# 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.

## Key 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.