Distinguishing the sources of silica nanoparticles by dual isotopic fingerprinting and machine learning

One of the key shortcomings in the field of nanotechnology risk assessment is the lack of techniques capable of source tracing of nanoparticles (NPs). Silica is the most-produced engineered nanomaterial and also widely present in the natural environment in diverse forms. Here we show that inherent isotopic fingerprints offer a feasible approach to distinguish the sources of silica nanoparticles (SiO₂ NPs). We find that engineered SiO₂ NPs have distinct Si-O two-dimensional (2D) isotopic fingerprints from naturally occurring SiO₂ NPs, due probably to the Si and O isotope fractionation and use of isotopically different materials during the manufacturing process of engineered SiO₂ NPs. A machine learning model is developed to classify the engineered and natural SiO₂ NPs with a discrimination accuracy of 93.3%. Furthermore, the Si-O isotopic fingerprints are even able to partly identify the synthetic methods and manufacturers of engineered SiO₂ NPs.