Dissipation-driven selection under finite diffusion: hints from equilibrium and separation of time-scales

TL;DR

This paper investigates how finite diffusion rates influence chemical selection in reaction networks exposed to thermal gradients, revealing new phenomena like maximized selection and state switching, using a time-scale separation framework.

Contribution

It introduces a novel equilibrium-based framework to analyze non-equilibrium features in reaction networks with finite diffusion, highlighting the role of fast-dissipation subnetworks.

Findings

Finite diffusion can enhance chemical selection.

Fast-dissipation subnetworks dominate steady states.

Heat capacity estimates entropy production.

Abstract

When exposed to a thermal gradient, reaction networks can convert thermal energy into the chemical selection of states that would be unfavourable at equilibrium. The kinetics of reaction paths, and thus how fast they dissipate available energy, might be dominant in dictating the stationary populations of all chemical states out-of-equilibrium. This phenomenology has been theoretically explored mainly in the infinite diffusion limit. Here, we show that the regime in which the diffusion rate is finite, and also slower than some chemical reactions, might give birth to interesting features, as the maximization of selection, or the switch of the selected state at stationarity. We introduce a framework, rooted in a time-scale separation analysis, which is able to capture leading non-equilibrium features using only equilibrium arguments under well-defined conditions. In particular, it is possible to identify fast-dissipation subnetworks of reactions whose Boltzmann equilibrium dominates the steady-state of the entire system as a whole. Finally, we also show that the dissipated heat (and so the entropy production) can be estimated, under some approximations, through the heat capacity of fast-dissipation subnetworks. This work provides a tool to develop an intuitive equilibrium-based grasp on complex non-isothermal reaction networks, which are important paradigms to understand the emergence of complex structures from basic building blocks.