Calibration of Metal Oxide Semiconductor Gas Sensors by High School Students

• Main Authors: Sebastian Höfner, Andreas Schütze, Michael Hirth, Jochen Kuhn, Benjamin Brück
• Format: Article
• Language: English
• Published: International Association of Online Engineering (IAOE) 2021-04-01
• Series: International Journal of Online and Biomedical Engineering
• Subjects: air pollution; calibration; electrochemical sensors; environmental monitor-ing; machine learning; neural networks; sensor phenomena and applications; signal analysis; student experiment
• Online Access: https://online-journals.org/index.php/i-joe/article/view/19215

A wide range of pollutants cannot be perceived with human senses, which is why the use of gas sensors is indispensable for an objective assessment of air quality. Since many pollutants are both odorless and colorless, there is a lack of awareness, in particular among students. The project SUSmobil (funded by DBU – Deutsche Bundesstiftung Umwelt) aims to change this. In three modules on the topic of gas sensors and air quality, the students (a) learn the functionality of a metal oxide semiconductor (MOS) gas sensor, (b) perform a calibration process and (c) carry out environmental measurements with calibrated sensors. Based on these introductory experiments, the students are encouraged to develop their own environmental questions. In this paper, the student experiment for the calibration of a MOS gas sensor for ethanol is discussed. The experiment, designed as an HTML-based learning, addresses both theoretical and practical aspects of a typical sensor calibration process, consisting of data acquisition, feature extraction and model generation. In this example, machine learning is used for generating the evaluation model as existing physical models are not sufficiently exact.