The oxygen vacancy in Li-ion battery cathode materials

Tang, Zhen‐Kun; Xue, Yufeng; Teobaldi, Gilberto; Liu, Limin

doi:10.1039/d0nh00340a

Cited by 106 publications

(78 citation statements)

References 102 publications

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“…The rational and controllable introduction of defects can adjust the key physicochemical properties of materials, including but not limited to electronic structure, geometric configuration, and surface adsorption. [ 14,15 ] For example, Zhang et al. [ 16 ] reported the fabrication of oxygen‐deficient MoO 3‐x ‐based anode via solution reduction method.…”

Section: Introductionmentioning

confidence: 99%

Kinetically Accelerated Lithium Storage in High‐Entropy (LiMgCoNiCuZn)O Enabled By Oxygen Vacancies

Liu

Xing

et al. 2022

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High‐entropy oxides (HEOs) are gradually becoming a new focus for lithium‐ion battery (LIB) anodes due to their vast element space/adjustable electrochemical properties and unique single‐phase retention ability. However, the sluggish kinetics upon long cycling limits their further generalization. Here, oxygen vacancies with targeted functionality are introduced into rock salt‐type (MgCoNiCuZn)O through a wet‐chemical molten salt strategy to accelerate the ion/electron transmission. Both experimental results and theoretical calculations reveal the potential improvement of lithium storage, charge transfer, and diffusion kinetics from HEO surface defects, which ultimately leads to enhanced electrochemical properties. The currently raised strategy offers a modular approach and enlightening insights for defect‐induced HEO‐based anodes.

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Section: Introductionmentioning

confidence: 99%

Kinetically Accelerated Lithium Storage in High‐Entropy (LiMgCoNiCuZn)O Enabled By Oxygen Vacancies

Liu

Xing

et al. 2022

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“…These limited studies suggest that the introduction of oxygen vacancies can improve electrical conductivity and increase active site, which is beneficial to enhancing lithium storage remarkably [28][29][30][31]. For example, Qiu and co-workers [32,33] obtained oxygen-deficient TiO 2 with a capacity of 128 mA h g −1 at 5 C when used as the anode in LIBs. Li et al [34] synthesized oxygen-vacancy-abundant ZnCo 2 O 4 for LIBs and found that it exhibited an excellent capacity of 746 mA h g −1 at 1 C. However, the effect of oxygen vacancies in TMOs remains ambiguous.…”

Section: Introductionmentioning

confidence: 99%

Oxygen vacancy-expedited ion diffusivity in transition-metal oxides for high-performance lithium-ion batteries

Wang

Liu

et al. 2022

Sci. China Mater.

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Rapid capacity decay and inferior kinetics are the vital issues of anodes in the conversion reaction for lithium-ion batteries. Vacancy engineering can efficiently modulate the intrinsic properties of transition-metal oxide (TMO)-based electrode materials, but the effect of oxygen vacancies on electrode performance remains unclear. Herein, abundant oxygen vacancies are in situ introduced into the lattice of different TMOs (e.g., Co 3 O 4 , Fe 2 O 3 , and NiO) via a facile hydrothermal treatment combined with calcination. Taking Co 3 O 4 as a typical example, results prove that the oxygen vacancies in Co 3 O 4−x effectively accelerate charge transfer at the interface and significantly increase electrical conductivity and pseudocapacitance contribution. The Li-ion diffusion coefficient of Co 3 O 4−x is remarkably improved by two orders of magnitude compared with that of Co 3 O 4 . Theoretical calculations reveal that Co 3 O 4−x has a lower Li-insertion energy barrier and more density of states around the Fermi level than Co 3 O 4 , which is favorable for ion and electron transport. Therefore, TMOs with rich vacancies exhibit superior cycling performance and enhanced rate capability over their counterparts. This strategy regulating the reaction kinetics would provide inspiration for designing other TMObased electrodes for energy applications.

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“…The app is free and open-source and it is available for download on Github 12 . We have also built a TEM ImageNet project website for searching, browsing, and downloading of the training images and labels 13 .…”

Section: Introductionmentioning

confidence: 99%

“…AtomSegNet is intended to become a pre-processing module of a complete TEM image processing workflow that include extensions of materials and zone axis recognition, crystal phase mapping, dislocation and defect detection, atomic counting, strain mapping, etc. The training data sets and labels are available for download, searching and browsing at the project website 13 .…”

Section: Introductionmentioning

confidence: 99%

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Lin

Zhang

Wang

et al. 2021

Sci Rep

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Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

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The oxygen vacancy in Li-ion battery cathode materials

Abstract: The substantial capacity gap between available anode and cathode materials for commercial Li-ion batteries (LiBs) remains, as of today, an unsolved problem. Oxygen vacancies (OVs) can promote Li-ion diffusion, reduce...

Cited by 106 publications

References 102 publications

Kinetically Accelerated Lithium Storage in High‐Entropy (LiMgCoNiCuZn)O Enabled By Oxygen Vacancies

Kinetically Accelerated Lithium Storage in High‐Entropy (LiMgCoNiCuZn)O Enabled By Oxygen Vacancies

Oxygen vacancy-expedited ion diffusivity in transition-metal oxides for high-performance lithium-ion batteries

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

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