A hybrid classification and evaluation method based on deep learning for decoration and renovation waste in view of recycling.

The escalating volume of decoration and renovation waste (D&RW) amid the rapid urbanization in China has posed significant challenges for the effective recycling of this waste stream, primarily due to the difficulty of accurately assessing its precise composition. To enhance the recycling of high-value materials from D&RW, a comprehensive understanding of its composition and quality is crucial before sorting. In this study, we propose a hybrid method that combines instance segmentation deep learning (DL) models with morphological machine learning (ML) models to automate the classification and evaluation of D&RW.

Filler effects inspired high performance polyurethane elastomer design: segment arrangement control.

Elastomers with high strength and toughness are in great demand. Previous research on elastomers focused mainly on the design of new chemical structures, but their complicated synthesis process and expensive monomers have restricted the practical application of these materials. Inspired by general filler effects, a strategy is proposed to remarkably enhance the mechanical properties of thermoplastic polyurethane (TPU) elastomers by designing the arrangement of hard/soft segments using traditional chemical compositions.