21:I[432972,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/1270dead247da766.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/2d7aa753e81324a8.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/263f2cdcb9aa2341.js","/_next/static/chunks/294b9525248c8e5f.js"],"default"] 22:T4fe,Abstract Graphitic carbon nanocages (CNCs) have garnered attention as viable candidates for potassium storage, primarily due to their notable crystallinity, large surface area, and rich porosity. Yet, the development of a rapid, scalable, and economically feasible synthesis approach for CNCs persists as a formidable challenge. This study presents a rapid (millisecond‐scale) and scalable (gram‐scale) method for fabricating mesoporous CNCs characterized by high purity and orderly graphitic structures, utilizing the flash Joule heating technique. Employed for potassium storage, the CNC electrode developed herein exhibits exceptional performance metrics, including initial capacity, rate capability, and cycling stability, surpassing numerous carbonaceous materials previously documented. Impressively, it delivers a high initial capacity of 312.3 mAh g −1 at 0.1 A g −1 , maintains 175.1 mAh g −1 at a high rate of 2.0 A g −1 , and retains 219.6 mAh g −1 over 1000 cycles at 1.0 A g −1 . Molecular dynamics simulations and in situ characterizations are employed to elucidate this robust behavior. This work underscores the significant advantages of the flash Joule heating technique in synthesizing carbonaceous materials for potassium storage applications.23:T46a,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.24:T537,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

#	Concept	H-Index	Publications	Citations
1	Engineering	2	2	40
2	Business	1	1	14
3	Chemical Engineering	1	1	26
4	Chemistry	1	1	26
5	Data Science	1	1	14

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