Ab initio molecular dynamics simulations investigating proton transfer in perfluorosulfonic acid functionalized carbon nanotubes

Literature Information

Publication Date 2010-06-16
DOI 10.1039/C0CP00130A
Impact Factor 3.676
Authors

Bradley F. Habenicht, Stephen J. Paddison, Mark E. Tuckerman


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Abstract

Proton dissociation and transfer were examined with ab initio molecular dynamics (AIMD) simulations of carbon nanotubes (CNT) functionalized with perfluorosulfonic acid (–CF2SO3H) groups with 1–3 H2O/SO3H. The CNT systems were constructed both with and without fluorine atoms covalently bound to the walls to elucidate the effects of the presence of a strongly hydrophobic environment, the fluorine, on proton dissociation, hydration, and stabilization. The simulations revealed that the dissociated proton was preferentially stabilized as a hydrated hydronium cation (i.e., Eigen like) in the fluorinated CNTs but as a Zundel (H5O2+) cation in the nonfluorinated CNTs. This feature is attributed to the fluorine atoms forming hydrogen bonds with the water molecules coordinated to the central hydronium ion.

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DOI: 10.1039/C8CP90068J

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
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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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