Files
Abstract
Reliable, real-time ion monitoring is essential for understanding and managing water quality, but gold-standard analytical methods (e.g., inductively coupled plasma and ion chromatography) remain laboratory-bound and poorly suited for continuous field deployment. Meanwhile, field-effect transistor (FET) sensors offer fast, sensitive, and low-power electrical readouts, but conventional back-gate and solution-gated configurations often suffer from drift, hysteresis, and device-to-device variability—especially when low-dimensional materials serve simultaneously as the sensing interface and the electronic transducer. This dissertation develops and validates a molecular-engineering framework for remote-gate field-effect transistor (RGFET) sensors that decouple the solution-facing interface from the semiconductor transducer, enabling stable, reproducible sensing of heavy metal and nutrient ions in water with varying pH values. Chapter 1 presents the background of ions in water environment, summarizes the current state of ion detection technologies, and introduces FET water sensors for ion detection. In Chapter 2, the dissertation first establishes how remote-gate structure suppresses non-ideal behaviors that commonly limit aqueous FET sensing. Using reduced graphene oxide (rGO) confined to the solution interface and capacitively coupled to a commercial metal-oxide-semiconductor field-effect transistor (MOSFET), the platform prevents current flow through the sensing film and mitigates interfacial redox processes, trapped-charge effects, and material instabilities that drive drift and hysteresis. A systematic mechanistic study links rGO sensing performance to film thickness/coverage and reduction conditions, and introduces intrinsic electrochemical metrics extracted from transfer characteristics to quantify stability. With an optimized multilayer rGO interface, the RGFET achieves near-Nernstian pH sensing with high linearity, minimal drift, and negligible hysteresis, while maintaining high device yield and reproducibility. Building on this foundation, Chapter 3 demonstrates molecular engineering of device interfaces to tune rGO electrochemical properties and enhance ion sensing. Self-assembled interlayers of (3-aminopropyl) trimethoxysilane (APTMS) and hexamethyldisilazane (HMDS) are used to control surface energy and selectively bias the chemical composition and hydrophilicity of deposited rGO, thereby modulating proton sensitivity and interfacial stability. In parallel, linker chemistry based on pyrene derivatives is quantitatively characterized using the RGFET, enabling estimation of surface charge density and linker surface density resulting from functionalization. By leveraging these insights to increase probe density on the sensing surface, the platform achieves improved heavy-metal response—illustrated by enhanced Pb²⁺ sensitivity using glutathione capture chemistry enabled by optimized linker/substrate interface. Finally, the dissertation translates these principles into deployable systems for practical water monitoring. In Chapter 4, a fully portable and reusable Pb²⁺ sensor is realized using a sensor printed circuit board (PCB) bearing a graphene film remote gate, a compact analyzer PCB with on-board electronics and wireless communication, and a smartphone interface for real-time display and threshold-based warnings. The portable sensor exhibits a high sensitivity of 21.7 % when detecting its LOD value of 1 nM (~ 0.2 ppb), while the sensor PCB provides adequate adhesion to the graphene ink, allowing facile deposition and removal, which results in the ability to reuse the sensor PCB repeatedly. In this way, the system detects sub-ppb lead rapidly (on the order of a minute) with strong selectivity and a regenerable sensing surface. To realize nutrient ion monitoring, a multiplexed RGFET array is presented in Chapter 5 for simultaneous nitrate, nitrite, and phosphate detection using ion-selective remote-gate modules. The array exhibits near-Nernstian potentiometric behavior (54.2, 48.7 and 40.5 mV/decade respectively) across environment-relevant concentration ranges (10-2–10-5 M). A machine-learning analysis model is integrated to resolve cross-sensitivity in mixed-ion environments, achieving prediction accuracies with R2 values exceeding 0.98 for all nutrient ions. The array exhibits near-Nernstian potentiometric behavior across relevant concentration ranges and supports continuous monitoring in a flow-cell configuration under dynamic conditions. Collectively, these advances position remote-gate FET sensing—paired with molecular engineering, portable electronics, and data-driven analysis—as a scalable pathway toward robust, real-time ion monitoring for environmental and water-quality applications. Intellectually, this dissertation establishes a mechanistic framework that connects molecular engineering and materials chemistry to sensor accuracy, stability, and key performance metrics, yielding general design principles for reliable FET-based water sensors. Broadly, the resulting portable and connected platforms enable low-cost, field-deployable monitoring of toxic metals and nutrients, shifting ion analysis from episodic laboratory testing to continuous, real-time monitoring that support faster response and wider access.