Capacity-Fading Mechanisms of LiNiO[sub 2]-Based Lithium-Ion Batteries

Muto, Shunsuke; Sasano, Yusuke; Tatsumi, Kazuyoshi; Sasaki, Tsuyoshi; Horibuchi, Kayo; Takeuchi, Yugo; Ukyo, Yoshio

doi:10.1149/1.3076137

Cited by 209 publications

(162 citation statements)

References 26 publications

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“…We also successfully applied NMF to a series of EELS datasets for the extraction of atom site-specific core-loss spectra, where the relative excitation probabilities of the spectra varied with the diffraction condition because of the electron channeling effects [22][23][24][25]. In these applied data analyses, the nonnegative constraint of the elements of extracted basis spectra and spatial intensity distributions were effective, and the resulting spectra extracted by NMF were consistent with the computational results obtained by first principles calculations [15][16][17][18][19][22][23][24][25].…”

Section: Introductionsupporting

confidence: 65%

“…Contrary to NMF, the methods mentioned above such as PCA allow the spatial intensities and spectra to have negative values, which hampers the direct physical interpretation of the resolved spectral profiles. We adopted the modified alternating least-square (MALS) fitting algorism of NMF [14] to map the different phases in the degradation of Li battery cathodes [15][16][17][18][19] and the chemical states of nitrogen in nitrogen-doped TiO 2 [20,21]. We also successfully applied NMF to a series of EELS datasets for the extraction of atom site-specific core-loss spectra, where the relative excitation probabilities of the spectra varied with the diffraction condition because of the electron channeling effects [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

Shiga

Muto

2018

Nanoinformatics

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Recent advances in scanning transmission electron microscopy (STEM) techniques have enabled us to obtain spectroscopic datasets such as those generated by electron energy-loss (EELS)/energy-dispersive X-ray (EDX) spectroscopy measurements in a PC-controlled way from a specified region of interest (ROI) even at atomic scale resolution, also known as hyperspectral imaging (HSI). Instead of conventional analytical procedures, in which the potential constituent chemical components are manually identified and the chemical state of each spectral component is successively determined, a statistical machine-learning approach, which is known to be more effective and efficient for the automatic resolution and extraction of the underlying chemical components stored in a huge three-dimensional array of an observed HSI dataset, is used. Among the statistical approaches suitable for processing HSI datasets, methods based on matrix factorization such as principal component analysis (PCA), multivariate curve resolution (MCR), and nonnegative matrix factorization (NMF) are useful to find an essential low-dimensional data subspace hidden in the HSI dataset. This chapter describes our developed NMF method, which has two additional terms in the objective function, and which is particularly effective for analyzing STEM-EELS/EDX HSI datasets: (i) a soft orthogonal penalty, which clearly resolves partially overlapped spectral components in their spatial distributions and (ii) an automatic relevance determination (ARD) prior, which optimizes the number of components involved in the observed

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Section: Introductionsupporting

confidence: 65%

Section: Introductionmentioning

confidence: 99%

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

Shiga

Muto

2018

Nanoinformatics

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“…31 Even though it was not explicitly shown in these latter reports, the observed phase transitions suggest a release of oxygen from the particle surface, which was also pointed out in the reports by Abraham et al, Muto et al, and Hwang et al [28][29][30][31] So far, a release of oxygen from the oxide lattice under battery operating conditions was shown only for overlithiated NMC materials (Li 1+x (Ni,Mn,Co) 1-x O 2 ), in which lithium additionally occupies the transition metal layers. [32][33][34][35][36] The exception is a recent publication by Guéguen et al, who showed oxygen evolution during battery cycling for NMC111.…”

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confidence: 63%

Oxygen Release and Its Effect on the Cycling Stability of LiNi_xMn_yCo_zO₂(NMC) Cathode Materials for Li-Ion Batteries

Jung

Metzger

Maglia

et al. 2017

J. Electrochem. Soc.

1,009

1,278

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Layered LiNi x Mn y Co z O 2 (NMC) is a widely used class of cathode materials with LiNi 1/3 Mn 1/3 Co 1/3 O 2 (NMC111) being the most common representative. However, Ni-rich NMCs are more and more in the focus of current research due to their higher specific capacity and energy. In this work we will compare LiNi 1/3 Mn 1/3 Co 1/3 O 2 (NMC111), LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622), and LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) with respect to their cycling stability in NMC-graphite full-cells at different end-of-charge potentials. It will be shown that stable cycling is possible up to 4.4 V for NMC111 and NMC622 and only up to 4.0 V for NMC811. At higher potentials, significant capacity fading was observed, which was traced back to an increase in the polarization of the NMC electrode, contrary to the nearly constant polarization of the graphite electrode. Furthermore, we show that the increase in the polarization occurs when the NMC materials are cycled up to a high-voltage feature in the dq/dV plot, which occurs at ∼4.7 V vs. Li/Li + for NMC111 and NMC622 and at ∼4.3 V vs. Li/Li + for NMC811. For the latter material, this feature corresponds to the H2 → H3 phase transition. Contrary to the common understanding that the electrochemical oxidation of carbonate electrolytes causes the CO 2 and CO evolution at potentials above 4.7 V vs. Li/Li + , we believe that the observed CO 2 and CO are mainly due to the chemical reaction of reactive lattice oxygen with the electrolyte. This hypothesis is based on gas analysis using On-line Electrochemical Mass Spectrometry (OEMS), by which we prove that all three materials release oxygen from the particle surface and that the oxygen evolution coincides with the onset of CO 2 and CO evolution. Interestingly, the onsets of oxygen evolution for the different NMCs correlate well with the high-voltage redox feature at ∼4.7 V vs. Li/Li + for NMC111 and NMC622 as well as at ∼4.3 V vs. Li/Li + for NMC811. To support this hypothesis, we show that no CO 2 or CO is evolved for the LiNi 0.43 Mn 1.57 O 4 (LNMO) spinel up to 5 V vs. Li/Li + , consistent with the absence of oxygen release. Lastly, we demonstrate by the use of 13 C labeled conductive carbon that it is the electrolyte rather than the conductive carbon which is oxidized by the released lattice oxygen. Taking these findings into consideration, a mechanism is proposed for the reaction of released lattice oxygen with ethylene carbonate yielding CO 2 , CO, and Li-Ion batteries have recently been used as power supply for electric vehicles (EVs). In order to penetrate the mass market, a significant reduction in costs and further performance improvements have to be achieved to realize a longer driving range of EVs.1 The latter highly depends on the choice of the cathode active material, for which several potential materials exist, 2 of which layered lithium nickel manganese cobalt oxide (LiNi x Mn y Co z O 2 , NMC) is one of the most promising class of cathode materials.3 This is due to the high specific capacity and good stability of the lay...

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“…The ALS method was applied to these EELS data [7][8][9][10][11][13][14][15]. As an initial ALS estimation, the orthogonal projection approach (OPA) was used [16,17].…”

Section: Methodsmentioning

confidence: 99%

“…Such a large matrix of spectral data is suitable for analysis via a chemometrics approach. To obtain individual pure spectra from the series of spectra without a reference spectrum, we applied a self-modeling curve-resolution technique (the alternating least-squares (ALS) method) [7][8][9][10][11] to the series of spectra to perform spectral decomposition. The degradation process of LiCoO 2 was analyzed based on the series of decomposed spectra.…”

Section: Introductionmentioning

confidence: 99%

Degradation Analysis of LiCoO<sub>2 </sub>Positive Electrode Material of a Li-Ion Battery Using the Li K-Edge Signal Obtained from STEM-EELS Measurements

Taguchi

Sakaebe

Tatsumi

et al. 2015

e-J. Surf. Sci. Nanotech.

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The Li distribution in positive electrode material (LiCoO2) was observed after a charge-discharge cycling test by analytical transmission electron microscopy (TEM). The electron energy-loss spectroscopy (EELS) imaging method revealed an inhomogeneous Li distribution in the LiCoO2 particles after the charge-discharge cycling test. Using the alternating least-squares (ALS) method for the data matrix of the EELS spectra, we obtained LiCoO2 and Li-poor LiCoO2 spectra from the LiCoO2 particles. Around the degraded LiCoO2 particles, Co3O4 and fluoride components were detected. It is suggested that inhomogeneous electrochemical reactions are one cause of LiCoO2 and electrolyte degradation.

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Capacity-Fading Mechanisms of LiNiO[sub 2]-Based Lithium-Ion Batteries

Cited by 209 publications

References 26 publications

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

Oxygen Release and Its Effect on the Cycling Stability of LiNi_xMn_yCo_zO₂(NMC) Cathode Materials for Li-Ion Batteries

Degradation Analysis of LiCoO<sub>2 </sub>Positive Electrode Material of a Li-Ion Battery Using the Li K-Edge Signal Obtained from STEM-EELS Measurements

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Capacity-Fading Mechanisms of LiNiO[sub 2]-Based Lithium-Ion Batteries

Cited by 209 publications

References 26 publications

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

Oxygen Release and Its Effect on the Cycling Stability of LiNixMnyCozO2(NMC) Cathode Materials for Li-Ion Batteries

Degradation Analysis of LiCoO<sub>2 </sub>Positive Electrode Material of a Li-Ion Battery Using the Li K-Edge Signal Obtained from STEM-EELS Measurements

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Oxygen Release and Its Effect on the Cycling Stability of LiNi_xMn_yCo_zO₂(NMC) Cathode Materials for Li-Ion Batteries