Subdiffusion of Sticky Dendrimers in an Associative Polymer Network

We investigate the static and dynamic properties of dendrimers diffusing through a network of linear associative polymers using coarse-grained Brownian dynamics simulations. Both dendrimers and network chains are modeled as bead–spring chain polymers, with hydrodynamic interactions incorporated for the accurate prediction of dynamic properties. Linear chains form a network via the associating groups distributed along their backbones, and the dendrimers interact attractively or repulsively with the network, enabling a direct comparison of sticky and nonsticky behaviors of dendrimers. Structural analysis reveals that while nonsticky dendrimers shrink with increasing network concentration, similar to linear polymer behavior, sticky dendrimers exhibit stretching at low concentrations due to binding interactions. Dendrimer dynamics are largely insensitive to network architecture but are strongly influenced by the strength of dendrimer–network interactions. Increasing attraction to the network leads to subdiffusive motion and non-Gaussian displacement statistics, even when dendrimers are smaller than the average mesh size. The long-time diffusivity aligns with theoretical predictions for nanoparticle transport in polymer networks. Additionally, dendrimers deform the network locally, altering the mesh size distribution depending on their stickiness. These findings offer insight into the interplay among macromolecular architecture, binding interactions, and transport in polymeric environments.

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Publication Details

Journal
Macromolecules
Published
2025-10-16
DOI
https://doi.org/10.1021/acs.macromol.5c01231
Citations
1
Primary Topic
Dendrimers and Hyperbranched Polymers
Type
article
Field-Weighted Citation Impact
0.61
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article

Subdiffusion of Sticky Dendrimers in an Associative Polymer Network

P. Sunthar, Jeremy J. Barr, J. Ravi Prakash, Silpa Mariya
1 citations
Macromolecules
Dendrimers and Hyperbranched Polymers
0.61
article

Subdiffusion of Sticky Dendrimers in an Associative Polymer Network

P. Sunthar, Jeremy J. Barr, J. Ravi Prakash, Silpa Mariya
article en
1 citations

Abstract

We investigate the static and dynamic properties of dendrimers diffusing through a network of linear associative polymers using coarse-grained Brownian dynamics simulations. Both dendrimers and network chains are modeled as bead–spring chain polymers, with hydrodynamic interactions incorporated for the accurate prediction of dynamic properties. Linear chains form a network via the associating groups distributed along their backbones, and the dendrimers interact attractively or repulsively with the network, enabling a direct comparison of sticky and nonsticky behaviors of dendrimers. Structural analysis reveals that while nonsticky dendrimers shrink with increasing network concentration, similar to linear polymer behavior, sticky dendrimers exhibit stretching at low concentrations due to binding interactions. Dendrimer dynamics are largely insensitive to network architecture but are strongly influenced by the strength of dendrimer–network interactions. Increasing attraction to the network leads to subdiffusive motion and non-Gaussian displacement statistics, even when dendrimers are smaller than the average mesh size. The long-time diffusivity aligns with theoretical predictions for nanoparticle transport in polymer networks. Additionally, dendrimers deform the network locally, altering the mesh size distribution depending on their stickiness. These findings offer insight into the interplay among macromolecular architecture, binding interactions, and transport in polymeric environments.

MacromoleculesVol. 58(20)
Indian Institute of Technology Bombay (IN), Australian Regenerative Medicine Institute (AU), IITB-Monash Research Academy (IN), Monash University (AU)
Openalex Percentile: Top 29%
Dendrimers and Hyperbranched Polymers
0.61
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Subdiffusion of Sticky Dendrimers in an Associative Polymer Network — P. Sunthar, Jeremy J. Barr, et al. · Macromolecules (2025) | TGRS Research Map | TGRS