Temperature & light modulation to enhance the selectivity of Pt-modified zinc oxide gas sensor

被引:48
|
作者
Deng, Qian [1 ]
Gao, Shi [2 ]
Lei, Tao [1 ,4 ]
Ling, Yansong [1 ]
Zhang, Shunping [1 ]
Xie, Changsheng [1 ,3 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Mat Proc & Die & Mould Technol, Wuhan 430074, Peoples R China
[2] Anti Chem Inst Chinese Peoples Liberat Army, Beijing 102205, Peoples R China
[3] Huazhong Univ Sci & Technol, Dept Mat Sci & Engn, Nanomat & Smart Sensor Res Lab, Wuhan 430074, Peoples R China
[4] Huazhong Univ Sci & Technol, Shenzhen Inst, Shenzhen 518000, Peoples R China
关键词
Metal oxide; Gas sensor; Selectivity improvement; Temperature & light modulationa; TIN OXIDE; PHOTOCATALYTIC OXIDATION; ELECTRICAL-CONDUCTIVITY; SENSING PROPERTIES; FILM; IDENTIFICATION; ETHANOL; SURFACE; TIO2; SENSITIVITY;
D O I
10.1016/j.snb.2017.03.107
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Metal oxide (MOX) has been used as resistive gas sensors for a long time. The gas sensitive response under constant temperature measurement is a broad-spectrum response, resulting in poor selectivity. To solve this problem and enhance the selectivity of a single sensing film, gas sensor based on Pt-modified zinc oxide was prepared by homogeneous precipitation and screen printing method. Afterwards, the temperature & light modulation method is first proposed herein. This method allows us to obtain different Resistance-Temperature (R-T) curves and characteristic parameters, photoresponse and photorelaxation. Combined with pattern recognition method, five tested gases, including ethanol, methanol, acetone, formic acid and ethyl ether, can be successfully classified. Compared with temperature modulation, which is usually used as a method to enhance selectivity, temperature & light modulation method can increase the classification rate from 95.55% to 100% for sensing these five tested gases. The results indicate that the temperature & light modulation method is an effective method for improving the selectivity of the gas sensor. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:903 / 915
页数:13
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