Understanding Dewetting Transitions On Nanotextured Surfaces: Implications For Designing Surfaces With Improved Wettability

Despite the early promise of superhydrophobic surfaces, their widespread technological adoption has been dawdled by the ease with which water can penetrate the surface texture, resulting in a breakdown of superhydrophobicity. Furthermore, this breakdown is believed to be irreversible, because large adhesion barriers impede the dewetting of the surface texture and the concomitant recovery of superhydrophobicity. Using molecular dynamics simulations in conjunction with advanced sampling techniques, in this thesis, we challenge this conventional argument. We show that while large barriers do typically impede the recovery of superhydrophobicity, it can nevertheless be recovered spontaneously on nanotextured surfaces, wherein collective water density fluctuations lead to non-classical dewetting path- ways and reduced dewetting barriers. An understanding of the complex dewetting pathways further enables us to uncover principles for the design of novel surface textures on which dewetting barriers vanish and superhydrophobicity can be spontaneously recovered. Our results thus promise to pave the way for robust superhydrophobic surfaces with widespread applicability under the most challenging conditions from applications involving sustained underwater operation to enabling drop-wise condensation in heat exchangers. Along with recent advances in the synthesis of surfaces with nanoscale texture, work in this thesis promises to revitalize the field of superhydrophobicity and its class of problems, from its prevalent trial-and-error approach to the rational design of surface textures

Sparse sampling of water density fluctuations near liquid-vapor coexistence

<p>The free energetics of water density fluctuations in bulk water, at interfaces, and in hydrophobic confinement inform the hydration of hydrophobic solutes as well as their interactions and assembly. The characterisation of such free energetics is typically performed using enhanced sampling techniques such as umbrella sampling. In umbrella sampling, order parameter distributions obtained from adjacent biased simulations must overlap in order to estimate free energy differences between biased ensembles. Many biased simulations are typically required to ensure such overlap, which exacts a steep computational cost. We recently introduced a sparse sampling method, which circumvents the overlap requirement by using thermodynamic integration to estimate free energy differences between biased ensembles. Here we build upon and generalise sparse sampling for characterising the free energetics of water density fluctuations in systems near liquid-vapor coexistence. We also introduce sensible heuristics for choosing the biasing potential parameters and strategies for adaptively refining them, which facilitate the estimation of such free energetics accurately and efficiently. We illustrate the method by characterising the free energetics of cavitation in a large volume in bulk water. We also use sparse sampling to characterise the free energetics of capillary evaporation for water confined between two hydrophobic plates. In both cases, sparse sampling is nearly two orders of magnitude faster than umbrella sampling. Given its efficiency, the sparse sampling method is particularly well suited for characterising free energy landscapes for systems wherein umbrella sampling is prohibitively expensive.</p