The detection of organic solvent vapor by using polymer coated chemocapacitor sensor

International Conference on Physical Instrumentation and Advanced Materials

物理学;材料科学

Indrapraja, Apik Rusdiarna^1 ; Rivai, Muhammad^1 ; Arifin, Achmad^1 ; Purwanto, Djoko^1

Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia^1

关键词: Capacitance values;    Capacitance-to-digital converter;    Digital conversion;    Interdigital electrode;    Organic solvent vapors;    Recognition systems;    Root mean square errors;    Sensitive layers;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/853/1/012033/pdf
DOI  :  10.1088/1742-6596/853/1/012033

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

A chemocapacitor consists of planar interdigital electrodes (IDE) made by two comb electrodes on a substrate. A dielectric film was applied on the electrodes in which the absorbed vapor will modify its permittivity. This study has fabricated chemocapacitor with the IDE distance of 0.5 mm, while the dielectric film was a sensitive layer consisting of a polymeric material. The deposition of the polymeric film was accomplished by drop casting. A sensor array consisting of four chemocapacitors coated with different polymers namely PEG-1540, PEG-20M, PEG-6000, and PVP was used to obtain the pattern of shift in the capacitance. The integrated circuit AD7746 was used as the capacitance to-digital converter (CDC). The organic solvents of ethanol, benzene, and aceton were used as the vapor samples in this experiment. The results showed that the change in the capacitance value increases proportionally to the concentration of vapour where sensors coated with PEG-1540 and PVP have higher sensitivity, i.e. 0.0028pF/part per thousand and 0.0027pF/part per thousand, respectively. Based on the capacitance to digital conversion capabilities, the system provides there solution of 0.4084ppm. The sensor array could produce a different pattern for each of the vapor sample. The Neural Network pattern recognition system could identify the type of vapor automatically with the root mean square error of 10-5