Carbon-assisted growth and high visible-light optical reflectivity of amorphous silicon oxynitride nanowires

Large amounts of amorphous silicon oxynitride nanowires have been synthesized on silicon wafer through carbon-assisted vapor-solid growth avoiding the contamination from metallic catalysts. These nanowires have the length of up to 100 μm, with a diameter ranging from 50 to 150 nm. Around 3-nm-sized nanostructures are observed to be homogeneously distributed within a nanowire cross-section matrix. The unique configuration might determine the growth of ternary amorphous structure and its special splitting behavior. Optical properties of the nanowires have also been investigated. The obtained nanowires were attractive for their exceptional whiteness, perceived brightness, and optical brilliance. These nanowires display greatly enhanced reflection over the whole visible wavelength, with more than 80% of light reflected on most of the wavelength ranging from 400 to 700 nm and the lowest reflectivity exceeding 70%, exhibiting performance superior to that of the reported white beetle. Intense visible photoluminescence is also observed over a broad spectrum ranging from 320 to 500 nm with two shoulders centered at around 444 and 468 nm, respectively

Cuckoo Algorithm Based on Global Feedback

This article proposes a cuckoo algorithm (GFCS) based on the global feedback strategy and innovatively introduces a “re-fly” mechanism. In GFCS, the process of the algorithm is adjusted and controlled by a dynamic global variable, and the dynamic global parameter also serves as an indicator of whether the algorithm has fallen into a local optimum. According to the change of the global optimum value of the algorithm in each round, the dynamic global variable value is adjusted to optimize the algorithm. In addition, we set new formulas for the other main parameters, which are also adjusted by the dynamic global variable as the algorithm progresses. When the algorithm converges prematurely and falls into a local optimum, the current optimum is retained, and the algorithm is initialized and re-executed to find a better value. We define the previous process as “re-fly.” To verify the effectiveness of GFCS, we conducted extensive experiments on the CEC2013 test suite. The experimental results show that the GFCS algorithm has better performance compared to other algorithms when considering the quality of the obtained solution

Analytical modeling of wetting dependence on surface nanotopography

An analytical model was developed to describe the mechanism of wetting dependence on surface nanotopography. This model relates the contact angle formation with the asperity geometry for application to a hydrophilic wafer surface, which is derived based on liquid-solid interfacial contact over the contact line. Experimental investigations were performed to verify the model. For much of the examined parameter room in the hydrophilic silicon wafer surface, it was found that the contact angle was strongly dependent on the ratio of asperity height to length, and the sharper asperity led to the higher contact angle. The observations are well consistent with Gibbs' contact-line theory

Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings

This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after dimension reduction. It makes full use of the labeled information and introduces a weight strategy in the feature extraction. The class-related weights are introduced to denote differences among the samples from different patterns, and genetic algorithm (GA) is implemented to seek out appropriate weights for optimizing the classification results. The features based on wavelet packet decomposition are derived from the original signals. Then the intrinsic geometric features extracted by WKECA are fed into the support vector machine (SVM) classifier to recognize different operating conditions of bearings, and we obtain the overall accuracy (97%) for the experimental samples. The experimental results demonstrated the feasibility and effectiveness of the proposed method