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.

多种挥发性有机化合物 (VOC) 的检测和定量正在成为包括医疗保健在内的多个利基应用的关键要求。需要使用最少数量的传感器快速识别多种气体。金属氧化物的异质结仍然是高效 VOC 传感的首选，因为除了增强的响应外，它们还展现了令人兴奋的传感特性。这项工作报告了 CuO、ZnO 和三种 CuO-ZnO pn纳米结构的合成使用简便的一锅水热法，结具有不同重量百分比（1-0.5、1-1 和 0.5-1）的 CuO 和 ZnO。使用 X 射线衍射、场发射扫描电子显微镜和紫外-可见光谱对纳米材料进行了表征。电阻传感器由所有五种纳米材料制成，并针对四种 VOC（异丙醇、甲醇、乙腈和甲苯）的浓度为 25–200 ppm 进行了测试。CuO 和 CuO–ZnO (1–0.5) 传感器在 250 °C、CuO–ZnO (1–1) 和 CuO–ZnO (0.5 –1) 在 350 °C 时分别对甲醇 (9–60%) 和异丙醇 (15–120%) 表现出最高响应，而在 400 °C 时本征 ZnO 对甲苯 (29–76%) 表现出最大响应。观察到所有传感层都对其他三种 VOC 表现出有限的响应，因此，尝试使用支持向量机 (SVM) 和多元线性回归 (MLR) 算法对四种 VOC 进行准确分类和量化。观察到两个传感器的响应和响应时间足以作为机器学习算法的输入，用于对所有四种 VOC 进行分类和量化。(CuO–ZnO (1–0.5) & (1-1) 和 CuO–ZnO (1-1) & (0.5–1) 的组合使用 SVM 证明了 98.13% 的最高分类精度。CuO–ZnO 的组合(1–0.5) 和 (1-1) 展示了使用 MLR 对四种 VOC 的最佳量化。观察到两个传感器的响应和响应时间足以作为机器学习算法的输入，用于对所有四种 VOC 进行分类和量化。(CuO–ZnO (1–0.5) & (1-1) 和 CuO–ZnO (1-1) & (0.5–1) 的组合使用 SVM 证明了 98.13% 的最高分类精度。CuO–ZnO 的组合(1–0.5) 和 (1-1) 展示了使用 MLR 对四种 VOC 的最佳量化。观察到两个传感器的响应和响应时间足以作为机器学习算法的输入，用于对所有四种 VOC 进行分类和量化。(CuO–ZnO (1–0.5) & (1-1) 和 CuO–ZnO (1-1) & (0.5–1) 的组合使用 SVM 证明了 98.13% 的最高分类精度。CuO–ZnO 的组合(1–0.5) 和 (1-1) 展示了使用 MLR 对四种 VOC 的最佳量化。