Feng Qiaohua

Synthesis of organic semiconductor hexadecachloro zinc phthalocyanine and its gas sensitivity

Due to the difficulty in synthesizing perhalogenated metallophthalocyanine, the method of ammonium molybdate solid phase catalysis was introduced, and by using tetrachlorophthalic anhydride and urea as the raw materials, hexadecachloro zinc phthalocyanine (ZnPcCl16) was synthesized. Components of the composite were analyzed by energy spectrum, and its functional group structures and absorption peaks were characterized by IR and UV-vis spectroscopy. The thin films of gas sensors were prepared in a vacuum evaporation system and evaporated onto SiO2 substrates, where sensing electrodes were made by MEMS micromachining. The optimal conditions for the films are: substrate temperature of 150 ◦ C evaporation current of 95 A and film thickness of 50 nm. The result showed that the sensors were ideally sensitive to Cl2 gas and could detect the minimum concentration of 0.3 ppm.

Sensitive properties of In-based compound semiconductor oxide to Cl2 gas

Aiming at detecting Cl2 gas, this study was made on how to make In-based compound semiconductor oxide gas sensor. The micro-property and sensitivity of In-based gas sensing material were analyzed and its gas sensitive mechanism was also discussed. Adopting constant temperature chemical coprecipitation, the compound oxides such as In–Nb, In–Cd and In–Mg were synthesized, respectively. The products were sintered at 600 °C and characterized by the Scanning Electron Microscope (SEM), showing the grain size almost about 50–60 nm. The test results show that the sensitivities of In–Nb, In–Cd and In–Mg materials under the concentration of 50 × 10−6 in Cl2 gas are above 100 times, 4 times and 10 times, respectively. The response time of In–Nb, In–Cd and In–Mg materials is about 30, 60 and 30 s, and the recovery time less than 2, 10 and 2 min, respectively. Among them, the In–Nb material was found to have a relatively high conductivity and ideal sensitivity to Cl2 gas, which showed rather good selectivity and stability, and could detect the minimum concentration of 0.5 × 10−6 with the sensitivity of 2.2, and the upper limit concentration of 500 × 10−6. The power loss of the device is around 220 mW under the heating voltage of 3 V.

Preparation of ZnO semiconductor formaldehyde gas sensor

Aimed at the current interior decoration formaldehyde pollution problems, through the analysis of the metal oxide semiconductor gas sensor characteristics and sensing mechanism, this paper combined with the influence of nanotechnology on sensor performance and designed semiconductor-type ZnO formaldehyde gas sensor. Prepared nano-ZnO using the sol-gel, doped H2PtCl6, La2O3 according to a certain proportion, sintered at 500°C for 3 hours and inorganic semiconductor sensitive material was compounded. By scanning electron microscopy (SEM) and differential thermal analyzer (DTA) the test results showed that the powder prepared was the nano-particle, the small particle size, and dispersion; sintering temperature was above 445°C. The sensor performance was test, the results showed that: the optimum operating voltage of the sensor was 4.8V, sensitivity was 5ppm, response time was 6s, detection range was 5-155ppm, and selectivity to alcohol, gasoline, carbon monoxide and other gases was high.

The fabrication of array wind speed and direction sensor based on Pt film

In order to meet the measurement requirements of the two-dimensional flow field in small-scale space, a new hot film wind speed and direction sensor based on Pt film is designed in this paper. The figure of array sensor is formed by magnetron sputtering and mechanical mask, whose process is simple and has good consistensy; the insulation groove is formed by micromachining technology, which can reduce the interaction between the heating elements and thermosensitive elements, so the power consumption is reduced, and the sensitivity is improved, the measurement of low wind speed is achieved.

Comparison of neural network algorithms based on gas qualitative analysis

For the problem of gas qualitatively identify in the field of gas detection, this paper is based on the multi-sensor and pattern recognition of neural network, the uniform change voltage of the sensor output is simulated by the gradient descent algorithm, the additional momentum algorithm and the LM algorithm of neural network, then compare the three simulation results of the three algorithms, the result proves that the LM algorithm is the optimal algorithm of the data simulation in this paper, in the range of allowable error, completed the gas qualitative identification.