Development of a Monte Carlo Random Walk Model for the Prediction of Linear and Branched Polymer Configuration and Connection to Radius of Gyration and GPC‐MALS Experimental Results

In this work, a self‐avoiding Monte‐Carlo off‐lattice 3D random walk model that is able to predict the configuration of linear and branched carbon‐based macromolecules, taking into account: i) the bond length, ii) the carbon‐carbon angle, and, iii) steric interferences, is presented. The methodology follows the theoretical framework proposed by the pioneering work of P. J. Flory. The Monte‐Carlo model developed is being validated based on experimental results of radius of gyration coming from linear polyethylene characterization by size exclusion chromatography multiangle light scattering (SEC‐MALS) technique. The most detailed version of the model manages to reach an accuracy level of 80% with regard to radius of gyration, without any tuning procedure involved. Furthermore, considering the solvent effect, the accuracy level reaches a value of 95%. On the contrary, it is shown that the simplest Monte Carlo random walk model version (including only the bond length restriction) is not able to quantitatively predict the experimentally acquired values of radius of gyration. Moreover, the developed Monte Carlo model is further employed to predict the expected radius of gyration values of more complex branched topologies, demonstrating the potential of this work. In this work, a self‐avoiding Monte‐Carlo off‐lattice 3D random walk model that is able to predict the configuration of linear and branched carbon‐based macromolecules, taking into account the bond length, the carbon–carbon angle, and steric interferences is presented and validated based on experimental results of radius of gyration coming from linear polyethylene characterization by gel permeation chromatography multiangle light scattering (GPC‐MALS) technique.