Development and validation of mathematical physical/chemical models for sensory systems.

Nowadays industrial processes are more and more advanced and therefore the effectiveness of detection systems has become fundamental to monitor and control processes. Hence, it is therefore essential to develop new control and monitoring technologies for sensor-based systems to ensure greater reliability and long-term competitiveness. There are several sensors types available for monitoring aspects of processing environments, in particular, my research activity has focused on the study of the behavior of three types of sensors systems noticeably important in industrial monitoring applications: chemoresistive gas sensors for the detection of low concentrations of polluting and greenhouse gases, such as NOX, Quartz Crystal Microbalance (QCM) for fluid analysis such as for the test of lubricating oils and a measurement system for evaluating the e ciency of enzyme accelerated CO2 capture. In detail, in studying the behavior of these sensor systems I worked on the development of mathematical models that could describe the sensing mechanisms of the devices with the aim of understanding their behavior in a more precise and complete way in order to speed up the design phase and contribute to the tuning of more efficient devices. In fact, the development of a dynamic mathematical model allows us to examine and control the phenomenon, and make predictions on its evolution. In particular, computational modeling allows us to obtain information on the behavior of complex systems which are difficult or impossible to obtain by direct measurements and observations.