2026-04-20T03:10:20Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/105242025-06-13T11:09:54Zcom_10259_6168com_10259_5086com_10259_2604col_10259_6169

In silico exploration of graphene nanoflakes: From DFT simulations to machine learning-driven toxicity predictions Aguilar Cuesta, Nuria Fuente Gamero, Patricia de la Fernández Pampín, Natalia Martel Martín, Sonia Gómez Cuadrado, Laura Marcos Villa, Pedro A. Bol Arreba, Alfredo Rumbo Lorenzo, Carlos Aparicio Martínez, Santiago Graphene nanoflakes Density Functional Theory (DFT) In silico toxicity Machine learning Nano-bio interactions The present theoretical work provides a ground-breaking and comprehensive study of graphene nanoflakes integrating Density Functional Theory (DFT) simulations, toxicity predictions and a machine learning approach. The properties of graphene nanoflakes as a function of size, shape, and symmetry are systematically analysed using DFT calculations. The interaction of these nanoflakes with human proteins and cell membranes, considered as Molecular Initiating Events for diverse Adverse Outcome Pathways, is explored to infer potential toxicity effects. Leveraging the generated data, machine learning models were developed to predict flake properties and biological interactions. A single score representing the biological interaction or impact of graphene nanoflakes on both proteins and plasma membranes is assigned to each evaluated nanoflake to infer its potential toxicity. Our multiscale approach bring valuable insights into the structure-property-toxicity relationships of graphene nanoflakes, paving the way for their safe and efficient design and application. 2025-06-09T07:42:40Z 2025-06-09T07:42:40Z 2025-06-09T07:42:40Z 2025-04 info:eu-repo/semantics/article 2452-0748 https://hdl.handle.net/10259/10524 10.1016/j.impact.2025.100563 eng NanoImpact. 2025, V. 38, 100563 https://doi.org/10.1016/j.impact.2025.100563 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Elsevier