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三电极碳纳米管传感器各电极之间的间距大小是影响检测精度的关键因素之一。在用传感器阵列检测多组分气体混合物时,各传感器的极间距很难确定。为三电极碳纳米管气体传感器提出一种基于粒子群算法(PSO)的极间距优化方法。该方法包括设计极间距、组建由不同极间距的多个传感器组成的传感器阵列、建立包括极间距及检测离子电流的数据库、建立混合气体定量分析模型及极间距优化等步骤。采用多组由不同极间距的三个碳纳米管传感器构成的传感器阵列对NO和SO2混合气体进行测量,其中各传感器的极间距均采用上述方法优化。实验结果显示,上述极间距优化方法能够有效地选择电极之间的最佳间距,优化极间距后的传感器也获得了更高的检测灵敏度。
Three-electrode carbon nanotube sensor spacing between the electrodes is one of the key factors affecting the accuracy of detection. When using a sensor array to detect multicomponent gas mixtures, the pole spacing of each sensor is difficult to determine. A three-electrode carbon nanotube gas sensor based on Particle Swarm Optimization (PSO) pole spacing optimization method. The method comprises the following steps: designing the pole pitch, forming the sensor array consisting of a plurality of sensors with different pole pitches, establishing the database including the pole pitches and the detecting ion currents, and establishing the quantitative analysis model of the mixed gas and optimizing the pole pitches. Multiple sets of sensor arrays consisting of three carbon nanotube sensors with different polar spacings are used to measure NO and SO2 mixed gas, wherein the pole spacings of each sensor are optimized using the above method. The experimental results show that the above-mentioned optimization of the pole spacing can effectively select the optimal spacing between the electrodes, and the sensor with the optimized pole spacing also obtains higher detection sensitivity.