Growth and thermal studies on pure ADP,KDP and mixed K1‐x(NH4)xH2PO4 crystals

The investigations on the formation of mixed crystals of ammonium dihydrogen orthophosphate (ADP) and potassium dihydrogen orthophosphate (KDP) i.e. potassium ammonium dihydrogen phosphate, K_1‐x(NH₄)_xH₂PO₄ have been presented in this paper. Pure and mixed crystals of ADP and KDP have been grown by slow evaporation technique from the supersaturated solution at an ambient temperature 26±1 °C for ammonium concentration x in the range 0.0 ≤ x ≤ 1.0 in the case of mixed crystals. Crystal compositions were determined by flame atomic absorption spectroscopy and chemical analysis. The results of the X‐ray analysis of the grown crystals are also reported. Thermogravimetric analysis (TGA) and differential thermal analysis (DTA) were used to study the kinetic process of dehydration and the high temperature phase behaviour. DTA showed the distinct thermal events attributed to dehydration of ADP, KDP and K_1‐x(NH₄)_xH₂PO₄. The results of thermal analysis and chemical analysis are consistent with each other. (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Comprehensive Kinetic Model of Free‐Radical‐Mediated Interfacial Polymerization

A mathematical model describing interfacial radical polymerization‐based film formation on hydrogels is elucidated. A glucose oxidase‐mediated multistage initiation reaction is used to accomplish interfacial film formation. A polymer concentration‐dependent diffusion coefficient is used to reflect the changing mass transport conditions as the film develops. Model predictions of the film thickness as a function of the species concentrations agree well with experiments. The model predicts that the degree of initiation reaction delocalization with the enzyme‐mediated initiation system is significantly higher than an enzyme‐independent system, thus affecting the film growth rate and structure. The mass transport properties of the film and its adhesion to the underlying substrate are also investigated.

     

Hierarchical microstructure in structural phase transitions

We consider a model in the context of martensitic materials in which hierarchical twinning near the habit plane (austenite-martensite interface) is a new and crucial ingredient. The model includes (1) a triple-well potential in local deviatoric (rectangular) strain, (2) strain gradient terms up to second order in strain and fourth order in gradient, and (3) all symmetry allowed compositional fluctuation-induced strain gradient terms. The last term favors branching of domain walls which enables communication between macroscopic and microscopic regions essential for shape memory. Below the transition temperature (T₀) we obtain the conditions under which branching of twins is energetically favorable. Above T₀ a hierarchy of branched domain walls also stabilizes tweed formation (criss-cross patterns of twins). External stress or pressure modulates (“patterns”) the spacing of domain walls. Results based on 2D time-dependent Ginzburg-Landau simulations are shown for twins, tweed and hierarchy formation.     

Composition maps in self-assembled alloy quantum dots

Nanoscale variations in composition arising from the competition between chemical mixing effects and elastic relaxation can substantially influence the electronic and optical properties of self-assembled alloy quantum dots. Using a combination of finite element and quadratic programming optimization methods, we have developed an efficient technique to compute the equilibrium composition profiles in strained quantum dots. We find that the composition profiles depend strongly on the morphological features such as the slopes and curvatures of their surfaces and the presence of corners and edges as well as the ratio of the strain and chemical mixing energy densities. More generally, our approach provides a means to quantitatively model the interplay among the composition variations, the temperature, the strain, and the shapes of small-scale lattice-mismatched structures.     

Analgesic and antimicrobial studies of some 2,4-dichloro-5-fluorophenyl containing arylidenetriazolothiadiazines

2,4-Dichloro-5-fluorophenyl containing 7-arylidenetriazolothiadiazines were obtained by the reaction of 4-amino-3-(2,4-dichloro-5-fluorophenyl)-5-mercapto-1,2,4-triazole with 2,3-dibromo-1,3-diarylpropan-1-ones, and also by the reaction of 4-amino-3-(2,4-dichloro-5-fluorophenyl)-5-mercapto-1,2,4-triazole with α-bromopropenones in the presence of a base. The structure of the 7-arylidenetriazolothiadiazines was confirmed by an alternative synthesis. A plausible mechanism for the formation of 7-arylidenetriazolothiadiazines is proposed. All newly synthesized compounds were screened for their analgesic and antimicrobial activities. Compounds bearing 4-chlorophenyl or 3,4-methylenedioxyphenyl moieties at position 7 of the arylidenetriazolothiadiazines showed excellent analgesic activity. Arylidenetriazolothiadiazines carrying a phenyl, 4-chlorophenyl, 4-methylphenyl, 3,4-dimethoxyphenyl, and 2,4-dichlorophenyl moieties at position 7 showed excellent antibacterial and antifungal activities. Correspondence: Mari Sithambaram Karthikeyan, Department of Chemistry, Mangalore University, Mangalagangothri 574199, Karnataka, India.     

A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores

Bayesian networks (BNs) are a useful tool for applications where dynamic decision-making is involved. However, it is not easy to learn the structure and conditional probability tables of BNs from small datasets. There are many algorithms and heuristics for learning BNs from sparse datasets, but most of these are not concerned with the quality of the learned network in the context of a specific application. In this research, we develop a new heuristic on how to build BNs from sparse datasets in the context of its performance in a real-time recommendation system. This new heuristic is demonstrated using a market basket dataset and a real-time recommendation model where all items in the grocery store are RFID tagged and the carts are equipped with an RFID scanner. With this recommendation model, retailers are able to do real-time recommendations to customers based on the products placed in cart during a shopping event.     

Simulation and experimental characterization of Raman/EDFA hybrid amplifier with enhanced performance

We present simulation and experimental characterization of a hybrid amplifier comprising of a Raman amplifier and an erbium doped fiber (EDF) amplifier, with enhanced performance. The incorporation of a pumped EDF section in a fiber Raman amplifier (FRA) employing a dispersion compensating fiber is demonstrated to provide superior performance than a sole FRA system. The hybrid amplifier is characterized in terms of single channel gain and noise figure, and the results of measurements are shown to be in close agreement with the simulated results. Polarization-dependent gain (PDG) and multi-channel measured and simulated gain characterization of the Raman/EDFA hybrid amplifier are also presented.     

Challenges and opportunities for next-generation intracortically based neural prostheses

Neural prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Intracortical electrode arrays measure action potentials and local field potentials from individual neurons, or small populations of neurons, in the motor cortices and can provide considerable information for controlling prostheses. Despite several compelling proof-of-concept laboratory animal experiments and an initial human clinical trial, at least three key challenges remain which, if left unaddressed, may hamper the translation of these systems into widespread clinical use. We review these challenges: achieving able-bodied levels of performance across tasks and across environments, achieving robustness across multiple decades, and restoring able-bodied quality proprioception and somatosensation. We also describe some emerging opportunities for meeting these challenges. If these challenges can be largely or fully met, intracortically based neural prostheses may achieve true clinical viability and help increasing numbers of disabled patients.