Thanks to unique Raman spectra of chemical substances, a growing number of applications in environmental and biomedical fields based on Raman scattering has been developed. However, the low probability of Raman scattering hindered its potential development and thus, many different techniques were developed to enhance Raman signal. A key step of surface-enhanced Raman scattering technique is to prepare active SERS substrate from noble metals. The main enhancement mechanism is electromagnetic enhancement resulted from surface plasmon resonance. The disadvantages of nanoparticles based SERS substrates include high randomness due to self - assembly process of nanoparticles. Recently, a new kind of SERS substrates with order nanostructures of semiconductors combining with noble metals can serve as active SERS substrates, which are expected to possess high enhancement of Raman signals. In this study, ordered ZnO nanorods were first prepared by galvanic hydrothermal method and gold was sputtered on the as prepared ZnO nanomaterials to enhance Raman. Our SERS substrates exhibit promising high enhancement factors, and can detect chemical substances at concentration in nano molar range.
Cu2ZnSnS4 (CZTS) is a p-type semiconductor with high absorption coefficient and direct bandgap from 1 to 1.5 eV, which is ideal for making absorber layer for solar cell. However, it is difficult to get single phase of CZTS due to the competitive formation of binary and ternary secondary phases. In this paper, we prepared CZTS nanoparticles by hydrothermal method and investigate the influence of hydrothermal temperature on the product. Raman scattering, X-ray diffraction, scanning electron microcopy, energy dispersive X-ray spectroscopy and diffusion reflective measurement were applied to characterize the products. The products are high quality nanocrystals of kesterite phase with uniform size which is applicable for solar absorber layer fabrication.
A low-energy adaptive clustering hierarchy (LEACH) routing protocol has been proposed specifically for wireless sensor networks (WSNs). However, in LEACH protocol the criteria for clustering and selecting cluster heads (CHs) nodes were not mentioned. In this paper, we propose to improve the LEACH protocol by combining the use of K-means algorithm for clustering and bat algorithm (BA) to select nodes as CHs. The proposed routing algorithm, called BA-LEACH, is superior to other algorithms, namely PSO-LEACH, which using particle swarm optimization (PSO) to improve LEACH. Simulation analysis shows that the BA-LEACH can obviously reduce network energy consumption and optimize the lifetime of WSNs.
PM2.5 (Particulate Matter) and PM10 are the most common pollutants, and the increasing of concentration in the air will threaten people’s health. The machine learning method has recently been of particular interest to many researchers due to its effectiveness in air quality prediction models. Many solutions employing deep learning-based techniques such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models to enhance air quality prediction accuracy have been developed. This paper proposes a hybrid Encoder STM model for predicting the next day to the next five days’ PM2.5 and PM10 concentrations in Hanoi. Additionally, we proposed five extended features to increase the accuracy of prediction. Then other models, namely the LSTM model and the Bidirectional LSTM model, are also considered for PM2.5 and PM10 concentration prediction. Our results show that the proposed approaches outperform other state-of-the-art deep learning-based methods on both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) due to low error and the small number of features.
CuO nanorods were prepared by thermal oxidation method in ozone ambient. The effect of annealing temprature in the range from 400 to 600 oC on morphology and structure of nanorods was studied thouroughly by scanning electron microscopy (SEM) and X-ray diffraction, combining with energy dispersive spectroscopy (EDS) and Raman spectroscopy. The results showed that annealing temprature strongly affected the structure and morphology of the produced CuO nanorods. The most uniform nanorods with highest crystal quality were obtained when annealing temperature is from 450 to 500 °C and annealing time was 2 h as suggested by SEM images together with Raman results.
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