Bragg Coherent Diffraction Imaging for In Situ Studies in Electrocatalysis

Abstract

Electrocatalysis is at the heart of a broad range of physicochemical applications that play an important role in the present and future of a sustainable economy. Among the myriad of different electrocatalysts used in this field, nanomaterials are of ubiquitous importance. An increased surface area/volume ratio compared to bulk makes nanoscale catalysts the preferred choice to perform electrocatalytic reactions. Bragg coherent diffraction imaging (BCDI) was introduced in 2006 and since has been applied to obtain 3D images of crystalline nanomaterials. BCDI provides information about the displacement field, which is directly related to strain. Lattice strain in the catalysts impacts their electronic configuration and, consequently, their binding energy with reaction intermediates. Even though there have been significant improvements since its birth, the fact that the experiments can only be performed at synchrotron facilities and its relatively low resolution to date (∼10 nm spatial resolution) have prevented the popularization of this technique. Herein, we will briefly describe the fundamentals of the technique, including the electrocatalysis relevant information that we can extract from it. Subsequently, we review some of the computational experiments that complement the BCDI data for enhanced information extraction and improved understanding of the underlying nanoscale electrocatalytic processes. We next highlight success stories of BCDI applied to different electrochemical systems and in heterogeneous catalysis to show how the technique can contribute to future studies in electrocatalysis. Finally, we outline current challenges in spatiotemporal resolution limits of BCDI and provide our perspectives on recent developments in synchrotron facilities as well as the role of machine learning and artificial intelligence in addressing them.

Lattice Strain and Defects Analysis in Nanostructured Semiconductor Materials and Devices by High‐Resolution X‐Ray Diffraction: Theoretical and Practical Aspects

“…[44] This relates to the development of new highresolution diffraction techniques like scanning X-ray diffraction microscopy, [38,45,46] which have emerged in very recent years. Considering also the development of diffracting devices such as Fresnel zone plates, gratings, pine-holes, etc., coherent diffraction imaging, [47,48] Bragg coherent diffraction imaging, [49][50][51] and others are in the center of extensive research. [52,53] Typical experimental setups used in HRXRD are show in Figure 2.…”

Nanoimaging of solid solution domains during phase transitions inside LiNi0.5Mn1.5O4

“…33 Li 0.5 −−→ Li 0 phase transition The second phase transition and voltage plateau occur at 4.75 V (Fig. 4b, images [15][16][17][18][19][20][21][22]. These images show a clear evolution in the strain gradients inside the crystal, unlike those from the first voltage plateau.…”

“…[15][16][17][18] Bragg coherent diffraction imaging (BCDI) has emerged as an alternative method for mapping strain inside LMNO cathode particles in situ. 8,[19][20][21][22] However, significant practical limitations on maximum crystallite size, crystal quality, beam damage, reconstruction accuracy, measurement speed, and the difficulty of data analysis have limited the utility of this competing technique. 23,24 In this work, high-resolution SXDM is used under operando conditions for the first time on battery materials 19 , to map the delithiation of a single LMNO crystal.…”

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

“…). , Spectra-based descriptors are derived from experimental measurements (e.g., X-ray diffraction, extended X-ray absorption fine structure (EXAFS), etc.) . The acquisition of these descriptors may be relatively costly and time-consuming.…”

“…88,89 Spectra-based descriptors are derived from experimental measurements (e.g., X-ray diffraction, extended X-ray absorption fine structure (EXAFS), etc.). 90 The acquisition of these descriptors may be relatively costly and timeconsuming. Clearly, computational descriptors are preferred because they do not require experiments to derive them.…”