Date of Award

December 2017

Degree Type


Degree Name

Doctor of Philosophy



First Advisor

Junhong Chen

Committee Members

Woo-Jin Chang, Benjamin C. Church, Yin Wang, Deyang Qu


2d Nanomaterial, Fet Sensor, Heavy Metal Ion Detection


Excessive intake of heavy metals damages the central nervous system and causes brain and blood disorders in mammals. Heavy metal contamination is commonly associated with exposure to mercury, lead, arsenic, and cadmium (arsenic is a metalloid, but classified as a heavy metal). Traditional methods to detect heavy metal ions include graphite furnace atomic absorption spectroscopy (GFAAS), inductively-coupled plasma optical emission spectroscopy (ICP-OES), and inductively-coupled plasma mass spectroscopy (ICP-MS). Recently, many new methods have been proposed to detect heavy metal ions, including atomic absorption spectrometry, fluorescent sensors, colorimetric sensors, electrochemical sensors, X-ray absorption fine structure spectroscopy, ultrasensitive dynamic light scatting assays, and ion selective electrodes. Although significant progress has been made, there are still some critical issues to be addressed, e.g., lack of portability, the need for well-trained personnel, highly expensive and complex instruments, long response time (tens of minutes or even longer), and the possibility of introducing additional contamination. Therefore, it is highly desirable to develop a real-time, low-cost, portable, user-friendly analytical platform for rapid inline analysis of mercury, lead and other heavy metal ions.

This dissertation research aims to investigate field-effect transistor (FET) sensors based on two-dimensional (2D) nanomaterials with specific probe-functionalized gold (Au) nanoparticles (NPs). The fundamental mechanism of the FET platform is to use a 2D nanomaterial as the conducting channel to transport charge carriers (electrons or holes). Upon the capture of target analytes, the charge carrier concentration and/or mobility changes correspondingly with a signal of current change within the channel. As a result, the FET characteristic changes upon the introduction of the heavy metal ion solution, varies with the metal concentration, and takes only a few seconds to respond. Control experiments are performed to verify the selectivity of the 2D nanomaterial/Au NP hybrid sensor to specific targets. The rapid, selective, sensitive, and stable detection performance indicates the promise of 2D nanomaterial/Au NP hybrid sensors for heavy metal ion detection in an aqueous solution.

This research is accomplished through several steps: First, various heavy metal ion contaminants, their damage, and the conventional detection methods are reviewed; Second, the FET-based plaform and its working mechanism are explored; Third, the understanding of various 2D nanomaterials, their unique properties pertinent to electronic sensing, and their selection to realize real-time, selective, and sensitive detection of heavy metal ions is carried out; Finally, improvement of stability, sensitivity and lifetime of FET sensors is investigated.

In this thesis work, sensitive and selective FET-based 2D nanomaterial/Au NP hybrid sensors for Pb2+, Hg2+, As(III), and As(V) have been demonstrated. The 2D nanomaterials include reduced graphene oxide (rGO), molybdenum disulfide (MoS2), and black phosphorus (BP). The hybrid structure consists of a nanomaterial film, homogeneously dispersed Au NPs, and specific probes. The detection is enabled by recording the electrical conductance of the device through monitoring the change in the drain current of the 2D nanomaterial sheets. The platform offers a promising route for real-time (1-2 seconds), high-performance and low-cost detection of heavy metal ions. The lower detection limit can reach the order of µg/L (parts-per-billion or ppb). The sensor also shows high selectivity against other co-existing metal ions.

To improve the sensitivity of the nanomaterial-based electronic sensor, theoretical analysis on the sensing mechanism has been carried out, together with experimental validation. Theoretical analysis indicates that sensitivity-related factors are semiconducting properties of nanomaterials (e.g., carrier mobility, band gap), number of probes, and adsorption capacity of Au NPs. Experimental results suggest that a higher sensitivity for sensors can be realized by forming hybrid structures with thinner 2D conducting materials with a larger band gap and a higher carrier mobility, increasing the areal density of anchoring sites on the sensor surface, and enhancing the adsorption of detection probes. Investigation into the stability of the nanomaterial-based electronic sensor includes the binding strength between the nanomaterial and electrodes, stability of the nanomaterials in ambient environment and water, the detachment of Au NPs, the lifetime and diffusion of probes, and the overall stability of the sensor platform. Subsequently, strategies to improve the stability of the nanomaterial-based FET sensor have been proposed. Finally, the FET sensor has been used for the accurate prediction of arsenic ions in lake water and integrated into a practical flowing water system for continuous detection of lead ions.

The rapid, selective, sensitive, and stable detection performance of the FET sensor for various heavy metal ions in water suggests a promising future for in-situ detection of contamination events. The thesis study provides a scientific foundation to engineer FET sensors with enhanced performance. An attempt has been made to practically develop the FET platform into standalone sensors and to integrate the sensor into flowing water equipment for heavy metal ion detection. The thesis results thus contribute to the future application of FET sensors for monitoring water contamination and mitigating the public health risk.