Abstract
Integration of multiple sensors in a so-called sensor array platform allows multi-analyte detection and enhanced selectivity. However, the demanding requirement of device integration comes along with less than desired sophistication of electrical interconnects with increasing circuit complexity. In the past decade, we have been focused on establishing a multi-modal sensing platform based on a single nanostructured device. Such a single device platform can be operated with different modes such as photocurrent, resistance, potential, and impedance. These nanoarray sensors have been fabricated with a good scalability using vapor to solution phase depositions. The array composition could be rationally designed from pristine to heterogeneous across metals, ceramics, and polymers. Depending on the comprised material composition, decoration of nanoparticle sensitizers such as perovskite, metal oxide, or noble metal may enable drastic boosting of sensor performance under various gaseous mixtures such as nitrogen oxide (NOx), hydrocarbons (HCs), carbon mono-oxide (CO), and hydrogen in atmospheres. Toward mixed multi-analyte conditions, such a new chemical sensor allows differentiated detection as well as low-concentration quantification of multiple species in a single-device platform. Machine learning as driven by the multimodal sensor datasets is utilized to add and enhance the single sensor platform performance in terms of accuracy, detection range, and speciation differentiation.
Published Version
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