New descriptors in toxicology prediction of nanomaterials: Using quasi-ab initio MD simulations for the estimation of aqueous ZnO and TiO2 surface structure parameters

DOI

This dataset houses a research poster, its poster abstract, and its award certificate. The set of documents was first presented at The Virtual 10th International Conference on Nanotoxicology (NanoTox2021) poster presentation, 20th - 22nd April 2021.

Poster Title: "New descriptors in toxicology prediction of nanomaterials: Using quasi-ab initio MD simulations for the estimation of aqueous ZnO and TiO2 surface structure parameters.”

Our research focuses on understanding the toxicity of nanomaterials, highlighting the need for in-silico methods due to their diverse structures and compositions. We investigate the interactions and surface parameters of ZnO and TiO2 nanoparticles with water using Molecular Dynamics simulations at Density Functional – Tight Binding level methods. By incorporating new structural parameters, we aim to contribute toxicology prediction models and improve safety assessments of nanomaterials.

The poster selected and awarded with attendees’ bursary, which is given to 49 attendees over 384 registered attendees, and one of the "Best Student Poster - Highly Commended" prize among 117 poster presentations.

MatLab, 2023b

DFTB+, 20.2.1

Identifier
DOI https://doi.org/10.34810/data1234
Metadata Access https://dataverse.csuc.cat/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34810/data1234
Provenance
Creator Çetin, Yarkın Aybars ORCID logo; Escorihuela, Laura ORCID logo; Martorell Masip, Benjamí ORCID logo; Serratosa, Francesc ORCID logo
Publisher CORA.Repositori de Dades de Recerca
Contributor Çetin, Yarkın Aybars; Martorell Masip, Benjamí; Universitat Rovira i Virgili
Publication Year 2024
Funding Reference European Commission (EC) H2020-NMBP-14-2018-814426
Rights CC BY-NC 4.0; info:eu-repo/semantics/embargoedAcces; http://creativecommons.org/licenses/by-nc/4.0
OpenAccess true
Contact Çetin, Yarkın Aybars (Universitat Rovira i Virgili); Martorell Masip, Benjamí (Universitat Rovira i Virgili)
Representation
Resource Type Experimental data; Dataset
Format application/pdf; text/plain
Size 76715; 5445111; 5644; 136343
Version 2.0
Discipline Chemistry; Natural Sciences