Rapid Electrostatic Capture of Rod-Shaped Particles on Planar Surfaces: Standing up to Shear.

LANGMUIR(2019)

引用 9|浏览6
暂无评分
摘要
We compare the electrostatically driven capture of flowing rod-shaped and spherical silica particles from dilute solutions onto a flow chamber wall that carries the opposite electrostatic charge from the particles. Particle accumulation and orientation are measured in time at a fixed region on the wall of a shear flow chamber. Rod-shaped particle aspect ratios are 2.5-3.2 and particle lengths are 1.3 and 2.67 mu m for two samples, while sphere diameters were 0.72, 0.96, and 2.0 mu m for three samples. At a moderate wall shear rate of 22 s(-1) , the particle accumulation for both rods and spheres is well described by diffusion-limited kinetics, demonstrating the limiting effect of particle diffusion in the near-wall boundary layer for electrostatically driven capture in this particle shape and size range. The significance of this finding is demonstrated in a calculation that shows that for delivery applications, nearly the same (within 10%) particle volume or mass is delivered to a surface at the diffusion-limited rate by rods and spheres. Therefore, in the absence of other motivating factors, the expense of developing rodshaped microscale delivery packages to enhance capture from flow in the diffusion-limited simple shear regime is unwarranted. It is also interesting that the captured orientations of the larger rods, 2.6 mu m in average length, were highly varied and insensitive to flow: a substantial fraction of rods were trapped in standing and slightly leaning orientations, touching the surface by their ends. Additionally, for particles that were substantially tipped over, there was only modest orientation in the flow direction. Taken together, these findings suggest that on the time scale of near-surface particle rotations, adhesion events are fast, trapping particles in orientations that do not necessarily maximize their favored adhesive contact or reduce hydrodynamic drag.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要