# Revealing factors influencing polymer degradation with rank-based machine learning

https://mdr.nims.go.jp/datasets/563a06f0-5aaf-4276-80ba-d0b23e7e3947

## File

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## Id

563a06f0-5aaf-4276-80ba-d0b23e7e3947

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-11-09T04:17:00.306108Z

## Updated at

2024-01-05T13:12:08.049675Z

## Published at

2023-11-10T04:30:10.248833Z

## Doi



## First published url

https://doi.org/10.1016/j.patter.2023.100846

## Date published

2023-09-25

## Recorded date published

2023-12

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Revealing factors influencing polymer degradation with rank-based machine
    learning
  title_type: original
  lang: en

## Description

- description: The efficient treatment of polymer waste is a major challenge to marine
    sustainability. It is useful to reveal the factors that dominate the degradability
    of polymer materials for developing new polymer materials in the future. In this
    study, we have developed a platform for evaluating the degradability of polymers
    based on machine learning techniques. However, the small number of available datasets
    on degradability and the diversity of experimental means and conditions hinder
    large-scale analysis. To avoid this difficulty, we have introduced RankSVM, which
    can learn the preference of the degradability of polymers. We have made a ranking
    model to evaluate the degradability of polymers, integrating three datasets on
    the degradability of polymers that are measured by different means and conditions.
    The analysis of this ranking model using a decision tree has revealed the factors
    that dominate the degradability of polymers.
  description_type: abstract
  lang: eng

## Creator

- name: Weilin Yuan
  role: author
- name: Yusuke Hibi
  role: author
  orcid: https://orcid.org/0000-0003-4006-1070
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Masato Sumita
  role: author
- name: Yasuyuki Nakamura
  role: author
  orcid: https://orcid.org/0000-0003-0078-6413
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Masanobu Naito
  role: author
  orcid: https://orcid.org/0000-0001-7198-819X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Koji Tsuda
  role: author
  orcid: https://orcid.org/0000-0002-4288-1606
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: polymer degradation
  schema: not_defined
- subject: rank-based machine learning
  schema: not_defined
- subject: PoLyInfo
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: Patterns
  issn: '26663899'
  volume: '4'
  start_page: 100846
  end_page: 100846

## Conference



## Related item



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## Instrument



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## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



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## Fileset

- id: b25bf4da-3883-4314-8250-7bff5304d85a
  filename: 1-s2.0-S2666389923002258-main.pdf
  content_type: application/pdf
  size: 3369325
  md5: 7bc87f2da53f81d66ede773a8fc60a8d

## Thumbnail

fileset_id: b25bf4da-3883-4314-8250-7bff5304d85a
filename: 1-s2.0-S2666389923002258-main.pdf