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Updated: May 6, 2026

A Microfluidic Approach for the Study of Ice and Clathrate Hydrate Crystallization

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Published on: August 18, 2022

Microstructured Surface Design for Ice Nucleation Control Assisted by a Hybrid Classical Nucleation Theory-Blackbox

Samaneh Keshavarzi¹, Olivier Gagné^2,3, Olivier A Zacharie¹

¹Department of Applied Sciences, Université du Québec à Chicoutimi, Saguenay, (QC) G7H 2B1, Canada.

Langmuir : the ACS Journal of Surfaces and Colloids

|May 4, 2026

Summary

Researchers developed a hybrid method combining experiments, classical nucleation theory (CNT), and optimization to design effective icephobic surfaces. This approach accurately predicts ice nucleation times and optimizes surface geometries for enhanced ice delay, crucial for aviation safety and cryopreservation.

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Area of Science:

Materials Science
Surface Science
Thermodynamics

Background:

Designing effective icephobic surfaces is critical for applications like aviation safety and cryopreservation.
Classical nucleation theory (CNT) offers a thermodynamic basis, but real-world complexities like stochastic effects and wetting states hinder design.
Surface structure significantly impacts icephobic performance, yet optimizing designs for specific conditions remains challenging.

Purpose of the Study:

To introduce and validate a hybrid approach combining experiments, CNT, and blackbox optimization for predicting and optimizing ice nucleation time on micropatterned surfaces.
To develop an analytical model for wetting states between Wenzel and Cassie-Baxter regimes and optimize its parameters.
To identify optimal micropillar geometries that maximize freezing delay.

Main Methods:

Fabrication of cylindrical SU-8 micropillar arrays with varying heights and spacings.
Measurement of apparent contact angles and freezing delay times at -10 °C and -20 °C.
Development of an analytical wetting model optimized using the Mesh adaptive direct search (MADS) algorithm.

Main Results:

The hybrid model accurately predicted contact angles (MAPE 2.09%, R² = 0.92) and nucleation times (MAPE 27.3%, R² = 0.75).
The method identified optimal micropillar geometries for maximizing ice formation delay.
Validation with an independent dataset confirmed the model's strong predictive capabilities.

Conclusions:

A hybrid physics-based and data-driven optimization approach enables rapid design of icephobic surfaces.
The developed method effectively predicts and optimizes ice nucleation time based on surface geometry.
This integrated strategy offers a powerful tool for advancing icephobic surface technology for critical applications.