Vector correlations in the 355 nm photolysis of thermal NO2

Literature Information

Publication Date 2000-01-07
DOI 10.1039/A907955F
Impact Factor 3.676
Authors

Richard P. Baker, Matthew L. Costen, Gus Hancock, Grant A. D. Ritchie, Dean Summerfield


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Abstract

Correlations between the NO2 transition moment μ, the fragment velocities ν and the NO rotational angular momentum J have been made for four NO(v=0, J) states which span the most populated rotational levels formed by photolysis at 355 nm of a sample of NO2 at 298 K. Correlations involving μ are seen to be decreased in comparison with those at lower temperatures, and in particular the translational anisotropy factor β is reduced by a factor of ca. 2. A speed independent value of β=0.7 is shown to describe the fast O(3P) atoms formed under these conditions and can be used in the analysis of the Doppler profiles measured in studies of their reactions with H2S by means of the ‘‘photoloc ’’ technique. Measured rotational distributions in v=0 show some deviations from phase space theory, particularly at high J.

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Back cover

Front/Back Matter

DOI: 10.1039/C1CP90039K

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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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