Arismendy Portorreal-Bottier, Inmaculada Márquez, Silvia Gutiérrez‐Tarriño, José Luis del Rio-Rodríguez, Juan José Calvente, Pascual Oña‐Burgos, Rafael Andreu, José Luis Olloqui‐Sariego
The development of reliable non‑enzymatic electrochemical sensors based on transition metal oxides has attracted great attention for monitoring and quantification of biomolecules, including glucose and hydrogen peroxide. Metal-organic frameworks (MOFs) serve as tunable sacrificial precursors for generating stable, porous metal oxide or oxyhydroxide phases, particularly for applications in electrocatalysis. In this work, we developed an in situ derivatization strategy consisting of the electrochemically-assisted surface reconstruction of a cobalt MOF to form the derived cobalt oxyhydroxide phase as an efficient electrochemical sensing platform for dual detection of glucose and hydrogen peroxide. The resulting catalytic phase exhibits electrocatalytic activity toward both glucose oxidation—mediated by the Co(III)/Co(IV) redox couple—and the direct electroreduction of hydrogen peroxide. The as-fabricated amperometric dual sensor provides fast response times, wide linear ranges (up to 10 mM), good reproducibility, and high selectivity against common interferents. Sensitivities up to 200 µA mM −1 cm −2 and 320 µA mM −1 cm −2 for glucose and hydrogen peroxide, respectively, are achieved, with detection limits in the low micromolar range. Structural stability of the derived phase under operational conditions was confirmed by operando Raman spectroscopy during prolonged amperometric experiments. Furthermore, a bipotentiostatic configuration enables the simultaneous quantification of both analytes using two independently biased working electrodes. The dual-in‑line platform maintains linearity across a broad concentration range and preserves a good sensitivity even under mixed‑analyte conditions. These results highlight the potential of electrochemically reconstructed MOF‑derived phases as robust and versatile materials for high‑performance multimodal sensing technologies.