Application of Surface Acoustic Waves in Gas Sensors
Theoretical Models and Advanced Sensor Structures
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Beschreibung
This textbook covers key aspects of applying surface acoustic waves (SAW) in gas sensors, presenting fundamental operating principles, theoretical models, research methodologies, and sensor structures in a clear and accessible manner. It discusses single, bilayer, and multilayer sensor structures, along with a detailed theoretical description of the acoustoelectrical effect in bilayer sensors. Special attention is given to semiconductor-metal bilayer structures, such as phthalocyanine-palladium and tungsten oxide-palladium, due to their strong potential for practical applications in SAW hydrogen sensors. The book also explores future applications and research directions, including advancements in light-activated photoconductive polymer structures.
This textbook covers key aspects of applying surface acoustic waves (SAW) in gas sensors, presenting fundamental operating principles, theoretical models, research methodologies, and sensor structures in a clear and accessible manner. It discusses single, bilayer, and multilayer sensor structures, along with a detailed theoretical description of the acoustoelectrical effect in bilayer sensors. Special attention is given to semiconductor-metal bilayer structures, such as phthalocyanine-palladium and tungsten oxide-palladium, due to their strong potential for practical applications in SAW hydrogen sensors. The book also explores future applications and research directions, including advancements in light-activated photoconductive polymer structures.
Outlines the provision of a unified treatment for data with overwhelming samples or parameters for stable predictions Provides advanced solutions to regression and classification problems that are crucial for complex systems Includes examples coded in Python and Matlab which provide students and instructors with mathematical insights
Autor*in
Wiesław Jakubik
Themen in »Application of Surface Acoustic Waves in Gas Sensors«
Learning methods Regression techniques Classification error Receiver characteristics Bridge regression Ensemble learning Instructor manual