Kinetic predictions are widely used in thermal runaway research. At present, the model-fitting method based on single experimental data is mainly adopted in the kinetic prediction of accelerating rate calorimetry (ARC), which has the problems related to difficult analysis of reaction mechanism and large uncertainty of kinetic parameters. The Kinetic Committee of the International Confederation for Thermal Analysis and Calorimetry has clearly proposed to avoid using this method. The development of prediction methods that use multiple temperature programs is essential in the field of ARC. To address these issues, the kinetic predictions based on isoconversional methods were studied. The data of the n -order reaction and autocatalytic reaction under adiabatic conditions were obtained by numerical simulations, and the kinetic parameters were estimated by the Vyazovkin and the Friedman methods. Then, the kinetic parameters are substituted into the equations for adiabatic and isothermal prediction. To solve the problem of large prediction error, a simple correction method of kinetic parameter was proposed. Finally, the kinetic prediction approaches were verified by experiments. The results show that the ARC can carry out model-free kinetic predictions based on the isoconversional methods. The Vyazovkin method has higher prediction accuracy than the Friedman method, and the simple correction method can effectively improve prediction accuracy. The kinetic prediction approaches based on the isoconversional methods are beneficial supplements and alternatives for traditional kinetic analysis method of the ARC.
At present, the kinetic approaches of accelerating rate calorimetry (ARC) are restricted to model-fitting methods, and the n -order reaction model is mainly used to estimate the kinetic parameters. However, the model-fitting method has the problems related to model selection and falling into a local optimum. To address these issues, kinetic analysis methods are introduced into ARC data treatment, and the kinetic parameters of complex reactions are solved by a model-free and model-fitting fusion method. First, the Friedman method is used to calculate the activation energy E α . Then, A α and the overall mathematical function f ( α ) are obtained through the compensation effect. It provides references for the selection of the reaction model and initial values of model fitting, and the kinetic parameters are estimated through two-step model fitting. The fitting in steps realizes the decoupling of E and A and reduces the data dimensions. These help to reduce the chance of falling into a local optimum and lower the experience requirements. Finally, numeric simulations and the experimental results showed the effectiveness of the method.
Cellulose is the most abundant natural biopolymer material, which has been widely used in film making and food packaging in recent years. However, lignin, a natural bioaromatic material, is always applied as a waste resource due to its low utilization efficiency. In this study, a ZnCl2/CaCl2/cellulose mixed system was used to prepare film materials via a regeneration method. The chemical structure and corresponding properties were characterized. The thermal decomposition process of film materials showed that with an increase of the heating rate, the maximum weight loss temperature gradually shifted to the higher-temperature region. Additionally, the combination of lignin with cellulose as composite films can effectively improve thermal stability. Furthermore, kinetics methods such as Kissing–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (FWO), and Friedman were used to calculate the average activation energy (E). This study proposed a facile method for preparing biobased multifunctional composite films using two kinds of naturally renewable materials.
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