Recent developments on gold nanostructures for surface enhanced Raman spectroscopy: Particle shape, substrates and analytical applications. A review

Surface enhanced Raman spectroscopy (SERS) is a powerful technique for sensitive analysis which is attracting great attention in the last decades. In this review, different gold nanostructures that have been exploited for SERS analysis are described, ranging from gold nanospheres to anisotropic and complex-shaped gold nanostructures, in which the presence of high aspect ratio features leads to an increment of the electromagnetic field at the surface of the nanomaterial, resulting in enhanced SERS response. In addition to the shape of the nanostructure, the interparticle nanogaps play a prominent role in the SERS efficiency. In this sense, different approaches such as nanoaggregation and formation of assemblies and ordered structures lead to the creation of the so-called hot spots. SERS measurements may be performed in solution, while usually the nanostructures are deposited building a SERS substrate, which can be created via attachment of chemically prepared gold nanostructures, as well as via top-down physical methods. Among the classical supports for creating the SERS substrates, in the last years there is a trend towards the development of flexible supports based on polymers as well as paper. Finally, some recent applications of gold nanostructures-based SERS substrates within the analytical field are discussed to spotlight the potential of this technique in real-world analytical scenarios.

Paper-based SERS substrate and one-class classifier to monitor thiabendazole residual levels in extracts of mango peels

The assessment of pesticide residue levels demands fast, low cost and easy-to-use procedures which are not found in conventional methods. In this work, SERS substrates based on the deposition of gold nanoparticles (GNPs) on common office paper were prepared using a wax printer. These substrates combined with Data Driven Soft Independent Modelling of Class Analogies (DD-SIMCA), a one-class classifier algorithm, were used for detection of pesticide residues in water extracts of mango peels. Paper-based substrates made sample collection easier compared with conventional SERS methods, since few microliters of the pesticide aqueous extract from fruit peels needed to be deposited onto the substrate. Moreover, one-class classifiers dismiss the need for quantification or calibration curves. Classification of a fruit with residue levels in accordance to regulatory bodies' limits is based on a mathematical threshold. Just as in an authentication problem, all the possibilities for a given analysed fruit are now restricted to agreeing or not agreeing with current regulations. The performance of the one-class model was demonstrated by detecting thiabendazole (TBZ) residues at various mango samples, with all results being confirmed by HPLC-DAD analysis. The final model could distinguish samples with TBZ levels above the ones allowed by the Brazilian Health Regulatory Agency with 94% of selectivity and 92% of sensitivity, even in the presence of other pesticides.