In the present work, single-phase CuO particles were synthesized by a hydrothermal method and characterized by SEM, TEM and XRD. The gas sensing properties of the CuO based sensor to some representative flammable VOC gases such as ethanol, isopropanol, acetone, benzene, para-xylene and decane were investigated. The sensor resistance increased when exposed to these flammable analytes, as well as when the concentration of oxygen in air decreased. That phenomenon is attributed to the surface accumulation conduction mechanism of the p-type metal oxide semiconductor, when negatively charged surface oxygen desorbs from the surface of CuO and releases electrons back to the CuO bulk. According to our research, the detection limit of the CuO based sensor to decane was better than 1 ppm, which indicated the potential applications of CuO based sensors in detecting low concentration decane.

Sensor drift is currently the most challenging problem in gas sensing. We propose a novel ensemble method with dynamic weights based on fitting (DWF) to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. The DWF method uses a dynamic weighted combination of support vector machine (SVM) classifiers trained by the datasets that are collected at different time periods. In the testing of future datasets, the classifier weights are predicted by fitting functions, which are obtained by the proper fitting of the optimal weights during training. We compare the performance of the DWF method with that of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the DWF method outperforms the other methods considered. Furthermore, the DWF method can be further optimized by applying a fitting function that more closely matches the variation of the optimal weight over time.

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied.

For sensors constructed by freestanding membranes, when the gap between a freestanding membrane and the substrate or between membranes is at micron scale, the effects of near-field radiative heat transfer on the sensors’ thermal performance should be considered during sensor design. The radiative heat flux is transferred from a membrane to a plane or from a membrane to a membrane. In the current study of the near-field thermal radiation, the scanning probe technology has difficulty in making a membrane separated at micron scale parallel to a plane or another membrane. A novel MEMS (micro electromechanical system) device was developed by sacrificial layer technique in this work to realize a double parallel freestanding membrane structure. Each freestanding membrane has a platinum thin-film resistor and the distance between the two membranes is 1 m. After evaluating the electrical and thermal characteristics of the lower freestanding membrane, experimental measurements of near-field radiative heat transfer between the lower membrane and the upper membrane were carried out by setting the lower membrane as a heat emitter and the upper membrane as a heat receiver. The near-field radiative heat transfer between the two membranes was validated by finding a larger-than-blackbody radiative heat transfer based on the experimental data.

Because the sensor response is dependent on its operating temperature, modulated temperature operation is usually applied in gas sensors for the identification of different gases. In this paper, the modulated operating temperature of microhotplate gas sensors combined with a feature extraction method based on Short-Time Fourier Transform (STFT) is introduced. Because the gas concentration in the ambient air usually has high fluctuation, STFT is applied to extract transient features from time-frequency domain, and the relationship between the STFT spectrum and sensor response is further explored. Because of the low thermal time constant, the sufficient discriminatory information of different gases is preserved in the envelope of the response curve. Feature information tends to be contained in the lower frequencies, but not at higher frequencies. Therefore, features are extracted from the STFT amplitude values at the frequencies ranging from 0 Hz to the fundamental frequency to accomplish the identification task. These lower frequency features are extracted and further processed by decision tree-based pattern recognition. The proposed method shows high classification capability by the analysis of different concentration of carbon monoxide, methane, and ethanol.