Topology induced modifications in the critical behavior of the Yaldram Khan catalytic reaction model

TL;DR

This study explores how complex network structures, specifically Erdos-Renyi and random geometric graphs, influence the phase transitions and reactive behavior of the Yaldram-Khan catalytic model, revealing topology-dependent critical phenomena.

Contribution

It introduces the impact of network topology on the critical behavior of the Yaldram-Khan model, highlighting differences between ER and RGG networks.

Findings

Erdos-Renyi networks preserve original phase transition nature.

Random geometric graphs exhibit two second-order phase transitions at low average degrees.

Network topology significantly affects the model's phase diagram and transition order.

Abstract

In this work, we investigated how the use of complex networks as catalytic surfaces can affect the phase diagram of the Yaldram-Khan model, as well as how the order of the phase transitions present in the seminal work behaves when the randomness is added to the model. The study was conducted by taking into consideration two well-known random networks, the Erdos-Renyi network (ERN), with its long-range randomness, and the random geometric graph (RGG), with its spatially constrained randomness. We perform extensive steady-state Monte Carlo simulations assuming the NO dissociation rate is equal to 1 and show the behavior of the reactive window as function of the average degree of the networks. Our results also show that, different from the ERN, which preserves the nature of the phase transitions of the original model for all considered average degrees, the RGG seems to have two second-order phase transitions for small values of average degree.