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

https://mdr.nims.go.jp/datasets/1d95b732-d144-4130-a667-1f3098d35983

## File

- [rm-2026-003077.pdf](https://mdr.nims.go.jp/filesets/17e60b8c-cbac-4762-9092-bcf9657ba169/download) ([Detail](https://mdr.nims.go.jp/filesets/17e60b8c-cbac-4762-9092-bcf9657ba169.md))

## Id

1d95b732-d144-4130-a667-1f3098d35983

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-10-13T06:53:06.342075Z

## Updated at

2026-10-14T00:26:52.193736Z

## Published at

2026-10-14T01:25:14.793446Z

## Doi



## First published url

https://doi.org/10.1021/acssusresmgt.6c00307

## Date published

2026-09-24

## Recorded date published

2026-9-24

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Physically Informed Multi-Magnification Learning for Scalable, Non-Destructive
    Assessment of Polypropylene Degradation in Recycled Materials
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: und

## Creator

- name: Kazuki Hammura
  role: author
  orcid: https://orcid.org/0009-0000-9671-5059
- name: Kiyotaka Hitomi
  role: author
- name: Kenji Nagata
  role: author
  orcid: https://orcid.org/0000-0001-9894-4461
- name: Masanobu Naito
  role: author
  orcid: https://orcid.org/0000-0001-7198-819X

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: polypropylene
  schema: not_defined
- subject: optical micrographs
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

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## Data origin



## Embargo



## Journal

- title: ACS Sustainable Resource Management
  issn: '28371445'
  volume: '3'
  issue: '9'
  start_page: 3048
  end_page: 3057

## Conference



## Related item



## Funding

- identifier: 23K26724
  funder_name: Japan Society for the Promotion of Science
- identifier: JPMXP1122714694
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: JPMJCR19J3
  funder_name: Japan Science and Technology Agency
- identifier: JPJ012290
  funder_name: Cabinet Office, Government of Japan

## Instrument



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## Specimen



## Chemical composition



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## Fileset

- id: 17e60b8c-cbac-4762-9092-bcf9657ba169
  filename: rm-2026-003077.pdf
  content_type: application/pdf
  size: 6207954
  md5: a2a478be1d26f3fb359c8f0f6379d53f

## Thumbnail

fileset_id: 17e60b8c-cbac-4762-9092-bcf9657ba169
filename: rm-2026-003077.pdf