In view of the growing importance of nanotechnologies, the detection of nanoparticles type in several contexts has been considered a relevant topic. Several organisms, including the National Institutes of Health, have been highlighting the urge of developing nanoparticles exposure risk assessment assays, since very little is known about their physiological responses. Although the identification/characterization of synthetically produced nanoparticles is considered a priority, there are many examples of “naturally” generated nanostructures that provide useful information about food components or human physiology. In fact, several nanoscale extracellular vesicles are present in physiological fluids with high potential as cancer biomarkers. However, scientists have struggled to find a simple and rapid method to accurately detect/identify nanoparticles, since their majority have diameters between 100-150 nm - far below the diffraction limit. Currently, there is a lack of instruments for nanoparticles detection and the few instrumentation that is commonly used is costly, bulky, complex and time consuming. Thus, considering our recent studies on particles identification through back-scattering, we examined if the time/frequency-domain features of the back-scattered signal provided from a 100 nm polystyrene nanoparticles suspension are able to detect their presence only by dipping a polymeric lensed optical fiber in the solution. This novel technique allowed the detection of synthetic nanoparticles in distilled water versus “blank solutions” (only distilled water) through Multivariate Statistics and Artificial Intelligence (AI)-based techniques. While the state-of-the-art methods do not offer affordable and simple approaches for nanoparticles detection, our technique can contribute for the development of a device with innovative characteristics.
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