Faculty of Physics - University of Bucharest

UNIVERSITY OF BUCHAREST
FACULTY OF PHYSICS

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2026-06-12 0:10

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Conference: Bucharest University Faculty of Physics 2026 Meeting

Section: Polymer Physics

Title:

Automated Analysis of Avrami Parameters from Polymer Crystallization Data

Authors:

Catalin BERLIC(1), Valentin BARNA(1), Cristina MIRON(1), Adrian BERLIC(1,2)

Affiliation:

1) University of Bucharest, Faculty of Physics, 405 Atomistilor Street, 077125, Magurele, Romania

2) National Meteorological Administration, 97 Soseaua Bucuresti - Ploiesti, Bucharest, Romania

E-mail

cataliniulian.berlic@g.unibuc.ro

Keywords:

Avrami equation, Python-based automated analysis, polymer crystallization, linear regression, computational workflow

Abstract:

The quantitative analysis of crystallization kinetics frequently requires the extraction of Avrami parameters from large datasets obtained either from experimental measurements or computational simulations. In this work, we present an automated computational workflow for the analysis of polymer crystallization data based on the Avrami formalism. The proposed framework combines numerical preprocessing, linear regression, parameter extraction, and uncertainty estimation within a unified and reproducible analysis pipeline. The methodology was designed to process crystallization datasets originating from both Monte Carlo simulations and experimental investigations of polymer systems. After converting the transformed fraction into Avrami coordinates, the datasets are analyzed automatically using a multi-stage fitting procedure. The algorithm identifies quasi-linear regions in the Avrami plots, determines the corresponding kinetic exponents and rate constants, and estimates parameters associated with transitions between distinct crystallization regimes. Special attention is given to numerical stability and error propagation. The workflow also supports automated handling of multiple datasets, generation of summary tables, statistical comparison of kinetic parameters, and graphical representation of fitting results as functions of physical or geometrical variables. The proposed approach substantially reduces manual intervention during kinetic analysis while improving consistency and reproducibility in large-scale studies of polymer crystallization. Although primarily developed for Avrami-type analyses of polymers, the methodology may also be extended to other phase-transition phenomena exhibiting sigmoidal transformation kinetics.