Desenvolvimento de substratos sers flexíveis para detecção de poluentes orgânicos

Abstract

The present work consists of the use of flexible SERS (surface-enhanced Raman scattering) substrates based on cellulose and the hydrophobic polymers PTFE (polytetrafluoroethylene) and PDMS (polydimethylsiloxane) for de identification of pesticides. Flexible substrates are promising as they can be produced according to demand, in different sizes and shapes and allow detection on the surface of samples. In the present work, substrates were initially developed with silver nanoparticles (AgNP) reduced by sodium citrate immobilized on quantitative and qualitative filter paper supports, with different pore sizes, treated with KCl and on hydrophobic supports to identify the cationic dye gentian violet (GV) and the anionic dye IR-820. The substrates were characterized by scanning electron microscopy (SEM) and diffuse reflectance in UV-VIS. Hydrophobic substrates, due to their ability to concentrate AgNP, showed the lowest relative standard deviation (RSD) values of SERS intensity using GV dye, followed by cellulose-based substrates. In the other hand, the PTFE substrate showed RSD greater than 25% in identifying IR-820. The pesticides imidacloprid and thiamethoxam, from the neonicotinoid classes, were studied in this work. Theorical calculations were carried out using DFT for free pesticide molecules and those linked to a cluster of 10 silver atoms. The main vibrational modes were assigned to aid in the identification of substances in the SERS experiments. For the SERS study of neonicotinoid pesticides, filter paper substrates treated with KCl and NaOH/chitosan and PTFE with AgNP obtained with reduction by NaBH4 and the D-SERS methodology were used. Samples of pesticides used in deionized water and drinking water. The minimum concentration for identification of imidacloprid by SERS obtained was below the maximum value allowed by legislation for drinking water. Finally, doped drinking water was used for the identification by SERS of isolated pesticides and mixture of pesticides with concentration varying between 1,0×10-4 mol L -1 and 1,0×10-7 mol L-1 through of the SIMCA classification model (flexible and independent modeling by class analogy), achieving 97% efficiency.