Loop expansion in polymer field theory: application to phase separation

TL;DR

This paper develops a loop expansion method in polymer field theory to improve predictions of phase separation, specifically refining the random phase approximation (RPA) for better accuracy in dilute regimes.

Contribution

It introduces a systematic loop expansion approach, identifying inverse polymer density as a quantum field theory parameter, to enhance RPA predictions for polymer phase behavior.

Findings

RPA+ improves dilute-phase coexistence density predictions

Loop expansion provides a systematic refinement over RPA

Critical point predictions remain similar to RPA

Abstract

Liquid-liquid phase separation underlies phenomena ranging from protein condensate formation to the phase coexistence of synthetic polymers. Although the random phase approximation (RPA) is widely used to predict such phase behavior, its quantitative accuracy for binodals of polymer solutions, particularly outside the high-density regime, remains incompletely characterized. Here, we develop a field theoretic loop expansion in homopolymer systems by identifying the inverse polymer density $\rho^{-1}$ as the Planck constant $\hbar$ in quantum field theory. We calculate the leading-order and next-to-leading-order corrections to the RPA free energy, denoted as RPA+ and RPA++, respectively. Testing the binodal predicted by the RPA+ against molecular dynamics simulations of bead-spring chains with Gaussian pair interactions, we find that the RPA+ qualitatively improves the dilute-phase coexistence density over the RPA, while the critical point error remains comparable to that of the RPA. Our results establish the loop expansion as a systematic route for refining the RPA-based binodal predictions for polymer phase separation.