Polyacrylonitrile/Reduced Graphene Oxide Free-Standing Nanofibrous Membranes for Detecting Endocrine Disruptors

Author(s)

Facure, Murilo H. M. ; Mercante, Luiza A. ; Correa, Daniel S.

• Document type: Journal article
• Source: ACS APPLIED NANO MATERIALS; v. 5, n. 5, p. 9-pg., 2022-04-27.

Abstract

Endocrine-disrupting chemicals (EDCs) are emerging pollutants whose uncontrolled release in natural aquatic environments can pose risks to human and animal health. In this scenario, the development of simple, low-cost, and sensitive methods to detect trace amounts of EDCs in the environment is highly sought. In this work, a nanofiber-based sensor was fabricated by functionalizing a polyacrylonitrile (PAN) electrospun nanofibrous membrane with reduced graphene oxide (rGO) to be used as a flexible and free-standing electrode to detect EDCs through electrical impedance measurements. The rGO was obtained from graphene oxide using a hydrothermal route, while the PAN/rGO composite membrane was prepared through vacuum filtration. The rGO provided high electrical conductivity to the membrane, which presented a good performance in the detection of endocrine hormones. The electrical resistance variation enabled the detection of 17 alpha-ethinylestradiol (EE2) in a linear range between 10(-5) and 10(-11) mol L-1. Moreover, by using the principal component analysis (PCA) for data treatment, the sensor was able to discriminate EE2, estrone, estradiol, and progesterone at 100 pmol L-1 in real river water samples. These results open up the possibility to fabricate in a simple way functionalized nanofibrous membranes to be used as sensing material for the detection of varied analytes with high sensitivity and low cost. (AU)

• FAPESP's process: 17/10582-8 - Production and characterization of graphene quantum dots and their application in chemical sensors
• Grantee: Murilo Henrique Moreira Facure
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis
• Grantee: Osvaldo Novais de Oliveira Junior
• Support Opportunities: Research Projects - Thematic Grants