CO2adsorption by nitrogen-doped carbon nanotubes predicted by density-functional theory with dispersion-correcting potentials

Literature Information

Publication Date 2010-12-14
DOI 10.1039/C0CP01537G
Impact Factor 3.676
Authors

Iain D. Mackie, Gino A. DiLabio


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Abstract

The interaction of CO2 to the interior and exterior walls of pristine and nitrogen-doped single-walled carbon nanotubes (SWNT) has been studied using density-functional theory with dispersion-correcting potentials (DCPs). Our calculations predict Gibbs energies of binding between SWNT and CO2 of up to 9.1 kcal mol−1, with strongest binding observed for a zigzag [10,0] nanotube, compared to armchair [6,6] (8.3 kcal mol−1) and chiral [8,4] (7.0 kcal mol−1). Doping of the [10,0] tube with nitrogen increases the Gibbs energies of binding of CO2 by ca. 3 kcal mol−1, but slightly reduced binding is found when [6,6] and [8,4] SWNT are doped in similar fashion. The Gibbs energy of binding of CO2 to the exterior of the tubes is quite small compared to the binding that occurs inside the tubes. These findings suggest that the zigzag SWNT show greater promise as a means of CO2 gas-capture.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
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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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