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The Multisensor Array Based on Grown-On-Chip Zinc Oxide Nanorod Network for Selective Discrimination of Alcohol Vapors at Sub-ppm Range

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The Multisensor Array Based on Grown-On-Chip Zinc Oxide Nanorod Network for Selective Discrimination of Alcohol Vapors at Sub-ppm Range

Anton Bobkov et al. Sensors (Basel).

Abstract

We discuss the fabrication of gas-analytical multisensor arrays based on ZnO nanorods grown via a hydrothermal route directly on a multielectrode chip. The protocol to deposit the nanorods over the chip includes the primary formation of ZnO nano-clusters over the surface and secondly the oxide hydrothermal growth in a solution that facilitates the appearance of ZnO nanorods in the high aspect ratio which comprise a network. We have tested the proof-of-concept prototype of the ZnO nanorod network-based chip heated up to 400 °C versus three alcohol vapors, ethanol, isopropanol and butanol, at approx. 0.2-5 ppm concentrations when mixed with dry air. The results indicate that the developed chip is highly sensitive to these analytes with a detection limit down to the sub-ppm range. Due to the pristine differences in ZnO nanorod network density the chip yields a vector signal which enables the discrimination of various alcohols at a reasonable degree via processing by linear discriminant analysis even at a sub-ppm concentration range suitable for practical applications.

Keywords: butanol; ethanol; gas sensor; isopropanol; multisensor array; nanorod; selectivity; sensitivity; zinc oxide.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The scheme of ZnO nanorods growth over the multielectrode chip by hydrothermal process. See the text for details.
Figure 2
Figure 2
The scheme of experimental setup to measure the gas response of ZnO NR network-based chip. See the text for details.
Figure 3
Figure 3
The electron microscopy characterization of the ZnO NRs grown over the multielectrode chip: (a) SEM image of the exemplary area of the chip surface; (b) EDX spectrum recorded from the chip surface comprising electrodes; the insert shows the Gauss distribution of NR length in the network analyzed from the SEM image.
Figure 4
Figure 4
The XPS characterization of the ZnO NRs grown over the multielectrode chip. The chosen energy ranges correspond to Zn 2p (a), O 1s (b) XP peaks, and Zn LMM Auger line (c). Blue points are experimental data; red curves indicate fitting calculations with @Thermo Avantage software.
Figure 5
Figure 5
The electrical characterization of the ZnO NRs network at the multielectrode chip in background air conditions under heating to ca. 400 °C. Data for exemplary segment are shown: (a) I–V curve taken in DC mode in two opposite direction of electrical potential variation, UDC = [−5,+5] V; insert shows the scheme of measurement; (b) Nyquist plot, [1:106] Hz range, the empty blue circles and filled red circles identify the experimental points recorded under UAC = 0.1 V and UAC = 5.0 V, respectively. The corresponding curves going around the points are built accounting for the equivalent electric scheme of the chemiresistors shown in the inset; (c) the ratio between full impedance, |Z|, its imaginary, Zim, and real, Zreal, components recorded under UAC = 0.1 V and UAC = 5.0 V in dependence on applied AC frequency.
Figure 6
Figure 6
The gas-sensing characterization of the ZnO NR network-based multielectrode chip heated up to ca. 400 °C at DC mode: (a) the typical resistance variation of exemplary segment upon chip exposure to isopropanol vapors mixed with air at concentration of 0.4 ppm, 1 ppm and 5 ppm; (b) the dependence of chemiresistive response of the segments in the multisensor array to three alcohol vapors on their concentration; the error bar shows the scatter of data over the multisensor array.
Figure 7
Figure 7
The processing of vector signal generated by ZnO NR network-based multisensor array by LDA: (a) the recognition of the signals to vapors at sub-ppm concentrations (0.7 ppm for ethanol, 0.4 ppm for isopropanol, 0.2 ppm for butanol) in mixture with background air, the circles are built around the cluster gravity centers with 0.95 confidence probability based on sampling of 20 training vector points; (b) the average distance between vapor-related clusters in LDA space when processing vector signals to vapors at sub-ppm, 1 ppm, 5 ppm, and all-range concentrations; (c) the recognition of vapors at all concentrations in range from sub-ppm to 5 ppm, the spheres are drawn to indicate the areas in the LDA space related to test vapors.

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