Tunable Icephobicity of Surface-Grown Metal-Organic Frameworks with Nanohierarchical Texture

Abstract

Metal-organic frameworks (MOFs) have emerged as promising candidates for advanced surface treatments due to their inherent porosity, structural tunability, and functional versatility. Herein, we utilize these unique attributes of MOFs to systematically investigate their potential as passive icephobic surfaces which is a critical requirement to mitigate ice accretion which significantly impacts safety and performance across numerous technologies. Specifically, we elucidate how MOFs' pore size, surface morphology, and chemical functionality synergistically influence ice nucleation temperature and ice adhesion strength. Employing surface-grown MOFs (UiO-66, UiO-67 and MOF-5), we demonstrate that these coatings consistently lowered the median ice nucleation temperature by approximately 4 – 5 °C and reduced ice adhesion strength by up to two-thirds compared to bare glass. Through a combined approach involving classical heterogeneous nucleation theory and density functional theory simulations, we uncover that the key mechanism driving this icephobic performance is the nanoconfinement effect arising from sub-nanometre pores which significantly elevates the energy barrier to ice nucleation and minimizes solid-ice contact through the void effect. Furthermore, we identify hydrophobic alkyl silane functionalisation as the most effective chemical strategy to enhance these icephobic properties. These findings provide critical insights into the structure-property relationships that govern icephobic performance, paving the way for the rational design of MOF-based anti-icing coatings for diverse technological applications.

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Article information

Article type
Paper
Submitted
14 Nov 2025
Accepted
21 Feb 2026
First published
23 Feb 2026
This article is Open Access
Creative Commons BY license

Nanoscale, 2026, Accepted Manuscript

Tunable Icephobicity of Surface-Grown Metal-Organic Frameworks with Nanohierarchical Texture

S. Bahal, J. Zhang, V. Singh, P. Kabi, A. Heydari and M. K. Tiwari, Nanoscale, 2026, Accepted Manuscript , DOI: 10.1039/D5NR04825G

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