(157d) Nanowire Sensor Array Based On Mixed Metal Oxide | AIChE

(157d) Nanowire Sensor Array Based On Mixed Metal Oxide

Authors 

Li, X. - Presenter, University of Massachusetts Lowell
Cho, J. - Presenter, University of Massachusetts Lowell
Yan, Q. - Presenter, University of Massachusetts Lowell
Sun, H. - Presenter, University of Massachusetts Lowell
Kurup, P. U. - Presenter, University of Massachusetts Lowell
Gu, Z. - Presenter, University of Massachusetts Lowell


Nanowire has been proved to be a promising candidate for sensor applications due to its high aspect ratio, improved specific surface area and enhanced sensitivity toward certain analytes. However, most of the nanowire sensors developed so far reply on single-elements and thus work individually; hence, they face challenges in selectivity and are incapable of discrimination and classification. The introduction of a secondary element into the sensor elements has the promise to solve the problems above. Here, we present our recent research work of fabricating mixed metal oxide nanowires and further integration of a nanowire-based sensor array. Mixed metallic nanowires were first synthesized with various element combinations, including tin, indium, nickel, copper, etc. Then the nanowires were integrated on to inter-digitated microelectrodes as a sensor chip using dielectrophoretic (DEP) assembly technique. A following thermal oxidation process converted mixed metallic nanowires into their corresponding oxides. Morphology of the nanowires before and after oxidation was characterized by scanning electron microscope (SEM) along with Energy Dispersive x-ray Spectroscopy (EDS) for compositional information, while their electrical properties were examined by a sourcemeter after each step. A sensor device containing up to four sensors and an auto-sampling system were designed, respectively, and were utilized for sensor testing. From the results, enhanced sensor performance was observed for mixed metal oxide nanowire sensors with particular compositions. Also, both principle component analysis (PCA) and artificial neural network (ANN) were applied to analyze the classification ability of the sensor array.

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