Ferromagnetic mixed tribridged dinuclear copper(II) complex [Cu2(OAc)2(OH)(dpa)2]PF6 · H2O (dpa = 2,2′-dipyridylamine): 3D superstructure based on hydrogen bond and π…π interaction

A new dinuclear complex [Cu₂(OAc)₂(OH)(dpa)₂] PF₆ · H₂O (1) is prepared and structurally and magneto-structurally characterized. The monocationic core contains one acetate in familiar bidentate η¹:η¹:μ₂-bridge and another in the rare monoatomic bridge along with one hydroxo intermediary. 1 packs through N–H…O and O–H…O hydrogen bonds and π…π interaction resulting a 3D supramolecular continuum and displays high-energy intraligand ¹(π − π^*) fluorescence and intraligand ³(π − π^*) phosphorescence in glassy solution.     

Polyaniline and polypyrrole modified conductive nanocomposites of polyfuran and polythiophene with montmorillonite clay

Nanocomposites of polyfuran (PF) and polythiophene (PTP) with montmorillonite clay (MMT) were prepared and modified by loading of polyaniline (PANI) and polypyrrole (PPY) moieties via polymerization of aniline (ANI) and pyrrole (PY) in aqueous dispersions of PF-MMT and PTP-MMT nanocomposites. Formation of PANI and PPY and their subsequent incorporation in the PF-MMT and PTP-MMT composites was confirmed by FTIR absorption studies. X-ray diffraction (XRD) patterns of PANI and PPY modified PF-MMT and PTP-MMT composites showed that PF-MMT and PTP-MMT intercalates were still present in the modified composites. Scanning electron microscopic analysis revealed distinctive morphological patterns of the various composite particles. The dc conductivity values of PANI and PPY modified PF-MMT and PTP-MMT composites were in the order of 10⁻² S/cm in either system – a value much improved compared to the same for both of the unmodified PF-MMT (10⁻⁷ S/cm) and PTP-MMT (10⁻⁵ S/cm) nanocomposites respectively.     

Novel wet-chemical synthesis and characterization of nanocrystalline CeO2 and Ce0.8Gd0.2O1.9 as solid electrolyte for intermediate temperature solid oxide fuel cell (IT-SOFC) applications

Novel wet-chemical methods of synthesis have been adopted to synthesize nano-crystalline CeO₂ and Gd-substituted compositions aiming to explore an efficient oxide ion conducting solid electrolyte for intermediate temperature solid oxide fuel cell (IT-SOFC) applications. Nano-crystalline CeO₂ powders were synthesized by combustion method using redox mixture of cerric ammonium nitrate or cerium nitrate and maleic acid or 1,3-dimethylurea and compared with high surface area CeO₂ powders prepared by hydrothermal technique with microwave precipitated precursor from aqueous solutions of (NH₄)₂Ce(NO₃)₆ and urea. The grain size achieved by the hydrothermal technique is ∼7 nm which is smaller than that of commercial nano CeO₂ powders. Conventional or microwave sintering was used to prepare dense Ce_0.8Gd_0.2O_1.9 pellets from the ceria powders made of redox mixture of cerium nitrate, 1,3-dimethylurea (DMU) and Gd₂O₃ as the starting ingredients. The samples were characterized by X-ray diffractometry (XRD), transmission electron microscopy (TEM), diffuse reflectance spectroscopy (DRS), scanning electron microscopy (SEM), and ac impedance spectroscopy. The ionic conductivity measured for the pellet sintered at 1400 °C is 1 × 10⁻² and 2.4 × 10⁻² S/cm at 700 °C and 800 °C respectively.     

Incorporating Ancestors' Influence in Genetic Algorithms

A new criterion of fitness evaluation for Genetic Algorithms is introduced where the fitness value of an individual is determined by considering its own fitness as well as those of its ancestors. Some guidelines for selecting the weighting coefficients for quantifying the importance to be given to the fitness of the individual and its ancestors are provided. This is done both heuristically and automatically under fixed and adaptive frameworks. The Schema Theorem corresponding to the proposed concept is derived. The effectiveness of this new methodology is demonstrated extensively on the problems of optimizing complex functions including a noisy one and selecting optimal neural network parameters.     

A mixture-of-experts framework for adaptive Kalman filtering

This paper proposes a modular and flexible approach to adaptive Kalman filtering using the framework of a mixture-of-experts regulated by a gating network. Each expert is a Kalman filter modeled with a different realization of the unknown system parameters such as process and measurement noise. The gating network performs on-line adaptation of the weights given to individual filter estimates based on performance. This scheme compares very favorably with the classical Magill filter bank, which is based on a Bayesian technique, in terms of: estimation accuracy; quicker response to changing environments; and numerical stability and computational demands. The proposed filter bank is further enhanced by periodically using a search algorithm in a feedback loop. Two search algorithms are considered. The first algorithm uses a recursive quadratic programming approach which extremizes a modified maximum likelihood function to update the parameters of the best performing filter in the bank. This particular approach to parameter adaptation allows a real-time implementation. The second algorithm uses a genetic algorithm to search for the parameter vector and is suited for post-processed data type applications. The workings and power of the overall filter bank and the suggested adaptation schemes are illustrated by a number of examples.     

Study of a simulated stream machine for dataflow computation

In this paper, a stream-based dataflow architecture is proposed, and its simulation model, which has helped to evaluate the effectiveness of the proposed architectural concept, is discussed. The machine integrates the conventional Von Neumann type of control flow subsystem with a dataflow processing element of token storage type. The control flow unit tackles the dynamic nature of the stream structure including input/output whereas the dataflow unit does the computation part in an applicative style. A pipelined version of the stream machine is also discussed. The effectiveness of the machine is studied by running a few example programs in the simulated machine. The machine is expected to be useful in real time signal processing applications.     

Conduction mechanism and dielectric properties of BiFeO3–BaTiO3 solid solutions

The first measurements are reported for the frequency-dependent conductivity of (1?x)BiFeO₃–xBaTiO₃ (x = 0.10, 0.15 and 0.30) solid solutions in the frequency range of 10⁰–10⁶ Hz and in the temperature range of 50–300 °C. Powder X-ray diffraction confirms the formation of solid solutions. The dielectric properties were seen to improve with increasing BaTiO₃ (BT) content. The conductivity (AC and DC) measurements reveal an inverse variation of the frequency exponent ‘s’ with temperature, high density of states and thermally activated process. The calculated density of states was found to be N(E_f) = 80.2 × 10³² eV^?1 cm^?1 at 1 kHz and 50 °C for BiFeO₃–10 % BaTiO₃ (BFO–10 % BT) solid solution. The impedance spectroscopy analysis confirms the presence of grain and grain boundary affecting the conductivity. Our results provide the first unambiguous evidence of conduction in crystallite BFO–BT solid solutions through correlated-barrier-hopping model.     

Lithium Implantation into Fullerite

Abstract

The depth profiles of lithium, implanted into fullerene at different fluences and temperatures are reported. They deviate considerably from the simple ballistic predictions. They can be understood in terms of depth dependent Li mobility immediately after the ion implantation. This mobility depends considerably on the temperature and on the degree of fullerene damage. It appears that the fullerene destruction products which act as traps for the mobile lithium are somewhat mobile themselves, essentially at high temperatures, and at low damage levels.