个人信息Personal Information
教授
博士生导师
硕士生导师
性别:女
毕业院校:大连理工大学
学位:博士
所在单位:化工学院
学科:应用化学. 精细化工. 有机化学
办公地点:大连市高新区凌工路2号西部校区化工楼E434房间
联系方式:0411-84986265
电子邮箱:zhangshf@dlut.edu.cn
High-sensitive and stable photonic crystal sensors for visual detection and discrimination of volatile aromatic hydrocarbon vapors
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论文类型:期刊论文
发表时间:2019-11-01
发表刊物:CHEMICAL ENGINEERING JOURNAL
收录刊物:EI、SCIE
卷号:375
ISSN号:1385-8947
关键字:Photonic crystal sensor; Volatile aromatic hydrocarbon vapors; Discriminative detection; Scanning recognition
摘要:Photonic crystals (PCs) are promising as intelligent sensors for detection of volatile organic compounds (VOCs) due to their visual identifiability, room-temperature operability and easy portability. In order to realize high-sensitive and repeated detection of the most hazardous volatile aromatic hydrocarbon vapors, a kind of nanoporous and multilayered organic-inorganic composite PC sensors are designed and prepared via alternate assembly of poly(styrene-acrylic acid) and TiO2 nanoparticles. The structures of the PC sensors are characterized and their optical properties are easily modulated. With a three-stack PC sensor, visual detection of different concentrations of benzene, toluene, xylene and 1,2,4-trimethylbenzene (TMB) vapors is realized and the detection limits of 1,2,4-TMB and xylene can reach 14.7 ppm and 99.2 ppm, respectively. When exposing to toluene gas stream, the sensor quickly generated a reversible shift of over 500 nm within 1.5 s and large red-shifts within several seconds are also achieved upon exposure to the other aromatic vapors. With toluene as an example, a colorimetric analysis program on smartphone is developed and through capturing the color with the program, the concentration value can be facilely obtained. In addition, the PC sensor is also served as visual recognizer for discriminating these aromatic vapors based on their differences in solubility parameters and polarity. With the excellent characteristics, the PCs are promising as high-performance aromatic VOCs sensors for environment quality monitoring.