NO gas sensor of PEDOT: PSS nanowires by using direct patterning DPN

Hui Hisn Lu, Chia Yu Lin, Yueh Yuan Fang, Tzu Chien Hsiao, Kuo Chuan Ho, Dongfang Yang, Chii Wann Lin

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

Nitric oxide (NO) detection is a critical issue for environmental safety and medical diagnosis due reasons with regard to the toxic properties for human body and metabolic index for respiratory disease, respectively. Development of gas sensor with high sensitivity is very important because the concentration of NO gas in the environment and respiratory tract is extremely low, therefore not readily detectable. The material with nanostructure can improve the sensitivity of sensor owning to surface effect and size effect. Herein, we developed a new type of gaseous nanosensor assembled by 34 nanowires of conducting polymer, PEDOT: PSS. The nanowires were fabricated by dip pen nanolithography (DPN) with the length of 55 um and diameter of 300 nm between golden wires. The NO gas measurement is based on chemiresistor based methods. The result of dynamic measurement of NO gas at 100 ppm shows repeatability and stability; the recovery time is 10.4 minutes. Moreover, the lowest concentration of NO gas in static measurement is l0ppm at 80 C, which also shows the ability sensing at low temperature.

原文English
主出版物標題Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
頁面3208-3211
頁數4
出版狀態Published - 2008
事件30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada
持續時間: 2008 八月 202008 八月 25

出版系列

名字Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

Other

Other30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
國家Canada
城市Vancouver, BC
期間08-08-2008-08-25

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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