A hybrid electrolyte composed of a high-concentration electrolyte made of dimethyl carbonate and lithium bis(fluorosulfonyl)imide and lithium exchanged faujasite-type zeolite exhibits solidification of the electrolyte with high ionic conductivity and excellent contact properties with a lithium metal anode and an olivine-type LiFePO₄ cathode. The assembled battery showed excellent cyclability for at least 100 cycles at a charge rate of 1 C at 60 °C with a discharge capacity retention of 98.6 % and high coulombic efficiency.

WO₃ with a high specific surface area of more than 100 m² g⁻¹ was synthesized by a simple sol-gel method to explore the potential of group VI metal oxides as the positive electrode active material for aluminum rechargeable batteries. Charge/discharge measurements indicated more than 130 mAh g⁻¹ capacity, which is the largest WO₃ electrode for an aluminum rechargeable battery. The capacity retention after 50 cycles was 80 %. From XPS measurements, the valence of tungsten changed during charging/discharging, and the content of aluminum also changed; therefore, intercalation/deintercalation type charge/discharge mechanism was confirmed for the WO₃ electrode. These results indicate that WO₃ with high specific surface area has high potential as a positive electrode material for aluminum rechargeable batteries.

Direct toluene electro-hydrogenation is gaining considerable attention for storing and transporting large amounts of energy. When a proton exchange membrane (PEM) is used in this system, dragged water molecules migrate with the protons from the anode to the cathode, and water inhibits the toluene hydrogenation reaction at the cathode. An anion exchange membrane (AEM) is expected to facilitate the migration of water from the cathode to the anode, and less water suppresses the side reactions. In this study, the quantity of water reaching the cathode is determined. Further, direct toluene electro-hydrogenation without side reactions except hydrogen generation is successfully performed using the AEM. Moreover, optimization of the catalyst loading results in an improved current efficiency exceeding 1.5 mg cm⁻². This technology is valuable as a first step in direct toluene electro-hydrogenation using AEM.

Lithium-ion batteries are widely used as power sources for various devices, so rapid and accurate estimation of the health status of lithium-ion batteries is an important means to reduce battery failures. This article conducts charging and discharging experiments on NCA batteries and LFP battery modules. A probability density function based method for predicting the health status of lithium-ion batteries has been proposed. The kurtosis at the peak of the probability density function (PDF) curve of the battery charging voltage was used as input for the model to achieve accurate prediction of battery SOH. The experimental results show that there is a good correlation between this health indicator and battery SOH, with Pearson correlation coefficients greater than 0.96. Therefore, it can be concluded that it can indirectly reflect the current situation of battery SOH and serve as input for the model to further predict SOH. Long short-term memory networks (LSTM) have become a popular deep learning network method for predicting the health status (SOH) of lithium-ion batteries. The LSTM method without optimizing hyperparameters can easily lead to low accuracy in battery SOH prediction models. A modified LSTM method based on Sparrow Search Algorithm (SSA) is proposed for the prediction of State of Health (SOH) in lithium-ion batteries. When the training set only accounts for 20 % of the total data, the root mean square error (RMSE) of LFP battery prediction results is within 0.85 %, and the maximum absolute error (AE) is less than 2.5 %, while the RMSE of NCA battery SOH prediction results is within 0.7 %, and the maximum AE is less than 2.0 %. SSA-LSTM can accurately predict battery SOH under limited training data and has good robustness.