Ding D, Gabbard C T, Bostwick J B
Department of Mechanical Engineering, Clemson University, Clemson, SC 29634, USA.
School of Engineering Brown University, Providence, RI 02912, USA.
Soft Matter. 2024 Oct 16;20(40):8068-8077. doi: 10.1039/d4sm00941j.
Dip coating a planar substrate with a suspension of particles in a shear-thinning liquid will entrain particles in the liquid film, facilitating filtration and sorting of particles. Experiments were performed for both monodisperse and bidisperse particle suspensions of shear-thinning Xanthan Gum solutions. Particle entrainment occurs when the coating thickness at the stagnation point of the thin film flow is larger than the particle diameter. A model is developed to predict the entrainment criteria using lubrication theory applied to an Ostwald power-law fluid which yields a modified Landau-Levich-Derjaguin (LLD) law governing the coating film thickness that depends upon a properly defined capillary number Ca. The critical withdrawal velocity for particle entrainment depends upon the particle size and fluid rheology through a relationship between Ca and the bond number Bo, which agrees well with our model predictions and prior experimental results of A. Sauret , 2019, , 054303 for the limiting case of Newtonian suspensions. Single particle entrainment and particle clustering is observed for monodisperse suspensions, which depends on Ca and the particle volume fraction . In bidisperse suspensions, particle sorting can occur whereby only the smaller particles are entrained in the film over an active filtration range of Ca and Bo, which also agrees well with our model predictions.
用颗粒悬浮在剪切变稀液体中对平面基板进行浸涂,会使颗粒夹带在液膜中,便于颗粒的过滤和分选。对剪切变稀的黄原胶溶液的单分散和双分散颗粒悬浮液都进行了实验。当薄膜流动驻点处的涂层厚度大于颗粒直径时,就会发生颗粒夹带。利用应用于奥斯特瓦尔德幂律流体的润滑理论建立了一个模型来预测夹带标准,该理论得出了一个修正的朗道 - 列维奇 - 杰里亚金(LLD)定律,该定律控制着取决于适当定义的毛细管数Ca的涂层膜厚度。颗粒夹带的临界抽出速度通过Ca与键数Bo之间的关系取决于颗粒大小和流体流变学,这与我们的模型预测以及A. Sauret在2019年发表的关于牛顿悬浮液极限情况的先前实验结果(论文编号054303)非常吻合。对于单分散悬浮液,观察到了单个颗粒夹带和颗粒聚集现象,这取决于Ca和颗粒体积分数。在双分散悬浮液中,在Ca和Bo的有效过滤范围内,可能会发生颗粒分选,即只有较小的颗粒被夹带在液膜中,这也与我们的模型预测非常吻合。