Multi-modal sensing with metal-organic frameworks: hierarchical design for analytical performance enhancement

The growing demand for reliable, sensitive, and precise analytical tools for complex biological and environmental matrices has driven a paradigm shift from single-modal to multi-modal sensing. Metal-organic frameworks (MOFs) offer a versatile platform for constructing multi-modal sensors due to their exceptional structural tunability, intrinsic multi-functionality, and well-defined host-guest chemistry. This review presents a strategy-driven framework that moves beyond conventional material-centric cataloguing. We systematically describe the hierarchical design of MOF-based multi-modal signal generation, starting from the intrinsic multi-functionality of single-component MOFs, through engineered composite systems (such as nanomaterial hybrids and energy transfer constructs), to programmable stimulus-responsive cascades. Subsequently, we discuss the strategic use of multi-modal signals to address key analytical challenges including enhancing reliability through orthogonal and spatially resolved verification, boosting sensitivity via enzyme-catalyzed and nucleic acid amplification cascades, enabling precise quantification with ratiometric self-calibration, and advancing toward intelligent and deployable sensing systems through functional platform integration and artificial intelligence-assisted data processing. Finally, we critically examine current challenges and future directions, including improving MOF stability and synthetic reproducibility, developing flexible and wearable sensing platforms, applying advanced data fusion algorithms, achieving multiplexed biomarker detection, and addressing regulatory aspects for clinical translation. The aim is to provide a guiding design philosophy for next generation MOF-based multi-modal sensors and to facilitate their translation from laboratory research to practical applications in clinical diagnostics, environmental monitoring, and food safety.