Composite polymer electrolytes provide an emerging solution for new battery development by replacing liquid electrolytes, which are commonly complexes of polyethylene oxide (PEO) with ceramic fillers. However, the agglomeration of fillers and weak interaction restrict their conductivities. By contrast with the prevailing methods of blending preformed ceramic fillers within the polymer matrix, here we proposed an in situ synthesis method of SiO₂ nanoparticles in the PEO matrix. In this case, robust chemical interactions between SiO₂ nanoparticles, lithium salt and PEO chains were induced by the in situ non-hydrolytic sol gel process. The in situ synthesized nanocomposite polymer electrolyte delivered an impressive ionic conductivity of ~1.1 × 10⁻⁴ S cm⁻¹ at 30 °C, which is two orders of magnitude higher than that of the preformed synthesized composite polymer electrolyte. In addition, an extended electrochemical window of up to 5 V vs. Li/Li⁺ was achieved. The Li/nanocomposite polymer electrolyte/Li symmetric cell demonstrated a stable long-term cycling performance of over 700 h at 0.01–0.1 mA cm⁻² without short circuiting. The all-solid-state battery consisting of the nanocomposite polymer electrolyte, Li metal and LiFePO₄ provides a discharge capacity of 123.5 mAh g⁻¹, a Coulombic efficiency above 99% and a good capacity retention of 70% after 100 cycles.

Inevitable mistuning in cyclic bladed disk structures would cause vibration amplification phenomena that seriously reduce the reliability of the bladed disk. The ability to accurately and quickly predict the dynamic responses is critical to investigating the dynamic behavior of the mistuned system. However, it is still challenging because the mistuned responses are extremely sensitive to the random mistuning parameters. In this work, a novel mistuned system deep neural network model (MS-DNN) is presented to predict the dynamic responses of mistuned bladed disks through the mistuning parameters for both the lumped parameter model and the large-scale finite element (FE) model, which decouples the vibration equations of the mistuned system and uses a neural network to replace the coupling process. MS-DNN is divided into two levels, namely, the blade and the disk. The blade-level neural networks are used for forward and backward propagation of mistuning parameters in the different blades, and the disk-level neural network is used to replace the physical coupling process in the disk of multiple mistuning parameters from individual blades, with data transmission between the neural networks via blade–disk boundary nodes. The expected physical response of the blade tip is predicted through MS-DNN. All neural networks in MS-DNN show high prediction accuracy on both training sets and unknown test sets. For the FE model of the industrial bladed disk, the effect of the number of boundary nodes selected as the data interface between neural networks on the prediction accuracy is also investigated. The results show that, for unknown test data, the predicted response has an [ ] value of 0.998 versus the actual response with an amplification factor error of less than 0.388%.

Garnet Li₇La₃Zr₂O₁₂ is one of the most promising solid electrolytes used for solid-state lithium batteries. However, low ionic conductivity impedes its application. Herein, we report Ta-doping garnets with compositions of Li_7-xLa₃Zr_2-xTa_xO₁₂ (0.1 ≤ x ≤ 0.75) obtained by solid-state reaction and free sintering, which was facilitated by graphene oxide (GO). Furthermore, to optimize Li_6.6La₃Zr_1.6Ta_0.4O₁₂, Mg²⁺ was select as a second dopant. The dual substitution of Ta⁵⁺ for Zr⁴⁺ and Mg²⁺ for Li⁺ with a composition of Li_6.5Mg_0.05La₃Zr_1.6Ta_0.4O₁₂ showed an enhanced total ionic conductivity of 6.1 × 10⁻⁴ S cm⁻¹ at room temperature. Additionally, spark plasma sintering (SPS) was applied to further densify the garnets and enhance their ionic conductivities. Both SPS specimens present higher conductivities than those produced by the conventional free sintering. At room temperature, the highest ionic conductivity of Li_6.5Mg_0.05La₃Zr_1.6Ta_0.4O₁₂ sintered at 1000 °C is 8.8 × 10⁻⁴ S cm⁻¹, and that of Li_6.6La₃Zr_1.6Ta_0.4O₁₂ sintered at 1050 °C is 1.18 × 10⁻³ S cm⁻¹.