Machine learning approach for prediction of the grafting yield in radiation-induced graft polymerization-Reference-Cited by-同舟云学术

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Machine learning approach for prediction of the grafting yield in radiation-induced graft polymerization

Published:2021-12 Issue: Volume:25 Page:101158
ISSN:2352-9407
Container-title:Applied Materials Today
language:en
Short-container-title:Applied Materials Today

Author:

Ueki Yuji,Seko Noriaki,Maekawa Yasunari

Funder

Japan Society for the Promotion of Science

Publisher

Elsevier BV

Subject

General Materials Science

Reference45 articles.

1. Radiation Chemistry of Polymeric Systems,1962

2. Radiation-grafted copolymers for separation and purification purposes: status, challenges and future directions;Nasef;Prog. Polym. Sci.,2012

3. Innovative Polymeric Adsorbents: Radiation-Induced Graft Polymerization;Saito,2018

4. Radiation Processing of Polymers and Its Applications;Tamada,2018

5. Metal adsorbent for alkaline etching aqua solutions of Si wafer;Tamada;Radiat. Phys. Chem.,2012

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2. Explainable Prediction of Hydrophilic/Hydrophobic Property of Polymer Brush Surfaces by Chemical Modeling and Machine Learning;The Journal of Physical Chemistry B;2024-07-01

3. Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective;Archives of Computational Methods in Engineering;2024-05-04

4. Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers;Polymers;2024-04-10

5. GFN‐xTB‐Based Computations Provide Comprehensive Insights into Emulsion Radiation‐Induced Graft Polymerization**;ChemPlusChem;2023-11-27

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