High-Precision Multiplex Chemical Sensors with Pattern-Recognition Capability via a Letter Printing-Inspired Transfer Method.

Graphene-based chemical sensors offer high sensitivity owing to exceptional charge transport and a large surface-to-volume ratio, yet reliable discrimination across diverse analytes remains challenging. In this study, a letterpress-inspired, one-step transfer-printing strategy integrated with pattern-recognition analysis is proposed to implement multiplexed chemical sensor arrays based on graphene field-effect transistors (GFETs). By tuning interfacial adhesion between functional materials and a polymer stamp, accurate and area-selective functionalization with sub-10 µm feature sizes is achieved, thereby enabling simultaneous multifunctionalization of graphene. The resulting 3 × 3 GFET sensor arrays, comprising both functionalized and pristine channels, generate distinct sensing response patterns to representative volatile organic compounds, including chlorobenzene, toluene, and methanol, at a fixed tested concentration, driven by molecule-specific charge-transfer interactions at the functional layer. An artificial neural network trained on sensor-derived patterns is further demonstrated with an increased number of sensors, delivering highly accurate classification results. This strategy highlights a versatile and scalable platform that combines lithography-free, low-cost transfer printing with intelligent analysis, offering a practical route toward next-generation chemical-sensing systems based on functionalized 2D materials.