Xie Guangzhong

The fabrication and optimization of OTFT formaldehyde sensors based on Poly(3-hexythiophene)/ZnO composite films

Formaldehyde (HCHO), a colorless and pungent-smelling gas, is confirmed be a huge threat to human health. The detection of formaldehyde is necessary and important. The Poly(3-hexythiophene) (P3HT)/ZnO organic-inorganic composite thin film was fabricated and used as the sensitive layer of organic thin film transistors (OTFT) by spray-deposited method to detect HCHO at room temperature. The process parameters such as P3HT/ZnO weight ratios and airbrushed masses were optimized. The results showed that P3HT/ZnO OTFT exhibited good sensing response to HCHO. Airbrushed mass of 1ml was the optimal mass, and the 1:1 and 1:5 weight ratios of P3HT/ZnO exhibited better sensing properties compared with others. OTFT gas sensors based on P3HT/ZnO composite film provides a novel promising approach to the detection of HCHO.

Fabrication and properties of an OLED-based gas sensor with ZnPc sensing film

Through vacuum evaporation, an OLED-based gas sensor is achieved by fabricating ZnPc (Zinc phthalocyanine) film as a sensing film between the metal cathode and the function layers in OLED (organic light emitting device). The responses of luminance and current, showing the sensing properties of OLED-based gas sensor in a given gas, are tested at room temperature. Results show that it has response both to NO2 and NH3, but better recovery for NH3.

Intelligent mine gas detector system based on pattern recognition

Intelligent mine gas detector system based on pattern recognition is developed with three metal oxide semiconductor and a catalytic combustion sensors. The identifying system is carried out by microcontroller unit, a pulse width modulation circuit is adopted to heat the sensors, and an analog-to-digital converter is designed to preprocess the voltage signals, the setup of the detector function and alarm values is implemented by the keyboard, the concentration of the gas and the temperature are showed on the liquid crystal display screen. In this research, pattern recognition technique based on back-propagation neural network was applied for testing and discriminating gases. The network was successfully trained and can recognize unknown mixed gases of various concentration and compositions. Once it is well-trained, the computed weights are inserted into the system from keyboard and then it can be employed anywhere even without computer so as to realize offline operation. Besides, it also has such merits as high accuracy, being portable and convenient, low-cost and good explosion preventing capacity.