Is hexagonal boron nitride always good as a substrate for carbon nanotube-based devices?

Literature Information

Publication Date 2015-01-07
DOI 10.1039/C4CP05478D
Impact Factor 3.676
Authors

Seoung-Hun Kang, Gunn Kim, Young-Kyun Kwon


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Abstract

Hexagonal boron nitride sheets have been noted especially for their enhanced properties as substrates for sp2 carbon-based nanodevices. To evaluate whether such enhanced properties would be retained under various realistic conditions, we investigate the structural and electronic properties of semiconducting carbon nanotubes on perfect and defective hexagonal boron nitride sheets under an external electric field as well as with a metal impurity, using density functional theory. We verify that the use of a perfect hexagonal boron nitride sheet as a substrate indeed improves the device performances of carbon nanotubes, compared with the use of conventional substrates such as SiO2. We further show that even the hexagonal boron nitride with some defects can show better performance as a substrate. Our calculations, on the other hand, also suggest that some defective boron nitride layers with a monovacancy and a nickel impurity could bring about poor device behavior since the imperfections impair electrical conductivity due to residual scattering under an applied electric field.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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