Characterizing Batteries by In Situ Electrochemical Atomic Force Microscopy: A Critical Review

Zhang, Zhenyu; Said, Samia; Smith, Keenan; Jervis, Rhodri; Howard, Christopher A.; Shearing, Paul R.; Brett, Dan J. L.; Miller, Thomas S.

doi:10.1002/aenm.202101518

Cited by 76 publications

(35 citation statements)

References 341 publications

(347 reference statements)

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“…[6] Different techniques such as focused-ion beam scanning electron microscopy (FIB-SEM), atomic force microscopy (AFM), and X-ray computed tomography (X-ray CT) have been employed to further elucidate the intrinsic link between electrode morphology and battery performance. [7][8][9][10][11] X-ray CT has garnered particular interest due to its non-destructive nature and multi-scale and in situ capabilities. The technique involves collecting 2D radiographs at different angular rotations and reconstructing these to 3D datasets whereby the resulting attenuation of the X-ray is inversely proportional to the contrast of the resulting image.…”

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confidence: 99%

Computer‐Vision‐Based Approach to Classify and Quantify Flaws in Li‐Ion Electrodes

et al. 2022

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X‐ray computed tomography (X‐ray CT) is a non‐destructive characterization technique that in recent years has been adopted to study the microstructure of battery electrodes. However, the often manual and laborious data analysis process hinders the extraction of useful metrics that can ultimately inform the mechanisms behind cycle life degradation. This work presents a novel approach that combines two convolutional neural networks to first locate and segment each particle in a nano‐CT LiNiMnCoO2 (NMC) electrode dataset, and successively classifies each particle according to the presence of flaws or cracks within its internal structure. Metrics extracted from the computer vision segmentation are validated with respect to traditional threshold‐based segmentation, confirming that flawed particles are correctly identified as single entities. Successively, slices from each particle are analyzed by a pre‐trained classifier to detect the presence of flaws or cracks. The models are used to quantify microstructural evolution in uncycled and cycled NMC811 electrodes, as well as the number of flawed particles in a NMC622 electrode. As a proof‐of‐concept, a 3‐phase segmentation is also presented, whereby each individual flaw is segmented as a separate pixel label. It is anticipated that this analysis pipeline will be widely used in the field of battery research and beyond.

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Computer‐Vision‐Based Approach to Classify and Quantify Flaws in Li‐Ion Electrodes

et al. 2022

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“…Under the right experimental conditions, the process can produce data with a very fine surface topography, usually with a lateral resolution of 1–20 nm and a height resolution of 0.025 nm, as well as a range of data on the physical nature of the material, such as conductivity, magnetic, electrostatic fields, and adhesion. [ 36,37 ]…”

Section: Technical Introductionmentioning

confidence: 99%

In Situ Atomic Force Microscopy and X‐ray Computed Tomography Characterization of All‐Solid‐State Lithium Batteries: Both Local and Overall

Chen

et al. 2023

Energy Tech

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All‐solid‐state lithium batteries (ASSLBs) are promising due to their high‐energy output and low‐risk profile, but their development has only just begun. Atomic force microscopy (AFM) and related techniques have had an impact on ASSLBs research by elucidating the interfacial, morphological, mechanical, electrical, and electrochemical properties of a wide range of electrodes and electrolytes. However, because a cross‐section cut is necessary to define the solid–solid interface, true in situ analysis is not practical. The first part of this review will assess recent advancements in the study of ASSLBs utilizing AFM and other scanning probe microscopy techniques. The interior solid–solid interfaces can be illuminated in situ using X‐ray computed tomography (X‐CT) and other nondestructive characterization techniques, whereas, in contrast, to deepen the subject, it is further examined how X‐CT vary from the use of other instruments for solid‐state battery characterization, compare the information that various methods may give, and assess how well they can accurately reflect real batteries. This review may serve as a reference and point researchers in the direction of future study on the solid–solid interface of ASSLBs.

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“…Recently, many visualization methods based on atomic force microscopy (AFM) have been developed to analyze battery materials. − The most important advantage of AFM is the simultaneous analysis of various material properties on the surface and topography with nanoscale spatial resolution . For example, Zhang et al visualized the evolution of SEI, including the change in the elastic modulus of the SEI, by in situ electrochemical AFM .…”

Section: Introductionmentioning

confidence: 99%

Nanoscale Visualization of the Electron Conduction Channel in the SiO/Graphite Composite Anode

Park

Choi

Shin³

et al. 2022

ACS Appl. Mater. Interfaces

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Conductive atomic force microscopy (C-AFM) is widely used to determine the electronic conductivity of a sample surface with nanoscale spatial resolution. However, the origin of possible artifacts has not been widely researched, hindering the accurate and reliable interpretation of C-AFM imaging results. Herein, artifact-free C-AFM is used to observe the electron conduction channels in Si-based composite anodes. The origin of a typical C-AFM artifact induced by surface morphology is investigated using a relevant statistical method that enables visualization of the contribution of artifacts in each C-AFM image. The artifact is suppressed by polishing the sample surface using a cooling cross-section polisher, which is confirmed by Pearson correlation analysis. The artifact-free C-AFM image was used to compare the current signals (before and after cycling) from two different composite anodes comprising single-walled carbon nanotubes (SWCNTs) and carbon black as conductive additives. The relationship between the electrical degradation and morphological evolution of the active materials depending on the conductive additive is discussed to explain the improved electrical and electrochemical properties of the electrode containing SWCNTs.

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Characterizing Batteries by In Situ Electrochemical Atomic Force Microscopy: A Critical Review

Cited by 76 publications

References 341 publications

Computer‐Vision‐Based Approach to Classify and Quantify Flaws in Li‐Ion Electrodes

Computer‐Vision‐Based Approach to Classify and Quantify Flaws in Li‐Ion Electrodes

In Situ Atomic Force Microscopy and X‐ray Computed Tomography Characterization of All‐Solid‐State Lithium Batteries: Both Local and Overall

Nanoscale Visualization of the Electron Conduction Channel in the SiO/Graphite Composite Anode

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