# A Nearly Zero-Cost Lot-by-Lot Inspection of Recycled Plastics: Prediction of Mechanical Properties from Viscosity Evolution during Melt Kneading

https://mdr.nims.go.jp/datasets/543df61c-d036-45b7-91ee-4c771da74eca

## File

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## Id

543df61c-d036-45b7-91ee-4c771da74eca

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-04-25T05:49:36.193930Z

## Updated at

2025-04-25T07:30:08.950552Z

## Published at

2025-04-25T07:24:12.613644Z

## Doi



## First published url

https://doi.org/10.1021/acssusresmgt.5c00040

## Date published

2025-04-24

## Recorded date published

2025-4-24

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: 'A Nearly Zero-Cost Lot-by-Lot Inspection of Recycled Plastics: Prediction
    of Mechanical Properties from Viscosity Evolution during Melt Kneading'
  title_type: original
  lang: en

## Description

- description: Driving mechanical recycling with minimal energy consumption has become
    increasingly urgent. However, recycled plastics derived from household plastic
    waste—which accounts for approximately half of all plastic waste—are contaminated
    with non-plastic substances and mixed polymers. These contamination levels vary
    significantly from lot to lot, limiting their use to low-grade applications where
    consistent quality is less critical. This study highlights that all recycled plastics
    undergo melting, kneading, and pelletizing processes. By predicting the mechanical
    properties of recycled products based on melt viscosity—auxiliary data obtained
    during kneading without additional costs—we propose a nearly zero-cost, lot-by-lot
    inspection method. Pre-production prediction of pellet properties during kneading
    enables the classification and extraction of high-quality, uniform recycled plastics
    tailored to specific applications. To validate this approach, we predict the tensile
    properties and Charpy impact energies of 23 lots of household polypropylene (PP)
    waste. Leveraging a bidirectional recurrent neural network, we develop a system
    to classify pellets prior to production based on predicted mechanical properties,
    achieving over 85% accuracy. This innovative analytical method provides a cost-effective
    solution for upcycling household waste, contributing to sustainability within
    the circular economy.
  description_type: abstract
  lang: und

## Creator

- name: Yusuke Hibi
  role: author
  orcid: https://orcid.org/0000-0003-4006-1070
- name: Shiho Uesaka
  role: author
- name: Kiyotaka Hitomi
  role: author
- name: Ken-ichi Niihara
  role: author
- name: Asami Imai
  role: author
- name: Sadaki Samitsu
  role: author
  orcid: https://orcid.org/0000-0002-4139-1656
- 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: Household Plastic Waste
  schema: not_defined
- subject: Mechanical Recycling
  schema: not_defined
- subject: Upcycling
  schema: not_defined
- subject: Property Prediction of Recycled Materials
  schema: not_defined
- subject: Melt Viscosity
  schema: not_defined
- subject: Recurrent Neural Network
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: ACS Sustainable Resource Management
  issn: '28371445'
  volume: '2'
  issue: '4'
  start_page: 673
  end_page: 680

## Conference



## Related item



## Funding

- identifier: JP24K08520
  funder_name: Japan Society for the Promotion of Science
- identifier: JPJ012290
  funder_name: Japan Society for the Promotion of Science

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



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## Fileset

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  filename: hibi-et-al-2025-a-nearly-zero-cost-lot-by-lot-inspection-of-recycled-plastics-prediction-of-mechanical-properties-from
    (1).pdf
  content_type: application/pdf
  size: 7425948
  md5: 8479fe6ecd56472c12edbe587f56f590
- id: e55df56c-ec9b-4f66-b717-f05f3594259b
  filename: rm5c00040_si_001.pdf
  content_type: application/pdf
  size: 490596
  md5: 876fc5502dc7d9ea82b7a8d92006abe9

## Thumbnail

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filename: hibi-et-al-2025-a-nearly-zero-cost-lot-by-lot-inspection-of-recycled-plastics-prediction-of-mechanical-properties-from
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