Droplet Memory on Liquid-Infused Surfaces.

Area of Science:

• Surface Science
• Fluid Dynamics
• Materials Science

Background:

• Understanding droplet friction on liquid-infused surfaces (LIS) is crucial for liquid manipulation technologies.
• Existing research primarily focuses on regular solid topographies, leaving a gap in understanding random nanostructures.
• Dissipation mechanisms on LIS are generally understood, but the influence of varied surface textures requires further investigation.

Purpose of the Study:

• To investigate the friction of water droplets on LIS featuring both random and regular polysilsesquioxane nanostructures.
• To explore the impact of surface texture on droplet dynamics and friction.
• To identify and characterize novel phenomena related to droplet motion on LIS.

Main Methods:

• Fabrication of LIS with random and regular polysilsesquioxane nanostructures.
• Experimental observation and measurement of water droplet friction and velocity on these surfaces.
• Analysis of droplet motion, including interactions with previously traversed paths.

Main Results:

• Established models for droplet friction are applicable to the tested nanostructured LIS.
• A novel droplet memory effect was observed: consecutive droplets accelerated along the same path, reaching a plateau velocity.
• Moving droplets leave a low-friction trace, matching their base diameter, attributed to temporary lubricant layer smoothing.

Conclusions:

• The study demonstrates that surface texture, including random nanostructures, influences droplet friction on LIS.
• A new droplet memory effect, linked to lubricant layer dynamics (Landau-Levich-Derjaguin deposition), was identified.
• The proposed mechanism for the memory effect is potentially applicable to a wide range of LIS with conformal lubricant layers.