Polypropylene (PP), a widely used recyclable plastic in packaging and engineering applications, is prone to thermo-oxidative degradation during reprocessing and molding at high temperatures. Antioxidants (AOs) are essential for stabilizing PP in both its virgin and recycled states. The quantity of AO added is critical: insufficient amounts can lead to poor stabilization, while excessive amounts can cause safety concerns due to build-up. This study presents a modified approach to measure the Oxidation Induction Temperature (OIT) using Differential Scanning Calorimetry (DSC), particularly for recycled PP from waste that contains unpredictable contaminations. This modified approach ensures the safety of the calorimetry cell by limiting the oxidation reaction and preventing the release of volatile compounds during measurements. By performing DSC measurements in inert environments, we obtain the OIT, which can be correlated to residual intact AO levels. This approach to monitoring AO levels is demonstrated in both open- and closed-loop recycling of rigid PP. Although the presence of contamination is known to catalyze thermo-oxidative degradation in PP, our results indicate that recycled PP from open-loop collection still contains sufficient residual AO that allows it to withstand limited thermal reprocessing. However, this tendency of AO retention leads to significant build-up during closed-loop recycling when AOs are added to each cycle, where the PP grade remains fairly homogeneous and the dispersity (Đ) does not significantly increase over multiple recycling loops.

The Microplastic Index (MPI) was presented in a previous paper as a method to assess the formation of microplastics during the application of impact and wear stresses, based on selected mechanical and physical properties of polymers. In this paper, the experimental validation of the MPI model is presented. A series of ten polymers was characterized to obtain the relevant parameters for the calculation of the MPI, i.e., the minimum particle size and volume of microplastics formed. The milling (addressing impact stress) and sanding experiments (addressing wear stress) resulted in particle sizes between 3 and 200 μm and 0.3 and 25 μm, respectively. These values were very well predicted by the MPI model, showing smaller particles for brittle polymers and larger ones for ductile polymers. In addition, the experimental-specific wear rates of impact and wear correlated well with the predicted ones, being 0.01–30 mm³/Nm for impact and 0.0002–0.012 mm³/Nm for wear. These results indicate that the MPI can be very well used to predict the tendency of a material to form microplastics. In the search for understanding and mitigating microplastic formation, the MPI can be used by both producers and end users to choose plastic grades that form fewer microplastics.