Modeling of Chain Sequence Length and Distribution in Random Copolyesters

The performances and properties of random copolyesters, including biodegradability, mechanical and thermal properties, transparency, etc., are highly influenced by their chain structures. However, obtaining detailed chain sequence information remains a significant challenge. This study introduces a mathematical model based on a probabilistic approach to determine the sequence length and distribution in random copolyesters. Two types of copolyesters, A₁A₁BB-A₂A₂BB, representing poly(butylene adipate-<i>co</i>-terephthalate) (PBAT), and A₁A₁B₁B₁-A₂B₂, using poly(butylene succinate-<i>co</i>-glycolic acid) (PBT-PGA) as an example, are the focus. The predicted sequence lengths of various copolyesters derived from the model are in good agreement with the values reported in the literature. The chain sequence distribution obtained from the model provides better insights into the unique properties of the copolyesters. It is observed that the incorporation of hydroxyl acid units into copolyester chains effectively reduces the sequence length without altering the copolymer composition, offering a strategic approach for enhancing degradation performance while maintaining mechanical properties of random copolyesters. The influence of the number-average sequence length becomes particularly significant when the copolymer composition ranges between 0.7 and 0.9, with a higher copolymer composition required for copolyesters containing hydroxyl acid monomer units. This model represents a powerful tool for researchers, enabling a deeper understanding of the relationship between copolymer composition and its structural characteristics in random copolyesters and facilitating the development of high-performance random copolyesters.