Non-monotonic lattice parameter variation with crystallite size in nanocrystalline solids

An anomalous dependence of the lattice parameter on the crystallite size of nanocrystalline ball-milled powders of metals was observed: lattice contraction followed by lattice expansion with decreasing crystallite size. These data were determined by application of detailed X-ray diffraction measurements. To this end the lattice parameters of the metals investigated – nickel, copper, iron and tungsten – were precisely determined by correcting for influences of stacking faults, in the face-centred cubic metals, as well as by correcting for instrument-related aberrations. The non-monotonic variation of the lattice constant was interpreted as the result of two competing mechanisms: interface-stress-induced contraction vs. expansion as a result of the stress field generated at the crystallite boundary due to the increased excess free volume in the crystallite boundary upon decreasing crystallite size.     

A kinetic model for the enzyme-catalyzed self-epoxidation of oleic acid

This paper reports a kinetic model for the selfepoxidation of oleic acid with toluene as solvent and Novozym 435 (a commercially available preparation of immobilized Candida antarctica lipase) as catalyst at 30°C. The effects of various parameters on the conversion and rates of reaction were studied. Both the initial rate and the progress curve data were used to fit an ordered bi-bi model. At low temperatures, the rate of epoxidation was faster than the rate of deactivation of the enzyme by hydrogen peroxide.     

Structural characteriztion of Nd-doped in silica host matrix prepared by net chemical process

Silica host matrix containing neodymium which is potentially important for the formation of nanocrystalline metal oxides was prepared by solgel method, using tetra-ethoxysilane and Nd(NO3)3 as precursor materials. The prepared samples were changed from amorphous to nanocrystallites phase at sintered temperature 550℃ (4 h), 750℃ (8 h) and 950 ℃ (12 h). The thermally treated sample microstructures were investigated using X-ray diffraction (XRD), Fourier transformation infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). While a further increase of the temperature at 750 ℃ and annealing time (8 h) resulted in the formation of cubic and hexagonal Nd2O3 nanocrystallites. At constant sintering temperature 950℃ for 12 h, the samples showed sharper and intense peaks. The sizes of Nd2O3 nanocrystallites were characterized by XRD with average size～46 nm.     

Frequency dependent electrical transport properties of 4,4′,4″-tris(N-3-methylphenyl-N-phenylamine)triphenylamine by impedance spectroscopy

The frequency dependent ac conduction mechanism in 4,4′,4″-tris(N-3-methylphenyl-N-phenylamine)triphenylamine (m-MTDATA) has been studied as a function of applied bias and temperature. The Cole–Cole plot shows a slightly depressed semicircle indicating Debye type relaxation. This result has been explained by an equivalent circuit of the device designed as a two parallel resistor and capacitance network in series with contact resistance. The ac conduction studies under dc bias for hole only devices shows an increase in device conductivity with the increase in bias. The variation of bulk resistance with applied bias indicates Space Charge Limited Conduction (SCLC) mechanism for hole conduction. The hole mobility of the material has also been evaluated from SCLC as 8.859 × 10^?6 cm²/V s. The temperature dependent impedance studies show two activation energies indicating two different phase of the material with a phase transition at 235 K.     

Facile preparation of <Emphasis Type="Italic">Chaetomorpha antennina</Emphasis> based porous polysaccharide–PMMA hybrid material by radical polymerization under microwave irradiation

A hybrid material was prepared from the hot water soluble sulphated polysaccharide of the green seaweed Chaetomorpha antennina (CM_sps) and polymethyl methacrylate (PMMA) by radical polymerization in aqueous medium under microwave irradiation. An insoluble material was formed with the progress of the polymerization. The product was characterized by the IR spectrum and elemental analysis, as well as by acid hydrolysis followed by mass spectrum of the hydrolysate of the hybrid CM_sps–PMMA for confirming the insertion of PMMA. Comprehensive characterization of the product included TGA, XRD, ESI-MS and BET surface analysis. The hybrid material was porous, and formed gel in water–DMSO mixture. It had the crystallinity index of 0.122, and had BET pore diameter of 3.71 nm, a size which is far larger than the molecular diameters of common natural gases and zeolites (0.5–1.0 nm). This material may be useful in the domain of certain adsorption and catalytic applications.     

Solution properties of methyl methacrylate–acrylonitrile copolymers

Characterization of methyl methacrylate-acrylonitrile copolymers is done through viscosity, swelling, and differential refractometric studies. Viscosities of the copolymers and homopolymers were determined at 30, 40, and 50°C. The activation parameters of viscous flow, voluminosity, and shape factor were also calculated. The average shape factor was observed to be 2.5 ± 0.005 for all copolymer systems. Viscosity molecular weights were calculated, and from intramolecular expansion factor (α), it was observed that copolymers are less flexible than are homopolymers. dn/dc values obtained from differential refractometry are in good agreement with those calculated theoretically. © 1993 John Wiley & Sons, Inc.     

An ensemble-based incremental learning approach to data fusion.

This paper introduces Learn++, an ensemble of classifiers based algorithm originally developed for incremental learning, and now adapted for information/data fusion applications. Recognizing the conceptual similarity between incremental learning and data fusion, Learn++ follows an alternative approach to data fusion, i.e., sequentially generating an ensemble of classifiers that specifically seek the most discriminating information from each data set. It was observed that Learn++ based data fusion consistently outperforms a similarly configured ensemble classifier trained on any of the individual data sources across several applications. Furthermore, even if the classifiers trained on individual data sources are fine tuned for the given problem, Learn++ can still achieve a statistically significant improvement by combining them, if the additional data sets carry complementary information. The algorithm can also identify-albeit indirectly-those data sets that do not carry such additional information. Finally, it was shown that the algorithm can consecutively learn both the supplementary novel information coming from additional data of the same source, and the complementary information coming from new data sources without requiring access to any of the previously seen data.     

Effect of low-density filler on mechanical properties of syntactic foams of cyanate ester

Syntactic foam composites of cyanate ester with varying volume fractions of resin and glass microballoon were processed and evaluated for tensile, flexural and compressive properties. The effect of nature and volume fraction of microballoon on the mechanical properties was studied. The mechanical properties showed a gradual decrease in strength with increase in volume fraction of microballoon. The specific strength values also manifested a similar order. A similar behaviour was observed for syntactic foams with microballoons of varying true density. The properties increased proportional to the strength of the microballoon in resin-rich systems implying a strong microballoon-resin interface, corroborated by Scanning Electron Microscopy studies. The compressive modulus showed a decreasing trend with enhanced microballoon loading.