Probabilistic numerical simulation for predicting spherulitic morphology from calorimetric crystallization conversion curves: An isothermal case

The present work introduces a novel method to estimate the morphology of semicrystalline polymers, namely the average spherulite size, size distribution, and nucleus density based on experimental crystallization conversion curves recorded by differential scanning calorimetry (DSC). A fast and accurate probabilistic numerical simulation method describing random nuclei formation and subsequent growth is introduced to handle the crystallization process of polymers. The developed method was used in a curve-fitting procedure with experimental crystallization curves to obtain structural parameters of neat and nucleated isotactic polypropylene. The simulated morphology was used with success to predict haze of injection-molded samples, proving the reliability and applicability of our new approach. The method presented here can be a useful technique to construct the spherulitic morphology based on crystallization conversion curves and predict properties dependent on the spherulite size, size distribution, and nucleus density, even when microscopic measurement cannot be carried out. CO 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Automated summary

This study introduces a novel method to estimate the morphology of semicrystalline polymers based on experimental crystallization conversion curves, utilizing a fast and accurate probabilistic numerical simulation method to handle the crystallization process. The method was successfully used to predict haze of injection-molded samples, demonstrating the reliability and applicability of the new approach.