JOURNAL ARTICLE
Tandem ADMET and CAMMP to Access Degradable Thermosets and Multiblock Copolymers.
Published In: Chemistry - A European Journal, 2025, v. 31, n. 32. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Cong, Ran; Nie, Nan; Chen, Changle; Si, Guifu 3 of 3
Abstract
The development of degradable polymers and multiblock copolymeric (MBCP) compatibilizers represents an appealing strategy to address ever‐growing concerns for treating waste plastics of traditional chemical degradation or mechanical recycling. Although recent advancements in cyclic–acyclic monomers metathesis polymerization (CAMMP) and tandem olefin metathesis polymerization (TOMP) have recently been reported to produce degradable polymers and MBCPs via copolymerization of cyclic monomers with acyclic diene comonomers, there have been no reports of preparing high‐performance thermosetting materials and multiblock copolymers within the same polymerization system solely through the selection of cycloolefin monomers. To achieve this objective, a TOMP system has been designed that combines acyclic diene metathesis (ADMET) polymerization of diene comonomers followed by CAMMP with cyclic olefin monomers (dicyclopentadiene DCPD or cyclooctene COE). The selection of different cyclic olefin monomers provided access to degradable cross‐linked thermosets and multiblock copolymers. Notably, this one‐pot, two‐step process is highly efficient and requires the addition of only one metathesis catalyst. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Chemistry - A European Journal. 2025/06, Vol. 31, Issue 32, p1
- Document Type:Article
- Subject Area:Engineering
- Publication Date:2025
- ISSN:0947-6539
- DOI:10.1002/chem.202500399
- Accession Number:185787229
- Copyright Statement:Copyright of Chemistry - A European Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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