In this paper we propose an original approach for the real-time detection of industrial organic pollutants in water. It is based on the monitoring of the time evolution of the electrical impedance of low-cost graphitic nanomembranes. The developed approach exploits the high sensitivity of the impedance of 2D graphene-related materials to the adsorbents. We examined sensitivity of the nanomembranes based on pyrolyzed photoresist, pyrolytic carbon (PyC), and multilayer graphene films. In order to realize a prototype of a sensor capable of monitoring the pollutants in water, the membranes were integrated into an ad hoc printed circuit board. We demonstrated the correlation between the sensitivity of the electric impedance to adsorbents and the structure of the nanomembranes, and revealed that the amorphous PyC, being most homogeneous and adhesive to the SiO2 substrate, is the most promising in terms of integration into industrial pollutants sensors.

Electrical impedance sensing of organic pollutants with ultrathin graphitic membranes

Sibilia S.
Investigation
;
Trezza G.
Investigation
;
Ferrigno L.
Methodology
;
Maffucci A.
Methodology
;
2022-01-01

Abstract

In this paper we propose an original approach for the real-time detection of industrial organic pollutants in water. It is based on the monitoring of the time evolution of the electrical impedance of low-cost graphitic nanomembranes. The developed approach exploits the high sensitivity of the impedance of 2D graphene-related materials to the adsorbents. We examined sensitivity of the nanomembranes based on pyrolyzed photoresist, pyrolytic carbon (PyC), and multilayer graphene films. In order to realize a prototype of a sensor capable of monitoring the pollutants in water, the membranes were integrated into an ad hoc printed circuit board. We demonstrated the correlation between the sensitivity of the electric impedance to adsorbents and the structure of the nanomembranes, and revealed that the amorphous PyC, being most homogeneous and adhesive to the SiO2 substrate, is the most promising in terms of integration into industrial pollutants sensors.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/87603
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