Modern monitoring requirements of gases for demanding applications such as medical diagnostics, environmental surveillance, industrial safety, and many others push the limits of existing detection concepts where we may reach their fundamental performance limits.
This tutorial is focused on multivariable sensing concepts and implementations that bridge the gap between the existing and required sensing capabilities. This tutorial (1) will pose several fundamental and practical questions on improvements of principles of gas sensing and (2) will answer these questions in sensors with previously unthinkable capabilities in wearable, disposable, wireless, and other formats.
Multivariable gas sensors utilize multi-dimensional response principles to overcome insufficient selectivity and stability limitations of existing sensors. The design rules of these multivariable sensors involve a sensing material with multi-response mechanisms to different chemicals and a multivariable transducer with independent outputs to recognize these different responses. Such sensors quantify individual chemical components in mixtures, reject interferences, and have enhanced stability. Such performance is attractive when selectivity advantages of traditional mature instruments are cancelled by application-specific requirements.
The tutorial will be structured as four segments. The first three segments will be lectures that will cover the fundamental and practical aspects of design rules of multivariable sensors, sensor requirements for emerging applications that demand low-power, wearable, unattended, or disposable implementations, comparison of performance of conventional and multivariable sensors with analytical instruments based on mature traditional analytical concepts. Further in these lectures, the role of data analytics, sensor fusion from the multivariable sensors will be demonstrated on several vivid examples where software-enabled enhancement of sensor performance was ten-fold, ten thousand-fold, and two million-fold. Machine learning examples will be presented and compared to more conventional data analysis techniques. The fourth segment will be discussion of analysis results of data submitted by participants prior the tutorial.
Segment 1 will include (1) Introduction: diversity of gas-monitoring needs and their specific requirements, (2) Field-portable conventional analytical instrumentation, (3) Advantages and limitations of conventional gas sensors vs. conventional analytical instrumentation, (4) Gas sensors with conventional and new sensing materials, (5) Conflicting requirements that lead to non-selective response of sensors, (6) Gas sensor arrays, basics of multivariate data analysis from arrays, and existing challenges, (7) Requirements for an ideal sensor system.
Segment 2 will include (1) Meeting requirements for an ideal sensor with an individual multivariable sensor, (2) Design principles of multivariable sensors, (3) Sensing materials with multiple gas-response mechanisms, (4) Multivariable transducers, (5) Learnings in data analytics from sensor arrays, (6) New data analytics tools.
Segment 3 will include (1) Rejection of ambient interferences with multivariable sensors, (2) Multi-gas quantitation with multivariable sensors, (3) Examples from field applications, (4) Stability of multivariable sensors, (5) Comparisons of selectivity and stability of multivariable sensors with sensor arrays, (6) Application areas enabled with multivariable sensors.
Segment 4 will include (1) Data analysis from participants: selectivity, (2) Data analysis from participants: stability.
Thus, by the end of the tutorial, the participants will have a good fundamental understanding and will see practical examples of operation of multivariable gas sensors and how these sensors may be utilized in their intended applications or research.