Raman Spectroscopy for LiNi&lt;sub&gt;1/3&lt;/sub&gt;Mn&lt;sub&gt;1/3&lt;/sub&gt;Co&lt;sub&gt;1/3&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt; Composite Positive Electrodes in All-Solid-State Lithium Batteries

Otoyama, Misae; Ito, Yusuke; Hayashi, Akitoshi; Tatsumisago, Masahiro

doi:10.5796/electrochemistry.84.812

Cited by 22 publications

(20 citation statements)

References 20 publications

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“…Our operando Raman spectra of NCM111 (Figure 8) start (OCP) and end (4.3 V vs. Li + /Li) with similar spectral envelopes as previously reported ex situ (Kerlau et al, 2007;Otoyama et al, 2016b). Unlike the case for NCA and LCO, the progressive evolution of the spectra upon de-/lithiation is hard to evaluate due to the difficulty of spectral deconvolution.…”

Section: Lini a Co B Mn 1-a-b O 2 (Ncms)supporting

confidence: 72%

In situ and Operando Raman Spectroscopy of Layered Transition Metal Oxides for Li-ion Battery Cathodes

2018

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In situ and operando Raman spectroscopy is proposed to provide unique means for deeper fundamental understanding and further development of layered transition metal LiMO 2 (M = Ni, Co, Mn) oxides suitable for Li-ion battery applications. We compare several spectro-electrochemical cell designs and suggest key experimental parameters for obtaining optimum electrochemical performance and spectral quality. Studies of the most practically relevant LiMO 2 compositions are exemplified with particular focus on two experimental approaches: (1) lateral and axial Raman mapping of the electrode's (near-) surface to monitor inhomogeneous electrode reactions and (2) time-dependent singleparticle spectra during cycling to analyze the Li x MO 2 lattice dynamics as a function of lithium content. Raman Spectroscopy is claimed to provide a unique real-time probe of the M-O bonds, which are at the heart of the electrochemistry of LiMO 2 oxides and govern their stability. We highlight the need for further fundamental understanding of the relationships between the spectroscopic response and oxide lattice structure with particular emphasis on the development of a theoretical framework linking the position and intensity of the Raman bands to the local Li x MO 2 lattice configuration. The use of complementary experimental techniques and model systems for validation also deserve further attention. Several novel LiMO 2 compositions are currently being explored, especially containing dopings and coatings, and Raman spectroscopy could offer a highly dynamic and convenient tool to guide the formulation of high specific charge and long cycle life LiMO 2 oxides for next-generation Li-ion battery cathodes.

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Section: Lini a Co B Mn 1-a-b O 2 (Ncms)supporting

confidence: 72%

In situ and Operando Raman Spectroscopy of Layered Transition Metal Oxides for Li-ion Battery Cathodes

2018

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“…Whereas the latter techniques provide average information from all particles within the path of the X‐ray or electron beam, Raman spectroscopy can be used to obtain information from individual oxide agglomerates and even individual particles within the agglomerates. Because the frequencies and intensities of Raman‐active bands emanating from NCM lattices are sensitive to crystal symmetry, coordination geometry, and oxidation states, the technique has been extensively used to characterize transition metal oxides in battery electrodes …”

mentioning

confidence: 99%

“…It is, however, important to recognize that virtually all of the prior Raman‐based studies of NCM materials (Otoyama et al being one exception) were conducted in the “top‐down” mode, wherein the excitation laser is introduced perpendicular to the electrode surface and the Raman scattering is detected perpendicular to the electrode surface. In this top‐down approach one obtains information mainly from the electrode surface because of the limited penetration depth of the excitation wavelength (a depth that is nominally in the range of at most a few micrometers, as will be made evident further on).…”

mentioning

confidence: 99%

Composition and Impedance Heterogeneity in Oxide Electrode Cross‐Sections Detected by Raman Spectroscopy

Gilbert

Maroni

Cui

et al. 2018

Adv Materials Inter

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the upper cutoff voltage (UCV) for cycling is increased. [4][5][6][7][8] The capacity fade in these cells mainly results from immobilization of Li + ions in the solid electrolyte interphase (SEI) of the graphite electrode; this immobilization is accelerated by the dissolution of TM ions from the positive electrode that deposit in the negative electrode SEI. [9,10] Cell impedance rise on aging arises mainly at the positive electrode. Contributing factors include the build-up of electrolyte decomposition products in the electrode, [11] crystal reconstruction at the oxide-particle surfaces, [12,13] increased separation of primary particles within the secondary particle agglomerates, [14,15] and intragranular cracking within the primary particles. [16] An interesting finding from our studies is the significant scatter in electrochemical performance data observed for cells cycled at a C/1 rate to an UCV > 4.3 V. [8] We attributed this nonuniform behavior to the fracture of sintered boundaries between the submicrometer-size primary particles, which occurs in an unpredictable and relatively random manner. Such "intergranular cracking" was also reported recently by Liu et al. [17] who obtained transmission X-ray tomograms on LiNi 0.8 Co 0.15 Al 0.05 O 2 (NCA) oxide electrodes cycled in operando to a UCV = 4.5 V. These authors described heterogeneities in the oxide behavior noting the presence of "active" and "sluggish" populations in cells that displayed ≈20% capacity fade. This observation points to an impedance inhomogeneity within the oxide particle population that develops on extended cycling, wherein the active and sluggish particles can be described as having low impedance and high impedance, respectively.Inhomogeneous behavior in lithium battery electrodes has been the subject of many recent articles. [18][19][20][21][22][23][24][25][26] The presence of these heterogeneities has been revealed by both in situ and ex situ techniques, which include conventional X-ray diffraction (XRD), [17,19] energy dispersive X-ray diffraction, [20] X-ray tomography, [21] X-ray absorption spectroscopy, [22] transmission X-ray microscopy, [23] and transmission electron microscopy. [24][25][26] Another technique, Raman spectroscopy, offers an opportunity to probe local structure in a manner that complements data obtained by the X-ray and electron beam techniques. Whereas the latter techniques provide average information from all particles within the path of the X-ray or electron beam, Raman Lithium-bearing layered transition metal oxides are the materials of choice for positive electrodes in high energy lithium-ion cells being developed for electric vehicle applications. During electrochemical cycling, the loss of mobile lithium-ions due to undesirable side reactions and an increase in cell resistance leads to a decline in the energy and power performance of the cells. This performance loss is often nonuniform across multiple cells, especially for those cycled at high voltages or high C-rates. This nonuniformity results from inhomoge...

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“…Notably, unsupervised intelligence (C-MCR-ALS) results show that carbon and LiMO 2 are a great match with univariate results (Fig. S9) 19 . In contrast to univariate analysis, the unsupervised intelligence extracted two additional components, namely; BG and LMO-II.…”

Section: Resultsmentioning

confidence: 60%

Machine Learning based Analytical Framework for Automatic Hyperspectral Raman Analysis of Lithium-ion Battery Electrodes

Baliyan

Imai

2019

Sci Rep

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The intelligence to synchronously identify multiple spectral signatures in a lithium-ion battery electrode (LIB) would facilitate the usage of analytical technique for inline quality control and product development. Here, we present an analytical framework (AF) to automatically identify the existing spectral signatures in the hyperspectral Raman dataset of LIB electrodes. The AF is entirely automated and requires fewer or almost no human assistance. The end-to-end pipeline of AF own the following features; (i) intelligently pre-processing the hyperspectral Raman dataset to eliminate the cosmic noise and baseline, (ii) extract all the reliable spectral signatures from the hyperspectral dataset and assign the class labels, (iii) training a neural network (NN) on to the precisely “labelled” spectral signature, and finally, examined the interoperability/reusability of already trained NN on to the newly measured dataset taken from the same LIB specimen or completely different LIB specimen for inline real-time analytics. Furthermore, we demonstrate that it is possible to quantitatively assess the capacity degradation of LIB via a capacity retention coefficient that can be calculated by comparing the LMO signatures extracted by the analytical framework (AF). The present approach is suited for real-time vibrational spectroscopy based industrial applications; multicomponent chemical reactions, chromatographic, spectroscopic mixtures, and environmental monitoring.

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Raman Spectroscopy for LiNi<sub>1/3</sub>Mn<sub>1/3</sub>Co<sub>1/3</sub>O<sub>2</sub> Composite Positive Electrodes in All-Solid-State Lithium Batteries

Cited by 22 publications

References 20 publications

In situ and Operando Raman Spectroscopy of Layered Transition Metal Oxides for Li-ion Battery Cathodes

In situ and Operando Raman Spectroscopy of Layered Transition Metal Oxides for Li-ion Battery Cathodes

Composition and Impedance Heterogeneity in Oxide Electrode Cross‐Sections Detected by Raman Spectroscopy

Machine Learning based Analytical Framework for Automatic Hyperspectral Raman Analysis of Lithium-ion Battery Electrodes

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