Strain engineering of bimetallic core@shell electrocatalysts is a promising approach to increase catalytic selectivity, activity, and durability of catalysts in electrochemical energy conversion applications [1,2]. The catalytic activity has been… Click to show full abstract
Strain engineering of bimetallic core@shell electrocatalysts is a promising approach to increase catalytic selectivity, activity, and durability of catalysts in electrochemical energy conversion applications [1,2]. The catalytic activity has been proposed to be controlled by the metal-ligand bond strengths [2,3]. Furthermore, surface strain has been demonstrated to be a controlling factor for the bond strength through modification of the metal-metal bond length and thus, strain is regarded as one of the most significant tuning variables available for enhancing catalytic activity. To better correlate strain at atomic-level interfaces with catalyst functionality, it is important to precisely measure the local lattice strain. Several approaches have been explored to quantify local strain: coherent X-ray diffraction, annular dark field (ADF) scanning transmission electron microscopy (STEM), and X-ray absorption spectroscopy among others [1-4]. Of all the available approaches, electron microscopy is by far the only technique that enables direct visualization of a catalyst nanoparticle’s atomic structure, allowing for the straightforward measurement of strain directly from the positions of the atomic columns. However, in reality the expected displacements due to strain are similar to the precision limits of ADF-STEM (~2pm). Additionally, scan and drift distortions are limiting factors in strain measurement derived from ADF-STEM datasets. Scanning nanodiffraction has been proposed as an alternative, robust technique for strain measurements, where the strain is calculated in the diffraction plane from the positions of the higher order diffraction disks [5]. Yet, depending on the disk fitting algorithms used, significant variations can be present in strain measurements, even when using the same underlying dataset.
               
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