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

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

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

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 $n_f$ of the trivalent configuration may be the implicit key variable even for the Kondo lattice. Three $(P, H, T)$ phase diagrams are discussed: CeRu$_2$Si$_2$, CeRhIn$_5$ and SmS

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