A-la-carte surface functionalization of organic materials via the combination of radiation-induced graft polymerization and multi-component reactions

Kakuchi, Ryohei; Matsubara, Kiho; Madrid, Jordan F.; Barba, Bin Jeremiah D.; Omichi, Masaaki; Ueki, Yuji; Seko, Noriaki

doi:10.1557/s43579-022-00255-9

Cited by 6 publications

(7 citation statements)

References 95 publications

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“…Among the various surface modification methods currently available, radiationinduced graft polymerization (RIGP), a synthetic method that allows the construction of new polymers on the membrane surface directly from commercially available polymeric materials in 3 an out-of-the-box fashion, is the most important. [15][16][17][18][19] In RIGP, existing membrane materials are irradiated with electron beams or other radiation to selectively generate radicals on the membrane surface. Vinyl polymerization from the generated radicals is then performed.…”

Section: Introductionmentioning

confidence: 99%

Building Machine Learning Models for Reactivity Prediction in Radiation-Induced Graft Polymerization Using Interpretable Parameters

Matsubara

Takahashi

Matsuda

et al. 2023

Preprint

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Machine learning prediction models for radiation-induced graft polymerization reactivity of methacrylate monomers were feasibly built with chemically interpretable parameters. The reactivity can be predicted based on the decision-tree-based machine learning algorithms. Among these algorithms, the XGBoost algorithm exhibited a good performance using five interpretable parameters: the solvation free energy of the methacrylate monomer in water, solvation free energy of the methacrylate monomer in hexane, methacrylate monomer radius, conformational entropy of the methacrylate monomers, and the dipole moments of the methacrylate monomers. The machine learning model building resulted in effective reactivity predictions and unveiled important factors for the radiation-induced graft polymerization in a chemically interpretable fashion.

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Section: Introductionmentioning

confidence: 99%

Building Machine Learning Models for Reactivity Prediction in Radiation-Induced Graft Polymerization Using Interpretable Parameters

Matsubara

Takahashi

Matsuda

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Among the various surface modification methods currently available, radiation-induced graft polymerization (RIGP), a synthetic method that allows the construction of new polymers on the membrane surface directly from commercially available polymeric materials in an out-of-the-box fashion, is the most important. [15][16][17][18][19] In RIGP, existing membrane materials are irradiated with electron beams or other radiation to selectively generate radicals on the membrane surface. Vinyl polymerization from the generated radicals is then performed.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, the functionality of a polymeric material is governed by the properties of the material surface that is solely in contact with the external environment. Among the various surface modification methods currently available, radiation‐induced graft polymerization (RIGP), a synthetic method that allows the construction of new polymers on the membrane surface directly from commercially available polymeric materials in an out‐of‐the‐box fashion, is the most important [15–19] . In RIGP, existing membrane materials are irradiated with electron beams or other radiation to selectively generate radicals on the membrane surface.…”

Section: Introductionmentioning

confidence: 99%

GFN‐xTB‐Based Computations Provide Comprehensive Insights into Emulsion Radiation‐Induced Graft Polymerization**

Matsubara,

Takahashi,

Matsuda

et al. 2023

ChemPlusChem

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In this article, a deep insight into emulsion radiartion‐induced graft polymerization (RIGP) was obtained by computing explicit solvation free energies, conformational entropy, monomer radius and dipole moments with the state‐of‐the‐art Conformer‐Rotamer Ensemble Sampling Tool (CREST) package primalily at semiempirical GFN‐xTB level. By leveraging the robustness of the CREST package, above parameters provided dynamic nature of methacrylate monoers with the consideration of realistic emulsion conditions. With the chemical and physical importance of the above results, CREST‐determined explanatory variables sufficiently led to the building of the prediction models for the RIGP of methacrylate monomers. The machine learning model building resulted in effective reactivity predictions and unveiled important factors for the radiation‐induced graft polymerization in a chemically interpretable fashion.

show abstract

“…License: CC BY-NC 4.0 polymers on the membrane surface directly from commercially available polymeric materials in an out-of-the-box fashion, is the most important. [15][16][17][18][19] In RIGP, existing membrane materials are irradiated with electron beams or other radiation to selectively generate radicals on the membrane surface. Vinyl polymerization from the generated radicals is then performed.…”

Section: Introductionmentioning

confidence: 99%

GFN-xTB Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization

Matsubara

Takahashi

Matsuda

et al. 2023

Preprint

View full text Add to dashboard Cite

In this article, a deep insight into emulsion radiartion-induced graft polymerization (RIGP) was obtained by computing explicit solvation free energies, conformational entropy, monomer radius and dipole moments with the state-of-the-art Conformer-Rotamer Ensemble Sampling Tool (CREST) package primalily at semiempirical GFN-xTB level. By leveraging the robustness of the CREST package, above parameters provided dynamic nature of methacrylate monoers with the consideration of realistic emulsion conditions. With the chemical and physical importance of the above results, CREST-determined explanatory variables sufficiently led to the building of the prediction models for the RIGP of methacrylate monomers. The machine learning model building resulted in effective reactivity predictions and unveiled important factors for the radiation-induced graft polymerization in a chemically interpretable fashion.

show abstract

A-la-carte surface functionalization of organic materials via the combination of radiation-induced graft polymerization and multi-component reactions

Cited by 6 publications

References 95 publications

Building Machine Learning Models for Reactivity Prediction in Radiation-Induced Graft Polymerization Using Interpretable Parameters

Building Machine Learning Models for Reactivity Prediction in Radiation-Induced Graft Polymerization Using Interpretable Parameters

GFN‐xTB‐Based Computations Provide Comprehensive Insights into Emulsion Radiation‐Induced Graft Polymerization**

GFN-xTB Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization

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