A single-chain nanoparticle-based mean-field theory for associative polymers
Marco Cappa, Stefano Chiani, Francesco Sciortino, Lorenzo Rovigatti

TL;DR
This paper develops a mean-field theory for associative polymers with stickers, explaining how sticker topology influences phase separation and network formation, and aligning well with simulation data.
Contribution
It introduces a simple yet effective theory that captures the thermodynamics of SCNPs, emphasizing the role of sticker sequence and microscopic assumptions.
Findings
The theory accurately predicts phase behavior based on sticker topology.
Phase transition nature varies with sticker types, from continuous to first-order.
Results align with coarse-grained simulation predictions.
Abstract
Associative polymers are a class of polymers containing attractive stickers that can reversibly bind to each other. Their fully-bonded state gives rise, in dilute conditions, to a fluid phase of so-called single-chain nanoparticles (SCNPs). These constructs have been used in a wide range of applications, from the design of new materials (e.g. biomolecular condensates) to drug-delivery vectors. The thermodynamic properties of SCNPs sensitively depend on the number of different sticker types, since numerical simulations show that a continuous transition to a network of chains upon increase of polymer concentration in the single sticker-type case can be replaced by an abrupt network formation (via a first-order phase transition) in the multiple sticker-type case. We present here a theory that, using the SCNP fluid as the reference system, quantifies the free energy change associated with…
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Taxonomy
TopicsBlock Copolymer Self-Assembly · Pickering emulsions and particle stabilization · Phase Equilibria and Thermodynamics
