Machine learning in nanomaterial electron microscopy data analysis

Yao, Lehan; Chen, Qian

doi:10.1016/b978-0-323-85796-3.00010-x

Cited by 2 publications

(1 citation statement)

References 73 publications

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“…Numerous factors, including material design and synthesis, enhanced ex situ and in situ characterization, simulation, and actual performance testing as feedback for greater understanding, influence the development of silicon-based battery materials (Figure a,b). Additionally, the combination of big data analysis with machine learning is a potent one that has shown to be useful in the field of materials science (Figure c). − …”

Section: Discussionmentioning

confidence: 99%

Imaging the Surface/Interface Morphologies Evolution of Silicon Anodes Using in Situ/Operando Electron Microscopy

Yang

Zhang

et al. 2023

ACS Appl. Mater. Interfaces

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Si-based rechargeable lithium-ion batteries (LIBs) have generated interest as silicon has remarkably high theoretical specific capacity. It is projected that LIBs will meet the increasing need for extensive energy storage systems, electric vehicles, and portable electronics with high energy densities. However, the Si-based LIB has a substantial problem due to the volume cycle variations brought on by Si, which result in severe capacity loss. Making Si-based anodesenabled high-performance LIBs that are easy to utilize requires an understanding of the fading mechanism. Due to its distinct advantage in morphological changes from microscale to nanoscale, even approaching atomic resolution, electron microscopy is one of the most popular methods. Based on operando electron microscopy characterization, the general comprehension of the fading mechanism and the morphology evolution of Si-based LIBs are debated in this review. The current advancements in compositional and structural interpretation for Si-based LIBs using advanced electron microscopy characterization methods are outlined. The future development trends in pertinent silicon materials characterization methods are also highlighted, along with numerous potential research avenues for Si-based LIBs design and characterization.

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Section: Discussionmentioning

confidence: 99%

Imaging the Surface/Interface Morphologies Evolution of Silicon Anodes Using in Situ/Operando Electron Microscopy

Yang

Zhang

et al. 2023

ACS Appl. Mater. Interfaces

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Unveiling the Dynamic Pathways of Metal–Organic Framework Crystallization and Nanoparticle Incorporation for Li–S Batteries

Song,

Huang,

Zhang

et al. 2024

Advanced Science

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Metal–organic frameworks (MOFs) present diverse building blocks for high‐performance materials across industries, yet their crystallization mechanisms remain incompletely understood due to gaps in nucleation and growth knowledge. In this study, MOF structural evolution is probed using in situ liquid phase transmission electron microscopy (TEM) and cryo‐TEM, unveiling a blend of classical and nonclassical pathways involving liquid–liquid phase separation, particle attachment–coalescence, and surface layer deposition. Additionally, ultrafast high‐temperature sintering (UHS) is employed to dope ultrasmall Cobalt nanoparticles (Co NPs) uniformly within nitrogen‐doped hard carbon nanocages confirmed by 3D electron tomography. Lithium–sulfur battery tests demonstrate the nanocage‐Co NP structure's exceptional capacity and cycling stability, attributed to Co NP catalytic effects due to its small size, uniform dispersion, and nanocage confinement. The findings propose a holistic framework for MOF crystallization understanding and Co NP tunability through ultrafast sintering, promising advancements in materials science and informing future MOF synthesis strategies and applications.

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Machine learning in nanomaterial electron microscopy data analysis

Cited by 2 publications

References 73 publications

Imaging the Surface/Interface Morphologies Evolution of Silicon Anodes Using in Situ/Operando Electron Microscopy

Imaging the Surface/Interface Morphologies Evolution of Silicon Anodes Using in Situ/Operando Electron Microscopy

Unveiling the Dynamic Pathways of Metal–Organic Framework Crystallization and Nanoparticle Incorporation for Li–S Batteries

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