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Macromolecular architecture is a critical parameter when tuning polymer material properties. Although the implementation of non-linear polymers in different applications has grown over the years, polymer grafted surfaces such as nanoparticles have traditionally been composed of linear thermoplastic polymers, with a limited number of examples demonstrating a diversity in polymer architectures. In an effort to combine polymer architecturally dependent material properties with polymer grafted particles (PGPs), as opposed to conventional methods of tuning polymer grafting parameters such as the number of chains per surface area (i.e., polymer graft density), a series of bottlebrush grafted particles were synthesized using surface-initiated ring-opening metathesis polymerization (SI-ROMP). These bottlebrush PGPs are composed of glassy, semi-crystalline, and elastomeric polymer side chains with controlled backbone degrees of polymerization (Nbb) at relatively constant polymer graft density on the surface of silica particles with diameters equaling approximately 160 or 77 nm. Bottlebrush polymer chain conformations, evaluated by measuring the brush height of surface grafted polymer chains in solution and the melt, undergo drastic changes in macromolecular dimensions in different environments. In solution, brush heights increase linearly as a function of Nbb, consistent with fully stretched chains, which is confirmed using cryogenic transmission electron microscopy (Cryo-TEM). Meanwhile, brush heights are consistently at a minimum in the melt, indicative of chains collapsed on the particle surface. The conformational extremes for grafted bottlebrush polymers are unseen in any linear polymer chain systems, highlighting the effect of macromolecular architecture and surface grafting. Bottlebrush grafted particles are an exciting class of materials where diversifying polymer architectures will expand PGP material design rules that harness macromolecular architecture to dictate properties.
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This content will become publicly available on June 25, 2026
Machine learning of the architecture–property relationship in graft polymers
Graft polymers are promising in energy and biomedical applications. However, the diverse architectures make it challenging to establish their structure–property relationships. We systematically investigate how backbone and side-chain architectures influence four key properties: glass transition temperature (Tg), self-diffusion coefficient (D), radius of gyration (Rg), and packing density (ρ). Using molecular dynamics simulations, we analyze a dataset of 500 graft polymers with randomly positioned side chains. Tg and D exhibit decoupled relationships due to the distinct topological effects. Furthermore, we develop dense neural networks (DNNs) and convolutional neural networks (CNNs) to pave the way to polymer design with desired properties.
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- Award ID(s):
- 2328188
- PAR ID:
- 10645827
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Physical Chemistry Chemical Physics
- Volume:
- 27
- Issue:
- 25
- ISSN:
- 1463-9076
- Page Range / eLocation ID:
- 13243 to 13247
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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