Self-driven movement of water droplets on anisotropic wetting surfaces: A lattice Boltzmann investigation

Controllable droplet transport is fundamentally crucial for diverse applications ranging from microfluidics and lab-on-a-chip devices to water harvesting, thermal management, and biomedical diagnostics. Passive strategies leveraging wettability gradients offer significant advantages by enabling self-driven droplet motion without complex structures and external energy input. By performing dynamic simulations using the pseudopotential multi-relaxation-time scheme of the lattice Boltzmann method, we investigate the fundamental dynamics governing the autonomous migration of liquid droplets on surfaces with spatially tailored wettability patterns. Through systematic analyses of the dynamics of droplets on surfaces with different wettability gradients, we elucidate the interplay among the gradient-induced Young's force, viscous dissipation, and contact line dynamics dictating droplet mobility. Key findings reveal that complementary wetting pattern angles generate similar unbalanced Young's force, yielding highly consistent horizontal velocities—enabling predictable droplet trajectory control. Furthermore, we calculated the wettability gradient induced migration velocity for different patterns, demonstrating tunable mean velocity modulation within 10–23 mm/s. Results on the coalescence of double droplets show that the maximum spreading length is greater than that of a single droplet, and this increase in spreading length decreases with an increase in the We number. These insights provide a foundational framework for engineering intelligent surfaces for directed droplet manipulation, advancing the development of functional interfacial materials.