Rationally Designing Co ₃ O ₄ Nanosheets with Extensive Surface Area and Rich Active Sites for Dual-Mode Colorimetric and Electrochemical Detection of Hydrogen Peroxide

The real-time and sensitive monitoring of hydrogen peroxide (H2O2) is critically important for predictive analytics and timely intervention in personalized healthcare, particularly for conditions such as diabetes mellitus. Herein, we developed a high-performance dual-mode sensor platform based on exfoliated Co3O4 nanosheets (E–Co3O4 NSs) for the simultaneous colorimetric and electrochemical detection of H2O2. The E–Co3O4 NSs induce a hierarchical flower-like structure, which exhibits an exceptionally large surface area (43.8 m2/g) with abundant mesoporous active sites. This unique architecture not only enhances adsorption/desorption kinetics but also significantly decreases the Fermi level, leading to encouraging the overall conductivity. Most importantly, the synthesized catalyst exhibits robust intrinsic peroxidase mimetic activity, facilitating the rapid conversion of colorless TMB to a blue oxidized product, as evidenced by a distinct UV–vis peak at 654 nm. The platform demonstrates excellent colorimetric sensing for H2O2 across a wide linear range of 0.1–80 μM with a low limit of detection (LOD) of 0.07 ± 0.05 μM (S/N = 3). Simultaneously, it delivers superior electrochemical performance, with DPV and amperometric measurements showing an identical linear range (0.1–80 μM) with lower LODs (0.08 ± 0.04 and 0.09 ± 0.04 μM) and electrochemical sensitivity of 490.2 μA mM–1·cm–2, respectively. In addition, this work displays key analytical advantages including outstanding selectivity, long-term stability, and excellent reproducibility. Most excitingly, the E–Co3O4 NSs sensor was successfully applied for the reliable and accurate detection of H2O2 in real clinical samples (urine) from diabetic mellitus patients. Thus, the proposed dual-mode E–Co3O4 NSs-based detection platforms set a benchmark for simultaneous colorimetric and electrochemical analysis. This work highlights a key prospect for developing highly reliable, cost-effective, and practical diagnostic tools that will significantly boost future clinical applications.