Scalable graphene sensor array for real-time toxins monitoring in flowing water

Arnab Maity, Haihui Pu, Xiaoyu Sui, Jingbo Chang, Kai J. Bottum, Bing Jin, Guihua Zhou, Yale Wang, Ganhua Lu and Junhong Chen ()
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Arnab Maity: University of Wisconsin-Milwaukee
Haihui Pu: University of Wisconsin-Milwaukee
Xiaoyu Sui: University of Wisconsin-Milwaukee
Jingbo Chang: University of Wisconsin-Milwaukee
Kai J. Bottum: University of Wisconsin-Milwaukee
Bing Jin: University of Wisconsin-Milwaukee
Guihua Zhou: University of Wisconsin-Milwaukee
Yale Wang: University of Wisconsin-Milwaukee
Ganhua Lu: University of Wisconsin-Milwaukee
Junhong Chen: University of Wisconsin-Milwaukee

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Risk management for drinking water often requires continuous monitoring of various toxins in flowing water. While they can be readily integrated with existing water infrastructure, two-dimensional (2D) electronic sensors often suffer from device-to-device variations due to the lack of an effective strategy for identifying faulty devices from preselected uniform devices based on electronic properties alone, resulting in sensor inaccuracy and thus slowing down their real-world applications. Here, we report the combination of wet transfer, impedance and noise measurements, and machine learning to facilitate the scalable nanofabrication of graphene-based field-effect transistor (GFET) sensor arrays and the efficient identification of faulty devices. Our sensors were able to perform real-time detection of heavy-metal ions (lead and mercury) and E. coli bacteria simultaneously in flowing tap water. This study offers a reliable quality control protocol to increase the potential of electronic sensors for monitoring pollutants in flowing water.

Date: 2023
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DOI: 10.1038/s41467-023-39701-0

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