The construction of thermal decomposition kinetic model of phenyl silicone oil requires the combination of thermogravimetric analysis (TGA) data and multiple kinetic methods, and the accuracy is optimized through model verification to support material stability evaluation and process optimization in high-temperature applications. The following is a specific analysis:

Model construction method
Thermogravimetric analysis (TGA) experimental design
Temperature program: Use programmed temperature increase (such as 5-20℃/min) or isothermal method to measure the thermal weight loss curve (TG/DTG) of phenyl silicone oil in an inert atmosphere (nitrogen/argon) or oxidizing atmosphere.
Key parameters: Record the initial decomposition temperature (T₅%), maximum weight loss rate temperature (Tₚ) and residual mass at different heating rates to provide a data basis for kinetic calculations.
Kinetic model selection
Isoconversion rate method (FWO/KAS): There is no need to preset the reaction mechanism function. The activation energy (Eₐ) is calculated by the decomposition temperature (Tₐ) corresponding to the same conversion rate (α) at different heating rates. It is suitable for the preliminary analysis of multi-step reaction systems.
Master Plots: Combine the Eₐ obtained by the equal conversion rate method with the integral or differential form of the theoretical model (such as n-order reaction, Avrami-Erofeev equation) to determine the most likely reaction mechanism.
Global fitting method (such as Coats-Redfern): Assuming a single reaction mechanism, nonlinearly fit the TG/DTG curve to simultaneously solve Eₐ, pre-exponential factor (A) and reaction order (n), which is suitable for systems with clear reaction mechanisms.
Model parameter solution
Activation energy (Eₐ) and pre-exponential factor (A): Calculate Eₐ under different α by FWO/KAS method. If Eₐ changes significantly with α, segmented fitting is required; master curve method or global fitting method can further determine A and reaction mechanism.
Reaction mechanism function (f(α)): Combine the morphology of the thermogravimetric curve (such as single peak/multi-peak) and the model matching degree to select the mechanism function that best fits the experimental data (such as three-dimensional diffusion, phase boundary reaction, etc.).
Model verification and optimization
Residual analysis and error assessment
Compare the α-T curve predicted by the model with the experimental data, calculate the residual sum of squares (RSS) or correlation coefficient (R²) to ensure the accuracy of the model over the entire temperature range.
For complex decomposition processes (such as multi-step reactions), it is necessary to introduce a distributed activation energy model (DAEM) or segmented kinetic processing to reduce the error of a single model.
Pyrolysis mechanism verification
The reaction mechanism assumed by the model is verified by combining pyrolysis product analysis (such as FTIR, GC-MS). For example, if the model predicts that the first step is side chain cleavage, the corresponding small molecule fragments must be detected in the product.
Model application direction
Material stability assessment
Predict the service life of phenyl silicone oil at different temperatures through kinetic parameters. For example, based on the Arrhenius equation, the thermal life (t₅₀%) at 300°C is extrapolated to guide the selection of high-temperature lubricants or sealing materials.
Thermal stability optimization
Analyze the effects of phenyl content, molecular weight, etc. on Eₐ to provide a basis for formulation design. For example, silicone oil with high phenyl content (>45%) is more suitable as a high-temperature lubricant resistant to temperatures above 300°C due to the increased Eₐ (>200 kJ/mol).


Process Condition Optimization
During the synthesis process, the reaction temperature and time are controlled by a kinetic model to avoid side reactions. For example, in the synthesis of cross-linked phenyl silicone oil, it is necessary to ensure that the cross-linking reaction rate is lower than the thermal decomposition rate to ensure the product yield.