Issue 9, 2024

Ti3C2Tx MXene-based hybrid nanocoating for flame retardant, early fire-warning and piezoresistive tension sensing smart polyester fabrics

Abstract

Flammability feature of textiles is a big underlying risk causing fire disasters. The fabrication of reliable fire resistant and quick fire warning fabrics is imperative but challenging. Herein, three types of early fire-warning polyester fabrics, namely, FPP@AM-X, FPP@PM-X and FPP@AX-M1, with good flame retardant and piezoresistive sensing performance were developed by fabricating polyethyleneimine (PEI), ammonium polyphosphate (APP), phytic acid (PA) and MXenes onto phosphorus-containing flame retardant polyethylene terephthalate (FRPET) via polydopamine (PDA) mediated layer-by-layer self-assembly. Owing to the improved thermoelectric properties of MXenes, FPP@A5-M1 exhibited a maximum thermoelectric voltage of 0.59 mV at a temperature difference of 130 °C and can provide an ideal cyclic early fire warning response within 4 s. In addition, due to the synergistic flame retardant effect of MXenes and APP in the coating layer, FPP@A5-M1 could be self-extinguished within 2 s after ignition and the value of peak heat release ratio and total smoke production decreased by 41.9% and 30.4%, respectively. Besides, the MXene-based hybrid coated fabric can detect the movement of human fingers and elbows, illustrating its potential application in piezoresistive tension sensing. This work provides a new route to designing and developing multi-functional and smart fire protection fabrics.

Graphical abstract: Ti3C2Tx MXene-based hybrid nanocoating for flame retardant, early fire-warning and piezoresistive tension sensing smart polyester fabrics

Supplementary files

Article information

Article type
Paper
Submitted
26 Dec 2023
Accepted
22 Jan 2024
First published
23 Jan 2024

Nanoscale, 2024,16, 4811-4825

Ti3C2Tx MXene-based hybrid nanocoating for flame retardant, early fire-warning and piezoresistive tension sensing smart polyester fabrics

M. Guo, W. Wang, B. Zhai, J. Li, L. Zhang, J. Li, K. Luo and R. Wang, Nanoscale, 2024, 16, 4811 DOI: 10.1039/D3NR06604E

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