Quantification of PLA degradation in the melt phase using a parallel plate rheometer

Bioplastics like poly(lactic acid) or PLA have received significant attention over the last years, triggered by global environmental and climate concerns linked to the use of petroleum-based plastics. However, the ester bonds in PLA that are responsible for its biodegradable character are also vulnerable linkages during polymer processing. Melt processing of polymers typically involves high temperatures and shear loading. Hence, understanding degradation of PLA in the melt phase is crucial to minimize the molecular weight decrease, since it is directly linked to deterioration of mechanical, thermal and rheological properties. In this study, we performed a 24 full factorial design to investigate melt-phase degradation using a parallel plate rheometer. We investigated the effect of four parameters on the molecular weight: moisture content in the PLA, processing temperature, residence time, and shear stress. In addition, we explored whether the percentage of d-isomer significantly influences the degradation, through selecting three PLA-grades with different optical purity. The results showed that moisture content is the dominant factor in the molecular weight decrease, followed by a significant effect of processing temperature. Notably, the significant interaction between moisture content and processing temperature highlights the importance of studying interaction effects, an aspect often overlooked in current literature. Moreover, only the effect of moisture content is influenced by the percentage of d-isomer, whereas the numerical values of other significant effects are similar across the three PLA-grades. The small, yet significant effect of residence time, along with the unexpected insignificant effect of shear stress, may be explained by the use of small amplitude oscillatory deformations in a parallel plate rheometer as testing method in this study. In conclusion, the findings of this study offer polymer processors and researchers valuable insights, which can help with the selection and prediction of processing conditions that minimize degradation, ultimately saving time and raw materials during production.