Distinguishing the sources of silica nanoparticles by dual isotopic fingerprinting and machine learning.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 30962437.
- Also identified by DOI 10.1038/s41467-019-09629-5 and PMC identifier 6453897.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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 (SiO<sub>2</sub> NPs). We find that engineered SiO<sub>2</sub> NPs have distinct Si-O two-dimensional (2D) isotopic fingerprints from naturally occurring SiO<sub>2</sub> NPs, due probably to the Si and O isotope fractionation and use of isotopically different materials during the manufacturing process of engineered SiO<sub>2</sub> NPs. A machine learning model is developed to classify the engineered and natural SiO<sub>2</sub> 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 SiO<sub>2</sub> NPs.