Pengjun Yao

Y-Doped ZnO nanorods by hydrothermal method and their acetone gas sensitivity

Pure and yttrium- (Y-) doped (1 at%, 3 at%, and 7 at%) ZnO nanorods were synthesized using a hydrothermal process. The crystallography and microstructure of the synthesized samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersiveX-ray spectroscopy (EDX). Comparing with pure ZnO nanorods, Y-doped ZnO exhibited improved acetone sensing properties. The response of 1 at% Y-doped ZnO nanorods to 100 ppm acetone is larger than that of pure ZnO nanorods. The response and recovery times of 1 at% Y-doped ZnO nanorods to 100 ppm acetone are about 30 s and 90 s, respectively. The gas sensor based on Y-doped ZnO nanorods showed good selectivity to acetone in the interfere gases of ammonia, benzene, formaldehyde, toluene, and methanol. The formation mechanism of the ZnO nanorods was briefly analyzed.

Detection of Formaldehyde in Mixed VOCs Gases Using Sensor Array With Neural Networks

A four-sensor array with neural networks was developed to identify formaldehyde in three possible interfering volatile organic vapors, such as acetone, ethanol, and toluene. The sensor array consisted of four metal oxide-based gas sensors: two of them are commercial SnO2 sensors, other two sensors are made in our laboratory. The responses of the sensors to each gas and to the mixture of two or all of them were tested and evaluated. It was found that every sensor has response to these four kinds of gases, and the response value of each sensor to the mixture gases was lower than the simple added value of the responses to each gas. This phenomenon is due to the properties of gas and the sensing materials. For recognizing formaldehyde in the background of ethanol, acetone, and toluene in air, 108 gas samples were tested taking into account of possible practical concentrations. Among these samples, 91 samples were used for training the pattern recognition methods and 17 samples for testing the robustness. Three neural networks were used in this report, including back propagation neural network support vector machines (SVM) and extreme learning machine (ELM) with principal component analysis (PCA). The PCA helps to improve the accuracy of the ELM by preprocessing the sensor data, while the SVM method achieves the best accuracy. The ELM method indicates a better way to train the sensor array and to identify the particular gas species with very less training time and good accuracy.

P1.7.8 Direct fabrication of La0.7Sr0.3FeO3 hollow nanofibers by electrospinning and their gas sensing properties

La0.7Sr0.3FeO3 hollow nanofibers are prepared by a facile single capillary electrospinning and calcination process. The hollow nanofibers are characterized by Scanning electron microscopy, Transmission electron microscopy and X-ray diffraction. The results indicate that the outer diameters of the hollow nanofibres range in 80-200 nm, and shell thickness are about 20 nm. Formaldehyde gas sensing properties are carried out in the concentration range of 0.1-100 ppm at the optimum operating temperature 240 o C. The response and recovery times, and the cross-response to ethanol, toluene, benzene, acetone, methanol, ammonia are measured.

Development of a wireless handheld terminal for indoor gas concentration detection

The development of a wireless handheld terminal for indoor gas concentration detection is presented in this paper. The major motivation for this work is to develop a distributed wireless measuring system suitable for metal-oxide semiconductor gas sensors, usually with a wide range resistance (e.g. 0∼100MΩ), for indoor gas concentration detection. The handheld data acquisition terminal is developed by an ARM-based board and gas concentration detection node is based on MSP430. Experimental results show that the developed wireless handheld terminal is proper for most normal metal-oxide semiconductor gas sensors and has high measurement accuracy (error range: 0.5%∼1.0%). In addition, the developed wireless handheld terminal has wireless transmission and control modules which make measurement and control operations more convenient and suitable for indoor air quality environmental inspection at home.

Development of a SOPC for reliably matching fingerprint images

The development of a SOPC for matching fingerprint images is presented in this paper. The major motivation for this work is to develop a fingerprint image matching handheld module, which can overcome the limitation of original phase correlation based algorithm and has more reliability performance to rotated and noisy images. By aid of Fourier-Mellin transform (FMT), frequency domain features are extracted and compared by peak value and distribution. Experimental results show that the improved algorithm is efficient for rotated and noisy images. Contrasted with the original phase correlation based algorithm, equal error ration (EER) is reduced 40.8%.

Increasing response of semiconductor gas sensor by using preconcentration method

Preconcentration method is usually used in gas chromatographic analysis system for gas condensation in detection of wound pathogen and measurement of low concentrations of odorous components [1]. Preconcentrators (traps) consist of a stainless-steel or glass tube packed with granular adsorbent material. Desorption occurs when a current is passed through the stainless-steel tube or through a metal wire coiled around the glass tube [2]. Recent years, there are some reports about the applications of preconcentration method in sensor systems, such as surface acoustic wave (SAW) sensor micro array for indoor air quality monitoring [3], and silicon microchannels with the carbon nano-powder for micro-fluidic reactor for benzene detecting [4]. In this report, preconcentration method is used in a gas sensor system. The response of semiconductor gas sensor to indoor formaldehyde (HCHO) vapor was increased by using this method.