A high-sensitivity and stable ethylene glycol sensor was successfully developed by employing a facile hydrothermal method and annealing process to fabricate flower-shaped NiO/ZnO hierarchical nanostructures. The flower-shaped NiO/ZnO was integrated as the sensing material into a Microelectromechanical Systems (MEMS) gas sensor. The MEMS-based hotplate provided favorable heating conditions for the sensitive nanomaterial. Gas-sensing tests demonstrated excellent sensing performance of the sensor based on the flower-shaped NiO/ZnO heterostructure towards ethylene glycol. At the optimal operating temperature of 300 °C, the sensor to 10 ppm of ethylene glycol exhibited a response value of approximately 17.46 with response and recovery times of approximately 18 and 150 s, respectively. Moreover, its selectivity towards ethylene glycol was significantly higher than other common volatile organic compounds. This enhanced sensing performance is primarily attributed to the formation of p-n heterojunctions at the interface, the porous structure of NiO nanosheets, and their effective catalytic activity, resulting in a remarkable enlargement of the surface depletion region and an increase in barrier height. This study provides a simple synthetic process not only applicable to the preparation of other semiconductor oxide composite materials but also offers an effective method for ethylene glycol detection.
The concentration prediction of mixed gases is crucial to the pattern recognition research of electronic nose (E-nose) systems. The response experiments of the E-nose towards the ethanol and propanol mixture with different concentrations are carried out. Five kinds of machine learning algorithms, including linear regression, support vector machine, K-nearest neighbor, random forest, and decision tree, are used for training the multiple output regressors to predict the content of each component simultaneously. The R2 score, root mean squared error, and mean absolute error are used to evaluate the performance of these models. The relationship between prediction accuracy and concentration distribution has also been studied. The results show that the model based on the random forest has superior performance for forecasting the concentration of ethanol and propanol, with the R2 score more than 0.98 in the 5-fold cross-validation. This study provides a significant inspiration for designing a multi-output regression model to realize the quantitative prediction of mixed gases by the E-nose.
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