High-Energy Lateral Mapping (HELM) Studies of Inhomogeneity and Failure Mechanisms in NMC622/Li Pouch Cells
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We calculate rates of ice thickness change and bottom melt for ice shelves in West Antarctica and the Antarctic Peninsula from a combination of elevation measurements from NASA–CECS Antarctic ice mapping campaigns and NASA Operation IceBridge corrected for oceanic processes from measurements and models, surface velocity measurements from synthetic aperture radar, and high-resolution outputs from regional climate models. The ice thickness change rates are calculated in a Lagrangian reference frame to reduce the effects from advection of sharp vertical features, such as cracks and crevasses, that can saturate Eulerian-derived estimates. We use our method over different ice shelves in Antarctica, which vary in terms of size, repeat coverage from airborne altimetry, and dominant processes governing their recent changes. We find that the Larsen-C Ice Shelf is close to steady state over our observation period with spatial variations in ice thickness largely due to the flux divergence of the shelf. Firn and surface processes are responsible for some short-term variability in ice thickness of the Larsen-C Ice Shelf over the time period. The Wilkins Ice Shelf is sensitive to short-timescale coastal and upper-ocean processes, and basal melt is the dominant contributor to the ice thickness change over the period. At the Pine Island Ice Shelf in the critical region near the grounding zone, we find that ice shelf thickness change rates exceed 40m/yr, with the change dominated by strong submarine melting. Regions near the grounding zones of the Dotson and Crosson ice shelves are decreasing in thickness at rates greater than 40m/yr, also due to intense basal melt. NASA–CECS Antarctic ice mapping and NASA Operation IceBridge campaigns provide validation datasets for floating ice shelves at moderately high resolution when coregistered using Lagrangian methods.
The combinatorial methodology offers a high-throughput process for developing radiation-resistant sensor materials for nuclear energy applications through the synthesis and screening of large numbers of compositions and processing conditions in a single process. The combinatorial materials fabrication coupled with an automated screening process enables us to identify the optimal materials composition and processing conditions that yield both desired properties and required irradiation resistance in the most efficient and economic manner. The report focuses on the development of scanning probe technique to map with high resolution the thermal conductivity, Seebeck coefficient and electrical conductivity of combinatory materials with gradient compositions, which has been a key challenge in combinatory materials science. The above three properties play important roles in many sensor materials for nuclear energy applications.
As lithium-on (Li-ion) batteries increase in their prevalence and the range of applications expands, there is a need to understand and exploit the limits of electrochemical performance. Probing the internal processes in Li-ion batteries provides insights into the electrochemical characteristics of the cells as well as information necessary for rational cell design. In this manuscript an in situ method, neutron imaging, is applied to Li-ion full cells to track the lithiation/delithiation processes in the electrodes during discharge. The electrodes comprise thick sintered films of only active material, which improves the number of discrete points that are mapped for net changes in neutron intensity through the electrode. The lithiation/delithiation processes are qualitatively consistent with calculations of the Li + concentration and discharge profiles of the cells. These results illustrate that neutron imaging can experimentally probe and confirm limitations in the electrochemical performance of Li-ion full cells, particularly those with thick electrodes.
We investigate a high temperature, high cooling-rate anneal Tabula Rasa (TR) and report its implications on n-type Czochralski-grown silicon (n-Cz Si) for photovoltaic fabrication. Tabula Rasa aims at dissolving and homogenizing oxygen precipitate nuclei that can grow during the cell process steps and degrade the cell performance due to their high internal gettering and recombination activity. The Tabula Rasa thermal treatment is performed in a clean tube furnace with cooling rates >100 degrees C/s. We characterize the bulk lifetime by Sinton lifetime and photoluminescence mapping just after Tabula Rasa, and after the subsequent cell processing. After TR, the bulk lifetime surprisingly degrades to <; 0.1ms, only to recover to values equal or higher than the initial non-treated wafer (several ms), after typical high temperature cell process steps. Those include boron diffusion and oxidation; phosphorus diffusion/oxidation; ambient annealing at 850 degrees C; and crystallization annealing of tunneling-passivating contacts (doped polycrystalline silicon on 1.5 nm thermal oxide). The drastic lifetime improvement during high temperature cell processing is attributed to improved external gettering of metal impurities and annealing of intrinsic point defects. Time and injection dependent lifetime spectroscopy further reveals the mechanisms of lifetime improvement after Tabula Rasa treatment. Additionally, we report the efficacy of Tabula Rasa on n-type Cz-Si wafers and its dependence on oxygen concentration, correlated to position within the ingot.
As one of NASA's open and free data systems, NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) provides a wide range of near real-time, low latency and expedited data products from NASA and other earth science satellite missions to support users in their understanding of the value and distribution of ecosystem services. LANCE data can support the System of Environmental Economic Accounting-Ecosystem Accounting (SEEA-EA) framework by both facilitating the creation of ecosystem spatial extent and condition accounts, as well as the quantification and mapping of ecosystem services, much more quickly than routine data processing allows. During the past 13 years, LANCE near real-time satellite data products (e.g., surface reflectance, albedo, vegetation height, thermal anomalies, soil moisture, snow cover etc.) have been used to produce ecosystem-related indicators such as vegetation indexes, biomass, land cover maps, fire, and flood products. These ecosystem-related indicators can be integrated into ecosystem services models, tools, and platforms to monitor land cover and land use changes in ecosystem composition in support of land and natural resource management. When using ecosystem services accounts in decision-making, low latency is essential because having up-to-date information can be critical to support decisions being made. With adequate spatial resolution, long time series length, and low latency data products, LANCE would promote the sustainable use of land and natural resources.
Soil zymography is commonly used to quantify spatial distribution of hydrolytic enzyme activities on soil and plant root surfaces. It is recommended to adjust pH in zymography substrates and calibration solutions with respect to soil/root pH. However, pH values may vary greatly within a few mm of plant rhizosphere, potentially altering the distribution of pH in zymography membranes. Despite the fact that the effect of pH on the calibration of zymography membranes is generally known, its potential impact on zymography results is unaccounted for in processing zymography images and calculations of enzyme activity. In this study we assessed the effect of pH variations on the persistency of the methylumbelliferone (MUF) calibration. The studied pH values ranged from 4.5 to 7.5. The MUF calibration curves greatly deviated from that at a reference pH of 6.5, with a marked nonlinear increase of deviation with greater membrane brightness. We suggest that the problem can be partially alleviated by reducing the membrane incubation time. However, such deviations suggest the need for a more comprehensive resolution via mapping pH and using pH-specific calibrations to process zymography images. Here, we developed a MATLAB code to implement a pixel-based correction of enzyme activity for pH in processing time-lapse zymography images.
ABSTRACT A crucial aspect of mass mapping, via weak lensing, is quantification of the uncertainty introduced during the reconstruction process. Properly accounting for these errors has been largely ignored to date. We present a new method to reconstruct maximum a posteriori (MAP) convergence maps by formulating an unconstrained Bayesian inference problem with Laplace-type l1-norm sparsity-promoting priors, which we solve via convex optimization. Approaching mass mapping in this manner allows us to exploit recent developments in probability concentration theory to infer theoretically conservative uncertainties for our MAP reconstructions, without relying on assumptions of Gaussianity. For the first time, these methods allow us to perform hypothesis testing of structure, from which it is possible to distinguish between physical objects and artefacts of the reconstruction. Here, we present this new formalism, and demonstrate the method on simulations, before applying the developed formalism to two observational data sets of the Abell 520 cluster. Initial reconstructions of the Abell 520 catalogues reported the detection of an anomalous ‘dark core’ – an overdense region with no optical counterpart – which was taken to be evidence for self-interacting dark matter. In our Bayesian framework, it is found that neither Abell 520 data set can conclusively determine the physicality of such dark cores at $99{{\ \rm per\ cent}}$ confidence. However, in both cases the recovered MAP estimators are consistent with both sets of data.
The present study reports a new development of the DAMAS microphone phased array processing methodology that allows the determination and separation of coherent and incoherent noise source distributions. In 2004, a Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) was developed which decoupled the array design and processing influence from the noise being measured, using a simple and robust algorithm. In 2005, three-dimensional applications of DAMAS were examined. DAMAS has been shown to render an unambiguous quantitative determination of acoustic source position and strength. However, an underlying premise of DAMAS, as well as that of classical array beamforming methodology, is that the noise regions under study are distributions of statistically independent sources. The present development, called DAMAS-C, extends the basic approach to include coherence definition between noise sources. The solutions incorporate cross-beamforming array measurements over the survey region. While the resulting inverse problem can be large and the iteration solution computationally demanding, it solves problems no other technique can approach. DAMAS-C is validated using noise source simulations and is applied to airframe flap noise test results.
The 18 May 1980 eruption of Mount St. Helens was initiated by a massive rockslide-debris avalanche which completely transformed the upper 25 km of the North Fork Toutle River valley. The debris was generated by one of the largest gravitational mass movements ever recorded on Earth. Moving at an average velocity of 35 m/s, the debris avalanche buried approximately 60 sq km of terrain to an average depth of 45 m with unconsolidated, poorly sorted volcaniclastic material, all within a period of 10 minutes. Where exposed and unaltered by subsequent lahars and pyroclastic flows, the new terrain surface was characterized predominantly by hummocks, closed depressions, and the absence of an identifiable channel network. Following emplacement of the debris avalanche, a complex interrelationship of fluvial and mass wasting processes immediately began operating to return the impacted area to an equilibrium status through the removal of material (potential energy) and re-establishment of graded conditions. In an attempt to chronicle the morphologic evolution of this unique environmental setting, a systematic series of interpretative maps of several selected areas was produced. These maps, which document the rate and character of active geomorphic processes, are discussed.
Abstract Rock salt is considered a suitable medium for the permanent disposal of heat‐generating radioactive waste due to its isolation properties. However, excavation damage and heating induce complex and heterogeneous thermal‐hydrological‐mechanical (THM) processes across different zones. Quantifying this heterogeneity is crucial for accurate long‐term performance assessment models, but traditional methods lack the necessary resolution. This study employs 4D electrical resistivity tomography (ERT) monitoring during controlled heating experiments in a salt formation to unravel the spatiotemporal dynamics of THM processes. Advanced time‐lapse inversion and clustering analysis quantify subsurface properties and map the heterogeneity of THM dynamics. The ERT results can estimate subsurface properties and delineate the damaged and intact zones, enabling appropriate parameterization and representation of processes for long‐term modeling. This approach may be used in further improving the predictive models and ensuring the safe long‐term disposal of radioactive waste in rock salt.
Hyperspectral imaging sensors greatly expand the potential of remote sensing to assess, map, and monitor marine coastal zones. Each pixel in a hyperspectral image contains an entire spectrum of information. As a result, hyperspectral image data can be processed in two very different ways: by image classification techniques, to produce mapped outputs of features in the image on a regional scale; and by use of spectral analysis of the spectral data embedded within each pixel of the image. The latter is particularly useful in marine coastal zones because of the spectral complexity of suspended as well as benthic features found in these environments. Spectral-based analysis of hyperspectral (AVIRIS) imagery was carried out to investigate a marine coastal zone of South Florida, USA. Florida Bay is a phytoplankton-rich estuary characterized by taxonomically distinct phytoplankton assemblages and extensive seagrass beds. End-member spectra were extracted from AVIRIS image data corresponding to ground-truth sample stations and well-known field sites. Spectral libraries were constructed from the AVIRIS end-member spectra and used to classify images using the Spectral Angle Mapper (SAM) algorithm, a spectral-based approach that compares the spectrum, in each pixel of an image with each spectrum in a spectral library. Using this approach different phytoplankton assemblages containing diatoms, cyanobacteria, and green microalgae, as well as benthic community (seagrasses), were mapped.
Innovative features of the anemometer include: (1) a rapid and efficient data acquisition process, (2) a detailed real-time graphic display of the data being accumulated, and (3) input laser beam positioning that maximizes the size of the intra-rotor region being mapped. Results demonstrate the anemometer's capability in flow mapping within a transonic axial-flow compressor rotor. The use of fluorescent seed particles allows flow measurements near the rotor hub and the casing window.
We discuss developments in calculating multi-hadron form-factors and transition processes via lattice QCD. Our primary tools are finite-volume scaling relations, which map spectra and matrix elements to the corresponding multi-hadron infinite-volume amplitudes. We focus on two hadron processes probed by an external current, and provide various checks on the finite-volume formalism in the limiting cases of perturbative interactions and systems forming a bound state. By studying model-independent properties of the infinite-volume amplitudes, we are able to rigorously define form-factors of resonances.
The definition of boundaries of discrete structural provinces on the surface of Mars has previously been made by the assumptions of circular or radial symmetry (in the case of the Tharsis olateau), by artificial quadrangle boundaries for regional studies, or simply by regional geographical features. Much of the planetary structural mapping has been presented in a line or spot format in existing consortium data. Methods are being generated to convert the detailed maps of tectonic features to a format that can be used with standard image processing techniques. In this manner such data can be more readily compared to age and other attributes deduced from geological mapping, gravity results, and topography. The 24 photomosaics covering the Eastern Hemisphere of Mars have already been examined for four types of structural features: erosional remnants, ridges, graben, and scarps. When digitized, each feature was flagged according to feature type, azimuth and length. These values were stored as text files on a quad-by-quad basis for each quad studied.
Quasi-Separatrix Layers (QSLs) are a useful proxy for the locations where current sheets can develop in the solar corona, and give valuable information about the connectivity in complicated magnetic field configurations. However, calculating QSL maps, even for two-dimensional slices through three-dimensional models of coronal magnetic fields, is a non-trivial task, as it usually involves tracing out millions of magnetic field lines with immense precision. Thus, extending QSL calculations to three dimensions has rarely been done until now. In order to address this challenge, we present QSL Squasher—a public, open-source code, which is optimized for calculating QSL maps in both two and three dimensions on graphics processing units. The code achieves large processing speeds for three reasons, each of which results in an order-of-magnitude speed-up. (1) The code is parallelized using OpenCL. (2) The precision requirements for the QSL calculation are drastically reduced by using perturbation theory. (3) A new boundary detection criterion between quasi-connectivity domains is used, which quickly identifies possible QSL locations that need to be finely sampled by the code. That boundary detection criterion relies on finding the locations of abrupt field-line length changes, which we do by introducing a new Field-line Length Edge (FLEDGE) map. We find FLEDGE maps useful on their own as a quick-and-dirty substitute for QSL maps. QSL Squasher allows construction of high-resolution 3D FLEDGE maps in a matter of minutes, which is two orders of magnitude faster than calculating the corresponding 3D QSL maps. We include a sample of calculations done using QSL Squasher to demonstrate its capabilities as a QSL calculator, as well as to compare QSL and FLEDGE maps.
Mars Reconnaissance Orbiter will launch in August 2005 and will achieve Mars Orbit Insertion in March of 2006. It will then take approximately six months to use the process of aerobraking to shape its orbit into the desired science mapping orbit. This six-month period is arguably the phase of the Mars Reconnaissance Orbiter mission with the highest risk to the spacecraft, dipping to within 100 km of the planet. This process requires enough atmospheric drag to slow the spacecraft and circularize the orbit while remaining high enough in the Mars atmosphere as to not risk thermal degradation of spacecraft components. This paper will discuss the trajectory simulation and several analyses performed at NASA Langley Research Center to support a successful aerobraking phase of the Mars Reconnaissance Orbiter mission.
A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata --- a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11,000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.