Issue 40, 2023

Mean-field models for the chemical fueling of transient soft matter states

Abstract

The chemical fueling of transient states (CFTS) is a powerful process to control the nonequilibrium structuring and the homeostatic function of adaptive soft matter systems. Here, we introduce a simple mean-field model of CFTS based on the activation of metastable equilibrium states in a tilted ‘Landau’ bistable energy landscape along a coarse-grained reaction coordinate (or ‘order parameter’) triggered by a nonmonotonic two-step chemical fueling reaction. Evaluation of the model in the quasi-static (QS) limit-valid for fast system relaxation-allows us to extract useful analytical laws for the critical activation concentration and duration of the transient states in dependence of physical parameters, such as rate constants, fuel concentrations, and the system's distance to its equilibrium transition point. We apply our model in the QS limit explicitly to recent experiments of CFTS of collapsing responsive microgels and find a very good performance with only a few global and physically interpretable fitting parameters, which can be employed for programmable material design. Moreover, our model framework also allows a thermodynamic analysis of the energy and performed work in the system. Finally, we go beyond the QS limit, where the system's response is slow and retarded versus the chemical reaction, using an overdamped Smoluchowski approach. The latter demonstrates how internal system time scales can be used to tune the time-dependent behavior and programmed delay of the transient states in full nonequilibrium.

Graphical abstract: Mean-field models for the chemical fueling of transient soft matter states

Supplementary files

Article information

Article type
Paper
Submitted
07 Jun 2023
Accepted
24 Sep 2023
First published
28 Sep 2023
This article is Open Access
Creative Commons BY license

Soft Matter, 2023,19, 7804-7814

Mean-field models for the chemical fueling of transient soft matter states

S. Pattloch and J. Dzubiella, Soft Matter, 2023, 19, 7804 DOI: 10.1039/D3SM00742A

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