Novel phenomenon of autoacceleration in the free radical oxidative polymerization of vinyl monomers: a thermochemical approach

A unique phenomenon of ‘autoacceleration’ was observed in a free radical polymerization of vinyl monomers and oxygen. Unlike the well known autoacceleration phenomenon in polymerization processes, this unusual phenomenon is not readily conceivable in terms of solution viscosity based reasoning. Surprisingly, we have observed manifestation of this new autoacceleration during free radical oxidative polymerization of some vinyl monomers at low conversions, where generally the polymerization reaction is zero order, the conversion–time plot are linear and viscosity effects are negligible. In the present paper, we interpret the mechanism of this new autoacceleration phenomenon on the basis of reactivity of the propagating radicals in terms of heat of formation data.

Polymerization of pyrrole and processing of the resulting polypyrrole as blends with plasticised PVC

Polypyrrole was synthesized by chemical oxidation of pyrrole in water containing various sulphonic acids like toluene sulphonic acid (TSA), sulphosalicylic acid (SSA), and camphor sulphonic acid (CSA), as well as a combination of each sulphonic acid with sodium dodecyl benzene sulphonate (NaDBS) to investigate the effect of doping on conductivity, yield, and processability of the conducting polymer. Free-standing blend films of polypyrrole and plasticized polyvinyl chloride (PVC) were obtained by casting an homogeneous suspension of the two polymers in tetrahydrofuran. The maximum conductivity of the blend film is similar to 0.3 S/cm, corresponding to a weight fraction of 0.16 w/w polypyrrole. The blend film is semiconducting in the range 300-10 K. A TG-DTA scan indicates the blend film to be amorphous with a stepwise decomposition process similar to pristine PVC. The choice of a dual dopant system during synthesis and the plasticised polymer during subsequent processing were keys to obtaining homogeneous high-quality films. (C) 2001 John Wiley & Sons, Inc.

Automatic Compilation of MATLAB Programs for Synergistic Execution on Heterogeneous Processors

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.

Multi-agent search using?sensors with heterogeneous capabilities

In this thesis we address the problem of multi-agent search. We formulate two deploy and search strategies based on optimal deployment of agents in search space so as to maximize the search effectiveness in a single step. We show that a variation of centroidal Voronoi configuration is the optimal deployment. When the agents have sensors with different capabilities, the problem will be heterogeneous in nature. We introduce a new concept namely, generalized Voronoi partition in order to formulate and solve the heterogeneous multi-agent search problem. We address a few theoretical issues such as optimality of deployment, convergence and spatial distributedness of the control law and the search strategies. Simulation experiments are carried out to compare performances of the proposed strategies with a few simple search strategies.

New Insights into Selective Heterogeneous Nucleation of Metal Nanoparticles on Oxides by Microwave-Assisted Reduction: Rapid Synthesis of High-Activity Supported Catalysts

Microwave-based methods are widely employed to synthesize metal nanoparticles on various substrates. However, the detailed mechanism of formation of such hybrids has not been addressed. In this paper, we describe the thermodynamic and kinetic aspects of reduction of metal salts by ethylene glycol under microwave heating conditions. On the basis of this analysis, we identify the temperatures above which the reduction of the metal salt is thermodynamically favorable and temperatures above which the rates of homogeneous nucleation of the metal and the heterogeneous nucleation of the metal on supports are favored. We delineate different conditions which favor the heterogeneous nucleation of the metal on the supports over homogeneous nucleation in the solvent medium based on the dielectric loss parameters of the solvent and the support and the metal/solvent and metal/support interfacial energies. Contrary to current understanding, we show that metal particles can be selectively formed on the substrate even under situations where the temperature of the substrate Is lower than that of the surrounding medium. The catalytic activity of the Pt/CeO(2) and Pt/TiO(2) hybrids synthesized by this method for H(2) combustion reaction shows that complete conversion is achieved at temperatures as low as 100 degrees C with Pt-CeO(2) catalyst and at 50 degrees C with Pt-TiO(2) catalyst. Our method thus opens up possibilities for rational synthesis of high-activity supported catalysts using a fast microwave-based reduction method.

Defect-Free Hyperbranched Polydithioacetal via Melt Polymerization

Degree of branching (DB) describes the level of structural perfection of a hyperbranched polymer when compared to its defect-free analogue, namely the dendrimer. The strategy most commonly used to achieve high DB values, specifically while using AB(2) type self-condensations, is to design an AB2 monomer wherein the reaction of the first B-group leads to an enhancement of the reactivity of the second one. In the present study, we show that an AB2 monomer carrying a dimethylacetal unit and a thiol group undergoes a rapid self-condensation in the melt under acid-catalysis to yield a hyperbranched polydithioacetal with no linear defects. NMR studies using model systems reveal that the intermediate monothioacetal is relatively unstable under the polymerization conditions and transforms rapidly to the dithioacetal; because this second step occurs irreversibly during polymer formation, it leads to a defect-free hyperbranched polydithioacetal. TGA studies of the polymerization process provided some valuable insights into the kinetics of polymerization. An additional virtue of this approach is that the numerous terminal dimethylacetal groups are very labile and can be quantitatively transformed by treatment with a variety of functional thiols; the terminal dimethylacetals were, thus, reacted with various thiols, such as dodecanethiol, benzyl mercaptan, ethylmercaptopropionate, and so on, to demonstrate the versatility of these systems as sulfur-rich hyperscaffolds to anchor different kinds of functionality on their periphery.

In Situ Electrochemical Polymerization at Air-Water Interface: Surface-Pressure-Induced, Graphene-Oxide-Assisted Preferential Orientation of Polyaniline

In situ electrochemical polymerization of aniline in a Langmuir trough under applied surface pressure assists in the preferential orientation of polyaniline (PANI) in planar polaronic structure. Exfoliated graphene oxide (EGO) spread on water surface is used to bring anilinium cations present in the subphase to air-water interface through electrostatic interactions. Subsequent electrochemical polymerization of aniline under applied surface pressure in the Schaefer mode results in EGO/PANT composite with PANT in planar polaronic form. The orientation of PANI is confirmed by electrochemical and Raman spectroscopic studies. This technique opens up possibilities of 2-D polymerization at the air-water interface. Electrochemical sensing of hydrogen peroxide is used to differentiate the activity of planar and coiled forms of PANI toward electrocatalytic reactions.

Combining heterogeneous classifiers for relational databases

Practical usage of machine learning is gaining strategic importance in enterprises looking for business intelligence. However, most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a flat form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. The proposed algorithm was evaluated on three diverse datasets. namely TPCH, PKDD and UCI benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy. (C) 2012 Elsevier Ltd. All rights reserved.

Thermal degradation kinetics of thermoresponsive poly(N-isopropylacrylamide-co-N,N-dimethylacrylamide) copolymers prepared via RAFT polymerization

Reversible addition-fragmentation chain transfer polymerization at 70 A degrees C in N,N-dimethylformamide was used to prepare poly(N-isopropylacrylamide-co-N,N-dimethylacrylamide) copolymers in various compositions to afford well-defined polymers with pre-determined molecular weight, narrow molecular weight distribution, and precise chain end structure. The copolymer compositions were determined by H-1 NMR spectroscopy. The reactivity ratios of N-isopropylacrylamide (NIPAM) and N,N-dimethylacrylamide (DMA) were calculated as r (NIPAM) = 0.838 and r (DMA) = 1.105, respectively, by the extended Kelen-Tudos method at high conversions. The lower critical solution temperature of PNIPAM can be altered by changing the DMA content in the copolymer chain. Differential scanning calorimetry and thermogravimetric analysis at different heating rates were carried out on these copolymers to understand the nature of thermal degradation and to determine its kinetics. Different kinetic models were applied to estimate various parameters like the activation energy, the order, and the frequency factor. These studies are important to understand the solid state polymer degradation of N-alkyl substituted polymers, which show great potential in the preparation of miscible polymer blends due to their ability to interact through hydrogen bonding.

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.

Runtime dependence computation and execution of loops on heterogeneous systems

GPUs have been used for parallel execution of DOALL loops. However, loops with indirect array references can potentially cause cross iteration dependences which are hard to detect using existing compilation techniques. Applications with such loops cannot easily use the GPU and hence do not benefit from the tremendous compute capabilities of GPUs. In this paper, we present an algorithm to compute at runtime the cross iteration dependences in such loops. The algorithm uses both the CPU and the GPU to compute the dependences. Specifically, it effectively uses the compute capabilities of the GPU to quickly collect the memory accesses performed by the iterations by executing the slice functions generated for the indirect array accesses. Using the dependence information, the loop iterations are levelized such that each level contains independent iterations which can be executed in parallel. Another interesting aspect of the proposed solution is that it pipelines the dependence computation of the future level with the actual computation of the current level to effectively utilize the resources available in the GPU. We use NVIDIA Tesla C2070 to evaluate our implementation using benchmarks from Polybench suite and some synthetic benchmarks. Our experiments show that the proposed technique can achieve an average speedup of 6.4x on loops with a reasonable number of cross iteration dependences.

Heterogeneous locational optimisation using a generalised Voronoi partition

In this paper a generalisation of the Voronoi partition is used for locational optimisation of facilities having different service capabilities and limited range or reach. The facilities can be stationary, such as base stations in a cellular network, hospitals, schools, etc., or mobile units, such as multiple unmanned aerial vehicles, automated guided vehicles, etc., carrying sensors, or mobile units carrying relief personnel and materials. An objective function for optimal deployment of the facilities is formulated, and its critical points are determined. The locally optimal deployment is shown to be a generalised centroidal Voronoi configuration in which the facilities are located at the centroids of the corresponding generalised Voronoi cells. The problem is formulated for more general mobile facilities, and formal results on the stability, convergence and spatial distribution of the proposed control laws responsible for the motion of the agents carrying facilities, under some constraints on the agents' speed and limit on the sensor range, are provided. The theoretical results are supported with illustrative simulation results.

Dynamics and Regulation of RecA Polymerization and De-Polymerization on Double-Stranded DNA

The RecA filament formed on double-stranded (ds) DNA is proposed to be a functional state analogous to that generated during the process of DNA strand exchange. RecA polymerization and de-polymerization on dsDNA is governed by multiple physiological factors. However, a comprehensive understanding of how these factors regulate the processes of polymerization and de-polymerization of RecA filament on dsDNA is still evolving. Here, we investigate the effects of temperature, pH, tensile force, and DNA ends (in particular ssDNA overhang) on the polymerization and de-polymerization dynamics of the E. coli RecA filament at a single-molecule level. Our results identified the optimal conditions that permitted spontaneous RecA nucleation and polymerization, as well as conditions that could maintain the stability of a preformed RecA filament. Further examination at a nano-meter spatial resolution, by stretching short DNA constructs, revealed a striking dynamic RecA polymerization and de-polymerization induced saw-tooth pattern in DNA extension fluctuation. In addition, we show that RecA does not polymerize on S-DNA, a recently identified novel base-paired elongated DNA structure that was previously proposed to be a possible binding substrate for RecA. Overall, our studies have helped to resolve several previous single-molecule studies that reported contradictory and inconsistent results on RecA nucleation, polymerization and stability. Furthermore, our findings also provide insights into the regulatory mechanisms of RecA filament formation and stability in vivo.