Exposing different facets on metal-organic frameworks (MOFs) is highly desirable to enhance the performance for various applications, however, exploiting a concise and effective approach to achieve facet-controlled synthesis of MOFs remains challenging. Here, by modulating the ratio of metal precursors to ligands, the facet-engineered iron-based MOFs (Fe-MOFs) exhibits enhanced catalytic activity for Fenton reaction are explored, and the mechanism of facet-dependent performance is revealed in detail. Fully exposed (101) and (100) facets on spindle-shaped Fe-MOFs enable rapid oxidation of colorless o-phenylenediamine (OPD) to colored products, thereby establishing a dual-mode platform for the detection of hydrogen peroxide (H2O2) and triacetone triperoxide (TATP). Thus, a detection limit as low as 2.06 nm is achieved, and robust selectivity against a wide range of common substances (>16 types) is obtained, which is further improved by incorporating a deep learning architecture with an SE-VGG16 network model, enabling precise differentiation of oxidizing agents from captured images. The present strategy is expected will shine light on both the rational synthesis of nanomaterials with modulated morphologies and the exploitation of high-performance trace chemical sensors.
Keywords: dual‐mode sensing; explosive detection; facet‐controlled synthesis; metal‐organic frameworks; peroxide.
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