标题： 蒸发胶体液滴中的皮肤形成 显示英文标题

标题： Skin formation in evaporating colloidal droplets

摘要： 当含有浓缩悬浮液的液滴在干燥环境中蒸发时，通常会在界面处形成一层，积累不挥发性物质。 这种“皮层”会经历强烈的应力，并在蒸发的最后阶段变得机械不稳定。 预测这种皮层或粒子壳的形成及其特性是一个重要的问题，对于应用而言具有重要意义，并构成了从粒子的微观/纳米尺度到液滴的毫米尺寸的多尺度问题。 有趣的是，其物理描述位于确定性宏观蒸发模型和微观随机粒子相互作用与扩散之间的界面上。 在本工作中，我们提出了一种通用的理论方法，以隐式方式获得球形液滴扩散限制蒸发的一般情况下的时间依赖粒子浓度分布，并更一般地适用于所有一维非线性扩散限制情况，其中粒子压力和迁移率项为有理形式。 该方法成功地与高佩克莱特数极限下使用有限元求解器得到的数值解以及二维布朗动力学模拟进行了比较。 我们的结果表明，浓度分布和壳层形成的起始点非平凡地依赖于初始填充分数。 通过分析这些分布，我们确定了玻璃层形成的位点，其形成预计在壳层屈曲中起关键作用。 该模型为预测所得簇的大小和最大长宽比提供了稳健的框架。 显示英文摘要

摘要： When a droplet containing a concentrated suspension evaporates in a dry environment, a layer often forms at the interface accumulating non-volatile material. Such a "skin layer" experiences strong stresses and eventually turns mechanically unstable at the last stage of evaporation. Predicting the formation of such skin layer or particle shell and its properties is a crucial problem for applications and constitutes a multi-scale problem, from the micro/nanoscopic scale of the particles to the millimetric size of the droplets. Interestingly, its physical description lies at the interface between deterministic macroscopic evaporation models and microscopic stochastic particles interactions and diffusion. In this work we present a general theoretical approach to obtain the time-dependent particle concentration profile in an implicit manner, for the general case of diffusion-limited evaporation of spherical droplets, and more generally to all 1D non linear diffusion-limited cases with particles pressure and mobility terms of rational form. This approach is compared successfully to numerical solutions obtained using a finite element solver in the limit of high P\'eclet numbers, and to 2D Brownian dynamics simulations. Our results show that the concentration profiles and shell formation onset depend nontrivially on the initial packing fraction. By analyzing these profiles, we determine the position where the glassy layer forms, whose formation is expected to play a critical role in shell buckling. This model provides a robust framework for predicting the size and maximum aspect ratio of the resulting clusters.