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The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the...
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Toward Recyclable Polymers: Ring-Opening Polymerization Enthalpy from First-Principles.

Huan Tran1, Aubrey Toland1, Kellie Stellmach2

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The Journal of Physical Chemistry Letters
|May 25, 2022
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Summary
This summary is machine-generated.

We developed a new computational method to accurately predict the ring-opening polymerization (ROP) enthalpy. This advancement accelerates the design of novel depolymerizable polymers with desired properties.

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Area of Science:

  • Polymer Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Ring-opening polymerization (ROP) enthalpy (ΔHROP) is crucial for controlling cyclic monomer polymerization.
  • Accurate computation of ΔHROP (within 5-10 kJ/mol) is vital for designing depolymerizable polymers.
  • Existing computational methods face challenges in accuracy for realistic polymer systems.

Purpose of the Study:

  • To develop a first-principles computational scheme for precise ΔHROP calculation.
  • To overcome challenges in conformational exploration and finite size effects in ROP enthalpy computations.
  • To validate the new scheme against experimental data for a diverse set of ROP polymers.

Main Methods:

  • Developed a first-principles computational scheme to address challenges in ΔHROP calculation.
  • Incorporated extensive conformational state exploration.
  • Accounted for finite size effects in the computational model.
  • Validated the scheme using a benchmark set of 42 ROP polymers with experimental ΔHROP values.

Main Results:

  • Achieved a root-mean-square error of approximately 7 kJ/mol for ΔHROP predictions on the benchmark set.
  • Demonstrated a 3-fold improvement in accuracy compared to conventional computational approaches.
  • Successfully validated the computational scheme against reliable experimental data.

Conclusions:

  • The developed computational scheme significantly enhances the accuracy of ROP enthalpy predictions.
  • This breakthrough enables the creation of high-quality ΔHROP databases for machine learning models.
  • Accelerates the design and discovery of new depolymerizable polymers with tailored properties.