Unusual distance dependences of electron transfer rates

文献情報

出版日 2016-06-29

DOI 10.1039/C6CP03124B

インパクトファクター 3.676

著者

Martin Kuss-Petermann, Oliver S. Wenger

原文を見る

要旨

Usually the rates for electron transfer (kET) decrease with increasing donor–acceptor distance, but Marcus theory predicts a regime in which kET is expected to increase when the transfer distance gets longer. Until recently, experimental evidence for such counter-intuitive behavior had been very limited, and consequently this effect is much less well-known than the Gaussian free energy dependence of electron transfer rates leading to the so-called inverted driving-force effect. This article presents the theoretical concepts that lead to the prediction of electron transfer rate maxima at large donor–acceptor distances, and it discusses conditions that are expected to favor experimental observations of such behavior. It continues with a consideration of specific recent examples in which electron transfer rates were observed to increase with increasing donor–acceptor distance, and it closes with a discussion of the importance of this effect in the context of light-to-chemical energy conversion.

掲載誌

Physical Chemistry Chemical Physics

CiteScore: 5.5

自己引用率: 10.3%

年間論文数: 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.