Anatase-rutile transformation in Fe/TiO2, Th/TiO2 and Cu/TiO2 catalysts and its possible role in metal-support interaction

Based on in-situ Mossbauer and X-ray diffraction studies, it is shown that in the Fe/TiO2 catalyst, the anatase-rutile transformation of the TiO2 support is facilitated by the Fe2+ ions formed during the reduction. The transformation occurs at lower temperatures in Th/TiO2 and Cu/TiO2 compared to pure TiO2. In general, the transformation of anatase to rutile seems to occur at or below the temperature (approximately 770 K) at which strong-metal-support-interaction manifests itself.

Mesoporous Carbon and Poly(3,4-ethylenedioxythiophene) Composite as Catalyst Support for Polymer Electrolyte Fuel Cells

In situ polymerization of 3,4-ethylenedioxythiophene with sol-gel-derived mesoporous carbon (MC) leading to a new composite and its subsequent impregnation with Pt nanoparticles for application in polymer electrolyte fuel cells (PEFCs) is reported. The composite exhibits good dispersion and utilization of platinum nanoparticles akin to other commonly used microporous carbon materials, such as carbon black. Pt-supported MC-poly(3,4-ethylenedioxythiophene) (PEDOT) composite also exhibits promising electrocatalytic activity toward oxygen reduction reaction, which is central to PEFCs. The PEFC with Pt-loaded MC-PEDOT support exhibits 75% of enhancement in its power density in relation to the PEFC with Pt-loaded pristine MC support while operating under identical conditions. It is conjectured that Pt-supported MC-PEDOT composite ameliorates PEFC performance/durability on repetitive potential cycling. (C) 2010 The Electrochemical Society. DOI: 10.1149/1.3486172] All rights reserved.

An electron spectroscopic study of the adsorption of nitrogen on a Ni/TiO2 catalyst prepared in situ in the spectrometer: evidence for dissociative adsorption in the strong-metal-support-interaction (SMSI) state

Nitrogen is dissociatively adsorbed on an annealed Ni/TiO2 surface just as on a Ti–Ni alloy surface while it is molecularly adsorbed on a Ni/Al2O3 surface.

Influence of an additional discrete elastic support on the stability of a Leipholz column

The stability characteristics of a conservatively loaded structure are expected to improve if additional supports are provided to the structure. The same, however, may not be said of a non-conservatively loaded structure; several factors, such as the location and stiffness of supports, type of structure and loading, have a significant influence on the stability characteristics. The influence of an arbitrarily located elastic support on the stability characteristics of a Leipholz column is examined.

Strong metal-support interaction in Ni/TiO2 catalysts: in situ EXAFS and related studies

In situ EXAFS and X-ray diffraction investigations of Ni/TiO2 catalysts show that NiTiO3 is formed as an intermediate during calcination of catalyst precursors prepared by the wet-impregnation method; the intermediate is not formed when ion-exchange method is used for the preparation. On hydrogen reduction, NiTiO3 gives rise to Ni particles dispersed in the TiO2(rutile) matrix. The occurrence of the anatase-rutile transformation of the TiO2 support, the formation and subsequent decomposition/reduction of NiTiO3 as well as the unique interface properties of the Ni particles are all factors of importance in giving rise to metal-support interaction. Active TiO2(anatase) prepared from gel route gives an additional species involving Ni3+.

Support vector machine for evaluating seismic-liquefaction potential using shear wave velocity

The use of the shear wave velocity data as a field index for evaluating the liquefaction potential of sands is receiving increased attention because both shear wave velocity and liquefaction resistance are similarly influenced by many of the same factors such as void ratio, state of stress, stress history and geologic age. In this paper, the potential of support vector machine (SVM) based classification approach has been used to assess the liquefaction potential from actual shear wave velocity data. In this approach, an approximate implementation of a structural risk minimization (SRM) induction principle is done, which aims at minimizing a bound on the generalization error of a model rather than minimizing only the mean square error over the data set. Here SVM has been used as a classification tool to predict liquefaction potential of a soil based on shear wave velocity. The dataset consists the information of soil characteristics such as effective vertical stress (sigma'(v0)), soil type, shear wave velocity (V-s) and earthquake parameters such as peak horizontal acceleration (a(max)) and earthquake magnitude (M). Out of the available 186 datasets, 130 are considered for training and remaining 56 are used for testing the model. The study indicated that SVM can successfully model the complex relationship between seismic parameters, soil parameters and the liquefaction potential. In the model based on soil characteristics, the input parameters used are sigma'(v0), soil type. V-s, a(max) and M. In the other model based on shear wave velocity alone uses V-s, a(max) and M as input parameters. In this paper, it has been demonstrated that Vs alone can be used to predict the liquefaction potential of a soil using a support vector machine model. (C) 2010 Elsevier B.V. All rights reserved.

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).

Graphitic Carbon as Durable Cathode-Catalyst Support for PEFCs

Long-term deterioration in the performance of PEFCs is attributed largely to reduction in active area of the platinum catalyst at cathode, usually caused by carbon-support corrosion. It is found that the use of graphitic carbon as cathode-catalyst support enhances its long-term stability in relation to non-graphitic carbon. This is because graphitic-carbon-supported- Pt (Pt/GrC) cathodes exhibit higher resistance to carbon corrosion in-relation to non-graphitic-carbon-supported- Pt (Pt/Non-GrC) cathodes in PEFCs during accelerated stress test (AST) as evidenced by chronoamperometry and carbon dioxide studies. The corresponding change in electrochemical surface area (ESA), cell performance and charge-transfer resistance are monitored through cyclic voltammetry (CV), cell polarisation and impedance measurements, respectively. The degradation in performance of PEFC with Pt/GrC cathode is found to be around 10% after 70 h of AST as against 77% for Pt/Non-GrC cathode. It is noteworthy that Pt/GrC cathodes can withstand even up to 100 h of AST with nominal effect on their performance. Xray diffraction (XRD), Raman spectroscopy, transmission electron microscopy and cross-sectional field-emission scanning electron microscopy (FE-SEM) studies before and after AST suggest lesser deformation in catalyst layer and catalyst particles for Pt/GrC cathodes in relation to Pt/Non-GrC cathodes, reflecting that graphitic carbon-support resists carbon corrosion and helps mitigating aggregation of Pt-particles.

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.

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.