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Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO₂ with periodic table descriptors using machine learning approaches

Joyita Roy, Souvik Pore and Kunal Roy

Beilstein J. Nanotechnol. 2023, 14, 939–950. https://doi.org/10.3762/bjnano.14.77

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Supporting Information File 1: Detailed information regarding heavy metals at different concentrations.
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Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO₂ with periodic table descriptors using machine learning approaches

Joyita Roy, Souvik Pore and Kunal Roy

Beilstein J. Nanotechnol. 2023, 14, 939–950. https://doi.org/10.3762/bjnano.14.77

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Roy, J.; Pore, S.; Roy, K. Beilstein J. Nanotechnol. 2023, 14, 939–950. doi:10.3762/bjnano.14.77

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aromatic	the word “aromatic”
aromatic aldehyde	the word “aromatic” OR “aldehyde”
+aromatic +aldehyde	both words “aromatic” AND “aldehyde”
+aromatic -aldehyde	the word “aromatic” but NOT “aldehyde”
“aromatic aldehyde”	the exact phrase “aromatic aldehyde”
benz*	words which begin with “benz”, such as “benzene” or “benzyl”
benz*yl	words that begin with “benz” and end with “yl”, such as “benzyl” or “benzoyl”
benzyl~	words that are close to the word “benzyl”, such as “benzoyl” (i.e., fuzzy search)

Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches

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Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO₂ with periodic table descriptors using machine learning approaches