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

Abstract

Nowadays, technologies involving nanoparticles, colloids, sensors, and artificial intelligence are widespread in society, media, and industry. It is thus mandatory to integrate them into the curricula of students enrolled in chemistry and materials science. To this purpose, we designed a simple assay for the detection of glutathione (GSH) using surface-clean gold nanoparticles (Au NPs). The alteration of the electric double layer of the Au NPs with increasing GSH concentration causes the particles to aggregate, producing a measurable change in color. This behavior, which is widely exploited for optical sensing, has been introduced in an undergraduate course to familiarize the students with the concepts of nanoparticles, colloids, colloidal stability, and sensor features (selectivity, sensitivity, detection range). Nonetheless, there are no analytical models to quantitatively relate the absorption of Au NP colorimetric sensors to analyte concentration, which is the ideal condition for resorting to machine learning (ML). Hence, an artificial neural network was instructed in a students' collective data-sharing experiment about machine learning. Overall, the laboratory experience is safe and highly tailorable to students' background, course duration, available instruments, and teacher's didactic objectives. For instance, it can be lifted to the Master's or Ph.D. level by improving the spectroscopic and ML contents or shifted toward the industrial ground by focusing on the nanoparticle synthesis. We propose the integration of this laboratory experience in the undergraduate and Master's academic programs to stimulate the students with a collection of hot topics that at the same time can consolidate their preparation on arguments of great relevance for their professional life.