Double Mpemba effect in the cooling of trapped colloids

TL;DR

This paper reports the discovery of a Double Mpemba effect in trapped colloids, where the cooling rate exhibits two non-monotonic regimes, and demonstrates that machine learning can predict these phenomena efficiently.

Contribution

It introduces the novel Double Mpemba effect in colloidal cooling and shows that machine learning methods can predict complex cooling behaviors.

Findings

Double Mpemba effect observed in colloids with two non-monotonic cooling regimes

Cooling time decreases twice as a function of initial temperature

Machine learning effectively predicts the Mpemba phenomena

Abstract

The Mpemba effect describes the phenomenon that a system at a hot initial temperature cools faster than at an initial warm temperature in the same environment. Such an anomalous cooling has recently been predicted and realized for trapped colloids. Here, we investigate the freezing behavior of a passive colloidal particle by employing numerical Brownian dynamics simulations and theoretical calculations with a model that can be directly tested in experiments. During the cooling process, the colloidal particle exhibits multiple non-monotonic regimes in cooling rates, with the cooling time decreasing twice as a function of the initial temperature-an unexpected phenomenon we refer to as the Double Mpemba effect. Additionally, we demonstrate that both the Mpemba and Double Mpemba effects can be predicted by various machine learning methods, which expedite the analysis of complex, computationally intensive systems.