The effect of parity violation on kinetic models of enantioselective autocatalysis

Literature Information

Publication Date 2007-10-11
DOI 10.1039/B711546F
Impact Factor 3.676
Authors

Gábor Lente


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Abstract

A detailed analysis of enantioselective autocatalytic reaction models is carried out using both deterministic and stochastic approaches. The models include the small differences between the total energies of enantiomers (ΔEPV ∼10−13 J mol−1) that arise due to parity violation. Different possible orders of autocatalysis are considered, and the reasons why the predictions of the stochastic and deterministic approaches are different under certain conditions are explored. The continuous time discrete state (CDS) stochastic approach is clearly superior to the deterministic approach. It is concluded that the small difference between enantiomers caused by ΔEPV cannot be amplified under conditions reasonable for the generation of biological chirality. It also seems highly unlikely that biological chirality was determined by any intrinsic difference between enantiomers.

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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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