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DROPLET IMPINGEMENT AND WETTING HYSTERESIS ON TEXTURED HYDROPHOBIC SURFACES

机译:疏水表面上的液滴撞击和润湿滞后

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We study the wetting energetics and wetting hysteresis of sessile and impacting water droplets on superhydrophobic surfaces as a function of surface texture and surface energy. Detailed experiments tracking contact line motion simultaneously with contact angle provides new insights on the wetting hysteresis, stick-slip behavior and dependence on contact line velocity. For sessile drops, we find three wetting regimes on these surfaces: equilibrium Cassie at small feature spacing, equilibrium Wenzel at large feature spacing, and an intermediate state at medium feature spacing. We observe minimum wetting hysteresis not on surfaces that exhibit Cassie wetting but rather on surfaces in the intermediate regime. We argue that droplets on these surfaces are metastable Cassie droplets whose internal Laplace pressure is insufficient to overcome the energy barrier required to homogeneously wet the surface. These metastable Cassie droplets show superior roll-off properties because the effective length of the contact line that is pinned to the surface is reduced. We develop a model that can predict the transition between the metastable Cassie and Wenzel regimes by comparing the Laplace pressure of the drop to the capillary pressure associated with the wetting-energy barrier of the textured surface. In the case of impacting droplets the water hammer and Bernoulli pressures must be compared with the capillary pressure. Experiments with impacting droplets show very good agreement with this simple pressure-balance model.
机译:我们研究了超疏水表面上无柄和撞击水滴的润湿能学和润湿滞后随表面织构和表面能的变化。跟踪接触线运动和接触角的详细实验,为润湿滞后,粘滑行为以及对接触线速度的依赖性提供了新的见解。对于无滴,我们在这些表面上发现了三种润湿方式:小特征间距处的平衡Cassie,大特征间距处的平衡Wenzel和中特征间距处的中间状态。我们观察到最小润湿滞后不是在表现出卡西润湿的表面上,而是在中间状态的表面上。我们认为这些表面上的液滴是亚稳态的Cassie液滴,其内部Laplace压力不足以克服均匀润湿表面所需的能垒。这些亚稳态的Cassie液滴显示出出色的滚降性能,因为固定在表面上的接触线的有效长度减少了。我们开发了一个模型,该模型可以通过比较液滴的拉普拉斯压力与与纹理化表面的湿能屏障相关的毛细压力来预测亚稳态Cassie和Wenzel机制之间的过渡。在撞击液滴的情况下,必须将水锤和伯努利压力与毛细管压力进行比较。冲击液滴的实验表明,这种简单的压力平衡模型具有很好的一致性。

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