TY  - JOUR 
AU  - Aguilar Cuesta, Nuria
AU  - Fuente Gamero, Patricia de la
AU  - Fernández Pampín, Natalia
AU  - Martel Martín, Sonia
AU  - Gómez Cuadrado, Laura
AU  - Marcos Villa, Pedro A.
AU  - Bol Arreba, Alfredo
AU  - Rumbo Lorenzo, Carlos
AU  - Aparicio Martínez, Santiago
PY  - 2025
SN  - 2452-0748
UR  - https://hdl.handle.net/10259/10524
AB  - 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...
LA  - eng
PB  - Elsevier
KW  - Graphene nanoflakes
KW  - Density Functional Theory (DFT)
KW  - In silico toxicity
KW  - Machine learning
KW  - Nano-bio interactions
KW  - Química
KW  - Chemistry
KW  - Química física
KW  - Chemistry, Physical and theoretical
TI  - In silico exploration of graphene nanoflakes: From DFT simulations to machine learning-driven toxicity predictions
DO  - 10.1016/j.impact.2025.100563
T2  - NanoImpact
VL  - 38
M2  - 100563
ER  -