Achieving High Substitutional Incorporation in Mn-Doped Graphene.
Renan VillarrealZviadi ZarkuaSilvan KretschmerVince HendriksJonas HillenHung Chieh TsaiFelix JungeMatz NissenTanusree SahaSimona AchilliHans C HofsässMichael MartinsGiovanni De NinnoPaolo LacovigSilvano LizzitGiovanni Di SantoLuca PetacciaSteven De FeyterStefan De GendtSteven BremsJoris van de VondelArkady V KrasheninnikovLino M C PereiraPublished in: ACS nano (2024)
Despite its broad potential applications, substitution of carbon by transition metal atoms in graphene has so far been explored only to a limited extent. We report the realization of substitutional Mn doping of graphene to a record high atomic concentration of 0.5%, which was achieved using ultralow-energy ion implantation. By correlating the experimental data with the results of ab initio Born-Oppenheimer molecular dynamics calculations, we infer that direct substitution is the dominant mechanism of impurity incorporation. Thermal annealing in ultrahigh vacuum provides efficient removal of surface contaminants and additional implantation-induced disorder, resulting in Mn-doped graphene that, aside from the substitutional Mn impurities, is essentially as clean and defect-free as the as-grown layer. We further show that the Dirac character of graphene is preserved upon substitutional Mn doping, even in this high concentration regime, making this system ideal for studying the interaction between Dirac conduction electrons and localized magnetic moments. More generally, these results show that ultralow energy ion implantation can be used for controlled functionalization of graphene with substitutional transition-metal atoms, of relevance for a wide range of applications, from magnetism and spintronics to single-atom catalysis.
Keyphrases
- transition metal
- molecular dynamics
- room temperature
- carbon nanotubes
- walled carbon nanotubes
- density functional theory
- metal organic framework
- quantum dots
- oxidative stress
- mass spectrometry
- drinking water
- molecular dynamics simulations
- machine learning
- diabetic rats
- high resolution
- preterm birth
- simultaneous determination