Malhotra Isha, Löwen Hartmut
Institut für Theoretische Physik II: Weiche Materie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany.
J Chem Phys. 2024 Oct 28;161(16). doi: 10.1063/5.0225749.
The Mpemba effect describes the phenomenon that a system at 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. In addition, 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.
在相同环境中，初始温度较高的系统比初始温度稍低的系统冷却得更快。最近，这种反常冷却现象已在捕获的胶体中得到预测和实现。在这里，我们通过数值布朗动力学模拟和理论计算，利用一个可在实验中直接测试的模型，研究了被动胶体粒子的冻结行为。在冷却过程中，胶体粒子的冷却速率呈现出多个非单调区域，冷却时间随初始温度的变化而减少了两倍——我们将这种意外现象称为双姆潘巴效应。此外，我们证明了姆潘巴效应和双姆潘巴效应都可以通过各种机器学习方法进行预测，这加快了对复杂、计算量大的系统的分析。
