Ternary nanocomposites based on cellulose nanocrystals, conductive materials and electrospun fibers applied in sensors for detection of heavy metals in water

Abstract

The monitoring of water resources destined for consumption is a subject of outmost importance for population and ecosystems life quality maintenance. The degradation of these resources with heavy metals, usually associated with the improper disposal of industrial wastes, is a current issue in Brazil and in the world. The use of strategies arising from nanoscience and nanotechnology can help the development of sensor devices able to provide a rapid and reliable diagnosis of water quality. In this sense, the present thesis reports the development of nanostructured chemical sensors with nanocomposites composed of polyamide 6 (PA6) electrospun fibers, cellulose nanocrystals (CNC) and conductive materials, such as silver nanoparticles (AgNP) and reduced graphene oxide (rGO). The studies revealed the possibility to use CNC as green reagent in the stabilization, reduction and dispersion of conductive species. In this way, CNC were added to AgNP and rGO syntheses, and the resulting hybrid materials were characterized in terms of morphology through scanning and transmission electron microscopy (SEM, FEG-SEM and TEM), and chemical composition using techniques as UV-Vis absorption spectroscopy and FTIR, X-ray diffraction and thermogravimetric analysis (TG). The hybrid materials CNC/AgNP and CNC/rGO were, subsequently, combined with PA6 electrospun fibers. The different possible strategies for combination of these materials were evaluated and the resultant nanocomposites were characterized in terms of morphology by SEM and FEG-SEM, composition by spectroscopy FTIR, interaction between components by differential scanning calorimetry (DSC) and ability of charge transfer by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). These nanocomposites were applied in the fabrication of sensing platforms which, making use of electrical and electrochemical techniques, enabled the detection of heavy metal ions even under low concentrations. The sensing platforms resultant from distinct combinations of CNC, AGNP and PA6 were arranged to compose an impedimetric electronic tongue. Capacitance data were statistically interpreted using multivariate data analysis, whereby it was possible to classify solution containing lead(II), cadmium(II), copper(II), nickel(II) and pure water. The same sensing array enabled distinguishing pure water and lead solutions under concentrations as low as 10 nmol. L-1. The sensor platform based on CNC, rGO and PA6 was applied to the electrochemical detection of Hg(II). With the use of differential pulse voltammetry (DPV), it was possible to detect mercury (II) in the range of 2.5-200 μM. The developed sensor was stable, selective and showed limit of detection of 0.52 μM.