Literature DB >> 20863059

Reactivity of alkyl polyhalides toward granular iron: development of QSARs and reactivity cross correlations for reductive dehalogenation.

David M Cwiertny1, William A Arnold, Tamar Kohn, Lisa A Rodenburg, A Lynn Roberts.   

Abstract

Attempts to develop quantitative structure-activity relationships (QSARs) for reductive dehalogenation by granular iron have been hindered by the unavailability of high quality predictor variables, have included relatively few compounds, and on occasion have relied on data lacking internal consistency. We herein investigate the reduction of 24 alkyl polyhalides by granular iron and the better-defined, homogeneous reductants Cr(H(2)O)(6)(2+) and an Fe(II) porphyrin. QSARs were constructed with a new set of computationally derived gas phase homolytic carbon-halogen bond dissociation energies and solvated one-electron reduction potentials determined using a quantum chemistry composite method (G3MP2). Reactivity cross correlations between reductant systems were also developed. Reactivity trends were generally consistent among all reductants and revealed pronounced structural influences. Compounds reduced at C-Br were orders of magnitude more reactive than analogues reduced at C-Cl; the number and identity of α- (Br ∼ Cl > CH(3) > F > H) and β-substituents (Br > Cl) also influenced reactivity. Nonlinearities encountered during QSAR and cross correlation development suggest that reactions of highly halogenated alkyl polyhalides with granular iron are limited by mass transfer, as supported by estimates of mass transfer coefficients. For species not suspected to exhibit mass transfer limitations, reasonably strong cross correlations and comparable substituent effects are consistent with dissociative electron transfer as the rate-determining step.

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Year:  2010        PMID: 20863059     DOI: 10.1021/es1018866

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  1 in total

1.  Quantitative structure activity relationships (QSARs) and machine learning models for abiotic reduction of organic compounds by an aqueous Fe(II) complex.

Authors:  Yidan Gao; Shifa Zhong; Tifany L Torralba-Sanchez; Paul G Tratnyek; Eric J Weber; Yiling Chen; Huichun Zhang
Journal:  Water Res       Date:  2021-01-15       Impact factor: 11.236

  1 in total

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