Ultra-Compact and Rugged Electrochemical Sensor for Monitoring Toxic Metals in Natural Waters Sources
CAIT project no.: CAIT-UTC-NC41
Fiscal Year: 2015/2016
Rutgers-CAIT Author(s): Mehdi Javanmard, Ph.D., Patrick Szary, Ph.D.
External Author(s): Ali Maher, Ph.D.
Sponsor(s): USDOT-FHWA, Rutgers' CAIT
We propose to develop a rugged in-situ battery-powered electronic sensing platform for continuous monitoring of key toxic compounds in natural water sources with high temporal and spatial density (> 1 measurement per 5 minutes and > 1 measurement per 5 meters) over long periods of time (>48 hours). This continuous enviornmental monitoring approach works by detecting toxic compounds in label-free multiplexed format using an electrochemical sensor, consisting of an ultra-compact graphene sensor capable of label-free detection of the following panel of compounds: lead, Arsenic, Mercury, Dioxin, Chlordane, and DDT. We will also build miniaturized readout instrumentation enabling handheld analysis and recording of the data. The proposed platform will also be GPS enabled, making it the first environmental monitoring tool, to the best of our knowledge, capable of geospatially mapping the panel of compounds.
We will demonstrate detection of Arsenic in purified buffer using nanofabricated reduced graphene oxide electrodes. We will utilize reduced graphene oxide thin films mounted on screen printed electrodes in conjunction with a Gamry potentiostat to detect and quantify the varying concentrations of Arsenic in purified buffer. We aim to achieve a detection limit of part per billion.
Demonstrate detection of Arsenic in “real” environmental samples: Upon fully characterizing the performance, detection limit, dynamic range, and accuracy of the nanoelectronic sensor, we will focus on validating the platform technology in pre-characterized environmental samples provided by the laboratory of Dr. Robert Miskowitz. The samples will be pre-characterized using gold-standard laboratory techniques (chromatography, etc..). Upon completion of aim 2, we will utilize the proposed platform for detection of alternative compounds in the panel of interest.