The main objective of this project is the development and application of an accurate model for the prediction of wetting behavior. Wettability was identified as the most prevalent mechanism associated with adhesion of liquids onto their enclosing material [1]. The overarching goal is to assist in the design of surfaces, e.g., of packaging materials or storage units, that repel the enclosed liquids as much as possible. This leads to saving a substantial amount of consumable commodities (e.g., water) and other domestic products (edible or not) of daily life. Besides saving food, controlling surface wettability has a great impact in other technological domains investigating, for example, non-wetting textiles [2, 3], anti-fogging/icing windowpanes [4, 5] and car windshields, improved hydrodynamics [6], buoyancy [7] and water collection from fog [8]. Existing state-of-the art models suffer from low accuracy [9, 10], while they are subject to rather oversimplifying assumptions which hold true only in certain special cases. In this project, we developed an accurate model for the prediction of surface topographies that exhibit omniphobicity, i.e., strongly repel all sorts of liquids [11]. This was accomplished by considering realistic curved liquid-air interfaces with the help of the sagging height which, in turn, was defined through the capillary length [12]. Single, double and triple level topographies were integrated into the predictive algorithm, while different combinations of the two were made possible. Our model showed that multiscale hierarchical roughness is a promising way for achieving enhanced liquid repellency [11, 13, 14]. In our latest work, we reported the development and application of a refined version of the classical Cassie-Baxter wetting model [15] for the prediction of surface topographies with superomniphobic traits. The sagging height defined through the capillary length was utilized to assess the relation between a curved liquid-air interface and the surface texture. The wettability, expressed in terms of the static apparent contact angle, was quantified for single- and double-scale surface topographies and for three representative liquids and the results were compared to those of the classical Cassie-Baxter model. Of the three single-scale topographies considered in this work, the fiber case exhibited the highest contact angle across length scales of surface topographies, whereas decreasing the length scale of surface patterns from a few hundreds of micrometers to a few hundreds of nanometers led to contact angle increase by15%–20%.A generic expression for modeling multiscale hierarchical roughness of arbitrarily large multiplicity n was derived and applied. Multiscale hierarchical roughness was corroborated to be a promising way for achieving enhanced liquid repellency. Double-scale roughness was more efficient when the two length scales differed in size by at least one order of magnitude. The ‘fiber on sinusoid’ hierarchical topography exhibiting re-entrant geometry yielded contact angles over 150° for all considered wetting liquids. Our model predictions were very recently validated against experimental data for a broad variety of surface materials, topography types, hierarchical levels and dimensions.