目的:具有不同表面张力的液-气界面的液滴聚结在自然界和工业应用中很常见,马兰戈尼驱动的电影传播是一个必不可少的过程。与受三重接触线动力学控制的不混溶流体不同,两种可混溶流体之间的混合与薄膜铺展过程强烈耦合,对于胶片半径的时间增加,预计将表现出明显的幂律关系。
方法:我们通过实验研究了具有较低表面张力的液滴滴落到可混溶的液滴上的Marangoni驱动的薄膜扩散现象,薄的液体层。通过使用一种新的深度卷积神经网络来检测胶片半径的时间增长,U2-net方法。进行缩放分析以解释膜的扩散动力学。
结果:我们发现胶片半径随时间表现出三级幂律关系,指数从1/2变化到1/8,再回到1/2。推导了这三个阶段受扩散影响的马兰戈尼应力,并考虑了粘性应力的两种估计。通过估计和平衡粘性应力与马兰戈尼应力,推导并验证了三阶段幂律关系。
OBJECTIVE: The coalescence of droplets with liquid-gas interfaces of different surface tensions is common in nature and industrial applications, where the Marangoni-driven film spreading is an essential process. Unlike immiscible fluids governed by triple contact line dynamics, the mixing between two miscible fluids strongly couples with the film spreading process, which are expected to manifest distinct power-law relations for the temporal increase in the film radius.
METHODS: We experimentally investigate the Marangoni-driven film spreading phenomenon for a droplet with lower surface tension dropping onto a miscible, thin liquid layer. The temporal growth of the film radius was detected by using a novel deep convolutional neural network, the U2-net method. Scaling analysis was performed to interpret the spreading dynamics of the film.
RESULTS: We find that the film radius exhibits a three-stage power-law relation over time, with the exponent varying from 1/2 to 1/8, and back to 1/2. The diffusion-affected Marangoni stresses in these three stages were derived, and two estimations of viscous stress were considered. Through estimating and balancing the viscous stress with the Marangoni stress, the three-stage power-law relation was derived and validated.