Superfund Research Program

Graphene-based Nanosensor Device for Rapid, Onsite Detection of Dissolved Lead in Tap Water

Project Leader: Ganhua Lu
Grant Number: R41ES028656
Funding Period: Phase I: August 2017 - July 2019

Final Progress Reports

Year:   2019 

There is an increasing public concern about monitoring water quality in the entire drinking water supply system, especially at the point of use, spurred by recent water catastrophes, such as the one in Flint, Michigan that caused severe health issues for thousands of children due to the unsafe level of lead in contaminated drinking water. Current quantitative detection methods for aqueous lead are often laboratory-based and are too expensive and time-consuming, unsuitable for end water users to perform fast and onsite detection.

This project intended to address the need for quantitative, real-time, in situ detection of total dissolved lead ions in tap water by developing a sensitive, specific, fast, portable, and cost-effective prototype handheld device that can be self-administered without any special training.

In this project, the research team investigated the feasibility of a handheld device for real-time, onsite detection of toxic lead in tap water. The device integrates a novel micro-sized sensor chip built upon a graphene-gold nanoparticle sensing platform with a portable digital signal meter for direct readout of testing results.

As part of this project, the research team:

• Carried out experiments to obtain a calibration chart for the sensing performance related to water pH value alone so that the actual free lead ion signal could be isolated from the overall sensor response.
• Computed the quantitative phase diagram and solved sets of proton and mass balance equations to establish the relationship between free lead ion concentration and total soluble lead in water with varying pH and Eh values.
• Studied sensor responses to various major disinfection by-products and found they are negligible compared with those toward lead, suggesting excellent selectivity of their sensors.
• Improved the sensor uniformity and developed a theoretical algorithm to predict the lead concentration, which was beyond the original project objectives.