Nakayama Koyuru 中山超

… Alternative Names

中山超ナカヤマコユル

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Researcher Number	00964280
Other IDs	https://orcid.org/0000-0003-0873-9486
Affiliation (Current)	2026: 国立研究開発法人産業技術総合研究所, 材料・化学領域, 研究員
Affiliation (based on the past Project Information) *help	2026: 国立研究開発法人産業技術総合研究所, 材料・化学領域, 研究員 2023 – 2024: 国立研究開発法人産業技術総合研究所, 材料・化学領域, 研究員
Review Section/Research Field	Principal Investigator Basic Section 34030:Green sustainable chemistry and environmental chemistry-related
Keywords	Principal Investigator 耐久性 / 疲労 / データ駆動型アプローチ / 複合材料 / 多変量解析 / 機会学習 / リグノセルロース / セルロース

Research Projects
(2 results)
Research Products
(7 results)

マルチモーダルAIによるリグニンの熱酸化特性評価法と安定化指針の構築Principal Investigator
- Principal Investigator
  
  中山超
- Project Period (FY)
  2026 – 2027
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 34030:Green sustainable chemistry and environmental chemistry-related
- Research Institution
  National Institute of Advanced Industrial Science and Technology
データ駆動型アプローチによるセルロース複合材料の耐久性設計の提案Principal Investigator
- Principal Investigator
  
  中山超
- Project Period (FY)
  2023 – 2025
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 34030:Green sustainable chemistry and environmental chemistry-related
- Research Institution
  National Institute of Advanced Industrial Science and Technology

All 2025 2024 2023

All Journal Article Presentation

[Journal Article] Machine learning-assisted sedimentation analysis of cellulose nanofibers to predict the specific surface area2025
- Author(s)
  Nakayama Koyuru、Kumagai Akio、Sakakibara Keita
- Journal Title
  
  Carbohydrate Polymer Technologies and Applications
  
  Volume: 9 Pages: 100697-100697
- DOI
  10.1016/j.carpta.2025.100697
- Data Source
  KAKENHI-PROJECT-23K13785
[Journal Article] Machine learning strategy to improve impact strength for PP/cellulose composites via selection of biomass fillers2024
- Author(s)
  Nakayama Koyuru、Sakakibara Keita
- Journal Title
  
  Science and Technology of Advanced Materials
  
  Volume: 25 Issue: 1 Pages: 2351356-2351356
- DOI
  10.1080/14686996.2024.2351356
- Data Source
  KAKENHI-PROJECT-23K13785
[Presentation] Rapid Prediction of Cellulose Nanofiber Specific Surface Area Using Sedimentation Tests and Machine Learning, and Its Application to Composite Material Property Estimation2025
- Author(s)
  中山超, 榊原圭太
- Organizer
  ISWST2025
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-23K13785
[Presentation] 機械学習を用いたセルロースナノファイバーの形態評価と樹脂複合材料の物性予測2025
- Author(s)
  中山超, 榊原圭太
- Organizer
  セルロース学会関東支部ミニシンポジウムセルロース素材の新展開 ‐セルロースの未来を拓く若手研究者達 Ⅹ
- Invited
- Data Source
  KAKENHI-PROJECT-23K13785
[Presentation] Machine learning Enhancement of Impact Strength in PP/Cellulose Composites through Strategic Biomass Fiber Selection2024
- Author(s)
  中山超, 榊原圭太
- Organizer
  ISF2024
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-23K13785
[Presentation] Machine learning assisted effective production of nanocellulose reinforced PP composites for high impact energy using ATR-IR spectroscopy of wooden materials2023
- Author(s)
  中山超,遠藤貴士,榊原圭太
- Organizer
  The 5th International Cellulose Conference
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-23K13785
[Presentation] ナノセルロース強化PP複合材料の疲労現象: 一軸引張疲労試験の温度依存性2023
- Author(s)
  中山超,遠藤貴士,榊原圭太
- Organizer
  マテリアルライフ学会第 34 回研究発表会
- Data Source
  KAKENHI-PROJECT-23K13785

Nakayama Koyuru 中山 超

中山 超 ナカヤマ コユル

Research Projects

Research Products

マルチモーダルAIによるリグニンの熱酸化特性評価法と安定化指針の構築Principal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

データ駆動型アプローチによるセルロース複合材料の耐久性設計の提案Principal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] Machine learning-assisted sedimentation analysis of cellulose nanofibers to predict the specific surface area2025

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Machine learning strategy to improve impact strength for PP/cellulose composites via selection of biomass fillers2024

Author(s)

Journal Title

DOI

Data Source

[Presentation] Rapid Prediction of Cellulose Nanofiber Specific Surface Area Using Sedimentation Tests and Machine Learning, and Its Application to Composite Material Property Estimation2025

Author(s)

Organizer

Data Source

[Presentation] 機械学習を用いたセルロースナノファイバーの形態評価と樹脂複合材料の物性予測2025

Author(s)

Organizer

Data Source

[Presentation] Machine learning Enhancement of Impact Strength in PP/Cellulose Composites through Strategic Biomass Fiber Selection2024

Author(s)

Organizer

Data Source

[Presentation] Machine learning assisted effective production of nanocellulose reinforced PP composites for high impact energy using ATR-IR spectroscopy of wooden materials2023

Author(s)

Organizer

Data Source

[Presentation] ナノセルロース強化PP複合材料の疲労現象: 一軸引張疲労試験の温度依存性2023

Author(s)

Organizer

Data Source

Nakayama Koyuru 中山超

中山超ナカヤマコユル