Connecting the complex microstructure of LDPE to its rheology and processing properties <i>via</i> a combined fractionation and modelling approach

Zentel, Kristina Maria; Bungu, Paul Severin Eselem; Degenkolb, Jonas; Pasch, Harald; Busch, Markus

doi:10.1039/d1ra03749h

Cited by 4 publications

(2 citation statements)

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“…The microscopic simulations are used to generate a representative sample of branched polymers, which are then fed into the theoretical machinery. The method was applied successfully by Read et al commercial Low density polyethylene (LDPE), and recently by Zentel and co-workers to predict the rheological properties of LDPE and polybutyl acrylate (PBA) from the reaction conditions in a miniplant. − …”

Section: Scale-bridging Strategiesmentioning

confidence: 99%

Understanding and Modeling Polymers: The Challenge of Multiple Scales

Schmid

2022

ACS Polym. Au

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Polymer materials are multiscale systems by definition. Already the description of a single macromolecule involves a multitude of scales, and cooperative processes in polymer assemblies are governed by their interplay. Polymers have been among the first materials for which systematic multiscale techniques were developed, yet they continue to present extraordinary challenges for modellers. In this Perspective, we review popular models that are used to describe polymers on different scales and discuss scale-bridging strategies such as static and dynamic coarse-graining methods and multiresolution approaches. We close with a list of hard problems which still need to be solved in order to gain a comprehensive quantitative understanding of polymer systems.

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Section: Scale-bridging Strategiesmentioning

confidence: 99%

Understanding and Modeling Polymers: The Challenge of Multiple Scales

Schmid

2022

ACS Polym. Au

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“…Such upgraded population-driven output includes, e.g., microscopic distributed compositional or topological information, which can be linked to macroscopic properties at the application level as relevant for manufacturers and end users. [33][34][35] It is thus not surprising that the kMC method has shown to be relevant to simulate distributed populations, with many examples in several fields such as electronic component design, [36] molecular interface dynamics, [37] heterogeneous catalysis, [38] film growth and crystallization, [39,40] biological population evolution, [41] nucleation kinetics, [42] and particle growth. [43][44][45] An additional field in which the kMC method has demonstrated its capabilities and versatility, and the main focus of the present work, is polymer reaction engineering (PRE).…”

Section: Introductionmentioning

confidence: 99%

A Signal‐To‐Noise‐Ratio‐Based Automated Algorithm to accelerate Kinetic Monte Carlo Convergence in Basic Polymerizations

Trigilio,

Marien,

De Smit

et al. 2023

Advcd Theory and Sims

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Kinetic Monte Carlo (kMC) modelling is ubiquitous to simulate the time evolution of (bio)chemical processes, specifically if populations are involved. A recurring task is the selection of the smallest control volume that leads to convergence, which means that the model outputs are accurate and sufficiently free from stochastic noise and do not significantly change upon further increasing this volume. Selecting a too high (safe) control volume leads to an excessive simulation time, while many small incremental control volume increases are inefficient. This work therefore presents an automated tool to determine the smallest control volume leading to convergence. The tool is illustrated for (intrinsic) free radical and nitroxide mediated polymerization (FRP/NMP), in which the chain length distribution (CLD) is a crucial output. The algorithm starts with a very low volume to then check if the desired (monomer) conversion can be reached, the number average chain length is accurate, and finally the signal‐to‐noise (SNR) ratio at the CLD level is below a threshold. The execution time of the algorithm is less than twice the time of running the converged simulation directly, hence, saving tremendous time in setting up a kMC simulation and facilitating benchmark studies even beyond polymer reaction engineering applications.

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Fractionation of Polymers

Lederer¹,

Ndiripo²

2023

Encyclopedia of Polymer Science and Technology

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Synthetic and natural polymers exist in microstructural distributions, such as chain length, size, chemical composition, branching, tacticity, or comonomer sequence distributions. Polymer fractionation techniques are essential for the analysis of these distributions and help in understanding polymerization mechanisms and material properties. In this article, the theoretical basics of general fractionation approaches and specific fractionation techniques are discussed. Details are given on fractionation techniques for molar mass and chemical composition distribution analysis, such as temperature rising elution fractionation (TREF), crystallization analysis fractionation (CRYSTAF), or crystallization elution fractionation (CEF). In addition, column‐based and channel‐based fractionation methods are extensively discussed. Theoretical and practical aspects of the analysis of molar mass distributions using size exclusion chromatography (SEC) as well as chemical composition distributions by interaction chromatography (SGIC, TGIC, LCCC) and multidimensional separations (2D‐LC) are reviewed and compared. General aspects and details on asymmetrical flow and thermal field flow fractionation techniques (AF4 and ThFFF) are discussed and examples of suitable physical and chemical detectors coupled to fractionation devices for the analysis of size, conformation, or chemical composition and topology are demonstrated.

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Connecting the complex microstructure of LDPE to its rheology and processing properties via a combined fractionation and modelling approach

Abstract: The complex microstructure of LDPE regarding branching and molecular weight was correlated to its rheology and processing properties via preparative fractionation and modelling.

Cited by 4 publications

References 61 publications

Understanding and Modeling Polymers: The Challenge of Multiple Scales

Understanding and Modeling Polymers: The Challenge of Multiple Scales

A Signal‐To‐Noise‐Ratio‐Based Automated Algorithm to accelerate Kinetic Monte Carlo Convergence in Basic Polymerizations

Fractionation of Polymers

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