Correction Model for Metal Oxide Sensor Drift Caused by Ambient Temperature and Humidity

Abstract

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16 °C to 30 °C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.

Reproducibility and Repeatability Tests on (SnTiNb)O2 Sensors in Detecting ppm-Concentrations of CO and Up to 40% of Humidity: A Statistical Approach

“…Since the MOX sensor responses are, in general, rather affected by humidity [ 12 , 13 , 24 , 25 , 26 , 27 , 28 ], the above CO concentration steps were performed at first in dry conditions and later in the presence of different relative humidities (RH: 10%, 15%, and 30%; Figure 8 ); the sensor chamber environment was therefore stabilized at the desired RH for at least six hours before the CO injection. The sensor measurements, in the presence of different CO concentrations and RHs, were repeated three times in order to assess the repeatability of their responses.…”

“…In any case, STN sensor behavior, as all the MOX sensors, is significantly affected by humidity, that, if present in the environment before the gas target injection, leads to a non-negligible sensor response decrease to the latter (here CO). This mechanism is related to an unavoidable adsorption on the sensor film surface of groups, originated by the water molecule dissociation (2) (at the WT of interest ranging from 300 to 600 °C), occupying most of the surface states [ 12 , 13 ]. …”

“…In this paper, thirteen thermo-activated thick-film (SnTiNb)O 2 sensors (named STN in the following), whose sensing material was obtained through a standardized sol-gel synthesis and applied on the sensor substrate by using a screen printer (see Materials and Methods) [ 3 ], were randomly selected from the same printing batch and concurrently tested with different concentrations of CO and water vapor (the latter was used as interfering and target gases separately [ 12 , 13 ]). In general, thick-film technology is used to produce components manufactured with films having a thickness between 1 and 100 μm by an additive process involving the deposition of several conductive, resistive, and/or dielectric layers onto a substrate usually using a lithographic process.…”

Breath VOC analysis and machine learning approaches for disease screening: a review

“…The issue of sensor drift is a common problem in E-Nose. Sensor drift may occur due to ageing and variations in temperature and humidity [126]. Dimensionality reduction-based approaches have been effective for drift compensation [127].…”

Piezo/Triboelectric Effect Driven Self‐Powered Gas Sensor for Environmental Sensor Networks

“…The increase in humidity also negatively affects the response of the gas sensors as molecules bind to the sensing area, reducing their adsorption activity and affecting their electronic and ionic conduction properties. [9] Therefore, to realize a gas sensor, it is also important to operate stably in various environments. In addition, owing to the rapid development of Internet of Things (IoT) technology in recent years, the development of portable sensors capable of implementing wireless, sustainable, multifunctional, and independent sensing network systems has received considerable attention.…”