For devices encountering long‐term stability challenges, a precise evaluation of degradation is of paramount importance. However, methods for comprehensively elucidating the degradation mechanisms in devices, particularly those undergoing dynamic chemical and mechanical changes during operation, such as batteries, are limited. Here, a method is presented using operando computed tomography combined with X‐ray absorption near‐edge structure spectroscopy (CT‐XANES) that can directly track the evolution of the 3D distribution of the local capacity loss in battery electrodes during (dis)charge cycles, thereby enabling a five‐dimensional (the 3D spatial coordinates, time, and chemical state) analysis of the degradation. This paper demonstrates that the method can quantify the spatiotemporal dynamics of the local capacity degradation within an electrode during cycling, which has been truncated by existing bulk techniques, and correlate it with the overall electrode performance degradation. Furthermore, the method demonstrates its capability to uncover the correlation among observed local capacity degradation within electrodes, reaction history during past (dis)charge cycles, and electrode microstructure. The method thus provides critical insights into the identification of degradation factors that are not available through existing methods, and therefore, will contribute to the development of batteries with long‐term stability.

1. Purpose Solid-state lithium-ion batteries (SSLIBs) have attracted extensive attentions because of their potentials to improve safety and to achieve higher power and energy densities compared with the conventional lithium-ion batteries. In composite electrodes for SSLIBs, particles of active material (AM) and solid electrolyte (SE) are three-dimensionally distributed and form complicated Li-ion and electron pathways. Then, especially during high rate charging/discharging, the inhomogeneous reaction distribution may be formed in the electrode. The reaction distribution can substantially deteriorate the battery performances of SSLIBs such as capacity, power output, rate capability, and cyclability. Therefore, understanding how the reaction distribution affects the battery performances is crucial for the development of high-performance SSLIBs. In this study, we performed operando three-dimensional (3D) observation of reaction distributions in composite electrodes for SSLIBs using CT-XAFS1 to elucidate the influence of reaction distributions on the battery performances of SSLIBs. 2. Experimental An SSLIB cell with a configuration of Li x CoO2 (LCO)-Li2.2C0.8B0.2O3 (LCBO) composite electrode|LCBO electrolyte|poly (ethylene oxide) (PEO)-based polymer electrolyte|Li metal electrode was fabricated based on the literature2. The composite electrode comprised 8 mg of LCO and 2 mg of LCBO. The SSLIB cell was charged to 4.5V and discharged to 2.0 V with a current of 240 μA (0.3 C) for 3 cycles at 100 °C. The reaction distribution in the composite electrode was observed every 40 minutes during charging and discharging using operando CT-XAFS. The CT-XAFS measurements were carried out in the energy range between 7725.5-7730 eV with an energy step of 0.1 eV under an exposure time of 20 ms per energy and projection angle from -65 to 65° with an angle step of 0.1°. The observation area was 517 × 517 × 49 μm. The spatial and time resolutions were 3.1 μm and 40 min., respectively. 3. Results and Discussion Figure 1(a) shows the charge-discharge curves of the SSLIB cell. The SSLIB cell exhibited the initial charge and discharge capacities of 84 and 46 mAh·g−1, respectively. Those degraded to 24 and 13 mAh·g−1 in the 2nd cycle, and 12 and 12 mAh·g−1 in the 3rd cycle, respectively. Figure 1(b) shows the 3D charging state (Li content) maps of the composite electrode and the representative 2D cross-sectional charging state maps in the in-plane direction at the end of 1st, 2nd, and 3rd charge. Colored/uncolored areas stand for the regions where the AMs exist or not, respectively. The red/blue areas stand for the charged (x = 0.6) /discharged (x = 1.0) AMs, respectively. After the 1st charge, the AMs generally showed a high charging state (red). However, the regions with high charging state significantly decreased after the 2nd charge, and most of the AMs showed a charging state corresponding to x = 0.7 ~ 0.8 (green). The regions with high charging state further decreased after the 3rd charge, and AMs with a low charging state were increased (blue). To more precisely investigate where the less-charged AMs existed after the 2nd and 3rd charge, we subtracted the charging state map after the 1st discharge from that after the 2nd charge, and also the charging state map after the 2nd discharge from that after the 3rd charge (Fig. 1(c)). The 2D maps in Fig. 1(c) thus indicate to what extent the charge reaction progressed during the 2nd or 3rd charge compared with the end of the 1st or 2nd discharge, respectively. The red/blue areas in Fig. 1(c) represent the AM regions where the reaction progressed (Δx = 0.4) or not (Δx = 0) from the end of the 1st or 2nd discharge, respectively. As shown in the left map in Fig. 1(c), the inner parts of the aggregated AMs were significantly less charged compared to the regions near AM/SE interfaces during the 2nd charge. Similarly to the 2nd charge, the charge reaction preferentially progressed near the AM/SE interfaces, while the reaction insufficiently progressed at the inner parts of the aggregated AMs during the 3rd charge. Such a variation in the charging state distribution during cycling could be explained by considering the microstructural change due to the expansion/shrinkage of AM particles. The above results suggest that the expansion/shrinkage of AM particles reduced the connections between AM particles but did not reduce the AM/SE interfaces. We further discuss the mechanism of the capacity degradation during cycling in the presentation. References: (1) H. Matsui et al., Angew. Chem. Int. Ed., 56, 9371 (2017). (2) T. Okumura et al., Solid State Ionics, 288, 248 (2016). Acknowledgement: This work was supported by JST ALCA-SPRING (JPMJAL1301). Figure 1

No abstract