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NSFDirectorate for Mathematical and Physical Sciences

CAS: Computation-Assisted Discovery of Enantioselective Polymerization Catalysts for Degradable Polyesters

Active
Institution
Virginia Polytechnic Institute and State University, VA
Principal investigator
Rong Tong
Funding
$600K
Duration
2024–2027
Open portfolio

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Abstract

The Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry supports the research groups of Prof. Rong Tong and Prof. Hongliang Xin at the Virginia Polytechnic Institution and State University to develop new chemistry for the synthesis and recycling of stereocomplex poly(lactic) acids (sc-PLA). With the co-funding support from the Official of Strategic Initiatives of the Directorate for Mathematical and Physical Sciences, computation-assisted approaches will be used to help identifying efficient catalysts for the polymerization process. Poly(lactic) acid (PLA) is a biodegradable and biocompatible polyester. The goal of this project is to improve understanding of the critical catalyst structural features affecting polymerization enantioselectivity and to enable efficient synthesis of sc-PLA, which is anticipated to have enhanced thermal-mechanical properties. Undergraduates and graduate students will receive interdisciplinary research training at the intersection of catalysis, polymer science, and data science. The accomplishments will be communicated to the public and scientific community, with a commitment to the open-source release of all datasets and models. Efficient enantioselective catalysts for scalable synthesis of recyclable sc-PLA from racemic lactide remain elusive. The two research teams aim to establish an integrated experimental and computational framework to identify efficient catalysts and to improve understanding of the structural features affecting polymerization enantioselectivity and mechanism. This research will focus on studying enantioselective ring-opening polymerization mediated by bimetallic chiral complexes. A combination of density-function theory (DFT) computations and machine learning models will be employed to assist the discovery of efficient catalysts. The effectiveness in the synthesis of sc-PLA from industrial monomer mixtures of racemic lactide and meso-lactide as well as the recyclability of sc-PLA will be evaluated. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Research team

  • Rong TongPI
  • Hongliang XinCo-PI

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Directorate for Mathematical and Physical SciencesArtificial Intelligence (AI)OFFICE OF MULTIDISCIPLINARY ACMacromolec/Supramolec/NanoCAS-Critical Aspects of Sustainabilityefficientcatalystsenantioselectivesynthesispolymerizationprofpolystructural features

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