Multivariate Model Based on UV-Vis Spectroscopy and Regression in Partial Least Squares for Determination of Diameter and Polydispersity of Silver Nanoparticles in Colloidal Suspensions

Abstract

In the universe of nanomaterials, silver nanoparticles (AgNPs) have attracted the attention of researchers because of their optical, catalytic, antimicrobial, fungicidal, and bactericidal properties. Recently, studies have correlated the toxicity and efficacy of antimicrobial activity with surface-volume ratio, morphology, polydispersity, ligand types, particle size, and stability of AgNPs. Soon, the need for characterization of properties such as diameter and polydispersity is clear. The methodologies conventionally used for characterization of AgNPs, although accurate, are generally expensive and laborious and can degrade the sample. Thus, the development of methodologies based on UV-Vis spectroscopy and chemometric techniques appears as an alternative for the characterization of diameter and polydispersity of the nanoparticles. For the development of the methodology in question, 50 samples were synthesized, varying the type, volume, and concentration of the reagents in order to increase the diameter and polydispersity values. All samples were analyzed by DLS and UV-Vis spectroscopy. For the construction of multivariate calibration models, the calibration and validation sets were selected using the SPXY algorithm, and their predictive capacity was evaluated based on the method figures. The model that presented the best predictive capacity was the one built with the pretreated spectra with the 1st derivative with a 15-point window and 2nd-order polynomial, providing prediction errors of 5.31% and 4.43% for diameter and polydispersity, respectively.