4.8 Article

Machine learning and materials modelling interpretation of in vivo toxicological response to TiO2 nanoparticles library (UV and non-UV exposure)

Journal

NANOSCALE
Volume 13, Issue 35, Pages 14666-14678

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/d1nr03231c

Keywords

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Funding

  1. European Commission [814426, 760928, 814530]
  2. FEDER through COMPETE Programa Operacional Factores de Competitividade (2020)
  3. Portuguese Science Foundation (FCT-FundacAo para a Ciencia e Tecnologia) [EXPL/AAG-MAA/0180/2013, PTDC/AAG-MAA/4084/2014]
  4. CESAM through FCT/MEC [UIDP/50017/2020 + UIDB/50017/2020]
  5. FEDER within the PT2020Partnership Agreement
  6. national funds (OE), through FCT
  7. Compete 2020
  8. Fundação para a Ciência e a Tecnologia [PTDC/AAG-MAA/4084/2014, EXPL/AAG-MAA/0180/2013] Funding Source: FCT

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The study aimed to evaluate the effects of Fe-doped TiO2 NPs on soil invertebrates and investigate the influence of different NPs characteristics on their effects. The findings revealed that toxicity of TiO2 NPs decreased under UV exposure conditions, and short-term water exposure induced lasting biological responses even after longer-term recovery.
Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability. In the present study we assessed the effects of a custom design Fe-doped TiO2 NPs library, using the soil invertebrate Enchytraeus crypticus (Oligochaeta), via a 5-day pulse via aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO2, realistic conditions should include UV exposure. The 11 Fe-TiO2 library contains NPs of size range between 5-27 nm with varying %Fe (enabling the photoactivation of TiO2 at energy wavelengths in the visible-light range). The NPs were each described by 122 descriptors, being a mixture of measured and atomistic model descriptors. The data were explored using single and univariate statistical methods, combined with machine learning and multiscale modelling techniques. An iterative pruning process was adopted for identifying automatically the most significant descriptors. TiO2 NPs toxicity decreased when combined with UV. Notably, the short-term water exposure induced lasting biological responses even after longer-term recovery in clean exposure. The correspondence with Fe-content correlated with the band-gap hence the reduction of UV oxidative stress. The inclusion of both measured and modelled materials data benefitted the explanation of the results, when combined with machine learning.

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