Abstract
Electrocatalysis plays a key role in clean energy innovation. In order to design more efficient, durable and selective electrocatalysts, a thorough understanding of the unique link between 3D structures and properties is essential yet challenging. Advanced 3D electron tomography offers an effective approach to reveal 3D structures by transmission electron microscopy. This mini-review summarizes recent progress on revealing 3D structures of electrocatalysts using 3D electron tomography. 3D electron tomography at nanoscale and atomic scale are discussed, respectively, where morphology, composition, porous structure, surface crystallography and atomic distribution can be revealed and correlated to the performance of electrocatalysts. (Quasi) in-situ 3D electron tomography is further discussed with particular focus on its impact on electrocatalysts’ durability investigation and post-treatment. Finally, perspectives on future developments of 3D electron tomography for eletrocatalysis is discussed.
Highlights
Clean energy innovation is vital to achieving a sustainable and resilient future energy system of carbon neutralization
X-ray computed tomography (XCT) is relatively low in spatial resolution, whereas atom probe tomography (APT) is less capable of resolving crystal structures
Transmission electron microscopy (TEM) is the most straightforward and widely-used characterization technique for materials, with spatial resolution spanning from sub-micron to atomic scale. 3D tomography performed in TEM is considered as an ideal approach to directly study the 3D structure of nanocatalysts
Summary
Clean energy innovation is vital to achieving a sustainable and resilient future energy system of carbon neutralization. Composition mapping by electron energy loss spectroscopy (EELS) and energy dispersive X-ray spectroscopy (EDX) can be acquired for electron tomography (Haberfehlner et al, 2014; Slater et al, 2016). In such scenario, tilt increment is sometimes increased to reduce electron dose and reconstruction quality is compromised (Midgley and Dunin-Borkowski, 2009; Hungría et al, 2019). The acquired tilt series are reconstructed using different algorithms, such as classic back projection or weighted back projection, iterative procedure, and more advanced compressive sensing, atomic electron tomography (AET) or deep-learning assisted algorithms (Midgley and DuninBorkowski, 2009; Zečević et al, 2013; Bals et al, 2014; Miao et al, 2016; Ding et al, 2019; Hovden and Muller, 2020; Wang, 2020). The reconstructed volume is visualized, segmented or quantified for detailed structural investigation
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