Physically Informed Multi-Magnification Learning for Scalable, Non-Destructive Assessment of Polypropylene Degradation in Recycled Materials

Kazuki Hammura; Kiyotaka Hitomi; Kenji Nagata; Masanobu Naito

doi:10.1021/acssusresmgt.6c00307

ジャーナル論文 Physically Informed Multi-Magnification Learning for Scalable, Non-Destructive Assessment of Polypropylene Degradation in Recycled Materials

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Kazuki Hammura, Kiyotaka Hitomi, Kenji Nagata, Masanobu Naito. Physically Informed Multi-Magnification Learning for Scalable, Non-Destructive Assessment of Polypropylene Degradation in Recycled Materials. ACS Sustainable Resource Management. 2026, 3 (9), 3048-3057. https://doi.org/10.1021/acssusresmgt.6c00307

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Reliable quality control of recycled polypropylene (PP) is essential for circular plastics management, but conventional degradation tests are often destructive or unsuitable for large-scale screening. This study introduces a physically informed, multi-magnification learning framework for non-destructive and scalable assessment of PP degradation from optical micrographs. A three-channel feature representation captures baseline-referenced yellowing, defect-associated contrast, and microstructural anisotropy. To address multi-scale variability, magnification-aware image patches are integrated via a hierarchical attention-based multiple instance learning model. The model jointly predicts continuous weathering cycles and ordinal degradation stages, enabling quantitative evaluation of heterogeneous material conditions. On the validation set, the approach achieved a balanced accuracy of 0.747, a stage-decoded mean absolute error of 5.6 cycles, and a quadratic weighted kappa of 0.908, with an accuracy within ±1 stage of 0.97. Notably, the model identified 10-cycle-related image signals despite the absence of a clear increase in attenuated total reflection infrared carbonyl index under the same conditions. These results demonstrate the feasibility of AI-enabled optical micrograph analysis as a rapid and non-destructive screening approach for weathering-related surface-state assessment under the controlled post-industrial recycled (PIR) PP conditions examined in this study. The framework can therefore serve as a basis for high-throughput image-based screening of weathering-related surface states, although extension to broader recycled PP streams and practical quality attributes requires further validation.

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