Understanding the resuspension of droplets from surfaces into air is important for elucidating a range of processes such as disease transmission of airborne pathogens and determining environmental contamination and the effectiveness of cleaning procedures. The resuspension condition is defined as the escape velocity of a droplet from a surface. This study investigated the dynamics of microliter-sized droplet resuspension off surfaces utilizing a novel free-fall device. We studied surfaces with three different wettabilities, three droplet volumes, and substrate velocities ranging from 0 to 3.5 m/s for deionized water and viscous droplets representing a prototype saliva substitute. Experimental results provide quantitative results for the increased propensity for drop resuspension for more hydrophobic surfaces, larger droplet volume, and higher velocity. By using high-speed imaging, we segment the resuspension process into four stages: initial equilibrium, deformation, elongation, and breakage. Experimental results are generalized as a machine-learning-derived decision surface, which predicts resuspension by defining a 2D decision boundary in our 3D parameter space. We present a simple physical model, corroborated by computational fluid dynamics simulations, for the dynamics of resuspension that explains the process and is in good agreement with the experiments.

中文翻译：

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微液滴重悬表面


了解液滴从表面重悬到空气中对于阐明一系列过程（例如空气传播病原体的疾病传播）以及确定环境污染和清洁程序的有效性非常重要。再悬浮条件定义为液滴从表面逸出的速度。本研究利用一种新型自由落体装置调查了微升大小的液滴从表面重悬的动力学。我们研究了具有三种不同润湿性、三种液滴体积和从 0 到 3.5 m/s 的基质速度范围的表面，用于去离子水和代表原型唾液替代品的粘性液滴。实验结果为更疏水表面、更大液滴体积和更高速度的液滴重悬倾向增加提供了定量结果。通过使用高速成像，我们将重悬过程分为四个阶段：初始平衡、变形、伸长和破损。实验结果被推广为机器学习衍生的决策表面，它通过在 3D 参数空间中定义 2D 决策边界来预测重新悬吊。我们提出了一个简单的物理模型，并得到了计算流体动力学仿真的证实，用于再悬浮的动力学，该模型解释了该过程并与实验非常吻合。