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基于虫型碳纳米纤维的气敏薄膜传感器对工作场所氨气的高灵敏性检测
引用本文:王家治,陈星,李民强,刘锦淮.基于虫型碳纳米纤维的气敏薄膜传感器对工作场所氨气的高灵敏性检测[J].中国化学,2008,26(4):649-654.
作者姓名:王家治  陈星  李民强  刘锦淮
作者单位:中国科学院合肥智能机械研究所仿生纳米控制中心,中国,安徽 230031 中国科学技术大学自动化系,中国,安徽 230026
摘    要:本文使用铝支持的铁催化剂,通过化学气相淀积方法合成了虫型碳纳米纤维;并把它以3:2的质量比与玻璃粉混合,借助松油醇的粘结,制备了薄膜传感器。使用扫描电镜和透射电镜对虫型碳纳米纤维,铁催化剂和传感器薄膜进行了形貌分析。最后在室温和常压下,采用新的检测方法在低电压(0-1伏)条件下进行了传感器的气敏测试,分析了它对氨气的灵敏性等特性。实验结果证实:在低浓度氨气条件下(0.175 mg/m3-0.35 mg/m3),基于虫型碳纳米纤维的薄膜传感器具有极高的灵敏性;经过反复的实验,证实薄膜传感器具有很高的稳定性,极快的反应时间(0.05 s)和恢复时间(1 min)。实验结果说明虫型碳纳米纤维是极好的气敏材料。

关 键 词:虫型碳纳米纤维  薄膜传感器  灵敏性  氨气  化学气相淀积
收稿时间:2007-9-21
修稿时间:2007-11-20

Highly Sensitive Thin Film Sensor Based on Worm‐like Carbon Nanofibers for Detection of Ammonia in Workplace
Jia‐Zhi WANG,Xing CHEN,Min‐Qiang LI,Jin‐Huai LIU.Highly Sensitive Thin Film Sensor Based on Worm‐like Carbon Nanofibers for Detection of Ammonia in Workplace[J].Chinese Journal of Chemistry,2008,26(4):649-654.
Authors:Jia‐Zhi WANG  Xing CHEN  Min‐Qiang LI  Jin‐Huai LIU
Institution:1. Center for Biomimetic Sensing and Control Research, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China;2. University of Science and Technology of China, Hefei, Anhui 230026, China
Abstract:A thin film sensor was fabricated using the mixture of worm‐like carbon nanofibers (WCNF), which were synthesized using aluminium supported iron catalysts via chemical vapour deposition, and glass dust in proportion of 3:2, combined by drops of terpineol. The morphology of the catalyst, the worm‐like carbon nanofibers and the film surface were investigated with the help of TEM and SEM. Low single‐potential signal was employed to investigate gas sensitivity of the sensor to the deleterious ammonia, in atmospheric pressure at room temperature. The results suggest that the sensor has high sensitivity at low concentration (0.175–0.35 mg/m3), perfect reproducibility, and a fast response time (0.05 s) and restoration time (1 min).
Keywords:worm‐like carbon nanofibers  thin film sensor  sensitivity  ammonia  chemical vapour deposition
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