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The recovery of valuable metals from spent ternary lithium-ion batteries (LIBs) has recently garnered significant attention due to the imperatives of the circular economy and environmental management. While the reclamation of lithium is generally straightforward, the hydrometallurgical methods most frequently employed for leaching and separating the remaining nickel, cobalt, and manganese from spent electrode material often yield secondary liquid and solid wastes. In this study, we present a mechanochemical strategy aimed at repurposing lithium-removed spent ternary LIBs cathode material as a precursor for perovskite oxides through a straightforward and scalable solid-state high-energy ball-milling synthesis. By optimizing the synthesis procedure, we have obtained a perovskite catalyst composed of LaNi0.6Co0.2Mn0.2O3 with a trace amount of phase-separated surface NiO nanocrystals. This catalyst demonstrates outstanding performance in the low-temperature oxidation of CO, exhibiting no degradation in performance over extended periods of service. Notably, it achieves a T50 of 162 °C and a T90 of 197 °C, which compares favorably with previously reported perovskite catalysts prepared via wet synthesis, utilizing fine chemicals as precursors. This approach not only presents a novel method for valorizing spent ternary LIBs but also expands the repertoire of metal precursors available for oxidation catalysts. [ABSTRACT FROM AUTHOR]

The dynamic operation and deep peak‐shaving of power‐generating units cause significant fluctuations in flue gas flowrate, thus affecting the accuracy of CO2 emissions measured by continuous emission monitoring systems (CEMS). This study established a long short‐term memory network with an attention mechanism (LSTM‐AM) for the soft measurement of the flue gas flowrate in real‐time. First, flue gas flowrate data and continuous operation parameters over 25 days were sampled from a typical 660 MW coal‐fired boiler in China. Then, a carbon balance model was established to verify the data reliability. The LSTM‐AM model was trained and testified at the 660 MW coal‐fired boiler. Results show that the LSTM‐AM model significantly surpassed the pristine LSTM model without attention, the convolutional neural network (CNN) with LSTM, and the static support vector regression (SVR) model in the real‐time prediction of flue gas flowrate. Finally, the LSTM‐AM model was generalized to a 630 MW coal‐fired power unit via transfer learning, which was further demonstrated to outperform the model re‐trained from scratch. This work manifests the feasibility of deep learning for the soft measurement of flue gas flowrate, which is promising to solve data‐lagging issues when measuring CO2 emissions from coal‐fired power plants. [ABSTRACT FROM AUTHOR]

Aqueous zinc‐ion batteries (ZIBs) have emerged as the most promising candidate for large‐scale energy storage due to their inherent safety, environmental friendliness, and cost‐effectiveness. Simultaneously, the utilization of organic electrode materials with renewable resources, environmental compatibility, and diverse structures has sparked a surge in research and development of aqueous Zn‐organic batteries (ZOBs). A comprehensive review is warranted to systematically present recent advancements in design principles, synthesis techniques, energy storage mechanisms, and zinc‐ion storage performance of organic cathodes. In this review article, we comprehensively summarize the energy storage mechanisms employed by aqueous ZOBs. Subsequently, we categorize organic cathode materials into small‐molecule compounds and high‐molecular polymers respectively. Novel polymer materials such as conjugated polymers (CPs), conjugated microporous polymers (CMPs), and covalent organic frameworks (COFs) are highlighted with an overview of molecular design strategies and structural optimization based on organic cathode materials aimed at enhancing the performance of aqueous ZOBs. Finally, we discuss the challenges faced by aqueous ZOBs along with future prospects to offer insights into their practical applications. [ABSTRACT FROM AUTHOR]

The wall-slip effect is observed in areas with magnetorheological fluids (MRFs). A slip layer is formed, which reduces the friction between the solid particles and working surface that causes relative movement of the particles. This leads to errors in the measurement of rheological parameters and an inaccurate braking torque model. Thus, here, a rheometer with a sandpaper on the rotor is used to change the working surface roughness to analyze the wall-slip effect of the MRFs. Based on the experimental results, the influence patterns of wall-slip effect on fluid viscosity and yield stress are obtained. Furthermore, a MRF model is established that considers wall-slip effect, which is different from the conventional models. The model is employed to establish a magnetorheological (MR) braking torque model. To verify the braking torque model, a prototype was manufactured, and its mechanical properties were tested. When compared with a smooth rotor, the braking torque of MR brakes with rectangular grooves is increased. This confirms the existence of the wall-slip effect and shows that the wall-slip effect of MRF can be effectively suppressed by incorporating grooves on the rotor surface. [ABSTRACT FROM AUTHOR]