Electrode separation optimization of triple-electrode carbon nanotube sensor

For triple-electrode carbon nanotube sensor, the electrode separation between electrodes is one of the key factors to influence the accuracy of detection. It is very difficult to decide the sensors' electrode separations when detecting the multi-component gas mixture using sensor array. A electrode separation optimization method for triple-electrode carbon nanotube gas sensor was presented. The method is based on particle swarm optimization(PSO) and includes following procedures: designing electrode separation, constructing sensor array using multi-sensor with different electrode separations, building data base including electrode separation and detection ionic current, creating quantitative analysis model of mixed gas, and optimizing electrode separation. The NO and SO2 gas mixtures were detected by multi-group sensor array, which were composed of three carbon nanotube sensors with different electrode separations. The electrode separations of three sensors was optimized using above-mentioned method. The experimental results show that the proposed method is able to select the optimal distances between electrodes effectively and the sensor with optimized electrode separation achieve higher detection accuracy.