Highly selective deprotection of tert-butyl esters using ytterbium triflate as a catalyst under mild conditions

Ytterbium triflate catalyses the deprotection of tert-butyl esters selectively in the presence of other esters under mild conditions in almost quantitative yields. The reactions are carried out in nitromethane (45degrees - 50degreesC) using 5 mole percent of the catalyst.

Influence of HZSM-5 catalyst on the thermal degradation of poly(vinyl chloride) in solution

The kinetics of thermal degradation of poly(vinyl chloride) (PVC) in solution was investigated at various temperatures (210-250degreesC). The degradation rate coefficients were determined from the time evolution of the molecular weight distribution (MWD). The energy of activation, determined from the temperature dependence of the rate coefficient, was 26.6 kcal/mol. The degradation of PVC was also studied in the presence of a catalyst (HZSM-5 zeolite). The results indicated that increase of the degradation rate of PVC is first order with the HZSM-5 concentration up to 50 g/L and zero order at higher concentrations. The thermal degradation kinetics of PVC in the presence of 50 g/L of the catalyst was studied at various temperatures. The temperature dependency of the rate coefficient was used to calculate the activation energy (21.5 kcal/mol). This is consistent with the observation that the presence of a catalyst generally decreases the activation energy and promotes degradation. (C) 2002 John Wiley Sons, Inc.

Pd-coated Ni nanoparticles by the polyol method: an efficient hydrogenation catalyst

Pd-coated Ni nanoparticles of 50 +/- 15 nm size are prepared by the polyol method and characterized by X-ray diffraction, transmission electron microscopy, X-ray photoelectron spectroscopy and thermogravimetry analysis. Surface coverage of Pd on Ni particles is less than a monolayer for 0.5 and 1 at% Pd-coated Ni. Quantitative conversion of nitrobenzene to aniline is observed over these Pd-coated Ni particles at 27degreesC under one atmospheric pressure of hydrogen. 0.5 and 1 at% Pd-coated Ni exhibits 10 times greater activity than that of typical colloidal palladium and platinum catalysts and 2.5 times higher activity than commercial 5 wt% Pd/C.

Study of local environment of Ag in Ag/CeO2 catalyst by EXAFS

The combustion synthesized Ag/CeO2 catalysts have been characterized by Extended Xray Absorption Fine Structure (EXAFS) spectroscopy at the Ag K-edge. It has been found that Ag+ like species is present in 1% Ag/CeO2 catalyst, whereas mostly Ag metal clusters are found in 3% Ag/CeO2. The analysis of EXAFS spectra indicates that about one oxygen atom is coordinated to Ag central atom at a distance of 2.19 Angstrom in 1% Ag/CeO2 catalyst along with eight coordinated Ag-Ag bond at 2.86 Angstrom. The Ag-O bond is absent in 3% Ag/CeO2. (C) 2002 Elsevier Science Ltd. All rights reserved.

Effect of inorganic ions, H(2)O(2) and pH on the photocatalytic inactivation of Escherichia coli with silver impregnated combustion synthesized TiO(2) catalyst

The photocatalytic antibacterial activity of Ag impregnated combustion synthesized TiO(2) (0.25 g/L) was studied against Escherichia coil in presence of UV irradiation. The effect of various parameters, such as anions, canons, hydrogen peroxide and pH, on the photocatalytic inactivation was investigated. The addition of inorganic ions showed a negative effect on inactivation. Among anions, the presence of chloride ions was observed to have a maximum negative effect and reduced the inactivation considerably. Among cations, the bacterial inactivation reduced significantly in the presence of Ca(2+) ions. Hydrogen peroxide addition in combination with Ag/TiO(2) photocatalysis, however, improved the inactivation. Photocatalysis with high concentration of H(2)O(2) yielded complete bacterial inactivation within few minutes. The photocatalytic inactivation of E. coil was not affected by variation in pH. (C) 2011 Elsevier B.V. All rights reserved.

An Approach for Distance Relay Co-ordination using Support Vector Machines

In this paper, an approach to enhance the Extra High Voltage (EHV) Transmission system distance protection is presented. The scheme depends on the apparent impedance seen by the distance relay during the disturbance. In a distance relay,the impedance seen at the relay location is calculated from the fundamental frequency component of the voltage and current signals. Support Vector Machines (SVMs) are a new learning-byexample are employed in discriminating zone settings (Zone-1,Zone-2 and Zone-3) using the signals to be used by the relay.Studies on 265-bus system, an equivalent of practical Indian Western grid are presented for illustrating the proposed scheme.

Pt-Au/C cathode with enhanced oxygen-reduction activity in PEFCs

Carbon-supported Pt-Au (Pt-Au/C) catalyst is prepared separately by impregnation, colloidal and micro-emulsion methods, and characterized by physical and electrochemical methods. Highest catalytic activity towards oxygen-reduction reaction (ORR) is exhibited by Pt-Au/C catalyst prepared by colloidal method. The optimum atomic ratio of Pt to Au in Pt-Au/C catalyst prepared by colloidal method is determined using linear-sweep and cyclic voltammetry in conjunction with cell-polarization studies. Among 3:1, 2:1 and 1:1 Pt-Au/C catalysts, (3:1) Pt-Au/C exhibits maximum electrochemical activity towards ORR. Powder X-ray diffraction pattern and transmission electron micrograph suggest Pt-Au alloy nanoparticles to be well dispersed onto the carbon-support. Energy dispersive X-ray analysis and inductively coupled plasma-optical emission spectroscopy data suggest that the atomic ratios of the alloying elements match well with the expected values. A polymer electrolyte fuel cell (PEFC) operating at 0 center dot 6 V with (3:1) Pt-Au/C cathode delivers a maximum power-density of 0 center dot 65 W/cm (2) in relation to 0 center dot 53 W/cm (2) delivered by the PEFC with pristine carbon-supported Pt cathode.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

Solar energy decision support system

Energy plays a prominent role in human society. As a result of technological and industrial development,the demand for energy is rapidly increasing. Existing power sources that are mainly fossil fuel based are leaving an unacceptable legacy of waste and pollution apart from diminishing stock of fuels.Hence, the focus is now shifted to large-scale propagation of renewable energy. Renewable energy technologies are clean sources of energy that have a much lower environmental impact than conventional energy technologies. Solar energy is one such renewable energy. Most renewable energy comes either directly or indirectly from the sun. Estimation of solar energy potential of a region requires detailed solar radiation climatology, and it is necessary to collect extensive radiation data of high accuracy covering all climatic zones of the region. In this regard, a decision support system (DSS)would help in estimating solar energy potential considering the region’s energy requirement.This article explains the design and implementation of DSS for assessment of solar energy. The DSS with executive information systems and reporting tools helps to tap vast data resources and deliver information. The main hypothesis is that this tool can be used to form a core of practical methodology that will result in more resilient in time and can be used by decision-making bodies to assess various scenarios. It also offers means of entering, accessing, and interpreting the information for the purpose of sound decision making.

Geographic Resources Decision Support System – an Open Source GIS

Fully structured and matured open source spatial and temporal analysis technology seems to be the official carrier of the future for planning of the natural resources especially in the developing nations. This technology has gained enormous momentum because of technical superiority, affordability and ability to join expertise from all sections of the society. Sustainable development of a region depends on the integrated planning approaches adopted in decision making which requires timely and accurate spatial data. With the increased developmental programmes, the need for appropriate decision support system has increased in order to analyse and visualise the decisions associated with spatial and temporal aspects of natural resources. In this regard Geographic Information System (GIS) along with remote sensing data support the applications that involve spatial and temporal analysis on digital thematic maps and the remotely sensed images. Open source GIS would help in wide scale applications involving decisions at various hierarchical levels (for example from village panchayat to planning commission) on economic viability, social acceptance apart from technical feasibility. GRASS (Geographic Resources Analysis Support System, http://wgbis.ces.iisc.ernet.in/grass) is an open source GIS that works on Linux platform (freeware), but most of the applications are in command line argument, necessitating a user friendly and cost effective graphical user interface (GUI). Keeping these aspects in mind, Geographic Resources Decision Support System (GRDSS) has been developed with functionality such as raster, topological vector, image processing, statistical analysis, geographical analysis, graphics production, etc. This operates through a GUI developed in Tcltk (Tool command language / Tool kit) under Linux as well as with a shell in X-Windows. GRDSS include options such as Import /Export of different data formats, Display, Digital Image processing, Map editing, Raster Analysis, Vector Analysis, Point Analysis, Spatial Query, which are required for regional planning such as watershed Analysis, Landscape Analysis etc. This is customised to Indian context with an option to extract individual band from the IRS (Indian Remote Sensing Satellites) data, which is in BIL (Band Interleaved by Lines) format. The integration of PostgreSQL (a freeware) in GRDSS aids as an efficient database management system.

WEPA: Wind energy potential assessment-spatial decision support system

Spatial Decision Support System (SDSS) assist in strategic decision-making activities considering spatial and temporal variables, which help in Regional planning. WEPA is a SDSS designed for assessment of wind potential spatially. A wind energy system transforms the kinetic energy of the wind into mechanical or electrical energy that can be harnessed for practical use. Wind energy can diversify the economies of rural communities, adding to the tax base and providing new types of income. Wind turbines can add a new source of property value in rural areas that have a hard time attracting new industry. Wind speed is extremely important parameter for assessing the amount of energy a wind turbine can convert to electricity: The energy content of the wind varies with the cube (the third power) of the average wind speed. Estimation of the wind power potential for a site is the most important requirement for selecting a site for the installation of a wind electric generator and evaluating projects in economic terms. It is based on data of the wind frequency distribution at the site, which are collected from a meteorological mast consisting of wind anemometer and a wind vane and spatial parameters (like area available for setting up wind farm, landscape, etc.). The wind resource is governed by the climatology of the region concerned and has large variability with reference to space (spatial expanse) and time (season) at any fixed location. Hence the need to conduct wind resource surveys and spatial analysis constitute vital components in programs for exploiting wind energy. SDSS for assessing wind potential of a region / location is designed with user friendly GUI’s (Graphic User Interface) using VB as front end with MS Access database (backend). Validation and pilot testing of WEPA SDSS has been done with the data collected for 45 locations in Karnataka based on primary data at selected locations and data collected from the meteorological observatories of the India Meteorological Department (IMD). Wind energy and its characteristics have been analysed for these locations to generate user-friendly reports and spatial maps. Energy Pattern Factor (EPF) and Power Densities are computed for sites with hourly wind data. With the knowledge of EPF and mean wind speed, mean power density is computed for the locations with only monthly data. Wind energy conversion systems would be most effective in these locations during May to August. The analyses show that coastal and dry arid zones in Karnataka have good wind potential, which if exploited would help local industries, coconut and areca plantations, and agriculture. Pre-monsoon availability of wind energy would help in irrigating these orchards, making wind energy a desirable alternative.

A Randomized Algorithm for Large Scale Support Vector Learning

We propose a randomized algorithm for large scale SVM learning which solves the problem by iterating over random subsets of the data. Crucial to the algorithm for scalability is the size of the subsets chosen. In the context of text classification we show that, by using ideas from random projections, a sample size of O(log n) can be used to obtain a solution which is close to the optimal with a high probability. Experiments done on synthetic and real life data sets demonstrate that the algorithm scales up SVM learners, without loss in accuracy. 1