Molecular Engineering of Materials and Surfaces – Development and Optimization of MXene-Based Formaldehyde Sensors

<p dir="ltr">This thesis focuses on the development of a low-cost, portable, and reliable MXene-based chemiresistive sensor for detecting formaldehyde (HCHO), a carcinogenic volatile organic compound (VOC) that is commonly present in both indoor and outdoor environments. This work is structured around three main projects. </p><p dir="ltr">First, nitrogen doped MXene sensors hybridized with silver (Ag) nanoparticles and encapsulated with poly(1,3,5,7-tetravinyl-1,3,5,7-tetramethylcyclotetrasiloxane) (PV4D4) were fabricated. The main aim of this study was to devise a method to overcome the susceptibility of MXene to oxidation. That was achieved by encapsulating it with the hydrophobic polymer PV4D4, deposited via initiated chemical vapor deposition (iCVD), and this was shown to improve the overall stability of the sensor. This encapsulant layer helped enhance the half-life span of the sensor by ~200%. It further enhanced the response of the sensor by 1.7 times by acting as a functional layer and selectively reacting with formaldehyde. Moreover, a simple, low-energy hydration process was observed to help regenerate the sensor performance after degradation, up to 90%. </p><p dir="ltr">Second, multiphysics simulations using COMSOL were performed to identify optimum design parameters for fabrication of these MXene based HCHO sensors. The main aim of this study was to improve the sensitivity of the sensor by precisely engineering the sensor architecture. The size of the Ag nanoparticles, sensing layer thickness, and the MXene interlayer spacing were systematically tuned. The size of the nanoparticles was found to have an inverse relation with the sensor response while the interlayer spacing was observed to have negligible influence. The sensing layer thickness exhibited a non-monotonic relation with sensor response owing to the trade-off between increase in surface area and the increase in diffusion pathway for the gas. </p><p dir="ltr">Finally, optimized Ag/N-MXene sensors were fabricated based on the insights obtained from the simulation results and statistical analysis of their response curves was performed. The main aim of this project was to validate the simulation results as well as to enhance the selectivity of the sensor via data analysis. The optimized sensor showed a 41% enhancement in the limit of detection and a 168% enhancement in response compared to the initial unoptimized sensor. The ratio of responses between the different sensor configurations tested matched closely with that of the simulated responses, confirming the validity of the model. Principal Component Analysis was applied to the dynamic sensing data of the optimized sensor to enable discrimination among the 6 different gases it was exposed to and the top three principal components obtained were able to explain up to 75% of the observed variance. </p><p dir="ltr">Thus, this work demonstrates a comprehensive strategy for designing stable, sensitive, and selective MXene-based gas sensors through material engineering, computational modeling, and data-driven analysis.</p>