Programming configurational changes in systems of functionalised polymers using reversible intramolecular linkages

ABSTRACT

We study polymers functionalised by complexes forming intramolecular linkages. Simulations of chains forming reversible linkages are difficult due to entropic barriers that hamper the sampling of different connectivity states specified by the list of pairs of reacted complexes. We address this problem by devising Monte Carlo (MC) moves that change the connectivity state of the system by regrowing parts of the chain while simultaneously reacting bond-forming complexes. Along with moves that link/unlink pairs of complexes, we develop two types of bond-exchange moves. We use these algorithms to study self-assembly of single chain polymeric nanoparticles. When considering monofunctional precursors, we find branched and linear nanoparticle morphologies dominated by long and short intramolecular loops, respectively, along with hierarchical structures in which complexes belonging to different loops are cross-linked. In the strong association limit, equilibrium structures are only reached when using bond-exchange MC moves. We also consider bifunctional precursors in which two different types of complexes decorate the two halves of the chain. We find different types of morphologies featuring different amounts of linkages between complexes of different types. Such findings corroborate our method as a valuable tool to design and predict self-assembly of functional polymers.

GRAPHICAL ABSTRACT

KEYWORDS:

• Configurational Bias Monte Carlo
• functionalised polymers

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Bortolo Matteo Mognetti http://orcid.org/0000-0002-7960-8224

Additional information

Funding

Financial support was provided by the Fédération Wallonie-Bruxelles (Actions de Recherches Concertées) for the project ‘Numerical Design of Supramolecular Interactions’. Computational resources have been provided by the Consortium des Equipements de Calcul Intensif (CECI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. 2.5020.11.