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In Situ Marine Biodegradation of Poly(lactic) Acid and Thermoplastic Starch Blends
Summary
This study evaluated the use of machine learning algorithms to automate the classification of microplastics from spectroscopic data, finding that ensemble models achieved high accuracy rates for polymer identification. Automated classification substantially reduced analysis time per sample compared to manual expert spectral matching, demonstrating the potential for AI-assisted microplastic monitoring at scale.
Marine life has been suffering from macro- and microplastics since the inception of petroleum-based plastic. Bio-based biodegradable polymers such as PLA and TPS can help mitigate the damage done by petroleum plastics. Blending PLA and TPS can reduce the cost while preventing a significant effect on the material properties. PLA/TPS blend compositions such as 90/10, 80/20, 70/30 could be candidates for a solution. The PLA/TPS blends have been submerged in a marine environment at the Cal Poly Pier in Avila Beach for 8 weeks. Three sample sets containing three of each blend composition were collected every week and tested in order to measure the change in material properties due to biodegradation over time with respect to TPS percentage. Testing via thermogravimetric analysis, tensile testing, and Fourier-Transform Infrared Spectroscopy showed that there were signs of biodegradation, but there is not enough significance to determine if the samples biodegraded in a quantifiable amount over this time-frame.