Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · cond-mat/0205488

Thermodynamics and structure of self-assembled networks

A. G. Zilman·S. A. Safran
View original source ↗Read source document ↗Export metadata ↓

Abstract

We study a generic model of self-assembling chains which can branch and form networks with branching points (junctions) of arbitrary functionality. The physical realizations include physical gels, wormlike micells, dipolar fluids and microemulsions. The model maps the partition function of a solution of branched, self-assembling, mutually avoiding clusters onto that of a Heisenberg magnet in the mathematical limit of zero spin components. The model is solved in the mean field approximation. It is found that despite the absence of any specific interaction between the chains, the entropy of the junctions induces an effective attraction between the monomers, which in the case of three-fold junctions leads to a first order reentrant phase separation between a dilute phase consisting mainly of single chains, and a dense network, or two network phases. Independent of the phase separation, we predict the percolation (connectivity) transition at which an infinite network is formed that partially overlaps with the first-order transition. The percolation transition is a continuous, non thermodynamic transition that describes a change in the topology of the system. Our treatment which predicts both the thermodynamic phase equilibria as well as the spatial correlations in the system allows us to treat both the phase separation and the percolation threshold within the same framework. The density-density correlation correlation has a usual Ornstein-Zernicke form at low monomer densities. At higher densities, a peak emerges in the structure factor, signifying an onset of medium-range order in the system. Implications of the results for different physical systems are discussed.

Explore related subjects

cond-mat.softcond-mat.stat-mech

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

A. G. Zilman, S. A. Safran. 2002-05-23. Thermodynamics and structure of self-assembled networks. https://doi.org/10.1103/physreve.66.051107

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Dynamic clustering of active chemotactic Janus particles

Modeling self-propulsion as an emergent diffusiophoretic effect, we come back to the dynamic clustering experiment of active Janus colloids. Working in the original three-dimensional geometry, and using the chemical solute concentration as a control parameter, we find that the dynamic clustering region occupies a robust fraction of the phase diagram. This behavior is rationalized using a linear stability analysis that efficiently captures the instability of the gas phase to dynamic clusters. We also find that dynamic clustering results from the combined effects of a Keller-Segel collapse and of a motility-induced phase separation, either being arrested by the orientational coupling to the chemical field in agreement with previous work.

cond-mat.soft↗
arXiv2026

Multi-Fidelity Machine Learning for Predicting Startup Stress Growth and Strain Hardening in Wormlike Micellar Solutions

Wormlike micellar solutions exhibit formulation-dependent nonlinear startup responses, including stress overshoot, undershoot, and strain hardening, making prediction across compositions and shear rates challenging. We developed a multi-fidelity machine-learning framework to predict stress-growth curves in cetylpyridinium chloride (CPyCl)-based wormlike micelles from sparse experimental measurements. The framework combines a low-fidelity analytical model describing the overall startup response with high-fidelity experimental data capturing formulation-specific nonlinear features. This approach addresses the limited and uneven coverage of salt chemistry, concentration, and shear rate without relying solely on data-intensive neural-network training. The trained model predicts complete stress-growth responses at untested shear rates and enables extraction of strain-hardening characteristics from interpolated curves. By augmenting sparse rheological measurements, the framework provides a basis for exploring relationships among formulation variables, linear viscoelastic properties, and nonlinear startup behavior, while supporting more efficient characterization of wormlike micellar solutions.

cond-mat.soft↗
arXiv2026

Topology Does Not Determine Defect Mobility in Active Polar Matter

Topological defects are commonly characterized by their charge, although defects with the same charge can have different internal morphologies. We study the annihilation of isolated $\pm1$ defect pairs in a two-dimensional compressible Toner--Tu model, varying the morphology of the $+1$ defect while keeping the initial charges and separation fixed. Activity produces a morphology-dependent mobility asymmetry: reversing the radial orientation of an aster reverses which defect travels farther, and asters show the strongest response. An early-time analysis reveals that the initial morphology selects distinct density modes whose gradients subsequently feed back on the polarization. Consistently, removing the density-gradient coupling strongly suppresses the asymmetry. These results establish defect morphology, in addition to topological charge, as a dynamical variable governing defect-pair motion in active polar matter.

cond-mat.soft↗