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.

纳米技术风险评估领域的主要缺陷之一是缺乏能够追踪纳米颗粒（NPs）来源的技术。二氧化硅是最生产的工程纳米材料，并且也以多种形式广泛存在于自然环境中。在这里，我们表明固有的同位素指纹图谱提供了一种区分二氧化硅纳米颗粒（SiO ₂ NPs）来源的可行方法。我们发现，工程化的SiO ₂纳米颗粒具有不同的的SiO二维（2D）同位素指纹从天然存在的SiO ₂纳米粒子，由于可能的Si和O的同位素分馏和期间的工程化的制造过程中使用的同位素不同的材料的SiO ₂NP。开发了机器学习模型以对工程和天然SiO ₂ NP进行分类，辨别精度为93.3％。此外，Si-O同位素指纹甚至能够部分鉴定合成的方法和工程SiO ₂ NP的制造商。