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Title: Sensitivity Enhancement of Benzene Sensor Using Ethyl Cellulose-Coated Surface-Functionalized Carbon Nanotubes
Authors: Thanattha Chobsilp
Worawut Muangrat
Chaisak Issro
Weerawut Chaiwat
Apiluck Eiad-Ua
Komkrit Suttiponparnit
Winadda Wongwiriyapan
Tawatchai Charinpanitkul
PTT Public Company Limited
Shinshu University
Chulalongkorn University
King Mongkut's Institute of Technology Ladkrabang
Mahidol University
Burapha University
Keywords: Engineering;Physics and Astronomy
Issue Date: 1-Jan-2018
Citation: Journal of Sensors. Vol.2018, (2018)
Abstract: © 2018 Thanattha Chobsilp et al. A hybrid sensor based on the integration of functionalized multiwalled carbon nanotubes (MWCNTs) with ethyl cellulose (EC) was fabricated for sensitivity enhancement of benzene detection. To functionalize the surface of MWCNTs, MWCNTs were treated with hydrochloric acid for 60 min (A60-MWCNTs), while other MWCNTs were treated with oxygen plasma for 30, 60, 90, and 120 min (P30-MWCNTs, P60-MWCNTs, P90-MWCNTs, and P120-MWCNTs, resp.). Pristine MWCNTs, A-MWCNTs, and P-MWCNTs were dispersed in 1,2-dichloroethane, then dropped onto a printed circuit board consisting of Cu/Au electrodes used as the sensor platform. Next, EC was separately spin coated on the pristine MWCNTs, A-MWCNTs, and P-MWCNTs (EC/MWCNTs, EC/A-MWCNTs, and EC/P-MWCNTs, resp.). All sensors responded to benzene vapor at room temperature by increasing their electrical resistance which was sensitive to benzene vapor. The EC/P90-MWCNTs enabled an approximately 11-fold improvement in benzene detection compared to EC/MWCNTs. The sensitivity of all sensors would be attributed to the swelling of EC, resulting in the loosening of the MWCNT network after benzene vapor exposure. The differences of the sensing responses of the EC/MWCNTs, EC/A-MWCNTs, and EC/P-MWCNTs would be ascribed to the differences in crystallinity and functionalization of MWCNT sidewalls, suggesting that acid and oxygen plasma treatments of MWCNTs would be promising techniques for the improvement of benzene detection.
ISSN: 16877268
Appears in Collections:Scopus 2018

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