Abstract
In this work, vacuum deposited thin films of PbPc, NiPc, VOPc, TiOPc and CoPc were employed as gas sensor to detect NO 2 and NO. Data collected from sensor responses were used to train a back-propagation network (BPN) for identifying the gas species and quantifying its concentration. The results show that among the metallophthalocyanines tested, PbPc and NiPc have better sensing characteristics towards NO 2 and NO. In BPN training, maximum error occurs for data collected by the TiOPc sensor, and minimum error occurs for array of PbPc and NiPc sensors. In the concentration prediction of NO or NO 2, the maximum predicted error is 6.94%. When Two-Stage BPN or Single-Stage BPN was use to identify and quantify a single gas (NO 2 or NO), the accuracy of recognition approaches 100% and the maximum error for concentration prediction is 7.45%.
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