Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform

Anastasios G. Papadiamantis; Antreas Afantitis; Andreas Tsoumanis; Eugenia Valsami-Jones; Iseult Lynch; Georgia Melagraki

doi:10.1016/j.impact.2021.100308

Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform

Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch^*, Georgia Melagraki

^*Corresponding author for this work

Earth and Environmental Sciences

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Abstract

The physicochemical characterisation data from a library of 69 engineered nanomaterials (ENMs) has been exploited in silico following enrichment with a set of molecular descriptors that can be easily acquired or calculated using atomic periodicity and other fundamental atomic parameters. Based on the extended set of twenty descriptors, a robust and validated nanoinformatics model has been proposed to predict the ENM ζ-potential. The five critical parameters selected as the most significant for the model development included the ENM size and coating as well as three molecular descriptors, metal ionic radius (r_ion), the sum of metal electronegativity divided by the number of oxygen atoms present in a particular metal oxide (Σχ/n_O) and the absolute electronegativity (χ_abs), each of which is thoroughly discussed to interpret their influence on ζ-potential values. The model was developed using the Isalos Analytics Platform and is available to the community as a web service through the Horizon 2020 (H2020) NanoCommons Transnational Access services and the H2020 NanoSoveIT Integrated Approach to Testing and Assessment (IATA).

Original language	English
Article number	100308
Number of pages	12
Journal	NanoImpact
Volume	22
Early online date	18 Mar 2021
DOIs	https://doi.org/10.1016/j.impact.2021.100308
Publication status	Published - Apr 2021

Bibliographical note

Funding Information:
This work was funded by the POST-DOC/0718/0070 project, co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation , the EU H2020 project NanoSolveIT (Grant Agreement No. 814572 ) and the EU H2020 research infrastructure project NanoCommons (grant agreement no. 731032 ).

Publisher Copyright:
© 2021 The Author(s)

Keywords

Engineered nanomaterials
Isalos Analytics Platform
Molecular descriptors
Nanoinformatics
Read across
Zeta potential

ASJC Scopus subject areas

Materials Science (miscellaneous)
Safety, Risk, Reliability and Quality
Safety Research
Public Health, Environmental and Occupational Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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PapadiamantisA2021Computational Final published version, 3.8 MBLicence: Creative Commons: Attribution (CC BY)

Cite this

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title = "Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform",

abstract = "The physicochemical characterisation data from a library of 69 engineered nanomaterials (ENMs) has been exploited in silico following enrichment with a set of molecular descriptors that can be easily acquired or calculated using atomic periodicity and other fundamental atomic parameters. Based on the extended set of twenty descriptors, a robust and validated nanoinformatics model has been proposed to predict the ENM ζ-potential. The five critical parameters selected as the most significant for the model development included the ENM size and coating as well as three molecular descriptors, metal ionic radius (rion), the sum of metal electronegativity divided by the number of oxygen atoms present in a particular metal oxide (Σχ/nO) and the absolute electronegativity (χabs), each of which is thoroughly discussed to interpret their influence on ζ-potential values. The model was developed using the Isalos Analytics Platform and is available to the community as a web service through the Horizon 2020 (H2020) NanoCommons Transnational Access services and the H2020 NanoSoveIT Integrated Approach to Testing and Assessment (IATA).",

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AU - Valsami-Jones, Eugenia

AU - Lynch, Iseult

AU - Melagraki, Georgia

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Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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