Artificial Neural Networks Applied to Colorimetric Nanosensors: An Undergraduate Experience Tailorable from Gold Nanoparticles Synthesis to Optical Spectroscopy and Machine Learning

Nowadays, technologies involving nanoparticles, colloids,sensors, and artificial intelligence are widespread in society, media, andindustry. It is thus mandatory to integrate them into the curricula ofstudents enrolled in chemistry and materials science. To this purpose, wedesigned a simple assay for the detection of glutathione (GSH) usingsurface-clean gold nanoparticles (Au NPs). The alteration of the electricdouble layer of the Au NPs with increasing GSH concentration causes theparticles to aggregate, producing a measurable change in color. Thisbehavior, which is widely exploited for optical sensing, has been introducedin an undergraduate course to familiarize the students with the concepts ofnanoparticles, colloids, colloidal stability, and sensor features (selectivity,sensitivity, detection range). Nonetheless, there are no analytical models toquantitatively relate the absorption of Au NP colorimetric sensors toanalyte concentration, which is the ideal condition for resorting to machinelearning (ML). Hence, an artificial neural network was instructed in a students'collective data-sharing experiment about machinelearning. Overall, the laboratory experience is safe and highly tailorable to students'background, course duration, availableinstruments, and teacher's didactic objectives. For instance, it can be lifted to the Master's or Ph.D. level by improving thespectroscopic and ML contents or shifted toward the industrial ground by focusing on the nanoparticle synthesis. We propose theintegration of this laboratory experience in the undergraduate and Master's academic programs to stimulate the students with acollection of hot topics that at the same time can consolidate their preparation on arguments of great relevance for their professionallife