United KingdomA new study from the Agricultural University of Athens introduces a machine-learning tool that rapidly predicts how quickly biodegradable plastics, specifically PHBV (poly(3-hydroxybutyrate-co-3-hydroxyvalerate)), break down in natural environments. Traditional testing methods can take months or years, but this tool uses data from 13 peer-reviewed studies covering nearly three decades to create a predictive model. Two machine learning algorithms—Random Forest and XGBoost—were trained on data including factors such as temperature, polymer composition, and microbial activity, achieving high accuracy (R² values of 0.95–0.97). The model is now available as a free, interactive web tool, enabling researchers and manufacturers to assess biodegradation rates based on formulation and environmental inputs. This development supports efforts to design safer, more sustainable biodegradable materials.
Bias read (Center): The article presents scientific research without overt ideological framing. It discusses technological advancements in biodegradable plastics, which have implications for environmental policy and sustainability, but does not take a partisan stance. The focus remains on technical findings and their应用
