2019
DOI: 10.1016/j.cjph.2019.01.009
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Effects of lithium impurities on electronic and optical properties of graphene nanoflakes: A DFT–TDDFT study

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Cited by 15 publications
(5 citation statements)
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“…Both geometry optimization and excited state calculations of GQDs have been performed with Gaussian16 by using the hybrid functional B3LYP with the basis set 6-31G(d). Past studies show that the model B3LYP/6-31G(d) strikes the best balance between computational cost and accuracy [14,20,[28][29][30] Water has been chosen as the solvent and it has been incorporated in the calculations via polarizable continuum model (PCM) during both geometry optimization and excited state calculations. During the optimization evaluations, we also calculated the vibrational frequencies to ensure the system is truly at the minimum of the potential energy surface.…”
Section: Computational Detailsmentioning
confidence: 99%
“…Both geometry optimization and excited state calculations of GQDs have been performed with Gaussian16 by using the hybrid functional B3LYP with the basis set 6-31G(d). Past studies show that the model B3LYP/6-31G(d) strikes the best balance between computational cost and accuracy [14,20,[28][29][30] Water has been chosen as the solvent and it has been incorporated in the calculations via polarizable continuum model (PCM) during both geometry optimization and excited state calculations. During the optimization evaluations, we also calculated the vibrational frequencies to ensure the system is truly at the minimum of the potential energy surface.…”
Section: Computational Detailsmentioning
confidence: 99%
“…Past studies show that the model B3LYP/6-31G(d) strikes the best balance between computational cost and accuracy. 14 , 20 , 28 30 …”
Section: Computational Detailsmentioning
confidence: 99%
“…Both geometry optimization and excited-state calculations of GQDs have been performed with Gaussian16 by using the hybrid functional B3LYP with the basis set 6-31G­(d). Past studies show that the model B3LYP/6-31G­(d) strikes the best balance between computational cost and accuracy. ,, …”
Section: Computational Detailsmentioning
confidence: 99%
“…Carbon-based materials, silicon-based materials, transition-metal oxides, and lithium-alloyed metals have been progressively used for negative electrodes for LIBs because of their higher specific charges and higher negative electrode potentials. To stabilize the alkali metal anodes, carbon materials such as graphene, polycyclic aromatic hydrocarbons (PAHs), carbon nanotubes, and carbon nanofibers are widely employed as conductive matrices. The high electrical conductivity, good structural stability, tunable surface functionality, and excellent mechanical properties of the carbon materials provide lithium batteries a better performance. The interaction of Li on graphene nanoflakes alters their optical properties as significant changes in the HOMO–LUMO gap occur depending on the position of the Li atom within the nanoflakes . The ionic bonding between the lithium atom and carbon is considered to describe effectively the interaction between Li and carbon in the sp 2 carbon system and the charge transfer controls the interaction of the Li–carbon nanostructure …”
Section: Introductionmentioning
confidence: 99%
“…The interaction of Li on graphene nanoflakes alters their optical properties as significant changes in the HOMO−LUMO gap occur depending on the position of the Li atom within the nanoflakes. 24 The ionic bonding between the lithium atom and carbon is considered to describe effectively the interaction between Li and carbon in the sp 2 carbon system and the charge transfer controls the interaction of the Li−carbon nanostructure. 25 PAHs are aromatic hydrocarbons having two or more fused benzene rings.…”
Section: ■ Introductionmentioning
confidence: 99%