Readily Degradable Aromatic Polyesters from Salicylic Acid

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Abstract

Polyesters constitute around 10% of the global plastic market with aromatic polyesters, such as poly(ethylene terephthalate) (PET), being the most prevalent because of their attractive properties. As for most commercial plastics, polyesters are primarily derived from fossil resources and are not readily degradable, which raises a number of sustainability concerns. Designing polymers with competitive properties from sustainable feedstocks that rapidly degrade under mild conditions is an attractive strategy for addressing the current plastic waste problem. Here, the detailed synthesis and characterization of degradable, high molar mass aromatic polyesters derived from salicylic acid, poly(salicylic glycolide) (PSG), and poly(salicylic methyl glycolide) (PSMG) are described. The synthesis of polymers was investigated through mechanistic experiments and complementary computational studies. The glass transition temperature (Tg ≈ 85 °C) and Young's modulus (E ≈ 2.3 GPa) of these polyesters are comparable to those of PET. In contrast to the poor hydrolytic degradability of PET, both PSG and PSMG are readily degradable in neutral aqueous solutions (e.g., complete degradation in seawater at 50 °C in 60 days). These aromatic polyesters derived from salicylic acid have potential as future high-performance, sustainable, and degradable plastics.

Original languageEnglish (US)
Pages (from-to)96-102
Number of pages7
JournalACS Macro Letters
Volume9
Issue number1
DOIs
StatePublished - Jan 21 2020

Bibliographical note

Funding Information:
We acknowledge our principal funding source, the National Science Foundation Center for Sustainable Polymers at the University of Minnesota, which is a National Science Foundation supported Center for Chemical Innovation (CHE-1901635). We thank Dr. Victor G. Young, Jr. and X-ray Crystallographic Laboratory at the University of Minnesota for crystallographic determinations, and PolyAnalytik (ON, Canada) for HFIP-SEC measurement. We also thank Prof. T. R. Hoye, X. Peng, Dr. K. Jin, Dr. J. Shim, G. D. Hoe, Dr. H. Ha, and Dr. A. Watts for helpful discussions, feedback, and suggestions. We thank the Minnesota Supercomputing Institute (MSI) for providing key computational resources.

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