Degradation analysis of a Ni-based layered positive-electrode active material cycled at elevated temperatures studied by scanning transmission electron microscopy and electron energy-loss spectroscopy

Kojima, Yukio; Muto, Shunsuke; Tatsumi, Kazuyoshi; Kondo, Hiroki; Oka, Hideaki; Horibuchi, Kayo; Ukyo, Yoshio

doi:10.1016/j.jpowsour.2011.05.017

Cited by 81 publications

(59 citation statements)

References 24 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: 63%

“…9.6), the third spectral components exhibit unnatural intensity decreases at 110 eV, where a sharp peak from the first spectral component is overlaid. This can happen in EELS-HSI under certain conditions [17]. This sudden lowering in intensity disappears when spatial orthogonality (w ≥ 0.01) is included, as seen in Fig.…”

Section: Xstem-eels Data From a Silicon Devicementioning

confidence: 83%

“…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: 63%

Section: Xstem-eels Data From a Silicon Devicementioning

confidence: 83%

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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“…8,9,17,18 An increase in the impedance is associated with these structural changes. 6 It is assumed that the impedance rise is caused by a hindrance of the lithium-ion diffusion through the altered surface structure. However, a deeper understanding of the reaction kinetics is still missing.…”

Section: Galvanostatic Intermittent Titration Techniquementioning

confidence: 99%

Unraveling the Degradation Process of LiNi_0.8Co_0.15Al_0.05O₂ Electrodes in Commercial Lithium Ion Batteries by Electronic Structure Investigations

Kleiner

Melke

Merz³

et al. 2015

ACS Appl. Mater. Interfaces

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The degradation of LiNi0.8Co0.15Al0.05O2 (LNCAO) is reflected by the electrochemical performance in the fatigued state and correlated with the redox behavior of these cathodes. The detailed electrochemical performance of these samples is investigated by galvanostatic and voltammetric cycling as well as with the galvanostatic intermittent titration technique (GITT). Near-edge X-ray absorption fine structure (NEXAFS) spectroscopy was used to investigate the oxidation state of all three materials at the Ni L2,3, O K, and Co L2,3 edges at five different states of charge. Surface and more bulklike properties are distinguished by total electron yield (TEY) and fluorescence yield (FY) measurements. The electrochemical investigations revealed that the changes in the cell performance of the differently aged materials can be explained by considering the reaction kinetics of the intercalation/deintercalation process. The failure of the redox process of oxygen and nickel at low voltages leads to a significant decrease of the reaction rates in the fatigued cathodes. The accompanied cyclic voltammogram (CV) peaks appear as two peaks because of the local minimum of the reaction rate, although it is one peak in the CV of the calendarically aged LNCAO. The absence of the oxidation/reduction process at low voltages can be traced back to changes in the surface morphology (formation of a NiO-like structure). Further consequences of these material changes are overpotentials, which lead to capacity losses of up to 30% (cycled with a C/3 rate).

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“…Chemical changes in Ni-based cathode materials have been explored with x-ray photoelectron spectroscopy (XPS), 10,11 energy dispersive spectroscopy (EDS), 12 and electron energy loss (EEL) spectroscopy. 8,13,14 A number of analytical tools have been utilized in order to elucidate the mechanism of degradation, but few studies have been conducted to determine the depth of structural evolution. This is despite the fact that identifying the degree of degradation inside a particle is critical to evaluate the stability of cathode materials inside lithium cells.…”

mentioning

confidence: 99%

Determination of the mechanism and extent of surface degradation in Ni-based cathode materials after repeated electrochemical cycling

et al. 2016

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We take advantage of scanning transmission electron microscopy and electron energy loss spectroscopy to investigate the changes in near-surface electronic structure and quantify the degree of local degradation of Ni-based cathode materials with the layered structure (LiNi0.8Mn0.1Co0.1O2 and LiNi0.4Mn0.3Co0.3O2) after 20 cycles of delithiation and lithiation. Reduction of transition metals occurs in the near-surface region of cathode materials: Mn is the major element to be reduced in the case of relatively Mn-rich composition, while reduction of Ni ions is dominant in Ni-rich materials. The valences of Ni and Mn ions are complementary, i.e., when one is reduced, the other is oxidized in order to maintain charge neutrality. The depth of degradation zone is found to be much deeper in Ni-rich materials. This comparative analysis provides important insights needed for the devising of new cathode materials with high capacity as well as long lifetime.

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Degradation analysis of a Ni-based layered positive-electrode active material cycled at elevated temperatures studied by scanning transmission electron microscopy and electron energy-loss spectroscopy

Cited by 81 publications

References 24 publications

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

Unraveling the Degradation Process of LiNi_0.8Co_0.15Al_0.05O₂ Electrodes in Commercial Lithium Ion Batteries by Electronic Structure Investigations

Determination of the mechanism and extent of surface degradation in Ni-based cathode materials after repeated electrochemical cycling

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Degradation analysis of a Ni-based layered positive-electrode active material cycled at elevated temperatures studied by scanning transmission electron microscopy and electron energy-loss spectroscopy

Cited by 81 publications

References 24 publications

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

Unraveling the Degradation Process of LiNi0.8Co0.15Al0.05O2 Electrodes in Commercial Lithium Ion Batteries by Electronic Structure Investigations

Determination of the mechanism and extent of surface degradation in Ni-based cathode materials after repeated electrochemical cycling

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Unraveling the Degradation Process of LiNi_0.8Co_0.15Al_0.05O₂ Electrodes in Commercial Lithium Ion Batteries by Electronic Structure Investigations