三电极碳纳米管传感器的极间距优化

郝惠敏; 张勇; 权龙

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三电极碳纳米管传感器的极间距优化

郝惠敏, 张勇, 权龙

文章导航 > 红外与激光工程 > 2015 > 44(10): 3061-3065

郝惠敏, 张勇, 权龙. 三电极碳纳米管传感器的极间距优化[J]. 红外与激光工程, 2015, 44(10): 3061-3065.

引用本文:

郝惠敏, 张勇, 权龙. 三电极碳纳米管传感器的极间距优化[J]. 红外与激光工程, 2015, 44(10): 3061-3065.

Hao Huimin, Zhang Yong, Quan Long. Electrode separation optimization of triple-electrode carbon nanotube sensor[J]. Infrared and Laser Engineering, 2015, 44(10): 3061-3065.

Citation:

Hao Huimin, Zhang Yong, Quan Long. Electrode separation optimization of triple-electrode carbon nanotube sensor[J]. Infrared and Laser Engineering, 2015, 44(10): 3061-3065.

三电极碳纳米管传感器的极间距优化

1.
太原理工大学新型传感器与智能控制教育部重点实验室,山西太原 030024;
2.
西安交通大学电力设备电气绝缘国家重点实验室,陕西西安 710049

基金项目:

国家自然科学基金(50877056);山西自然科学基金(2013011026-1)

详细信息

作者简介:
郝惠敏(1971-),女,博士,主要从事智能检测、微纳米传感器、多传感信息融合等方面的研究。Email:haohuimin@tyut.edu.cn

中图分类号: TB383.1;TH744.4

Electrode separation optimization of triple-electrode carbon nanotube sensor

1.
Key Laboratory of Advanced Transducers and Intelligent Control Systems,Ministry of Education China,Taiyuan University of Technology,Taiyuan 030024,China;
2.
State Key Laboratory of Electrical Insulation and Power Equipment,Xi'an Jiaotong University,Xi'an 710049,China

摘要: 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。
- 碳纳米管传感器 /
- 极间距 /
- 优化 /
- 粒子群优化
Abstract: 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.
- carbon nanotube sensor /
- electrode separation /
- optimization /
- particle swarm optimization

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三电极碳纳米管传感器的极间距优化

1. 太原理工大学新型传感器与智能控制教育部重点实验室,山西太原 030024;

2. 西安交通大学电力设备电气绝缘国家重点实验室,陕西西安 710049

作者简介:
郝惠敏(1971-),女,博士,主要从事智能检测、微纳米传感器、多传感信息融合等方面的研究。Email:haohuimin@tyut.edu.cn

收稿日期: 2015-02-20
修回日期: 2015-03-21
刊出日期: 2015-10-25

基金项目:

国家自然科学基金(50877056);山西自然科学基金(2013011026-1)

中图分类号: TB383.1;TH744.4

关键词:

摘要: 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。

English Abstract

Electrode separation optimization of triple-electrode carbon nanotube sensor

1. Key Laboratory of Advanced Transducers and Intelligent Control Systems,Ministry of Education China,Taiyuan University of Technology,Taiyuan 030024,China;

2. State Key Laboratory of Electrical Insulation and Power Equipment,Xi'an Jiaotong University,Xi'an 710049,China

Received Date: 2015-02-20
Rev Recd Date: 2015-03-21
Publish Date: 2015-10-25

Keywords:

Abstract: 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.