CuO–ZnO p-n junctions for accurate prediction of multiple volatile organic compounds aided by machine learning algorithms

Abstract

Detection and quantification of multiple volatile organic compounds (VOCs) are emerging as critical requirements for several niche applications including healthcare. It is desirable to get multiple gases identified rapidly and using minimum number of sensors. Heterojunctions of metal oxides are still among the top-picks for efficient VOC sensing because they unfold exciting sensing characteristics in addition to enhanced response. This work reports the synthesis of nanostructures of CuO, ZnO, and three CuO–ZnO p-n junctions having different weight percentages (1–0.5, 1-1, and 0.5–1) of CuO and ZnO, using a facile one-pot hydrothermal method. The nanomaterials were characterized using X-ray diffraction, field emission scanning electron microscopy, and UV–Visible spectroscopy. Resistive sensors were fabricated of all five nanomaterials and were tested for 25–200 ppm of four VOCs – isopropanol, methanol, acetonitrile, and toluene. The CuO and CuO–ZnO (1–0.5) sensors showed the highest response for isopropanol (7.5–65.3% and 19–122%, respectively) at 250 °C, CuO–ZnO (1-1) and CuO–ZnO (0.5–1) exhibited the highest responses for methanol (9–60%) and isopropanol (15–120%), respectively at 350 °C, and the intrinsic ZnO showed maximum response to toluene (29–76%) at 400 °C. All the sensing layers were observed to exhibit finite responses to the other three VOCs so, an attempt to classify and quantify the four VOCs accurately was made using support vector machine (SVM) and multiple linear regression (MLR) algorithms. The response and response times of two sensors were observed to be sufficient as inputs to the machine learning algorithms for classifying and quantifying all the four VOCs. The combinations of (CuO–ZnO (1–0.5) & (1-1) and CuO–ZnO (1-1) & (0.5–1) demonstrated the highest classification accuracy of 98.13% with SVM. The combination of CuO–ZnO (1–0.5) & (1-1) demonstrated the best quantification of the four VOCs using MLR.