The role of strain in oxygen evolution reaction

The oxygen evolution reaction(OER)is a crucial step in metal-air batteries and water splitting technologies,playing a significant role in the efficiency and achievable heights of these two technologies.However,the OER is a four-step,four-electron reaction,and its slow kinetics result in high overpotentials,posing a challenge.To address this issue,numerous strategies involving modified catalysts have been proposed and proven to be highly efficient.In these strategies,the introduction of strain has been widely reported because it is generally believed to effectively regulate the electronic structure of metal sites and alter the adsorption energy of catalyst surfaces with reaction intermediates.However,strain has many other effects that are not well known,making it an important yet unexplored area.Based on this,this review provides a detailed introduction to the various roles of strain in OER.To better explain these roles,the review also presents the definition of strain and elucidates the potential mechanisms of strain in OER based on the d-band center theory and adsorption volcano plot.Additionally,the review showcases various ways of introducing strain in OER through examples reported in the latest literature,aiming to provide a comprehensive perspective for the development of strain engineering.Finally,the review analyzes the appropriate proportion of strain introduction,compares compressive and tensile strain,and examines the impact of strain on stability.And the review offers prospects for future research directions in this emerging field.

An Adaptive Padding Correlation Filter With Group Feature Fusion for Robust Visual Tracking

In recent visual tracking research,correlation filter(CF)based trackers become popular because of their high speed and considerable accuracy.Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter.However,the related studies are insufficient.By exploring the potential of trackers in these two aspects,a novel adaptive padding correlation filter(APCF)with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework.In the tracker,three feature groups are fused by use of the weighted sum of the normalized response maps,to alleviate the risk of drift caused by the extreme change of single feature.Moreover,to improve the adaptive ability of padding for the filter training of different object shapes,the best padding is selected from the preset pool according to tracking precision over the whole video,where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames.The sequence features include three traditional features and eight newly constructed features.Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.