Simulações atomísticas e previsão de espectros de RMN em materiais carbonosos

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Ambrozio, Alan Johnny Romanel
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Espírito Santo
BR
Doutorado em Física
Centro de Ciências Exatas
UFES
Programa de Pós-Graduação em Física
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
RMN
53
Link de acesso: http://repositorio.ufes.br/handle/10/10382
Resumo: In this work, the 13C NMR spectral parameters of both ordered and disordered carbon materials were calculated using computational simulations. The 13C NMR shielding in a single graphene sheet was calculated using density functional theory (DFT) via the gauge-including projector augmented plane wave (GIPAW) method. After performing convergence tests involving changes of k-sampling and supercell size, the calculations were extended to graphene-based systems, including graphene bilayer and stacked graphene sheets, finally leading to hexagonal graphite. Regarding the disordered carbon materials, the 13C NMR chemical shifts corresponding to different sites in atomistic models of amorphous hydrogenated carbons were also computed at different H contents by employing molecular dynamics and firstprinciples methods. The models were validated by the pair distribution functions and further bonding analyses were carried out to determine the amounts of sp3 and sp2 carbons in the structures. Specifically, the obtained results allowed the distinction of the chemical shifts associated with different types of carbon sites, with different hybridization states and bonded or not to a hydrogen atom. The calculated results showed good agreement with experimental 13C NMR spectra of different types of carbon materials, evidencing the power of the DFT calculations to predict NMR parameters in graphene-based nanocarbons and to identify local structural features of disordered carbon materials.