Synthesis of ZnO nanowires using ball-milling and annealing method

Nanowires represent a new class of ZnO morphologies with many exiting new properties and applications. The research in the synthesis and characterization of ZnO nanowires has received enormous attention in recent years. However, most synthesis methods using vapor deposition process can only produce small amount of sample, mass production has not been achieved yet. Large-quantity production of ZnO nanowires needs to be realized for large-scale property and application studies. One of the promising approaches to the large scale synthesis is a ball-milling and annealing method. This paper first introduces several common synthesis methods of ZnO nanowires and then summarizes the one dimensional nanomaterials produced by the ball milling and annealing method. Finally, some preliminary results of ZnO nanowire synthesis are presented.

Conical boron nitride nanorods synthesized via the ball-milling and annealing method

A boron nitride (BN) nanostructure, conical BN nanorod, has been synthesized in a large quantity on Si substrates for the first time via the ball-milling and annealing method. Nitridation of milled boron carbide (B₄C) powders was performed in nitrogen gas at 1300°C on the surface of the substrates to form the BN nanorods. The highly crystallized nanorods consist of conical BN basal layers stacked along the nanorod axis. Ball milling of the B₄C powders can significantly enhance the nitridation of the powders and thus facilitate the formation of nanorods during the annealing process.

Efficient production of ZnO nanowires by a ball milling and annealing method

ZnO powder was mechanically milled in a ball mill. This procedure was found to greatly increase its evaporation ability. The anomalous evaporation behaviour was caused by the disordered structure of the milled material and was not related to the increase in its surface area after milling. ZnO nanowires were synthesized by evaporation of this milled precursor. Nanowires with smooth and rough surfaces were present in the sample; the latter morphology was dominant. A green emission band centred at 510 nm was dominant in the cathodoluminescence spectrum of the nanowires.

Cache and replica lookup, indexing and synchronization in P2P environments

The major outcomes of this research project were the development of a set of decentralized algorithms to index, locate and synchronize replicated information in a networked environment. This study exploits the application specific design constraints of networked systems to improve performance, instead of relying on data structures and algorithms best suited to centralized systems.

Selecting the individual numerical scale and prioritization method in the analytic hierarchy process : a 2-tuple fuzzy linguistic approach

The validity of the priority vector used in the analytic hierarchy process (AHP) relies on two factors: the selection of a numerical scale and the selection of a prioritization method. The traditional AHP selects only one numerical scale (e.g., the Saaty scale) and one prioritization method (e.g., the eigenvector method) for each particular problem. For this traditional selection approach, there is disagreement on which numerical scale and prioritization method is better in deriving a priority vector. In fact, the best numerical scale and the best prioritization method both rely on the content of the pairwise comparison data provided by the AHP decision makers. By defining a set of concepts regarding the scale function and the linguistic pairwise comparison matrices (LPCMs) of the priority vector and by using LPCMs to unify the format of the input and output of AHP, this paper extends the AHP prioritization process under the 2-tuple fuzzy linguistic model. Based on the extended AHP prioritization process, we present two performance measure criteria to evaluate the effect of the numerical scales and prioritization methods. We also use the performance measure criteria to develop a 2-tuple fuzzy linguistic multicriteria approach to select the best numerical scales and the best prioritization methods for different LPCMs. In this paper, we call this type of selection the individual selection of the numerical scale and prioritization method. We also compare this individual selection with traditional selection by using both random and real data and show better results with individual selection.

A novel defect detection and identification method in optical inspection

Optical inspection techniques have been widely used in industry as they are non-destructive. Since defect patterns are rooted from the manufacturing processes in semiconductor industry, efficient and effective defect detection and pattern recognition algorithms are in great demand to find out closely related causes. Modifying the manufacturing processes can eliminate defects, and thus to improve the yield. Defect patterns such as rings, semicircles, scratches, and clusters are the most common defects in the semiconductor industry. Conventional methods cannot identify two scale-variant or shift-variant or rotation-variant defect patterns, which in fact belong to the same failure causes. To address these problems, a new approach is proposed in this paper to detect these defect patterns in noisy images. First, a novel scheme is developed to simulate datasets of these 4 patterns for classifiers' training and testing. Second, for real optical images, a series of image processing operations have been applied in the detection stage of our method. In the identification stage, defects are resized and then identified by the trained support vector machine. Adaptive resonance theory network 1 is also implemented for comparisons. Classification results of both simulated data and real noisy raw data show the effectiveness of our method.

A simple and effective method to ameliorate the interfacial properties of cellulosic fibre based bio-composites using poly (ethylene glycol) based amphiphiles

In order to overcome interfacial incompatibility issues in natural fibre reinforced polymer bio-composites, surface modifications of the natural fibres using complex and environmentally unfriendly chemical methods is necessary. In this paper, we demonstrate that the interfacial properties of cellulose-based bio-composites can be tailored through surface adsorption of polyethylene glycol (PEG) based amphiphilic block copolymers using a greener alternative methodology. Mixtures of water or water/acetone were used to form amphiphilic emulsions or micro-crystal suspensions of PEG based amphiphilic block copolymers, and their deposition from solution onto the cellulosic substrate was carried out by simple dip-coating. The findings of this study evidence that, by tuning the amphiphilicity and the type of building blocks attached to the PEG unit, the flexural and dynamic thermo-mechanical properties of cellulose-based bio-composites comprised of either polylactide (PLA) or high density polyethylene (HDPE) as a matrix, can be remarkably enhanced. The trends, largely driven by interfacial effects, can be ascribed to the combined action of the hydrophilic and hydrophobic components of these amphiphiles. The nature of the interactions formed across the fibre-matrix interface is discussed. The collective outcome from this study provides a technological template to significantly improve the performance of cellulose-based bio-composite materials.

Constructing optimal prediction intervals by using neural networks and bootstrap method

This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.

Consensus building in a local context for the AHP-GDM with the individual numerical scale and prioritization method

The methodology for selecting the individual numerical scale and prioritization method has recently been presented and justified in the analytic hierarchy process (AHP). In this study, we further propose a novel AHP-group decision making (GDM) model in a local context (a unique criterion), based on the individual selection of the numerical scale and prioritization method. The resolution framework of the AHP-GDM with the individual numerical scale and prioritization method is first proposed. Then, based on linguistic Euclidean distance (LED) and linguistic minimum violations (LMV), the novel consensus measure is defined so that the consensus degree among decision makers who use different numerical scales and prioritization methods can be analyzed. Next, a consensus reaching model is proposed to help decision makers improve the consensus degree. In this consensus reaching model, the LED-based and LMV-based consensus rules are proposed and used. Finally, a new individual consistency index and its properties are proposed for the use of the individual numerical scale and prioritization method in the AHP-GDM. Simulation experiments and numerical examples are presented to demonstrate the validity of the proposed model.

A hybrid simulated annealing and perturb and observe method for maximum power point tracking in PV systems under partial shading conditions

This paper presents a hybrid method for Maximum Power Point Tracking (MPPT) of a Photovoltaic (PV) system which experiences non-uniform environmental conditions or partial shading conditions. The hybrid method combines two simple techniques with complementary strengths in achieving Global MPPT. Simulated Annealing (SA) has only recently been applied to PV MPPT and is very effective at locating global maxima with limited implementation complexity. Perturb and Observe (P&O) is a very common technique which provides continuous tracking of the MPP in a simple and easy to implement manner. The P&O method is generally incapable of locating global maxima, and the SA based method is unable to perform continuous searching. By merging these techniques in a hybrid MPPT method consisting of a global searching stage and a local searching stage, the tracking performance is improved compared to what each technique could achieve independently. Simulation results are presented to demonstrate the effectiveness of the proposed hybrid technique.