Liu, Hongwei (2022) Modeling the Rheology of Ordinary and Associating Unentangled Polymer Melts. [Tesi di dottorato]
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Tipologia del documento: | Tesi di dottorato |
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Lingua: | English |
Titolo: | Modeling the Rheology of Ordinary and Associating Unentangled Polymer Melts |
Autori: | Autore Email Liu, Hongwei hongwei.liu@unina.it |
Data: | 11 Gennaio 2022 |
Numero di pagine: | 173 |
Istituzione: | Università degli Studi di Napoli Federico II |
Dipartimento: | Ingegneria Chimica, dei Materiali e della Produzione Industriale |
Dottorato: | Ingegneria dei prodotti e dei processi industriali |
Ciclo di dottorato: | 33 |
Coordinatore del Corso di dottorato: | nome email D'Anna, Andrea andrea.danna@unina.it |
Tutor: | nome email Ianniruberto, Giovanni [non definito] |
Data: | 11 Gennaio 2022 |
Numero di pagine: | 173 |
Parole chiave: | Sticky Rouse Model; Associating Polymers; Coarse-grained Modeling; Friction Reduction; Fast Flow |
Settori scientifico-disciplinari del MIUR: | Area 02 - Scienze fisiche > FIS/02 - Fisica teorica, modelli e metodi matematici Area 08 - Ingegneria civile e Architettura > ICAR/11 - Produzione edilizia |
Depositato il: | 28 Gen 2022 09:21 |
Ultima modifica: | 07 Giu 2023 11:22 |
URI: | http://www.fedoa.unina.it/id/eprint/13532 |
Abstract
We are aiming at studying the rheological behavior of ordinary and associating polymers. Ordinary polymers refer to polymers without stickers but under fast flows. Associating polymers are a group of polymers which are held together through reversible non covalent bonds, such as ionic interactions, metal-ligand, $\pi$-$\pi$ stacking, and hydrogen bonds. Both topics have attracted enormous attention either due to the non-universal features in fast flows of polymer melts or because of their unique abilities for working as self-healing, stimuli-sensitive, and shape-memory materials. Fast flows and large deformation rates typically are involved in polymer processing operations and no general constitutive equation can be written that describes these nonlinear rheological behavior exhibited by polymer flows. Therefore, molecular modeling of the rheology of polymers in fast flow was developed many years ago. The friction reduction due to coalignment of polymer chains has been confirmed both through molecular dynamics simulations and extensional flow experiments. In spite of the results achieved by the friction reduction model, the flow-induced reduction of the friction coefficient is still highly controversial. For instance, the Einstein relationship between $\zeta$ and the diffusion coefficient probably does not hold true away from equilibrium. This thesis partly focuses on enhancing our understanding of the mechanisms behind the nonlinear responses of the unentangled polymer melts under fast shear flow through a coarse-grained modeling approach. We take advantage of the rich set of data of Kremer-Grest melts in fast steady shear flows. First, we apply the data to the latest Watanabe's model. Then we reproduce them by suitable Brownian dynamics simulations, where the beads are endowed with isotropic and anisotropic friction separately. Likewise, molecular modeling is typically used to study the viscoelastic response of associating polymers as well. The most famous one is the sticky Rouse model. This model is capable of accounting for the slowing down of the relaxation caused by reversible bonds. However, the effects of the lifetime, density, and distribution of stickers on the dynamics of associating polymers is still a matter of some debate. For instance, a mismatch between data and predictions of this model at the intermediate frequencies always appears. The rest of this thesis concentrates on studying the dynamics of associating polymers. A multi-chain model and a single-chain model are proposed. The predictions of the multi-chain model are able to describe the topological structure of polymer networks. Meanwhile, those effects on rheological behavior are studied. The results are compared with the predictions of the sticky Rouse model. The single-chain model, which was designed to save running time, allows stickers to be distributed randomly. The predictions are compared with data.
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