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.

In this paper, a gas detection system with an environmental compensation algorithm based on nondispersive infrared (NDIR) technology was designed. The prepared infrared pyroelectric detector was a dual-channel type based on the lithium tantalate (LiTaO₃) wafer. The design of the optical gas chamber adopted a combination of two ellipsoids and a spherical top surface, which not only enhanced the coupling efficiency of the light propagation but also facilitated the miniaturization of the sensor module. In addition to this, a temperature and humidity compensation algorithm based on the least square method was proposed to make the measurement accuracy up to ±0.9% full scale (FS).

Metal oxide semiconductor (MOS) gas sensors are an important part of electronic nose technology because they are sensitive, cheap, and work well with microfabrication for system integration. But sensor drift makes them less useful for long-term, continuous gas monitoring. Changes in how sensors respond over time make pattern recognition models that were trained at first less accurate. This review looks at new ways to deal with sensor drift, with a focus on transfer learning and deep learning methods that have been developing continuously in the last five years. It emphasizes the shift from conventional recalibration and component correction to sophisticated methodologies, including deep domain adaptation, contrastive representation learning, and attention-based models. The review does not just list these methods; it also analyzes their pros and downsides, especially in situations where there is not much labeled data, drift is hard to anticipate, or the computational resources are limited, which is often the case with edge sensors.