the large magnitude of the effect. Here, we showed that MR in lightly doped n-Si can be significantly enhanced by introducing two diodes and proper design of the carrier path [Wan, Nature 477, 304 (2011)]. We designed a geometrical enhanced magnetoresistance (GEMR) device whose room-temperature MR ratio reaching 30% at 0.065 T and 20 000% at 1.2 T, respectively, approaching the performance of commercial MR devices. The mechanism of this GEMR is: the diodes help to define a high resistive state (HRS) and a low resistive state (LRS) in device by their openness and closeness, respectively. The ratio of apparent resistance between HRS and LRS is determined by geometry of silicon wafer and electrodes. Magnetic field could induce a transition from LRS to HRS by reshaping potential and current distribution among silicon wafer, resulting in a giant enhancement of intrinsic MR. We expect that this GEMR could be also realized in other semiconductors. The combination of high sensitivity to low magnetic fields and large high-field response should make this device concept attractive to the magnetic field sensing industry. Moreover, because this MR device is based on a conventional silicon/semiconductor platform, it should be possible to integrate this MR device with existing silicon/semiconductor devices and so aid the development of silicon/semiconductor-based magnetoelectronics. Also combining MR devices and semiconducting devices in a single Si/semiconductor chip may lead to some novel devices with hybrid function, such as electric-magnetic-photonic properties. Our work demonstrates that the charge property of semiconductor can be used in the magnetic sensing industry, where the spin properties of magnetic materials play a role traditionally.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Engineered magnetic domain textures in exchange bias bilayer systems

A magnetic domain texture has been deterministically engineered in a topographically flat exchange-biased (EB) thin film system. The texture consists of long-range periodically arranged unit cells of four individual domains, characterized by individual anisotropies, individual geometry, and with non-collinear remanent magnetizations. The texture has been engineered by a sequence of light-ion bombardment induced magnetic patterning of the EB layer system. The magnetic texture's in-plane spatial magnetization distribution and the corresponding domain walls have been characterized by scanning electron microscopy with polarization analysis (SEMPA). The influence of magnetic stray fields emerging from neighboring domain walls and the influence of the different anisotropies of the adjacent domains on the Néel type domain wall core's magnetization rotation sense and widths were investigated. It is shown that the usual energy degeneracy of clockwise and counterclockwise rotating magnetization through the walls is revoked, suppressing Bloch lines along the domain wall. Estimates of the domain wall widths for different domain configurations based on material parameters determined by vibrating sample magnetometry were quantitatively compared to the SEMPA data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation.

42 ENGINEERING↗