Multiple linear regression (mlr) models used to predict the thermal stability of some polyimides

Catalin Lisa1, Corneliu Hamciuc2, Elena Hamciuc2, Gabriela Lisa1

1 Gheorghe Asachi Tehnical University, Iasi, Faculty of Chemical Engineering and Environmental Protection Cristofor Simionescu , 73 Prof.dr.doc. Dimitrie Mangeron, 700050 Iasi, Romania
2 Petru Poni Institute of Macromolecular Chemistry, Aleea Gr. Ghica Voda 41A, 700487 Iasi, Romania


Two multiple linear regression (MLR) models were developed with the aim to estimate the decomposition temperature of a series of polyimides. Two parameters, Tni and Tai, corresponding to the temperature of 10 % weight loss of the sample, determined by dynamic thermogravimetric analysis under conditions of N2 inert atmosphere and air, respectively, were used as a criterion for thermal stability. The obtained MLR models correlate thermostability with a series of characteristics of the studied polymers, such as Van der Waals volume, density, molecular weight, number of aromatic cycles, number of C=O bonds, number of CH3 groups and the number of CF3 groups. The results showed that the MLR models can be successfully used to predict the thermal stability of polyimides, the mean percentage errors being below 3%, regardless of the work environment.


MLR models; polyimides; prediction; thermal stability

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