Fully Inkjet-Printed Chemiresistive Sensor Array Based on Molecularly Imprinted Sol-Gel Active Materials.

The fabrication of chemiresistive sensors by inkjet printing is recognized as a breakthrough in gas-sensing applications. One challenge of this technology, however, is how to enhance the cross-selectivity of the sensor array. Herein, we present a ketjen black (KB) ink and molecularly imprinted sol-gel (MISG) inks to support the fabrication of a fully inkjet-printed chemiresistive sensor array, enabling the highly accurate recognition of volatile organic acids (VOAs) on the molecular level. The MISG/KB sensor array was prepared on a glossy photographic paper with a three-layer structure: a circuit layer by a commercial silver ink, a conductive layer by a KB ink, and an active selective layer by MISG inks imprinted by different templates. Hexanoic acid (HA), heptanoic acid, and octanoic acid were used as templates to prepare the MISGs and as targets to evaluate the detection and discrimination performance of the sensor array. Three resultant MISG/KB sensors exhibited high sensitivity and selectivity to VOA vapors. The limit of detection and imprinting factor were 0.018 ppm and 7.82, respectively, for HA-MISG/KB sensors to the corresponding target. With linear discriminant analysis of the gas responses, the MISG/KB sensor array can realize high discrimination to VOAs in single and binary mixtures. Furthermore, the proposed sensor array showed strong sensor robustness with excellent consistency, durability, bending, and humidity resistance. This work developed a fully inkjet-printed chemiresistive sensor array, enabling the realization of high cross-selectivity detection, achieving low-cost, scalable, and highly reproducible sensor fabrication, moving it closer to reliable, commercial, and wearable multi-analyte human body odor analysis potential.

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training.

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.