Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform.
Engineered nanomaterials
Isalos Analytics Platform
Molecular descriptors
Nanoinformatics
Read across
Zeta potential
Journal
NanoImpact
ISSN: 2452-0748
Titre abrégé: NanoImpact
Pays: Netherlands
ID NLM: 101676795
Informations de publication
Date de publication:
04 2021
04 2021
Historique:
received:
30
11
2020
revised:
01
02
2021
accepted:
01
03
2021
entrez:
13
5
2022
pubmed:
14
5
2022
medline:
20
5
2022
Statut:
ppublish
Résumé
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
Identifiants
pubmed: 35559965
pii: S2452-0748(21)00017-3
doi: 10.1016/j.impact.2021.100308
pii:
doi:
Substances chimiques
Metals
0
Oxides
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
100308Informations de copyright
Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.