Nano-engineered PEDOT(MXene)/PVDF(HFP) bilayer membranes for dual-mode flexible sensing and machine learning-guided signal recognition

Abstract

Flexible nanostructured sensors that combine electrical conductivity, mechanical robustness, and multimodal recognition capability are crucial for next-generation wearable electronics and intelligent human–machine interfaces. This study reports a dual-mode flexible sensor based on a bilayer PEDOT(MXene)–PVDF(HFP) composite membrane (denoted as PMPH), rationally designed via electrochemical polymerization and solvent-driven interface regulation. Through dual-dopant optimization and dimethyl sulfoxide-induced structural rearrangement, the PEDOT chains exhibit a transition from benzenoid to quinoid configurations, while MXene nanosheets provide a highly conductive and mechanically resilient framework. The resulting PMPH membrane displays superior conductivity, large stretchability (up to 1200%), and stable pressure and strain responses. The dual-mode flexible sensor not only distinguishes stretching and pressing behaviors but also achieves an intelligent classification accuracy of 92.13% through a machine learning-guided data recognition model. This study demonstrates a simple yet efficient strategy to couple conductive polymer–MXene hybrids with flexible fluoropolymers, bridging materials design and intelligent sensing toward advanced wearable devices.