(Invited) Single Nanoarray Sensor for Multi-Analyte Detection

Abstract

Chemical sensors usually adopted a single resistance-metric mode, which has a drawback of significant cross-sensitivities in mixed analytes scenario, thus showing a poor selectivity towards target analytes. By integration of multiple sensors in a so-called sensor array platform, multi-analyte detection and enhanced selectivity can be achieved. However such a strategy requires significant device integration work involving sophisticated electrical interconnects with increasing circuit complexity. In the past few years, we has been focused on establishing a multi-mode sensing platform based on a single nanostructure array (nanoarray) device. Such a single device platform can be operated at variable temperatures with different modes such as photocurrent, resistance, and impedance. These nanoarray sensors have been fabricated with good scalability using a variety of synthetic methods ranging from vapor to solution phase depositions. The array composition could be rationally designed from pristine homogeneous to dissimilar heterogeneous across metals, ceramics, and polymers. Depending on the comprised material composition, decoration of a nanolayer of perovskite, metal oxide, or noble metal may enable formation of electronic and catalytic sensitizers, as well as hetero-contacts that can drastically boost sensor performance under various gaseous analyte atmospheres. Toward mixed multi-analyte conditions, such a new type of multi-mode nanoarray sensors allows selective and sensitive detection of multiple chemical species in a single-device configuration. By relating the multiple sensing modes using a single nanoarray sensor, efficient and selective detection of multiple gas analytes has been accomplished. A database was established based on multiple sets of data, namely the sensitivities for different analytes obtained from the outputs of different sensing modes. The signals of multiple gas analytes could be differentiated through the principal component analysis process. The linear and non-linear decision boundaries could be readily derived for pattern recognition, and the multi-analyte quantification and detection.