Cyclization in Linear Step-Growth Polymerizations.

Area of Science:

• Polymer Chemistry
• Chemical Engineering
• Computational Chemistry

Background:

• Intramolecular cyclization is a critical but often overlooked phenomenon in step-growth polymerizations (SGPs).
• Limited theoretical understanding hinders prediction of polymer structure and reaction design, particularly under dilute conditions.
• Experimental data confirms concentration's significant influence on cyclization.

Purpose of the Study:

• To develop a quantitative framework for understanding and predicting intramolecular cyclization in SGPs.
• To derive explicit formulas for key polymerization parameters as functions of monomer conversion and concentration.
• To provide practical tools for predicting molecular structures and properties in real-world polymerization scenarios.

Main Methods:

• Employed a reverse-engineering strategy using experimental data from a classical A2 + B2 SGP system.
• Combined analytical derivations with symbolic regression, a machine learning technique.
• Generated closed-form expressions for cyclization probability, degree of cyclization, degree of linear polymerization, and molecular weights.

Main Results:

• Obtained explicit, data-driven formulas accurately describing cyclization dynamics.
• Formulas show excellent agreement with experimental results across a wide range of concentrations.
• Successfully captured the interplay between monomer conversion, concentration, and cyclization.

Conclusions:

• The developed quantitative framework effectively incorporates cyclization into SGP theory.
• The derived expressions offer practical predictive capabilities for polymer molecular structures and properties.
• This work enhances the ability to guide reaction design and control polymerization outcomes.