A method was used to fabricate a fully inkjet-printed gas sensor matrix on photographic paper. An electrode matrix comprising 36 interdigital electrodes in a high-density layout that is easy to integrate has been fabricated using a combination of insulating ink and commercial silver ink. Molecular-imprinted polymer (MIP) inks were then made using a simple solution mixing method, and these inks were printed together with carbon black ink on the electrode matrix to complete production of the sensor. Finally, experimental dynamic sensing of volatile organic compounds verifies that for detection of gases corresponding to the MIP template molecules, the MIP layer offers improvements in both sensitivity and selectivity when compared with non-imprinted polymer layers. The matrix can produce a response of more than 20% to 3 ppm propenoic acid gas through adjustment of the printing times for the carbon black layer and the MIP layer.
Facile detection of melamine in milk was realized through a portable multi-channel sensing device using Au nanoparticles with urchin-like structures as sensing probes.
When an electronic nose (E-nose) is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde). Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training) is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.
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