4.7 Article

Probing robust electrochemical monitoring of dimetridazole using nanocrystalline hydroxyapatite-graphene oxide decorated Mn3O4 nanocomposite

Journal

SURFACES AND INTERFACES
Volume 42, Issue -, Pages -

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ELSEVIER
DOI: 10.1016/j.surfin.2023.103535

Keywords

Electrocatalyst; Composite materials; Modified electrodes; Hazardous pollutant; Electrochemical sensors

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This study developed a novel ternary nanocomposite for enhanced DMZ sensors, which exhibited improved electrocatalytic activity and wide linear ranges. The developed sensor demonstrated its ability to accurately monitor DMZ food contamination samples.
Developing low-cost electrocatalysts with efficient, long-lasting electrode materials is of significant interest and presents a considerable challenge for electrochemical sensing applications in food contamination, particularly in the monitoring of dimetridazole (DMZ). In this study, we introduce a novel ternary nanocomposite, namely hydroxyapatite-intercalated graphene oxide-decorated manganese oxide (HAp/GO@Mn3O4), synthesized through straightforward hydrothermal and ultrasonication methods. The nanocomposite is subjected to thorough physicochemical and electrochemical characterization and utilized as an electrode material to enhance DMZ sensors. The ternary nanocomposite of HAp/GO@Mn3O4 delivers improved electrocatalytic activity because the synergistic features offer an enhanced electron transfer rate compared to the other GCE-modified electrodes. The developed sensitive chemical sensor probe exhibits optimized electrochemical conditions and significantly improves the electrode/electrolyte system. It demonstrates wide linear ranges (0.005-440.3 mu M), a low limit of detection (LOD, 0.86x10- 9 M), and excellent sensitivity (23.12 mu A mu M-1 cm-2). The sensor also displays reliable reproducibility, repeatability, selectivity, and cycle stability. Moreover, the developed sensor demonstrates its ability to accurately monitor DMZ food contamination samples with an excellent recovery rate in trace-level detection.

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