Nanosized silica‐supported metallocene/MAO catalyst for propylene polymerization

A nanosized silica particle was used as the support to prepare an Et[Ind]₂ZrCl₂/MAO catalyst for propylene polymerization of polypropylene. The catalyst and the polymer produced were characterized with nitrogen adsorption, ICP, DSC, SEM, TEM, XRD, solution viscometer, ¹³C NMR and optical microscopy. The effects of polymerization temperature and [Al]/[Zr] ratio on catalyst activity and polymer melting point were investigated. Under identical reaction conditions, nanosized catalyst exhibited better polymerization activity than the microsized catalyst (e.g., the former had 64% higher activity than the latter at the optimum polymerization temperature (50°C) and [Al]/[Zr] = 570). DSC results indicated that polymer melting point increased with the increase of [Al]/[Zr] ratio and with the decrease of polymerization temperature. XRD results showed that the percentage of γ crystals increased with decreasing [Al]/[Zr] ratio. Electron microscopic results showed that the polymer particle size increased with increasing polymerization temperature. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 2573–2580, 2006     

Study of SO2 adsorption and thermal regeneration over activated carbon-supported copper oxide catalysts

The mechanisms of SO₂ adsorption and regeneration over activated carbon-supported copper oxide sorbent/catalysts were analyzed. Studies were carried out in a fixed-bed reactor equipped with a non-dispersive infrared gas analyzer to detect the reaction products and by using X-ray powder diffraction (XRPD) and temperature-programmed desorption (TPD) experiments to characterize the nature of the sulfate species and surface oxygen complexes. The results indicate that SO₂ was catalytically oxidized to SO₃ over a copper phase in the presence of gaseous oxygen, and then reacted with a copper site to form a sulfate linked to copper without desorption into the gas phase. The activated carbon support did not participate in this sulfation reaction. After the adsorption of SO₂, the exhausted sorbent/catalysts could be regenerated by direct heat treatment in inert gas at temperatures between 260 and 480 °C, while the neighboring surface oxygen complexes on the carbon surface were acting as the reducing agents to reduce CuSO₄ to Cu. During the subsequent adsorption process, the copper is rapidly oxidized by oxygen in the flue gas.     

Emergence of Ag particles and their effects on the mechanical properties of TaN-Ag nanocomposite thin films

TaN-Ag nanocomposite films were deposited by reactive co-sputtering on tool steel substrates. The films were then annealed using RTA (Rapid Thermal Annealing) at 350 °C for 2, 4, 8 min respectively to induce the nucleation and growth of Ag particles in TaN matrix and on film surface. C-AFM (Conductive Atomic force Microscopy) and FESEM (Field-Emission Scanning Electron Microscopy) were applied to examine the Ag nano-particles emerged on the surface of these thin films. A nano-indenter and a pin-on-disk tribometer were used to study the effect of annealing on the films' mechanical properties. The results reveal that annealing by RTA can cause Ag nano-particles to emerge on the TaN surface. Consequently, the mechanical properties of the films will vary depending on annealing conditions, Ag content, and Ag particle emergence.     

PREACO: A fast ant colony optimization for codebook generation

This paper presents an effective and efficient method for speeding up ant colony optimization (ACO) in solving the codebook generation problem. The proposed method is inspired by the fact that many computations during the convergence process of ant-based algorithms are essentially redundant and thus can be eliminated to boost their convergence speed, especially for large and complex problems. To evaluate the performance of the proposed method, we compare it with several state-of-the-art metaheuristic algorithms. Our simulation results indicate that the proposed method can significantly reduce the computation time of ACO-based algorithms evaluated in this paper while at the same time providing results that match or outperform those ACO by itself can provide.     

Using simulation and multi-criteria methods to provide robust solutions to dispatching problems in a flow shop with multiple processors

Dispatching rules are important to the performance of a manufacturing system. Selective applications of different priority rules at different processing stages in a multiple workstation manufacturing system have a positive impact on shop performance. This type of problem is a combinatorial dispatching decision. However, no dispatching rule can consistently produce better performance than all other rules under a variety of operating conditions and criteria. It is the purpose of this study to provide a robust solution for a dispatching decision that will have ‘good’ performance under different operating scenarios. In this paper a simulation case of a flow shop with multiple processors is proposed, specifically a multi-layer ceramic capacitor manufacturing system. Two multiple criteria decision-making methods – techniques for order preference by similarity to ideal solution (TOPSIS) and an analytic hierarchy process (AHP) – in combination with Taguchi orthogonal array are used to find the most suitable dispatching rule for every workstation. The results show that for 15 production scenarios and 4 criteria this combinatorial dispatching rule is robust, in the sense that it outperforms other commonly employed strategies.     

An efficient bit-based feature selection method

Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach.     

Photonic page buffer based on GaAs multiple-quantum-well modulators bonded directly over active silicon complementary-metal-oxide-semiconductor (CMOS) circuits

We present a 2-kbit, 50-Mpage/s, photonic first-in, first-out page buffer based on gallium arsenide/aluminium-gallium arsenide multiple-quantum-well diodes that are flip-chip bonded to submicrometer silicon complementary-metal-oxide-semiconductor circuits. This photonic chip provides nonvolatile storage (buffering), asynchronous-to-synchronous conversion, bandwidth smoothing, tolerance to jitter or skew, spatial format conversion, wavelength conversion, and independent flow control for the input and the output channels. It serves as an interface chip for parallel-accessed optical bit-plane data. It represents the first smart-pixel array that accomplishes the vertical integration of multiple-quantum-well modulators and detectors directly over active silicon VLSI circuits and provides over 340 transistors per optical input-output. Results from high-speed single-channel testing and real-time array operation of the photonic page buffer are reported.     

Capillary HPLC-NMR Coupling: High-Resolution (1)H NMR Spectroscopy in the Nanoliter Scale

Coupling HPLC and NMR is one of the most powerful techniques for simultaneous separation and structural elucidation of unknown compounds in mixtures. To date, however, minimizing the detection volume, as is required when coupling NMR with miniaturized separation techniques, has been accompanied by a dramatic loss in resolution of the NMR spectra. Here, we report on the coupling of gradient capillary HPLC with on-column, high-resolution NMR detection. On-line stopped-flow and static (1)H NMR spectra were acquired with capillary columns of 75-315 μm i.d. With detection over a length of 1.2 cm, cell volumes cover a range of 50-900 nL. An on-line-detected NMR separation of dansylated amino acids was carried out in a 315 μm i.d. fused silica capillary packed to a length of 12 cm with C(18) stationary phase. The low solvent consumption makes the use of fully deuterated solvents economically feasible. NMR spectra with resolution on the order of 3 Hz were obtained using a 50 nL detection cell to measure 1.1 nmol of dansylated γ-aminobutyric acid under static conditions in a 75 μm i.d. capillary.