E-nose based on a high-integrated and low-power metal oxide gas sensor array

To improve the poor selectivity of a semiconductor gas-sensor system, an array of sensors can be utilized. However, this increases the system's size and power consumption. To overcome these limitations, we propose a high-integrated and highly selective electronic nose (E-nose) comprising two independent gas-sensing elements on a low-power microhotplate (MHP). Pd-SnO2 nanoflowers and Pd-WO3 microparticles were prepared and printed on a bridge-structured MHP 20 mu m apart and over an area of 110 mu m x 45 mu m. This was achieved using electrohydrodynamic inkjet printing aided by in-situ infrared laser curing to form an array of sensors. A power of 17 mW was required to increase the temperature of the MHP to 300 degrees C, which is the optimal operating temperature of the two gas sensors. Thus, a high response and low cross-sensitivity were achieved for hydrogen, ammonia, hydrogen-ammonia, ethanol, acetone, ethanol-acetone, toluene, and formaldehyde. A wavelet transform was used to reduce the noise and dimensionality of the signals from the gas-sensor array. Qualitative identification of the eight gases with an accuracy of 99.86% was achieved using the k-nearest neighbor (kNN) model. A p neighbors back propagation neural network (pN-BPNN) model was established to remove interfering samples to quantitatively estimate the gas concentration. The quantitative identification accuracy of pN-BPNN was higher than that of the standard back propagation neural network (BPNN) model with the average absolute percentage error of hydrogen detection in the range of 15-500 ppm, decreasing from 5.44% to 2.08%.

Automated summary

To improve the poor selectivity of semiconductor gas-sensor systems, a high-integrated and highly selective electronic nose (E-nose) with two independent gas-sensing elements on a low-power microhotplate (MHP) was proposed. Pd-SnO2 nanoflowers and Pd-WO3 microparticles were used to form an array of sensors on the MHP through electrohydrodynamic inkjet printing and in-situ infrared laser curing. The gas-sensor array achieved high response and low cross-sensitivity for various gases. Wavelet transform was applied to reduce noise and dimensionality of the signals. Qualitative identification accuracy of 99.86% was achieved using the k-nearest neighbor (kNN) model, while the p neighbors back propagation neural network (pN-BPNN) model improved quantitative identification accuracy for gas concentration estimation, especially for hydrogen.