Magnetic and binding properties of Co-doped single-walled carbon nanotubes: a first principles study
Source abstract
Abstract
An attempt has been made to analyze the magnetic-spin quenching property of Co, as a representative of transition metals, in Co-doped single-walled carbon nanotubes (SWCNTs) as well as the binding property of CO with the side walls of the Co-doped SWCNTs by means of hybrid density functional theory (DFT) calculations. Four different types of SWCNTs are considered: semi-conducting (5,0) zigzag, metallic (5,2) and semi conducting (5,3) chirals, and metallic (5,5) armchair. The results show that while the spin states of Co in the whole of the present Co-doped SWCNTs were preserved, the combined effects of adsorbate (CO) and substrate (Co-doped SWCNT) were strong enough to favor the low-spin states, and to quench the spins in the Co-doped SWCNTs (5,0) and (5,2). The doped Co atom converts the endothermic reactions of CO molecules on the outer surfaces of the pure SWCNTs into exothermic reactions. The nature of charge transfer between the d-orbitals of Co, and the π* orbital of the nearby C of CO is clarified. Natural bond orbital (NBO) analysis reveals that the electronic configuration of the doped Co metal represents a qualitative change with respect to that of the free-metal. The binding of CO precursor is mostly dominated by the metal E(i)Co..CO pairwise additive contributions, and the role of the SWCNTs is not restricted to supporting the metal. The spin quenched SWCNTs are characterized in terms of isodensity contours of frontier orbitals. Molecular electrostatic potentials (MEPs) indicate that SWCNTs can act as effective gas sensors for nucleophiles. The results show that Co-doped SWCNTs can be useful in spintronics applications and sensor technology.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Carbon Nanotubes in Composites · Graphene research and applications · Boron and Carbon Nanomaterials Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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