Laser-Assisted Patterning of Metallized Fabrics for Washable and Durable Electronic Textiles

Wearable electronic textiles (e-textiles) are increasingly being explored for continuous, noninvasive physiological monitoring; however, scalable fabrication of high-resolution circuitry with long-term mechanical and washing durability remains a major challenge. Conventional approaches, including screen printing, inkjet printing, and conductive-thread embroidery, enable the integration of conductive traces into garments but often suffer from limited patterning resolution, binder-related degradation, mechanical instability, and unreliable interfacing with electronic components. Here, we present a selective laser-assisted ablation method for directly defining conductive circuit traces on single-sided metallized fabrics (MFs) composed of woven, multi-stranded silver fiber yarns. This mask-free and chemical-free process enables rapid, digitally programmable patterning while preserving the structural integrity, flexibility, and breathability of the underlying textile. Importantly, localized laser heating simultaneously removes conductive fibers in selected regions and thermally fuses the exposed ends of adjacent silver yarns, thereby suppressing fraying and enhancing the mechanical robustness of the patterned circuit edges. Using a neodymium-doped yttrium aluminum garnet (Nd:YAG) laser tuned for high absorption by silver, controlled ablation at energy densities of 8–26.5 J·cm −2 produced well-defined insulating regions without compromising the conductivity of neighboring traces. Surface and elemental analyses confirmed nearly complete silver removal, with residual silver content below 3% in the ablated areas. The resulting laser-ablated metallized fabric (LA-MF) exhibited low sheet resistance (approximately 2 mΩ/sq.), retained more than 94% of its initial conductivity after 10000 bending cycles, and maintained stable electrical performance after more than 20 machine-washing cycles. As a proof of concept, a 16-electrode electrical impedance tomography (EIT) array was patterned directly onto the MF and integrated into a compression garment for noninvasive lung-function monitoring. The textile electrodes enabled real-time impedance imaging, and an embedded convolutional neural network accurately classified respiratory states, including normal breathing and breath-hold conditions, even after repeated laundering. This laser-assisted patterning framework provides a robust, scalable, and manufacturing-compatible route for integrating high-performance circuit architectures directly into fabrics for wearable sensing, healthcare monitoring, and next-generation smart textile applications.