Committee members

• Advisor: Prof. Carlos A Grande
• Chair: Prof. Shadi Fatayer
• ME Member: Prof. Peter Schmid, Prof. Mohammad Younis,
• External examiner: Prof. Paulo Batista Gonçalves

Abstract

Microelectromechanical systems (MEMS) resonators exhibit rich nonlinear dynamics, including hardening, softening, saturation, and buckling, which can be leveraged to enhance sensitivity in gas sensing applications. This thesis explores the integration of compound structures, such as U-shaped resonators and auxetic architectures, with electrothermal and electrostatic actuation to improve gas detection selectivity and performance. Traditional gas sensing methods, like surface coating and thermal effects, often suffer from cross-sensitivity and limited distinguishability, particularly for biomarkers like acetone, ethylene, and CO2. To address these challenges, machine learning algorithms are employed to extract features from resonance shifts, saturation curves, and recovery times, enabling accurate classification of gases and concentrations. Through Analytical mathematical modeling (MATLAB and Mathematica), Finite Element Method (FEM) through COMSOL simulations, and experimental validation, the study investigates optimal set points for resistor-type sensors, exploits 2:1 internal resonance for viscosity sensing in U-beams, and utilizes the negative Poisson's ratio of auxetic structures for inert atmosphere detection. The proposed approach aims to advance smart, reusable MEMS sensors for environmental monitoring, healthcare, and Internet of Things (IoT) applications, demonstrating enhanced reliability and reduced false positives via nonlinear dynamics and data-driven techniques.

Biography

Wagner Barth Lenz received his Bachelor’s degree in Mechanical Engineering, in 2016, one of his master’s degree in Mechanical engineering, in 2019, and another master’s degree in Electrical Engineering, in 2020, all from the Federal University of Technology – Parana, Ponta Grossa - PR, Brazil. He is currently a Ph.D. Candidate at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, in Mechanical Engineering in the PSE division. His research interests include linear and nonlinear dynamics of MEMS-based resonators, chaos, control, optimization, and their application in MEMS sensors and actuators.