Nanoparticles (NPs) in the environment can occur naturally or originate from engineered nanomaterials released by human activities. Distinguishing the sources of NPs is of extreme importance for nanotechnology risk assessment. However, it is still a very intractable problem that is extremely difficult to be solved by the conventional characterization approaches, e.g., by morphology, crystalline structure, or chemical composition. 

Stable isotopic fingerprints (or signatures) of elements in samples contain valuable information on sources and processes, which can reflect the history of the samples. For nanotechnology, stable isotopic tracing was also expected to be a valuable tool. Unfortunately, up to now, the use of stable isotopic fingerprints in source tracing of NPs has not succeeded. Two previous studies investigated the Zn or Ce isotopic compositions of ZnO or CeO₂ NPs, respectively, but they did not find distinct differences between natural and anthropogenic materials.  

In this study, we report that it is possible to distinguish the sources of SiO₂ NPs by their intrinsic dual isotopic fingerprints (Si and O). The sources and morphologies of silica in natural environment are rather complex (Fig 1), and, until now, there are no effective methods to distinguish them. Firstly, we collect SiO₂ NPs from a variety of sources. For natural SiO₂ NPs, we select two major forms of silica, quartz (NQ) and diatomite (ND), representing geologically and biologically originating silica. For engineered SiO₂ NPs, we collect SiO₂ NP samples synthesized by three dominating methods used in the industrial production, i.e., fumed silica (EF), precipitated silica (EP), and sol-gel silica (ES).

Fig. 1 Morphology of different sources of silica particles. (a) Engineered silica standard, (b-c) industrial white carbon black, (d) natural quartz, (e-f) natural diatomite (intact and fragmented). 

Interestingly, we find that NQ, ND, and engineered SiO₂ NPs (ES + EP + EF) can be fully differentiated into three isolated zones by two straight lines (δ¹⁸O = 13‰ and δ³⁰Si = 0.1‰; Fig. 2a), which reveals the possibility of distinguishing engineered SiO₂ NPs from their naturally occurring counterparts. To make the method more precise and quantitative, we develop a machine learning model to identify the source of a SiO₂ NP sample with linear discriminant analysis (LDA) into five classes (Fig. 2b).

Fig. 2 Si and O isotopic fingerprints of SiO₂ NPs of different origins. a, Si-O 2D isotopic fingerprints of SiO₂ NPs with source differentiation by two straight lines (δ¹⁸O = 13‰ and δ³⁰Si = 0.1‰). b, Si-O 2D isotopic fingerprints of SiO₂ NPs with linear discriminant analysis (LDA) into five classes.

Our results reveal the tremendous potentials of isotopic fingerprinting in source tracing of NPs, which actually breaks through the previous knowledge on stable isotopic tracing of NPs. It should be stressed that this technique is based on the inherent isotopic fingerprints of NPs and thus should be suitable for application in complex systems (e.g., natural environment, biological, and industrial systems). In our opinion, this technique has the potential to emerge as a common tool for source distinguishing of NPs (e.g., TiO₂, Fe_xO_y, ZrO₂, quantum dots).

Publication link: https://doi.org/10.1038/s41467-019-09629-5