Issue 20, 2021

A self-healing, recyclable, and degradable fire-retardant gelatin-based biogel coating for green buildings

Abstract

Wood is one of the oldest building materials and commonly employed in construction. However, the inherent fire hazard of wood restricts its practical application. Application of fire retardant coatings has been proved to be a highly efficient method for improving the fire retardancy of structural materials during combustion. However, developing sustainable, renewable and environmentally-friendly coatings is challenging because of the dependence on traditional flame retardants. In this study, a self-healable, fully-recyclable and biodegradable biogel coating was proposed, derived entirely from natural and food-safe constituents, which has rarely been demonstrated for wood safety. A uniform and strongly-adhesive coating could be obtained on wood surfaces via a facile preparation process without compromising the inherent mechanical properties of wood. Meanwhile, the coating showed excellent self-healing properties after damage, full degradability and good recyclability when disposed. Remarkably, biogel-coated wood exhibited enhanced fire-retardant properties, reflected by a 24.0% decrease in peak heat release rate and 17.2% reduction in total heat release with a 350 μm thick coating, along with a sixfold enhancement in ignition delay time and self-extinguishing behavior. We merged all merits in one fire-retardant coating which can be easily reproduced, and is low cost and scalable, making the biogel-coated wood a promising candidate for widespread application in green buildings.

Graphical abstract: A self-healing, recyclable, and degradable fire-retardant gelatin-based biogel coating for green buildings

Supplementary files

Article information

Article type
Paper
Submitted
22 Marts 2021
Accepted
20 Apr. 2021
First published
20 Apr. 2021

Soft Matter, 2021,17, 5231-5239

A self-healing, recyclable, and degradable fire-retardant gelatin-based biogel coating for green buildings

L. Zhang, Y. Huang, P. Sun, Y. Hai and S. Jiang, Soft Matter, 2021, 17, 5231 DOI: 10.1039/D1SM00435B

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