Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint-Reference-Cited by-同舟云学术

Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint

Published:2024-03-11 Issue:17 Volume:31 Page:25114-25128
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Qi Qi,Wang Zhuang^ORCID

Funder

National Natural Science Foundation of China

Qinglan Project of Jiangsu Province of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11356-024-32723-1.pdf

Reference68 articles.

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3. Banerjee A, Kar S, Pore S, Roy K (2023) Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach. Nanotoxicology 17(1):78–93

4. Batley GE, Kirby JK, McLaughlin MJ (2013) Fate and risks of nanomaterials in aquatic and terrestrial environments. Acc Chem Res 46(3):854–862

5. Book F, Backhaus T (2022) Aquatic ecotoxicity of manufactured silica nanoparticles: a systematic review and meta-analysis. Sci Total Environ 806:150893

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