Analytical Rheology of Polymer Melts: State of the Art

Shanbhag, Sachin

doi:10.5402/2012/732176

Cited by 18 publications

(16 citation statements)

References 342 publications

(425 reference statements)

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“…They found that their simulation results agreed with the Likhtman-McLeish theory [14] by using the double reptation approximation for the CR effect and removing high-frequency CLF contributions [15]. The related theories have also been extensively reviewed in the literature [7,[10][11][12][13][16][17][18][19][20].…”

Section: Introductionsupporting

confidence: 51%

Challenges in Multiscale Modeling of Polymer Dynamics

et al. 2013

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The mechanical and physical properties of polymeric materials originate from the interplay of phenomena at different spatial and temporal scales. As such, it is necessary to adopt multiscale techniques when modeling polymeric materials in order to account for all important mechanisms. Over the past two decades, a number of different multiscale computational techniques have been developed that can be divided into three categories: (i) coarse-graining methods for generic polymers; (ii) systematic coarse-graining methods and (iii) multiple-scale-bridging methods. In this work, we discuss and compare eleven different multiscale computational techniques falling under these categories and assess them critically according to their ability to provide a rigorous link between polymer chemistry and rheological material properties. For each technique, the fundamental ideas and equations are introduced, and the most important results or predictions are shown and discussed. On the one hand, this review provides a comprehensive tutorial on multiscale computational techniques, which will be of interest to readers newly entering this field; on the other, it presents a critical discussion of the future opportunities and key challenges in the multiscale geometric mapping matrix between all-atomistic and coarse-grained models N number of monomers per chain N e entanglement length, which is the number of monomers between two entanglements n v number of polymer chains per unit volume n ij (n 0 (r ij )) entanglement number (at equilibrium) between particle pairs i and j p r , P R configurational probability distributions in all-atomistic and coarse-grained models, respectively R ee end-to-end distance of polymer chain R G radius of gyration of polymer chain s segment index/contour length variable along primitive chain S(q) single chain coherent dynamic scattering function Z number of entanglements per chain, defined as Z = N/N e α exponent in standard Mittag-Leffler function σ

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Section: Introductionsupporting

confidence: 51%

Challenges in Multiscale Modeling of Polymer Dynamics

et al. 2013

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“…In order to consider these components in the calculation, a stepwise procedure is adopted. Since the polymer's architecture is also considered in this model, it is necessary to determine the viscosity ratio between a linear polymer used as reference and a (hyper)branched one which has the same total molecular weight [41][42][43][44][45][46]. Eq.…”

Section: Phase Viscositiesmentioning

confidence: 99%

Numerical Modeling of Nanotechnology-Boosted Chemical Enhanced Oil Recovery Methods

Druetta¹

2020

Computational Fluid Dynamics Simulations

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Since it was theorized more than 50 years ago, nanotechnology has become the perfect boost for existing old technologies. The unique properties exhibited by materials at these scales have a potential to improve the performance of mature oil fields along with enhanced oil recovery (EOR) processes. Regarding polymer flooding, the influence of the (macro) molecules' architecture on the fluid properties has been lately stressed. This chapter presents the numerical simulation of the combination of both agents in a single, combined recovery process. The presence of the nanoparticles affects the rheological behavior and the rock's wettability, increasing the organic phase mobility. Undesirable effects such as (nano) particle aggregation and sedimentation are also considered. The polymer's architecture has a major influence on the recovery process, improving the rheological and viscoelastic properties. On the other hand, although nanoparticles improve the viscosity as well, its main mechanism is their adsorption onto the rock and wettability modification. This chapter shows the importance of a good polymer characterization for EOR, the potential of nanoparticles acting as a boost of traditional EOR processes, and the vital role CFD techniques play to assess the potential of these agents and the optimization of the recovery strategies.

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“…The last two decades have seen an increasing level of interest in the synthesis of star, branched, and hyperbranched polymers because of the differentiated material properties that they exhibit compared to their linear counterparts. For example, they have shown significant potential to deliver differentiated application performance in areas such as melt flow, drug/gene delivery, and medical imaging . Branched structures are of particular interest in the case of biosourced polymers because: (a) they are typically more sensitive to processing conditions, e.g., temperature, and/or (b) the physical properties of linear biopolymers are often on the limit of viability for use in many medical, healthcare, and pharma applications …”

Section: Introductionmentioning

confidence: 99%

“…The last two decades have seen an increasing level of interest in the synthesis of star, branched, and hyperbranched polymers because of the differentiated material properties that they exhibit compared to their linear counterparts. For example, they have shown signifi cant potential to deliver differentiated application performance in areas such as melt fl ow, [1][2][3] drug/gene delivery, [4][5][6] and medical imaging. [7][8][9] Branched structures are of particular media dielectric properties could be used to follow ROP reactions in real time.…”

Section: Introductionmentioning

confidence: 99%

Facile Determination of Molecular Structure Trends in Amphiphilic Core Corona Star Polymer Synthesis via Dielectric Property Measurement

Hild

Nguyen

Deng

et al. 2016

Macromol. Rapid Commun.

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The use of dielectric property measurements to define specific trends in the molecular structures of poly(caprolactone) containing star polymers and/or the interbatch repeatability of the synthetic procedures used to generate them is demonstrated. The magnitude of the dielectric property value is shown to accurately reflect: (a) the number of functional groups within a series of materials with similar molecular size when no additional intermolecular order is present in the medium, (b) the polymer molecular size for a series of materials containing a fixed core material and so functional group number, and/or (c) the batch to batch repeatability of the synthesis method. The dielectric measurements are validated by comparison to spectroscopic/chromatographic data.

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Analytical Rheology of Polymer Melts: State of the Art

Cited by 18 publications

References 342 publications

Challenges in Multiscale Modeling of Polymer Dynamics

Challenges in Multiscale Modeling of Polymer Dynamics

Numerical Modeling of Nanotechnology-Boosted Chemical Enhanced Oil Recovery Methods

Facile Determination of Molecular Structure Trends in Amphiphilic Core Corona Star Polymer Synthesis via Dielectric Property Measurement

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