Surface modification imparts selectivity, facilitating redox catalytic studies: quinone mediated oxygen reduction

Literature Information

Publication Date 2013-04-22
DOI 10.1039/C3CP50607J
Impact Factor 3.676
Authors

Joseph Mason, Christopher Batchelor-McAuley, Richard G. Compton


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Abstract

Modifying a gold electrode surface with hydroxy-alkyl-thiols significantly reduces the observed rate of electron transfer. This designed and controlled decrease in electron transfer rate readily allows reversible and irreversible electrode processes to be more clearly delineated. Separation of such voltammetric responses can facilitate the direct study of redox catalytic processes, which would, under other experimental conditions be obscured. The reduction of oxygen by anthraquinone hydroxy derivatives are used as a paradigmatic example, demonstrating the importance of the hydroxyl groups in the reduction process. Specifically, the reduced form of 1,8-dihydroxy-anthraquinone is shown to be significantly more reactive towards oxygen reduction than the 1,4-dihydroxyl analogue.

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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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