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An acoustic particle patterning method generated ordered structures in battery electrodes to facilitate lithium-ion diffusion and charge transport kinetics, allowing superior rate capability and cycling stability over conventional electrodes.

 

State of health (SOH) estimation of lithium-ion batteries has typically been focused on estimating present cell capacity relative to initial cell capacity. While many successes have been achieved in this area, it is generally more advantageous to not only estimate cell capacity, but also the underlying degradation modes which cause capacity fade because these modes give further insight into maximizing cell usage. There have been some successes in estimating cell degradation modes, however, these methods either require long-term degradation data, are demonstrated solely on artificially constructed cells, or exhibit high error in estimating late-life degradation. To address these shortfalls and alleviate the need for long-term cycling data, we propose a method for estimating the capacity of a battery cell and diagnosing its primary degradation mechanisms using limited early-life degradation data. The proposed method uses simulation data from a physics-based half-cell model and early-life degradation data from 16 cells cycled under two temperatures and C rates to train a machine learning model. Results obtained from a four-fold cross validation study indicate that the proposed physics-informed machine learning method trained with only 60 early life data (five data from each of the 12 training cells) and 30 high-degradation simulated data can decrease estimation error by up to a total of 9.77 root mean square error % when compared to models which were trained only on the early-life experimental data. 

Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 (LLZTO) is a promising inorganic solid electrolyte due to its high Li + conductivity and electrochemical stability for all-solid-state batteries. Mechanical characterization of LLZTO is limited by the synthesis of the condensed phase. Here we systematically measure the elastic modules, hardness, and fracture toughness of LLZTO polycrystalline pellets of different densities using the customized environmental nanoindentation. The LLZTO samples are sintered using the hot-pressing method with different amounts of Li 2 CO 3 additives, resulting in the relative density of the pellets varying from 83% to 98% and the largest grain size of 13.21 ± 5.22 μm. The mechanical properties show a monotonic increase as the sintered sample densifies, elastic modulus and hardness reach 158.47 ± 10.10 GPa and 11.27 ± 1.38 GPa, respectively, for LLZTO of 98% density. Similarly, fracture toughness increases from 0.44 to 1.51 MPa⋅m 1/2 , showing a transition from the intergranular to transgranular fracture behavior as the pellet density increases. The ionic conductivity reaches 4.54 × 10 −4 S/cm in the condensed LLZTO which enables a stable Li plating/stripping in a symmetric solid-state cell for over 100 cycles. This study puts forward a quantitative study of the mechanical behavior of LLZTO of different microstructures that is relevant to the mechanical stability and electrochemical performance of all-solid-state batteries.