GOPY is a free and open-source Python tool specifically written to automate the generation of 2D graphene-based molecular models such as pristine graphene (PG) and several graphene derivatives i.e. graphene oxide (GO), reduced graphene oxide (rGO), aminated polyethylene glycol functionalised reduced graphene oxide (rGO-PEG-NH2), and N-doped graphene (NG) in the Protein Data Bank file format (PDB). These models are generally built manually, but the process can become lengthy and cumbersome. That is especially the case when investigating larger molecules such as those used in Molecular Dynamics (MD) simulations. Using GOPY significantly speeds up the process from hours to minutes, reducing potential bias that may come with the manual placement of functional groups on a graphene layer. Moreover, the building procedure becomes effortless for the researcher, granting the possibility of producing larger and more complex molecular models than one would be able to build manually. Of its more intensive tasks, the generation of a 4 x 4 nm2 rGO-PEG-NH2 layer takes about 9 min on a CodeOcean capsule. Each model is generated in the PDB format, which is easily convertible to a wide array of other molecular formats.

GOPY: A tool for building 2D graphene-based computational models

Burns J. S.
Secondo
Funding Acquisition
;
2020

Abstract

GOPY is a free and open-source Python tool specifically written to automate the generation of 2D graphene-based molecular models such as pristine graphene (PG) and several graphene derivatives i.e. graphene oxide (GO), reduced graphene oxide (rGO), aminated polyethylene glycol functionalised reduced graphene oxide (rGO-PEG-NH2), and N-doped graphene (NG) in the Protein Data Bank file format (PDB). These models are generally built manually, but the process can become lengthy and cumbersome. That is especially the case when investigating larger molecules such as those used in Molecular Dynamics (MD) simulations. Using GOPY significantly speeds up the process from hours to minutes, reducing potential bias that may come with the manual placement of functional groups on a graphene layer. Moreover, the building procedure becomes effortless for the researcher, granting the possibility of producing larger and more complex molecular models than one would be able to build manually. Of its more intensive tasks, the generation of a 4 x 4 nm2 rGO-PEG-NH2 layer takes about 9 min on a CodeOcean capsule. Each model is generated in the PDB format, which is easily convertible to a wide array of other molecular formats.
2020
Muraru, S.; Burns, J. S.; Ionita, M.
File in questo prodotto:
File Dimensione Formato  
Muraru et al 2020a.pdf

accesso aperto

Descrizione: Full text editoriale
Tipologia: Full text (versione editoriale)
Licenza: Creative commons
Dimensione 3.02 MB
Formato Adobe PDF
3.02 MB Adobe PDF Visualizza/Apri
Muraru et al 2020 738.pdf

accesso aperto

Descrizione: Informazione Supplementare
Tipologia: Altro materiale allegato
Licenza: Creative commons
Dimensione 6.27 MB
Formato Adobe PDF
6.27 MB Adobe PDF Visualizza/Apri
Muraru et al 2020 290.pdf

solo gestori archivio

Descrizione: Informazione Supplementare
Tipologia: Altro materiale allegato
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 5.97 MB
Formato Adobe PDF
5.97 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2438727
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 11
social impact