| Description |
Indoor Air Quality (IAQ) refers to the air quality within and around buildings. Volatile Organic Compounds (VOCs) are one of the most harmful indoor air pollutants as they contribute to various health problems. The market for VOC sensors is currently dominated by expensive optical sensors or power-hungry resistive sensors. Polymer-swelling-based capacitive sensors are a promising alternative, if some of their limitations can be improved upon. These devices are versatile, low-cost, and consume low power, which makes them excellent candidates to form an Internet of Things-based IAQ monitoring system. This dissertation presents two new families of polymer-based mechanically leveraged vapor sensors for improving the sensitivity and reducing the temperature dependence of capacitive VOC sensors. The first sensor developed in this work is a Laterally Amplified Chemo-Mechanical humidity sensor based on parametric amplification. This bending-type device features an intrinsic gain due to electrostatic spring softening resulting in enhanced sensitivity. Unlike electronic amplifiers, spring softening amplifies the sensor output without amplifying or adding electronic noise to the circuit, leading to a higher signal-to-noise ratio. Eleven-fold sensor response magnification was observed via parametric amplification. The sensor showed a repeatable and recoverable capacitance change of 11% when exposed to a change in relative humidity from 25-85%. The device also demonstrated a competitive response compared to a commercially available reference chip with a response time of ~1 sec. The second part of this dissertation focuses on the development of a VOC sensor based on mechanical self-leveling for low-power applications. This device features passive temperature compensation without needing externally powered temperature compensation systems. Self-leveling VOC sensors have been developed using three sensing polymers (Polyimide, Polyurethane, and PDMS), and device response to five VOCs (Ethanol, Acetone, Benzene, Hexane, and Water) has been presented. A Support Vector Machine-based algorithm has also been used to demonstrate target identification. We demonstrate that the new sensor provides the same gas response as a simple microcantilever geometry, showing ~20% change in capacitance when subjected to 35-85% RH change while showing near-zero baseline drift, when the ambient temperature is increased from 23-72°C, which is ~52-fold better than the uncompensated sensor. |
