3D Quantification of Microstructural Properties of LiNi0.5Mn0.3Co0.2O2 High‐Energy Density Electrodes by X‐Ray Holographic Nano‐Tomography

Nguyen, Trong-Khoa; Villanova, Julie; Su, Zeliang; Tucoulou, R.; Fleutot, Benoît; Delobel, Bruno; Delacourt, Charles; Demortière, Arnaud

doi:10.1002/aenm.202003529

Cited by 40 publications

(41 citation statements)

References 76 publications

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“…It was found that the lithium‐ion distribution is extremely uneven during the lithiation process due to the uneven spatial distribution of CBD, as shown in Figure 7b. Similar results were obtained by Nguyen et al [ 96 ] when characterizing the NCM523 electrode. They found that the non‐uniformly distributed CBD phase leads to the inhomogeneous ion concentration, thereby reducing the overall electrochemical performance of the cell.…”

Section: Applications Of Synchrotron X‐ray Tomography For Battery Researchsupporting

confidence: 91%

Synchrotron X‐Ray Tomography for Rechargeable Battery Research: Fundamentals, Setups and Applications

Tang

Yang

et al. 2021

Small Methods

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Understanding the complicated interplay of the continuously evolving electrode materials in their inherent 3D states during the battery operating condition is of great importance for advancing rechargeable battery research. In this regard, the synchrotron X‐ray tomography technique, which enables non‐destructive, multi‐scale, and 3D imaging of a variety of electrode components before/during/after battery operation, becomes an essential tool to deepen this understanding. The past few years have witnessed an increasingly growing interest in applying this technique in battery research. Hence, it is time to not only summarize the already obtained battery‐related knowledge by using this technique, but also to present a fundamental elucidation of this technique to boost future studies in battery research. To this end, this review firstly introduces the fundamental principles and experimental setups of the synchrotron X‐ray tomography technique. After that, a user guide to its application in battery research and examples of its applications in research of various types of batteries are presented. The current review ends with a discussion of the future opportunities of this technique for next‐generation rechargeable batteries research. It is expected that this review can enhance the reader's understanding of the synchrotron X‐ray tomography technique and stimulate new ideas and opportunities in battery research.

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Section: Applications Of Synchrotron X‐ray Tomography For Battery Researchsupporting

confidence: 91%

Synchrotron X‐Ray Tomography for Rechargeable Battery Research: Fundamentals, Setups and Applications

Tang

Yang

et al. 2021

Small Methods

View full text Add to dashboard Cite

show abstract

“…Electrochemical investigations have qualitatively shown that chemical composition and volume fraction of the CBD directly influence charge transport in the porous electrode and the cycling performance of the battery [11–15] . However, detailed quantitative descriptions of CBD morphology and spatial CBD distribution in electrodes as well as the expected impact on ionic transport and overall battery performance are still rare [6,7,16–24] …”

Section: Introductionmentioning

confidence: 99%

Three‐Phase Reconstruction Reveals How the Microscopic Structure of the Carbon‐Binder Domain Affects Ion Transport in Lithium‐Ion Batteries

Kroll

Karstens

Cronau

et al. 2021

Batteries & Supercaps

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The morphology of the electrolyte‐filled pore space in lithium‐ion batteries is determined by the solid microstructure formed by μm‐sized active material particles and the smaller‐featured carbon binder domain (CBD). Tomographic reconstructions have largely neglected the CBD, resulting in inadequately defined pore space morphologies at odds with experimental ionic tortuosity values. We present a three‐phase reconstruction of a LiCoO2 composite cathode by focused ion‐beam scanning electron microscopy tomography. Morphological analysis proves that the reconstruction, which combines an unprecedented volume (20 μm minimum edge length) with the hitherto highest resolution (13.9×13.9×20 nm3 voxel size), represents the cathode's pore space morphology. Pore‐scale diffusion simulations show consideration of the resolved CBD as indispensable to reproduce ionic tortuosity values from electrochemical impedance spectroscopy. Our results reveal the CBD as a convoluted network that dominates the pore space morphology and limits Li+ transport through tortuous and constricted diffusion pathways.

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“…CBD is the most difficult to segment among these three phases in this dataset and tends to have inconsistencies between experimenters. Potential ways to improve IoUs of thin objects could be to use higher resolution and smaller FoV with interlaced scans or other advanced XCT techniques 4,40 or reconstruction algorithms 41 .…”

Section: Reveal the Influence Of Biases Diluted In Ground Truth (Gt)mentioning

confidence: 99%

Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

Decencière

Nguyen

et al. 2022

npj Comput Mater

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The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.

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3D Quantification of Microstructural Properties of LiNi_0.5Mn_0.3Co_0.2O₂ High‐Energy Density Electrodes by X‐Ray Holographic Nano‐Tomography

Cited by 40 publications

References 76 publications

Synchrotron X‐Ray Tomography for Rechargeable Battery Research: Fundamentals, Setups and Applications

Synchrotron X‐Ray Tomography for Rechargeable Battery Research: Fundamentals, Setups and Applications

Three‐Phase Reconstruction Reveals How the Microscopic Structure of the Carbon‐Binder Domain Affects Ion Transport in Lithium‐Ion Batteries

Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

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