Literature DB >> 22391938

Chemical reaction networks as a model to describe UVC- and radiolytically-induced reactions of simple compounds.

Daniele Dondi1, Daniele Merli, Angelo Albini, Alberto Zeffiro, Nick Serpone.   

Abstract

When a chemical system is submitted to high energy sources (UV, ionizing radiation, plasma sparks, etc.), as is expected to be the case of prebiotic chemistry studies, a plethora of reactive intermediates could form. If oxygen is present in excess, carbon dioxide and water are the major products. More interesting is the case of reducing conditions where synthetic pathways are also possible. This article examines the theoretical modeling of such systems with random-generated chemical networks. Four types of random-generated chemical networks were considered that originated from a combination of two connection topologies (viz., Poisson and scale-free) with reversible and irreversible chemical reactions. The results were analyzed taking into account the number of the most abundant products required for reaching 50% of the total number of moles of compounds at equilibrium, as this may be related to an actual problem of complex mixture analysis. The model accounts for multi-component reaction systems with no a priori knowledge of reacting species and the intermediates involved if system components are sufficiently interconnected. The approach taken is relevant to an earlier study on reactions that may have occurred in prebiotic systems where only a few compounds were detected. A validation of the model was attained on the basis of results of UVC and radiolytic reactions of prebiotic mixtures of low molecular weight compounds likely present on the primeval Earth.

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Year:  2012        PMID: 22391938     DOI: 10.1039/c2pp00005a

Source DB:  PubMed          Journal:  Photochem Photobiol Sci        ISSN: 1474-905X            Impact factor:   3.982


  1 in total

1.  What makes a reaction network "chemical"?

Authors:  Stefan Müller; Christoph Flamm; Peter F Stadler
Journal:  J Cheminform       Date:  2022-09-19       Impact factor: 8.489

  1 in total

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