In this work, the thermal stability of four types of 18,650 lithium-ion batteries with LiCoO₂ (LCO), LiFePO₄ (LFP), LiNi_0.8Co_0.1Mn_0.1O₂ (NCM811) and LiNi_0.8Co_0.15Al_0.05O₂ (NCA) materials as cathodes are experimentally investigated by the accelerating rate calorimeter (ARC) and the isothermal battery testing calorimeter (iso-BTC) under adiabatic and isothermal conditions, respectively. The thermal runaway danger level of these batteries can be ranked as LCO > NCA > NCM811 >> LFP by judging from the values of T_max and HR_{max, nominal}. The higher the nickel and cobalt content, the higher the lithium-ion battery capacity, but the worse the thermal stability. The Q_total of NCA is the largest in the complete standard charge and discharge process, due to that the capacity of NCA is significantly higher than that of the other three batteries, resulting in remarkable increase in Q_irre proportioned to the square of the current. When the ambient temperature rises, the energy release decreases owing to the decrease in the internal resistance of the battery. These studies are expected to have important implications for the subsequent safe design of commercial lithium-ion batteries with different cathode materials.

The excess sludge in the petrochemical industry is large in quantity, complex in composition, and highly harmful, and its rational disposal is of great significance for environmental protection and sustainable development. In the present study, a classification and disposal strategy for the excess sludge in the petrochemical industry is proposed. The strategy first analyzes the dioxin, flammability, corrosivity, reactivity, and leaching properties of the sludge, from which the waste type of the sludge (general waste or hazardous waste) can be determined. Then, methods of disposal can be selected depending on the type of waste and the corresponding risk analysis, enabling rationalized disposal of the sludge. To verify the effectiveness and practicability of the proposed sludge classification and disposal strategy, research on petrochemical excess sludge samples (i.e., Ah, Bl, and Cq) originated from three different regions in China is carried out as a case study. The component analysis of the above three sludge samples revealed that they are all general wastes. In addition, the possibility of employing Cq sludge for landfill, soil modification, and greening mud, as well as the risk of landfill and incineration disposal in solid waste landfills are investigated. Furthermore, natural radioactive elements uranium and thorium in Cq sludge sample are studied. The results show that Cq sludge cannot be used for landfill, soil modification, and greening mud due to excessive arsenic content. The proposed strategy provides a basis for the selection of reasonable petrochemical excess sludge disposal methods.

The heat of decomposition of reactive chemicals is a critical parameter for characterizing their thermodynamic properties and has broad application across various essential fields, including hazard identification, chemical classification, and safety risk assessment of chemical reactions. Currently, the primary method for determining the heat of decomposition of compounds relies on experimental techniques, which are not only complex but also inherently associated with significant potential hazards. The existing predictive methodologies are still in the developmental stage. This paper first analyzes and summarizes the current research progress on the prediction of heat of decomposition, then provides an in-depth exploration of the three most prominent predictive methods: the Chemical Thermodynamic and Energy Release Computer Program (CHETAH) program, quantum chemistry calculation, and Quantitative Structure-Property Relationship (QSPR) methods. It focuses on their underlying prediction mechanisms, accuracy, advantages, and limitations. Among these, quantum chemical computation methods and the QSPR approach have demonstrated superior predictive performance. The study specifically emphasizes that integrating deep learning techniques can overcome existing bottlenecks: transfer learning can mitigate the challenge of limited samples in QSPR modeling, while large language models (LLMs) in chemistry can address the prediction difficulties of decomposition reaction equations. These innovative directions are expected to significantly enhance predictive accuracy and provide crucial technical pathways for future research.