The ORNL-SNAP shielding program

The effort in the ORNL-SNAP shielding program is directed toward the development and verification of computer codes using numerical solutions to the transport equation for the design of optimized radiation shields for SNAP power systems. A brief discussion is given for the major areas of the SNAP shielding program, which are cross-section development, transport code development, and integral experiments. Detailed results are presented for the integral experiments utilizing the TSF-SNAP reactor. Calculated results are compared with experiments for neutron and gamma-ray spectra from the bare reactor and as transmitted through slab shields

Cymantrene–Triazole "Click" Products: Structural Characterization and Electrochemical Properties

We report the first known examples of triazole-derivatized cymantrene complexes (η5-[4-substituted triazol-1-yl]cyclopentadienyl)tricarbonylmanganese(I), obtained via a “click” chemical synthesis, bearing a phenyl, 3-aminophenyl, or 4-aminophenyl moiety at the 4-position of the triazole ring. Structural characterization data using multinuclear NMR, UV–vis, ATR-IR, and mass spectrometric methods are provided, as well as crystallographic data for (η5-[4-phenyltriazol-1-yl]cyclopentadienyl)tricarbonylmanganese(I) and (η5-[4-(3-aminophenyl)triazol-1-yl]cyclopentadienyl)tricarbonylmanganese(I). Cyclic voltammetric characterization of the redox behavior of each of the three cymantrene–triazole complexes is presented together with digital simulations, in situ infrared spectroelectrochemistry, and DFT calculations to extract the associated kinetic and thermodynamic parameters. The trypanocidal activity of each cymantrene–triazole complex is also examined, and these complexes are found to be more active than cymantrene alone

Rate-Dependent Nucleation and Growth of NaO2 in Na-O2 Batteries

Understanding the oxygen reduction reaction kinetics in the presence of Na ions and the formation mechanism of discharge product(s) is key to enhancing Na–O2 battery performance. Here we show NaO2 as the only discharge product from Na–O2 cells with carbon nanotubes in 1,2-dimethoxyethane from X-ray diffraction and Raman spectroscopy. Sodium peroxide dihydrate was not detected in the discharged electrode with up to 6000 ppm of H2O added to the electrolyte, but it was detected with ambient air exposure. In addition, we show that the sizes and distributions of NaO2 can be highly dependent on the discharge rate, and we discuss the formation mechanisms responsible for this rate dependence. Micron-sized (∼500 nm) and nanometer-scale (∼50 nm) cubes were found on the top and bottom of a carbon nanotube (CNT) carpet electrode and along CNT sidewalls at 10 mA/g, while only micron-scale cubes (∼2 μm) were found on the top and bottom of the CNT carpet at 1000 mA/g, respectively.Seventh Framework Programme (European Commission) (Marie Curie International Outgoing Fellowship, 2007-2013))National Science Foundation (U.S.) (MRSEC Program, award number DMR-0819762)Robert Bosch GmbH (Bosch Energy Research Network (BERN) Grant)China Clean Energy Research Center-Clean Vehicles Consortium (CERC-CVC) (award number DE-PI0000012)Skolkovo Institute of Science and Technology (Skoltech-MIT Center for Electochemical Energy Storage

Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning

Landslide susceptibility mapping using support vector machine and GIS at the Golestan province, Iran

The main goal of this study is to produce landslide susceptibility map using GIS-based support vector machine (SVM) at Kalaleh Township area of the Golestan province, Iran. In this paper, six different types of kernel classifiers such as linear, polynomial degree of 2, polynomial degree of 3, polynomial degree of 4, radial basis function (RBF) and sigmoid were used for landslide susceptibility mapping. At the first stage of the study, landslide locations were identified by aerial photographs and field surveys, and a total of 82 landslide locations were extracted from various sources. Of this, 75% of the landslides (61 landslide locations) are used as training dataset and the rest was used as (21 landslide locations) the validation dataset. Fourteen input data layers were employed as landslide conditioning factors in the landslide susceptibility modelling. These factors are slope degree, slope aspect, altitude, plan curvature, profile curvature, tangential curvature, surface area ratio (SAR), lithology, land use, distance from faults, distance from rivers, distance from roads, topographic wetness index (TWI) and stream power index (SPI). Using these conditioning factors, landslide susceptibility indices were calculated using support vector machine by employing six types of kernel function classifiers. Subsequently, the results were plotted in ArcGIS and six landslide susceptibility maps were produced. Then, using the success rate and the prediction rate methods, the validation process was performed by comparing the existing landslide data with the six landslide susceptibility maps. The validation results showed that success rates for six types of kernel models varied from 79% to 87%. Similarly, results of prediction rates showed that RBF (85%) and polynomial degree of 3 (83%) models performed slightly better than other types of kernel (polynomial degree of 2 = 78%, sigmoid = 78%, polynomial degree of 4 = 78%, and linear = 77%) models. Based on our results, the differences in the rates (success and prediction) of the six models are not really significant. So, the produced susceptibility maps will be useful for general land-use planning

Atomic Energy Commission Report K-1472

Summary: The following narrative interspersed with figures and attached reference exhibits is designed to acquaint the reader with the scheduling procedure developed at ORGDP, trial results and evaluation, subsequent improvement, further application, and use in conjunction with our IBM 7090 Computer