Printing Nacre‐Mimetic MXene‐Based E‐Textile Devices for Sensing and Breathing‐Pattern Recognition Using Machine Learning

Abstract Wearable sensors enable continuous monitoring of physiological parameters and detection of deviations from healthy baselines, offering significant potential for personalized health management and disease diagnosis. The COVID‐19 pandemic accelerates the adoption of smart face masks among the general population, particularly for respiratory health monitoring. In response to this need, a sustainable ink formulation for 3D direct‐write ink printing is developed by integrating MXene with waterborne polyurethane (WPU), resulting in nanocomposite interconnects that exhibit excellent mechanical strength and electrical performance. The nanocomposites are printed directly on textiles compatible for wearable sensing applications to obtain devices that exhibit low resistance (∼1.8 Ω cm ⁻² ), good biocompatibility (cell viability > 94%), and nacre‐mimetic microstructure with high tensile strength (∼120 MPa). The nanocomposite formulation is utilized to print textile‐integrated near field communication (NFC) coils and humidity sensors, with high sensitivity, stability, and reproducibility. By combining the time‐series data recorded by the wireless humidity sensor node with convolutional neural network‐based machine learning model, the sensor is able to distinguish between a range of respiratory patterns with an accuracy of ≈90%. This work should serve as an example for scalable manufacturing, high‐performance, and multifunctional e‐textiles suitable for wireless healthcare and other related applications.