| Autor: |
Lundberg DJ; Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Ko K; Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Kilgallon LJ; Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Johnson JA; Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States. |
| Jazyk: |
angličtina |
| Zdroj: |
ACS macro letters [ACS Macro Lett] 2024 May 21; Vol. 13 (5), pp. 521-527. Date of Electronic Publication: 2024 Apr 16. |
| DOI: |
10.1021/acsmacrolett.4c00106 |
| Abstrakt: |
The incorporation of cleavable comonomers as additives into polymers can imbue traditional polymers with controlled deconstructability and expanded end-of-life options. The efficiency with which cleavable comonomer additives (CCAs) can enable deconstruction is sensitive to their local distribution within a copolymer backbone, which is dictated by their copolymerization behavior. While qualitative heuristics exist that describe deconstructability, comprehensive quantitative connections between CCA loadings, reactivity ratios, polymerization mechanisms, and deconstruction reactions on the deconstruction efficiency of copolymers containing CCAs have not been established. Here, we broadly define these relationships using stochastic simulations characterizing various polymerization mechanisms (e.g., coltrolled/living, free-radical, and reversible ring-opening polymerizations), reactivity ratio pairs (spanning 2 orders of magnitude between 0.01 and 100), CCA loadings (2.5% to 20%), and deconstruction reactions (e.g., comonomer sequence-dependent deconstruction behavior). We show general agreement between simulated and experimentally observed deconstruction fragment sizes from the literature, demonstrating the predictive power of the methods used herein. These results will guide the development of more efficient CCAs and inform the formulation of deconstructable materials. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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