Introduction: The accurate and efficient detection of Uric Acid (UA) is of great importance for monitoring metabolic and clinical conditions, which are linked to human health, disease diagnosis, and medical analysis. Conventional analytical techniques often face limitations in sensitivity, cost, and operational simplicity. To address these challenges, this study introduces an electrochemical sensor utilizing a graphitic carbon nitride (g-C3N4) and cobalt oxide (Co3O4) nanocomposite designed to achieve high-performance UA sensing. Methods: Co3O4 nanoparticles were synthesized through a controlled hydrothermal method, while g-C3N4 was obtained via thermal exfoliation. The as-prepared materials were combined to form the g-C3N4/Co3O4 nanocomposite. Comprehensive physicochemical characterization was performed using Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX), X-Ray Diffraction (XRD), UV–Visible (UV–Vis) spectroscopy, and Fourier-Transform Infrared (FTIR) spectroscopy to analyze morphological and structural properties. Results: Electrochemical measurements revealed that the g-C3N4/Co3O4-modified electrode exhibited a broad linear range for UA detection from 20 to 200 µM, along with an exceptionally low Limit of Detection (LOD) of 0.165 µM. These findings demonstrated the excellent electrocatalytic activity and sensitivity of the developed sensing platform. Discussion: The superior sensing behavior can be attributed to the synergistic effect between gC3N4 and Co3O4, where g-C3N4 provides a high surface area and excellent conductivity, while Co3O4 contributes active sites for redox reactions. This hybrid structure facilitated rapid charge transfer and efficient UA oxidation, ensuring improved analytical performance compared with conventional sensors. Conclusion: The g-C3N4/Co3O4 nanocomposite-based electrochemical sensor demonstrated remarkable sensitivity, a wide detection range, and reliable stability, positioning it as a highly promising platform for uric acid detection in biomedical and environmental samples.
Sathappan et al. (Tue,) studied this question.