Do defects in PAHs promote catalytic activity in space? Stone–Wales pyrene as a test case

Literature Information

Publication Date 2020-02-28
DOI 10.1039/C9CP06523G
Impact Factor 3.676
Authors

Dario Campisi


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Abstract

Using density functional theory (DFT), we studied the formation of Stone–Wales defects in pyrene, as a prototype PAH molecule. In addition, we studied the reactivity of the defective and pristine pyrenes toward hydrogenation, a process that can occur in some regions of the interstellar medium. We found that the formation of the defect requires overcoming energies of the order of 8.4 eV, but the defective structure is stable due to the high reverse reaction barrier (approx. 6 eV). We also found that the presence of the defect decreases the sticking barrier for the first hydrogenation and promotes more stable singly and doubly hydrogenated intermediates with respect to that of the pristine pyrene. Finally, our results show that both Stone–Wales pyrene and pristine pyrenes can lead to the formation of H2 through an extraction mechanism involving H atoms attached on distal carbon atoms with energy barriers below 2 eV.

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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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