Computational study of 5<Emphasis Type="Italic">d</Emphasis> transition metal mononitrides and monoborides using density functional method

5d-metal mononitrides and monoborides viz. X-N and X-B (X = La, Hf, Ta, W, Re, Os, Ir, Pt, Au, Hg) are studied using density functional method based B3LYP functional with LANL2DZ and SDD basis set. The lowest spin state, electron affinities, ionization potentials and binding energies for mononitrides and monoborides are obtained. The electronic state and electronic configuration of mononitrides and monoborides are discussed. Orbitals involved in bond formation are identified. The properties of mononitrides and monoborides are compared. It is found that 5d-metal atoms form stronger bond with nitrogen atom than the boron atom. The range of binding energy, electron affinity and ionization potential is wider for mononitrides than that for monoborides. The properties of 5d-metal mononitrides and 3d-metal mononitrides are also compared. The binding energies for the former are lower than those for the latter.     

Biomimetic synthesis and patterning of silver nanoparticles

The creation of nanoscale materials for advanced structures has led to a growing interest in the area of biomineralization. Numerous microorganisms are capable of synthesizing inorganic-based structures. For example, diatoms use amorphous silica as a structural material, bacteria synthesize magnetite (Fe3O4) particles and form silver nanoparticles, and yeast cells synthesize cadmium sulphide nanoparticles. The process of biomineralization and assembly of nanostructured inorganic components into hierarchical structures has led to the development of a variety of approaches that mimic the recognition and nucleation capabilities found in biomolecules for inorganic material synthesis. In this report, we describe the in vitro biosynthesis of silver nanoparticles using silver-binding peptides identified from a combinatorial phage display peptide library.     

Bidirectional segmented-memory recurrent neural network for protein secondary structure prediction

The formation of protein secondary structure especially the regions of β-sheets involves long-range interactions between amino acids. We propose a novel recurrent neural network architecture called segmented-memory recurrent neural network (SMRNN) and present experimental results showing that SMRNN outperforms conventional recurrent neural networks on long-term dependency problems. In order to capture long-term dependencies in protein sequences for secondary structure prediction, we develop a predictor based on bidirectional segmented-memory recurrent neural network (BSMRNN), which is a noncausal generalization of SMRNN. In comparison with the existing predictor based on bidirectional recurrent neural network (BRNN), the BSMRNN predictor can improve prediction performance especially the recognition accuracy of β-sheets.     

A multiple-scale analysis of grating-assisted couplers

Optical directional couplers with longitudinal periodic perturbations or gratings are analyzed by a multiple-scale solution to the coupled-mode equations. The use of two length scales in the analysis becomes the key to obtaining globally valid analytic solutions, which are shown to be in excellent agreement with the exact numerical solutions. Because of the nonorthogonality of the coupled modes in the structure, two different coupling lengths, a maximum power transfer length and a zero crosstalk length, are predicted for the power exchange. The spectral properties of the coupler are also studied through a generalized multiple scale method     

Model for oxygen transfer in rotating biological contactor

Rotating biological contactor is being widely used for wastewater treatment but there is an apparent lack of knowledge about the rate at which oxygen transfer occurs, in physical and biological system. In this study the transfer of oxygen from air to water by a rotating disc air-liquid contactor in physical system is investigated. The oxygen transfer model suggested by Kim and Molof, Water Sci. Technol. 14 (1982) 569, was modified and the developed model is termed as modified Kim and Molof model. The model was calibrated by using available data in literature and validated by experiments conducted in this study. The effect of significant physical parameters was integrated into a single term and is termed as volume renewal number. The modified Kim and Molof model was compared with the other available models. The coefficient of determination (R(2)) for the modified Kim and Molof model obtained is 0.95 which is much higher than in the other available models. Thereby the model is expected to estimate oxygen transfer more accurately. Further, a simplified linear model between K(L)a and the volume renewal number is proposed. Both modified Kim and Molof and linear model estimate the overall oxygen transfer coefficient (K(L)a) accurately.     

Techniques based on genetic algorithms for large deflection analysis of beams

A couple of non-convex search strategies, based on the genetic algorithm, are suggested and numerically explored in the context of large-deflection analysis of planar, elastic beams. The first of these strategies is based on the stationarity of the energy functional in the equilibrium state and may therefore be considered weak. The second approach, on the other hand, attempts to directly solve the governing differential equation within an optimisation framework and such a solution may be thought of as strong. Several numerical illustrations and verifications with ‘exact’ solutions, if available, are provided For communication     

Analog CMOS circuit design and characterization for optical coherence tomography signal processing

We have developed a custom analog CMOS circuit to perform the signal processing for an optical coherence tomography imaging system. The circuit is realized in a 1.5 /spl mu/m low-noise analog CMOS technology. The circuitry extracts the Doppler frequency from the signal and electrically mixes this with the original signal to provide a filtered A-scan. The circuitry was used to produce a two-dimensional image of an onion.     

Optimal Design of Level 1 Redundant Water Distribution Networks Considering Nodal Storage

A water distribution network (WDN) is designed to meet time-varying demands with sufficient pressure, taking into consideration an appropriate demand during peak hours. Therefore, a network has inherent redundancy in the sense that under abnormal conditions such as those arising due to pipe breaks or pump failures, deficiency in supply during peak hours can be met through additional supply during off-peak periods. However, this necessitates a storage facility at the consumer end of the network, which is normally available in the form of a sump or an overhead tank in developing countries. Such a storage enables the consumer to store water during the off-peak period and then use it during the peak period. Reliability of a WDN is assessed herein considering nodal storage, and an iterative method is proposed for the optimal design of Level 1 redundant WDNs, i.e., networks that can sustain a single pipe failure without affecting consumer services either in part or in full. The method is illustrated through an example and the designs of a network with and without storage are compared. Provision of a nodal storage is found to reduce the total cost of the network.     

Photocatalytic degradation of dyes and organic contaminants in water using nanocrystalline anatase and rutile TiO2

Nanocrystalline TiO₂ was synthesized by controlled hydrolysis of titanium tetraisopropoxide. The anatase phase was converted to rutile phase by thermal treatment at 1023 K for 11 h. The catalysts were characterized by X-ray diffraction (XRD), diffuse reflectance spectroscopy (DRS), Fourier-transform infrared absorption spectrophotometry (FT-IR) and N₂ adsorption (BET) at 77 K. This study compare the photocatalytic activity of the anatase and rutile phases of nanocrystalline TiO₂ for the degradation of acetophenone, nitrobenzene, methylene blue and malachite green present in aqueous solutions. The initial rate of degradation was calculated to compare the photocatalytic activity of anatase and rutile nanocrystalline TiO₂ for the degradation of different substances under ultraviolet light irradiation. The higher photocatalytic activity was obtained in anatase phase TiO₂ for the degradation of all substances as compared with rutile phase. It is concluded that the higher photocatalytic activity in anatase TiO₂ is due to parameters like band-gap, number of hydroxyl groups, surface area and porosity of the catalyst.