期刊
POLYMER CHEMISTRY
卷 13, 期 7, 页码 982-988出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d1py01643a
关键词
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资金
- National Science Foundation (NSF) [DMR-1904631]
- DoD through the ARO [W911NF-17-1-0326]
- NIH [S10 OD021758-01A1]
By designing suitable activated monomers and implementing post-polymerization modification, statistical olefin-acrylate copolymers can be prepared, overcoming challenges associated with traditional radical copolymerization processes.
The advantageous material properties that arise from combining non-polar olefin monomers with activated vinyl monomers have led to considerable progress in the development of viable copolymerization strategies. However, unfavorable reactivity ratios during radical copolymerization of the two result in low levels of olefin incorporation, and an abundance of deleterious side reactions arise when attempting to incorporate many polar vinyl monomers via the coordination-insertion pathway typically applied to olefins. We reasoned that design of an activated monomer that is not only well-suited for radical copolymerization with polar vinyl monomers (e.g., acrylates) but is also capable of undergoing post-polymerization modification to unveil an olefin repeat unit would allow for the preparation of statistical olefin-acrylate copolymers. Herein, we report monomers fitting these criteria and introduce a post-polymerization modification strategy based on single-electron transfer (SET)-induced decarboxylative radical generation directly on the polymer backbone. Specifically, SET from an organic photocatalyst (eosin Y) to a polymer containing redox-active phthalimide ester units under green light leads to the generation of reactive carbon-centered radicals on the polymer backbone. We utilized this approach to generate statistical olefin-acrylate copolymers by performing the decarboxylation in the presence of a hydrogen atom donor such that the backbone radical is capped by a hydrogen atom to yield an ethylene or propylene repeat unit. This method allows for the preparation of copolymers with previously inaccessible comonomer distributions and demonstrates the promise of applying SET-based transformations to address long-standing challenges in polymer chemistry.
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