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Chemosphere|August 12, 2022
Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicitySurendra Balraadjsing, Willie J G M Peijnenburg, Martina G VijverEnvironment International|May 24, 2024
Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset sizeSurendra Balraadjsing, Willie J G M Peijnenburg, Martina G VijverNanoimpact|February 15, 2026
Generating allometric scaling relationships for aquatic species and metallic nanomaterials using nano-QSARsSurendra Balraadjsing, Willie J G M Peijnenburg, Martina G VijverNanoimpact|February 9, 2025
Predicting the dissolution of metal-based nanoparticles by means of QSPRs and the effect of data augmentationYuchao Song, Surendra Balraadjsing, Willie J G M Peijnenburg, et al.Environmental Science & Technology|February 2, 2023
Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic OrganismsYunchi Zhou, Ying Wang, Willie Peijnenburg, et al.Environmental Toxicology and Chemistry|March 14, 2026
Harmonizing ecotoxicological data reporting: A guide for toxicologists and chemistsSurendra Balraadjsing, Philipp Kropf, Annetrude Boeije, et al.Environmental Science & Technology|August 7, 2024
Application of Machine Learning in Nanotoxicology: A Critical Review and PerspectiveYunchi Zhou, Ying Wang, Willie Peijnenburg, et al.Pageof 1