# Chemical Structure Evaluations of Amine Hardeners to Ensure and Predict the Performance of Wet Adhesion of Epoxies

https://mdr.nims.go.jp/datasets/84d5b5b4-2972-42a6-bbf3-07644d652a32

## File

- [Nakamura et al. - 2023 - Chemical Structure Evaluations of Amine Hardeners to Ensure and Predict the Performance of Wet Adhes.pdf](https://mdr.nims.go.jp/filesets/41f0a0be-01b6-4acc-abc4-a1af8014e319/download) ([Detail](https://mdr.nims.go.jp/filesets/41f0a0be-01b6-4acc-abc4-a1af8014e319.md))

## Id

84d5b5b4-2972-42a6-bbf3-07644d652a32

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-07-16T09:48:09.933846Z

## Updated at

2024-07-17T07:30:21.213327Z

## Published at

2024-07-17T07:30:21.642345Z

## Doi



## First published url

https://doi.org/10.1246/bcsj.20230218

## Date published

2023-12-15

## Recorded date published

2023-12-15

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Chemical Structure Evaluations of Amine Hardeners to Ensure and Predict the
    Performance of Wet Adhesion of Epoxies
  title_type: original
  lang: en

## Description

- description: 吸水したエポキシ接着剤の化学構造と性能の関係は湿潤環境や水中で使用される際の基礎データとなる。しかし化学構造が湿潤状態での接着性に及ぼす影響についての実験データは不十分である。本研究では各種アミン硬化剤を用いたエポキシ接着剤について、アミンの化学構造と接着性能を系統的に検討した。実験データと理論計算により得られたパラメータの解析により、湿潤状態で接着に寄与する構造的特徴が見いだされた。さらに、機械学習による回帰分析によりアミンの化学構造から接着強度を予測するモデルを作成した。実験的検証により、わずか14個の実験値を用いたモデルながら妥当な精度を有することを示した。この結果は、湿潤条件や水中で用いられるエポキシ樹脂の設計や性能理解に大きく貢献する。
  description_type: abstract
  lang: und

## Creator

- name: Yasuyuki Nakamura
  role: author
  orcid: https://orcid.org/0000-0003-0078-6413
  organization: National Institute for Materials Science
- name: Yusuke Hibi
  role: author
  orcid: https://orcid.org/0000-0003-4006-1070
  organization: National Institute for Materials Science
- name: Kimiyoshi Naito
  role: author
  orcid: https://orcid.org/0000-0002-3334-4876
  organization: National Institute for Materials Science
- name: Norie Yamamoto
  role: author
- name: Misato Hanamura
  role: author

## Contact agent



## Publisher

organization: Oxford University Press (OUP)

## Managing organization



## Keyword

- subject: Epoxy
  schema: not_defined
- subject: Wet adhesion
  schema: not_defined
- subject: Amine hardener
  schema: not_defined
- subject: Machine-learning prediction
  schema: not_defined
- subject: Experimental verification
  schema: not_defined

## Rights

- identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Bulletin of the Chemical Society of Japan
  issn: '13480634'
  volume: '96'
  issue: '12'
  start_page: 1339
  end_page: 1345

## Conference



## Related item



## Funding

- identifier: 23K04845
  funder_name: 日本学術振興会

## 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



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 41f0a0be-01b6-4acc-abc4-a1af8014e319
  filename: Nakamura et al. - 2023 - Chemical Structure Evaluations of Amine Hardeners
    to Ensure and Predict the Performance of Wet Adhes.pdf
  content_type: application/pdf
  size: 2538675
  md5: f7efbdad8ead84c3855e5936b6c4f29b

## Thumbnail

fileset_id: 41f0a0be-01b6-4acc-abc4-a1af8014e319
filename: Nakamura et al. - 2023 - Chemical Structure Evaluations of Amine Hardeners
  to Ensure and Predict the Performance of Wet Adhes.pdf