Self-assembled Electronic Nose based on Air-bridge-structured Nanowire Junction Arrays
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- Self-assembled Electronic Nose based on Air-bridge-structured Nanowire Junction Arrays
- Park, Won Jeong
- Baik, Jeong Min
- Issue Date
- Graduate School of UNIST
- In this article, we introduce a facile synthesis and characterization of alternatively driven dual nanowire arrays with single crystalline nanostructures of ZnO and CuO that capable of reliably discriminating between three gases such as hydrogen (H2), carbon monoxide (CO), and nitric dioxide (NO2) in air.
In the Part 1, we describe the fabrication process for multiple nanowires species (ZnO and CuO) growth via chemical vapor deposition at 773-873 K, without the incorporation of foreign atoms. This process can be simply achieved by a direct and simultaneous heating of metallic film with Zn and Cu followed by a conventional lithography. It was clearly observed that there is no chemical cross-contamination between them, separated by 5 µm, during the growth. Additionally, the sensing properties for the single crystalline nanowires of ZnO and CuO are then tested and compared for their ability to distinguish three gases (H2, CO and NO2) in air, which they were able to do unequivocally.
In the Part 2, we also introduce a strategy for creating an air-bridge-structured nanowire junction array platform for their ability to distinguish three gases (H2, CO and NO2) in air. Alternatively driven dual nanowire species of ZnO and CuO on single substrate are used and decorated with metallic nanoparticles to form two dimensional microarray, which do not need to consider the post fabrications. Each individual nanowires in the array form n-n, p-p and p-n junctions act as electrical conducting path for carrier. The adsorption of gas molecules to the surface changes the potential barrier height formed at the junctions and the carrier transport inside the straight semiconductors, which provide the ability of a given sensor array to differentiate among the junctions. The sensor were tested for their ability to distinguish three gases (H2, CO and NO2), which they were able to do unequivocally when the data was classified using linear discriminant analysis.
- Materials Science Engineering
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