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

Author

Listed:
  • Xuezhi Yang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xian Liu

    (Chinese Academy of Sciences)

  • Aiqian Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Dawei Lu

    (Chinese Academy of Sciences)

  • Gang Li

    (Chinese Academy of Sciences)

  • Qinghua Zhang

    (Chinese Academy of Sciences)

  • Qian Liu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Jianghan University)

  • Guibin Jiang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

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 (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.

Suggested Citation

  • Xuezhi Yang & Xian Liu & Aiqian Zhang & Dawei Lu & Gang Li & Qinghua Zhang & Qian Liu & Guibin Jiang, 2019. "Distinguishing the sources of silica nanoparticles by dual isotopic fingerprinting and machine learning," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09629-5
    DOI: 10.1038/s41467-019-09629-5
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    Cited by:

    1. Evie L. Papadopoulou & Giulia Biffi & Anitha Senthamizhan & Beatriz Martín-García & Riccardo Carzino & Roman Krahne & Athanassia Athanassiou, 2021. "Paper Sensors Based on Fluorescence Changes of Carbon Nanodots for Optical Detection of Nanomaterials," Sustainability, MDPI, vol. 13(21), pages 1-13, October.

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