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Motion of a Droplet on an Anisotropic Microgrooved Surface.

Manish KumarRajneesh BhardwajKirti Chandra Sahu
Published in: Langmuir : the ACS journal of surfaces and colloids (2019)
We experimentally characterize the sliding angle of water droplets (volume 3.1-22.2 μL) migrating on inclined microgrooved surfaces along the longitudinal and transverse directions of the grooves. The rectangular microgrooves are manufactured on silicon wafers using standard photolithography techniques. We tilt the surface gradually using a rotating stage mechanism until the incipience of the sliding. The droplet migration in the longitudinal and transverse directions to the grooves is recorded using a high-speed camera. For the droplets migrating downward in the transverse direction, the contact line exhibits a "stick-slip" type motion, that is, the advancing contact line is attached to the surface, whereas the receding contact line is detached from the surface. However, no significant change in the relative position of the advancing and receding contact lines is observed in the case of the longitudinal migration of the droplets. The sliding behavior of the droplet in the longitudinal direction is similar to that observed in the case of a smooth surface. The sliding angle in the longitudinal direction of motion is found to be smaller as compared to that in the transverse motion of the droplet. In both longitudinal and transverse migrations, increasing the pitch of the grooves increases the contact angle, which in turn decreases the sliding angle. As the droplet volume is increased, the component of the gravitational force in the direction of inclination increases, which acts to decrease the sliding angle. A theoretical analysis is also conducted to predict the sliding angle of a droplet on microgrooved surfaces. The model predictions agree with the trends observed in our experiments and thus validate the proposed sliding mechanisms in the longitudinal and transverse migrations of the droplet.
Keyphrases
  • high speed
  • single cell
  • high resolution
  • high throughput
  • cross sectional
  • atomic force microscopy
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  • mass spectrometry
  • convolutional neural network