Pontos Quânticos de Carbono e Nanopartículas de Prata: nanomateriais com potencial aplicação para o sensoriamento de pesticidas

Abstract

Chemical sensing strategies are being developed as a possible alternative to detect pesticides in food samples, in a simple way and low cost. Among the nanomaterials used, the carbon quantum dots (PQCs) are a great option for the construction of a sensor, whose sensing mechanism is based on the Förster Resonance Energy Transfer (FRET). Then, silver nanoparticles (AgNPs) can be used in the quenching of luminescence of the PQCs, which make them excellent coadjuvants in the identification of the analytes. Therefore, the objective of this work is to propose a strategy of sensing using PQCs and AgNPs, in order to identify pesticides of different chemical classes, by means of chemometrics methods. The PQCs were synthesized by the solvothermic method and the AgNPs obtained were stabilized with sodium polyacrylate (PAA) and polyethyleneimine (BPEI). The samples were characterized by UVVis spectrophotometry, fluorescence spectroscopy, TEM, FTIR, Raman spectroscopy and XPS. The samples selected for the sensing showed emission bands close to 420 nm, the region of the AgNPs absorption band. Ag@PAA and Ag@BPEI samples had particle sizes of 2.24 ± 0.49 nm and 17.95 ± 12.59 nm, respectively. After optimization of the experimental conditions and through discriminant analysis, the pesticides propanyl, parathion, dimethoate, chlorpyrifos and pyrimicarb were identified with a confidence level of 95%. Subsequently, the power of discrimination of the strategy was evaluated for three of these pesticides, using samples of rice, carrot, orange and pepper. The discriminating of pesticides was then evaluated using lower concentrations of analytes by applying the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) methods. With this, it was possible to verify the sensitivity of the sensing strategy up to a concentration of 0.025 ppm of pesticide. Therefore, it was possible to develop a PQC-based sensing strategy for the identification of pesticides in food.