Thirty years of contact angles reveal universal design rules for wetting control

TL;DR

This paper identifies universal thresholds for superhydrophilic and superhydrophobic surfaces using a large dataset of contact angle measurements.

Contribution

The study provides empirically validated universal design rules for wetting behavior across diverse materials and surface geometries.

Findings

Superhydrophilicity is defined by contact angles ≲ 20°, and superhydrophobicity by ≳ 150°.

These thresholds are geometry-dominated and consistent across material types.

The dataset serves as a benchmark for predictive wettability design and machine learning models.

Abstract

Wettability, commonly quantified by the static contact angle (θ), governs critical interfacial phenomena including anti-icing, self-cleaning, adhesion control, and lubrication. Although conceptual thresholds for superhydrophilic and superhydrophobic states are widely cited, their empirical validation across material classes has remained limited by the absence of a comprehensive, rigorously verified dataset spanning diverse surfaces and liquids. Here, we compile and systematically analyze 110 curated static contact-angle measurements reported between 1995 and 2025, encompassing polymers, metals, oxides, self-assembled monolayers (SAMs), and micro-/nano-textured surfaces measured with multiple probe liquids. Our meta-analysis quantitatively confirms the existence of universal critical thresholds, with θ ≲ 20° defining superhydrophilicity and θ ≳ 150° defining superhydrophobicity. Crucially, for textured and hierarchical surfaces, these limits emerge as geometry-dominated properties, persisting across material classes and largely independent of intrinsic surface chemistry. These validated thresholds establish clear, principle-driven design rules for engineering functional wetting behavior, moving beyond trial-and-error approaches. The resulting dataset provides a reliable benchmark for the community, supporting predictive wettability design, cross-study meta-analysis, and the development of data-driven and machine-learning models without the need for repetitive experimental measurements.

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