Capitalizing on the Iodometric Reaction for Energetic Aqueous Energy Storage.

Iodometric and iodimetric titrations represent a prevailing technique to determine the concentration of Cu2+ ions in aqueous solutions; However, their utilization in electrochemical energy storage has been overlooked due to the poor reversibility between CuI and Cu2+ related to the shuttling effect of I3- species. In this work, we developed a 4A zeolite separator capable of suppressing the free shuttling of I3- ions, thus achieving a record-high capacity retention of 95.7% upon 600 cycles. Theoretical and experimental studies reveal that the negatively charged zeolite can effectively impede the approach and penetration of I3- ions, as a result of electrostatic interaction between them. To explore the practical potential, a hybrid cell of Zn∥I2 consisting of Cu2+ redox agent has been assembled with a discharge capacity of 356 mA h g-1. The cell affords a specific energy of 443 W h kg-1 based on I2, or 193 W h kg-1 based on both electrodes. This work offers insight on the energy utilization of the iodometric reactions and advocates a Cu2+-mediated cell design that could potentially double the capacity and energy of conventional aqueous battery systems.

Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting.

Low-carbon and environmentally friendly living boosted the market demand for new energy vehicles and promoted the development of the new energy vehicle industry. Accurate demand forecasting can provide an important decision-making basis for new energy vehicle enterprises, which is beneficial to the development of new energy vehicles. From the perspective of an intelligent supply chain, this study explored the demand forecasting of new energy vehicles, and proposed an innovative SARIMA-LSTM-BP combination model for prediction modeling. The data showed that the RMSE, MSE, and MAE values of the SARIMA-LSTM-BP combination model were 2.757, 7.603, and, 1.912, respectively, all of which are lower values than those of the single models. This study therefore, indicated that, compared with traditional econometric forecasting models and deep learning forecasting models, such as the random forest, support vector regression (SVR), long short-term memory (LSTM), and back propagation neural network (BP) models, the SARIMA-LSTM-BP combination model performed outstandingly with higher accuracy and better forecasting performance.