616 research outputs found
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Passive Corrosion Behavior of Alloy 22
Alloy 22 (N06022) was designed to stand the most aggressive industrial applications, including both reducing and oxidizing acids. Even in the most aggressive environments, if the temperature is lower than 150 F (66 C) Alloy 22 would remain in the passive state having particularly low corrosion rates. In multi-ionic solutions that may simulate the behavior of concentrated ground water, even at near boiling temperatures, the corrosion rate of Alloy 22 is only a few nanometers per year because the alloy is in the complete passive state. The corrosion rate of passive Alloy 22 decreases as the time increases. Immersion corrosion testing also show that the newer generation of Ni-Cr-Mo alloys may offer a better corrosion resistance than Alloy 22 only in some highly aggressive conditions such as in hot acids
Probing the phase diagram of CeRu_2Ge_2 by thermopower at high pressure
The temperature dependence of the thermoelectric power, S(T), and the
electrical resistivity of the magnetically ordered CeRu_2Ge_2 (T_N=8.55 K and
T_C=7.40 K) were measured for pressures p < 16 GPa in the temperature range 1.2
K < T < 300 K. Long-range magnetic order is suppressed at a p_c of
approximately 6.4 GPa. Pressure drives S(T) through a sequence of temperature
dependences, ranging from a behaviour characteristic for magnetically ordered
heavy fermion compounds to a typical behaviour of intermediate-valent systems.
At intermediate pressures a large positive maximum develops above 10 K in S(T).
Its origin is attributed to the Kondo effect and its position is assumed to
reflect the Kondo temperature T_K. The pressure dependence of T_K is discussed
in a revised and extended (T,p) phase diagram of CeRu_2Ge_2.Comment: 7 pages, 6 figure
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Modeling the Effects of Crevice Former, Particulates , and the Evolving Surface Profile in Crevice Corrosion
Crevice corrosion may initiate in confined regions due to transport limitations, followed by an accumulation of a highly corrosive chemistry, capable of dissolving the metal. The metal and the crevice former surface roughness, the presence of particulates under the crevice former and the accumulation of solid corrosion products at the corroding site would significantly affect the current and potential distribution at the anode by increasing the ohmic potential drop. Most crevice corrosion models focus on a smooth walled crevice of uniform gap and do not account for the changing profile after crevice corrosion has been initiated. In this work we analyze the crevice (anodic) region and apply current and potential distribution models to examine the effects of the perturbed surface topography. The analysis focuses on three related issues: (1) the effects of surface roughness of the metal and the crevice former, (2) the effects of particulates under the crevice former, and (3) the evolution of the crevice profile with corrosion product accumulation at the active, anodic region
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Modeling the Cathodic Region in Crevice Corrosion Under a Thin Electrolyte Film Including Particulates
Crevice corrosion may be limited by the capacity of the external cathodic region to support anodic dissolution currents within the crevice. The analysis here focuses on behavior of metal surfaces covered by a thin ({approx}microns) layer of the electrolyte film including particulates. The particulates can affect the cathode current capacity (I{sub total}) by increasing the solution resistance (''volume effect'') and by decreasing the electrode area (''surface effect''). In addition, there can be particulate effects on oxygen reduction kinetics and oxygen transport. This work simulates and characterizes the effect of a uniform particulate monolayer on the cathode current capacity for steady state conditions in the presence of a thin electrolyte film. Particulate configurations with varying particle size, shape, arrangement, volume fraction, and electrode area coverage were numerically modeled as a function of the properties of the system. It is observed that the effects of particles can be fully accounted for in terms of two corrections: the volume blockage effect on the electrolyte resistivity can be correlated using Bruggeman's equation, and the electrode coverage effect can be modeled in terms of a simple area correction to the kinetics expression. For the range of parameters analyzed, applying these two correction factors, cathodes covered with thin electrolyte films that contain particles can be represented in terms of equivalent homogeneous electrolytes that can then be analyzed using simpler approaches. Continuing work will examine the effects of greater volume fractions of particles and multiple particle layers
Patterns in the diagnosis and treatment of osteoporosis in men : a questionnaire-based survey in Central and Eastern European countries
Kondo engineering : from single Kondo impurity to the Kondo lattice
In the first step, experiments on a single cerium or ytterbium Kondo impurity
reveal the importance of the Kondo temperature by comparison to other type of
couplings like the hyperfine interaction, the crystal field and the intersite
coupling. The extension to a lattice is discussed. Emphasis is given on the
fact that the occupation number of the trivalent configuration may be the
implicit key variable even for the Kondo lattice. Three phase
diagrams are discussed: CeRuSi, CeRhIn and SmS
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Present and future nitrogen deposition to national parks in the United States: critical load exceedances
National parks in the United States are protected areas wherein the natural habitat is to be conserved for future generations. Deposition of anthropogenic nitrogen (N) transported from areas of human activity (fuel combustion, agriculture) may affect these natural habitats if it exceeds an ecosystem-dependent critical load (CL). We quantify and interpret the deposition to Class I US national parks for present-day and future (2050) conditions using the GEOS-Chem global chemical transport model with 1/2° × 2/3° horizontal resolution over North America. We estimate CL values in the range 2.5–5 kg N ha−1 yr−1 for the different parks to protect the most sensitive ecosystem receptors. For present-day conditions, we find 24 out of 45 parks to be in CL exceedance and 14 more to be marginally so. Many of these are in remote areas of the West. Most (40–85%) of the deposition originates from NOx emissions (fuel combustion). We project future changes in N deposition using representative concentration pathway (RCP) anthropogenic emission scenarios for 2050. These feature 52–73% declines in US NOx emissions relative to present but 19–50% increases in US ammonia (NH3) emissions. Nitrogen deposition at US national parks then becomes dominated by domestic NH3 emissions. While deposition decreases in the East relative to present, there is little progress in the West and increases in some regions. We find that 17–25 US national parks will have CL exceedances in 2050 based on the RCP8.5 and RCP2.6 scenarios. Even in total absence of anthropogenic NOx emissions, 14–18 parks would still have a CL exceedance. Returning all parks to N deposition below CL by 2050 would require at least a 50% decrease in US anthropogenic NH3 emissions relative to RCP-projected 2050 levels.Engineering and Applied Science
High-pressure transport properties of CeRu_2Ge_2
The pressure-induced changes in the temperature-dependent thermopower S(T)
and electrical resistivity \rho(T) of CeRu_2Ge_2 are described within the
single-site Anderson model. The Ce-ions are treated as impurities and the
coherent scattering on different Ce-sites is neglected. Changing the
hybridisation \Gamma between the 4f-states and the conduction band accounts for
the pressure effect. The transport coefficients are calculated in the
non-crossing approximation above the phase boundary line. The theoretical S(T)
and \rho(T) curves show many features of the experimental data. The seemingly
complicated temperature dependence of S(T) and \rho(T), and their evolution as
a function of pressure, is related to the crossovers between various fixed
points of the model.Comment: 9 pages, 10 figure
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
Image landmark detection aims to automatically identify the locations of
predefined fiducial points. Despite recent success in this field,
higher-ordered structural modeling to capture implicit or explicit
relationships among anatomical landmarks has not been adequately exploited. In
this work, we present a new topology-adapting deep graph learning approach for
accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection.
The proposed method constructs graph signals leveraging both local image
features and global shape features. The adaptive graph topology naturally
explores and lands on task-specific structures which are learned end-to-end
with two Graph Convolutional Networks (GCNs). Extensive experiments are
conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as
well as three real-world X-ray medical datasets (Cephalometric (public), Hand
and Pelvis). Quantitative results comparing with the previous state-of-the-art
approaches across all studied datasets indicating the superior performance in
both robustness and accuracy. Qualitative visualizations of the learned graph
topologies demonstrate a physically plausible connectivity laying behind the
landmarks.Comment: Accepted to ECCV-20. Camera-ready with supplementary materia
Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks
Both image registration and label fusion in the multi-atlas segmentation
(MAS) rely on the intensity similarity between target and atlas images.
However, such similarity can be problematic when target and atlas images are
acquired using different imaging protocols. High-level structure information
can provide reliable similarity measurement for cross-modality images when
cooperating with deep neural networks (DNNs). This work presents a new MAS
framework for cross-modality images, where both image registration and label
fusion are achieved by DNNs. For image registration, we propose a consistent
registration network, which can jointly estimate forward and backward dense
displacement fields (DDFs). Additionally, an invertible constraint is employed
in the network to reduce the correspondence ambiguity of the estimated DDFs.
For label fusion, we adapt a few-shot learning network to measure the
similarity of atlas and target patches. Moreover, the network can be seamlessly
integrated into the patch-based label fusion. The proposed framework is
evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework
is effective in both cross-modality registration and segmentation
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