Carbon monoxide can cause severe harm to humans even at low concentrations. Metal Oxide Semiconductor (MOS) carbon monoxide gas sensors have excellent sensing performance regarding sensitivity, selectivity, response speed, and stability, making them very desirable candidates for carbon monoxide monitoring. However, MOS gas sensors generally work at temperatures higher than room temperature, and need a heating source that causes high power consumption. High power consumption is a great problem for long-term portable monitoring devices for point-of-care or wireless sensor nodes for IoT application. Room-temperature MOS carbon monoxide gas sensors can function well without a heater, making them rather suitable for IoT or portable applications. This review first introduces the primary working mechanism of MOS carbon monoxide sensors and then gives a detailed introduction to and analysis of room-temperature MOS carbon monoxide sensing materials, such as ZnO, SnO₂, and TiO₂. Lastly, several mechanisms for room-temperature carbon monoxide sensors based on MOSs are discussed. The review will be interesting to engineers and researchers working on MOS gas sensors.

The rapid development of signal processing technology has improved the stability and anti-interference ability of gas sensors in electronic noses (E-noses). However, the interference and noise caused by temperature and humidity in the environment are still inevitable in real detection conditions, which can cause data fluctuation during the recognition process. In traditional pattern recognition, the data fluctuation would reduce the difference between extracted features and the accuracy of gas classification. This study proposes a One-dimensional Convolutional Neural Network (1DCNN) with the Savitzky-Golay (SG) filter. The SG filter is added before the convolution layer in the 1DCNN to automatically remove the noise of the sensor array data. The model can improve the effectiveness of the convolution layer to obtain features, omit the tedious preprocessing steps, and directly complete the identification process from raw data to results. The 1DCNN with SG filter is employed for the recognition of four gases: methane, ethanol, ethylene, and carbon monoxide. The results show that the accuracy of the 1DCNN with SG filter (99.21%) is 4% higher than that of the CNN (95.31%). Furthermore, the 1DCNN with SG filter is utilized for classifying a diverse assortment of mixed gases, ultimately achieving a classification accuracy of 99.8%. This study demonstrates the effectiveness of a novel CNN model with a Savitzky-Golay filter, which streamlines the data processing and significantly improves the accuracy and efficiency of gas recognition in E-noses.