Xuerui Zhang

Structure and photoluminescence of Mn-passivated nanocrystalline ZnO thin films

We have studied the structure and the photoluminescence of Mn-passivated nanocrystalline ZnO thin films. The ZnO thin films were prepared by thermally oxidizing ZnS:Mn films grown by low-pressure metalorganic chemical vapor deposition. The structural properties of the ZnO films were examined by X-ray diffraction and X-ray photoelectron spectroscopy. It was demonstrated that the Mn passivation could dramatically change the emission characteristics of nanocrystalline ZnO thin films. The photoluminescence spectra of the films with an optimized Mn-doped concentration showed only ultraviolet emission, while the green emission was fully quenched due to the Mn passivation of the ZnO nanocrystallite surface. A core-shell structure model of the surface passivation is presented. The note of the oxygen vacancy (Vo**) as the dominant recombination center for green emission is discussed.

Structure and optically pumped lasing from nanocrystalline ZnO thin films prepared by thermal oxidation of ZnS thin films

In this article, we observe the optically pumped lasing from the high-quality nanocrystalline ZnO thin films obtained by thermal oxidation of ZnS thin films, which were grown on SiO2 substrates by low-pressure-metalorganic chemical vapor deposition technique. The x-ray diffraction (XRD) patterns indicate that high-quality ZnS films possess a preferred (111) orientation. ZnS has a transformation to ZnO at an annealing temperature (Ta) of 500 °C, and fully transforms into ZnO at Ta⩾700 °C from the XRD patterns. The obtained ZnO films possess a polycrystalline hexagonal wurtzite structure. The fifth-order Raman scattering is observed in the films, which indicates that a large deformation energy exists in the lattice. In photoluminescence (PL), spectra, for all the samples with different annealing temperatures, the near-band-edge (NBE) PL peak has a pronounced blueshift with increasing annealing temperature, while the full width at half maximum (FWHM) decreases gradually. We think that emissions of the bound ...

Effects of thermal annealing on ZnO films grown by plasma enhanced chemical vapour deposition from Zn(C2H5)(2) and CO2 gas mixtures

To improve ZnO thin film quality, the ZnO thin films grown on silicon (100) by plasma enhanced chemical vapour deposition from Zn(C2H5)2 and CO2 gas mixtures at a low temperature of 120°C are annealed in an oxygen ambient at temperature ranging from 600°C to 1000°C. X-ray diffraction spectra indicate that ZnO films possess a polycrystalline hexagonal wurtzite structure. Atomic force microscopy results show an increase of ZnO grain size with the increase of annealing temperature. The photoluminescence is closely related to the annealing temperature. The free exciton binding energy deduced from the temperature-dependent PL spectra is about 59 meV for the ZnO film annealed at 900°C, suggesting that the film quality can be improved by annealing process.

Room-temperature blue luminescence from ZnO:Er thin films

Abstract In this paper, room-temperature blue cathodoluminescence from ZnO:Er thin films has been studied using different electron beam currents. The ZnO:Er thin films used in our experiment were prepared by simultaneous evaporation from ZnO and Er sources. The X-ray diffraction spectra showed that the thin films had a strong preferential c -axis (0002) orientation with a hexagonal crystalline structure. The blue light emission at 455 nm originating from the intra-4f shell transition ( 4 F 5/2 → 4 I 15/2 ) in Er 3+ ions was observed at room temperature. This is because many Er ions in the ground states resonantly absorb the energy from the emission related to deep-level defects and cathode ray, then fill in the 2 H 11/2 state, the non-radiative relaxation rates from the 4 F 5/2 state to the 2 H 11/2 state are completely suppressed. The non-linear dependence of the cathodoluminescence intensity on the electron beam current showed a blue light emission above the threshold electron beam current ( I th ) of 0.6 μA, which was attributed to the phonon bottleneck effect. Furthermore, the near infrared luminescence at 1.54 μm was obtained at room temperature.

A survey of smart water quality monitoring system.

The smart water quality monitoring, regarded as the future water quality monitoring technology, catalyzes progress in the capabilities of data collection, communication, data analysis, and early warning. In this article, we survey the literature till 2014 on the enabling technologies for the Smart Water Quality Monitoring System. We explore three major subsystems, namely the data collection subsystem, the data transmission subsystem, and the data management subsystem from the view of data acquiring, data transmission, and data analysis. Specifically, for the data collection subsystem, we explore selection of water quality parameters, existing technology of online water quality monitoring, identification of the locations of sampling stations, and determination of the sampling frequencies. For the data transmission system, we explore data transmission network architecture and data communication management. For the data management subsystem, we explore water quality analysis and prediction, water quality evaluation, and water quality data storage. We also propose possible challenges and future directions for each subsystem.

A multi-granularity combined prediction model based on fuzzy trend forecasting and particle swarm techniques

In spite of the impressive diversity of multi-factors fuzzy time series models, there is still a burning need to develop models that can handle the uncertainty inherent in certain data with some certain methods and obtain high forecasting accuracy. This paper proposes a novel multi-granularity combined prediction model for multi-factors fuzzy time series forecasting. The proposed model utilizes the clustering algorithm to generate different lengths of intervals, and forecasts the fuzzy trend in different granular spaces. It calculates the final forecasted value by using the forecasted fuzzy trend in each granular space and the optimal weighting vector obtained by particle swarm techniques. The proposed model can transform the uncertain problem to a certain problem through the granular computing theory. The experiments show that the presented model can not only obtain higher forecasting accuracy than several existing methods to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the civilian unemployment rate, but also capture and interpret the fuzzy trend.

An Improved Hybrid ARIMA and Support Vector Machine Model for Water Quality Prediction

Traditionally, the hybrid ARIMA and support vector machine model has been often used in time series forecasting. Due to the unique variability of water quality monitoring data, the hybrid model cannot easily give perfect forecasting. Therefore, this paper proposed an improved hybrid methodology that exploits the unique strength in predicting water quality time series problems. Real data sets of water quality provided by the Ministry of Environmental Protection of People’s Republic of China during 2008-2014 were used to examine the forecasting accuracy of proposed model. The results of computational tests are very promising.

Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China

Harmful cyanobacterial blooms are exemplified as a major environmental concern due to producing toxin, and have generated a serious threat to public health. Knowledge on the spatial-temporal distribution of cyanobacterial blooms is therefore crucial for public health organizations and environmental agencies. In this study, field data and charge coupled device (CCD) image were collected in Lakes Gaoyang and Hanfeng of the Three Gorges Reservoir (TGR), China. We conducted the risky grade index (RGI) and coverage area index to develop a feasible estimation framework of cyanobacterial blooms. First, the close relationships between CCD reflectance spectral indices and water quality parameters were constructed based on water optical classification. Then, a regional algorithm for the RGI classification was established by density peaks. Finally, our proposed algorithm was applied to investigate dynamics of cyanobacterial blooms in the two lakes from 6-year series of CCD images. Encouraging results demonstrated that satellite remote sensing in conjunction with field observation can aid in the estimation of cyanobacterial blooms in the TGR.

Water quality prediction based on a novel hybrid model of ARIMA and RBF neural network

Improving the accuracy of the water quality prediction is an important and difficult task facing decision makers in water resources management. Many researchers have argued that combining different models can be an effective way of improving upon their predictive performance. The hybrid models of autoregressive integrated moving average (ARIMA) and neural network, as one of the most popular hybrid models for time series forecasting, have recently been shown successfully for water quality prediction. However, these models have many assumptions and limitations. In this paper, a novel hybrid model of ARIMA and Radial Basis Function Neural Network (RBF-NN) is proposed in order to yield more general and higher accuracy prediction model than traditional hybrid ARIMA-ANNs models for water quality prediction. The proposed model consist of an ARIMA model, which was a linear model and used to obtain the existing linear structures, and an RBF-NN model that is used to capture the nonlinear architectures and do the prediction. Experiments results show that the proposed model can be an available and effective way to improve the accuracy of the water quality prediction.

Remote sensing image enhancement based on wavelet analysis and histogram specification

In order to monitor the water quality widely and rapidly in The Three Gorges zone, its useful to gain the information in real-time by remote sensing images, but there are some defects in remote sensing images, such as bad visual contrast, low-resolution, and low brightness. Other than general optical images, remote sensing images contain a large amount of information; its processing is time consuming. In order to improve the contrast and details of the image and reduce the complication of compute, an image enhancement approach based on wavelet and histogram specification is proposed in this paper. From the experiment results and comparison with the original image, we can see that the contrast improved by about 200% and information entropy improved by about 107%. Our proposed algorithm not only enhances the details of image greatly, but also preserves the color information of original remote sensing image effectively. Since this method do not convert the color image to other color space, the computation complexity is simple, so it is an efficient algorithm for remote sensing image enhancement in real-time.