Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany.
Nanoscale. 2021 Sep 17;13(35):14666-14678. doi: 10.1039/d1nr03231c.
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 TiO NPs library, using the soil invertebrate (Oligochaeta), a 5-day pulse aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO, realistic conditions should include UV exposure. The 11 Fe-TiO library contains NPs of size range between 5-27 nm with varying %Fe (enabling the photoactivation of TiO 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. TiO 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.
在各种环境条件下评估纳米材料/纳米颗粒（NMs/NPs）的风险需要更系统的方法，包括比较具有不同但相关特征/描述符的许多 NM 的影响。因此，迫切需要提供具有广泛适用性的综合（生态）毒理学数据集，其中包含与各种 NPs 特征相关的全面毒性信息。这些数据集是开发具有更广泛适用性的整体方法的测试平台。在本研究中，我们使用土壤无脊椎动物（寡毛纲）评估了定制设计的 Fe 掺杂 TiO NPs 库的影响，采用了为期 5 天的脉冲水暴露，随后在土壤中进行了 21 天的恢复期（生存、繁殖评估）。显然，在测试 TiO 时，实际条件应包括 UV 暴露。该 11 个 Fe-TiO 库包含尺寸在 5-27nm 之间的 NPs，具有不同的%Fe（使 TiO 在可见光范围内的能量波长下发生光激活）。每个 NPs 都用 122 个描述符来描述，其中包括测量和原子模型描述符的混合物。数据通过单变量和多变量统计方法进行了探索，结合机器学习和多尺度建模技术。采用迭代修剪过程自动识别最重要的描述符。当与 UV 结合时，TiO NPs 的毒性降低。值得注意的是，即使在清洁暴露下进行更长时间的恢复，短期水暴露也会引起持久的生物学反应。与 Fe 含量的相关性与能带隙相关，从而降低了 UV 氧化应激。当与机器学习结合使用时，包含测量和建模材料数据有助于解释结果。
