Multiple dehydrogenation of fluorene cation and neutral fluorene using the statistical molecular fragmentation model

Literature Information

Publication Date 2021-04-08
DOI 10.1039/D0CP06100J
Impact Factor 3.676
Authors

Nguyen-Thi Van-Oanh, Lejin Xu, Yining Luo


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Abstract

The statistical molecular fragmentation (SMF) model was used to analyze the 306 fragmentation channels (containing 611 different species) that result from the fluorene (C13H10+) cation losing up to three hydrogen atoms (neutral radicals and/or a proton). Breakdown curves from such analysis permit one to extract experimentally inaccessible information about the fragmentation of the fluorene cation, such as the locations of the lost hydrogen atoms (or proton), yields of the neutral fragments, electronic states of the residues, and quantification of very low probability channels that would be difficult to detect. Charge localization during the fragmentation pathways was studied to provide a qualitative understanding of the fragmentation process. Breakdown curves for both the fluorene cation and neutral fluorene were compared. The SMF results match the rise and fall of the one hydrogen loss yield experimentally measured by imaging photoelectron-photoion coincidence spectroscopy using a VUV synchrotron.

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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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