Abstract
Iron-based oxides have been widely used in investigating the performance of electrochemical sensing due to their abundant sources, excellent biocompatibility, and high catalytic activity. However, previous studies mainly focused on the effect of the morphology or crystal phases of iron oxides on the electrochemical sensing performance, while the influence of different valence states was rarely reported. Therefore, for designing iron-based oxides with highly active components, it is meaningful to understand the effect of their oxidation states on the electrochemical sensing performance. In this study, monodisperse sphere-like Fe2O3 nanoparticles (Fe2O3 NPs) with a diameter of 80–100 nm were synthesized through a novel emulsion hydrothermal method and a subsequent annealing process in Ar atmosphere. The results of X-ray photoelectron spectroscopy (XPS) indicated that the Fe2+/Fe3+ ratio and oxygen vacancy content in Fe2O3 NPs can be tuned by changing the annealing temperature. As a modified electrode, the electrochemical sensing performance of Fe2O3 NPs improved with the increase in the Fe2+/Fe3+ ratio, indicating that the oxidation state of Fe played an important role in the electrochemical sensing of heavy metal ions (HMIs). As a result, a typical electrode, namely, Fe2O3 NPs-550 annealed at 550 °C with optimal Fe2+/Fe3+ ratio and oxygen vacancy content exhibited the best performance for simultaneously detecting Pb2+ and Cu2+ (limits of detection: 9.48 nM and 38.31 nM, respectively). Electrochemical adsorption experiments revealed that the Fe2O3 NPs-550 electrode (Fe2O3 NPs-550/GCE) showed the highest adsorption capacity for Pb2+ and Cu2+, demonstrating that the presence of Fe2+ and oxygen vacancies were beneficial for the adsorption of heavy metal ions and enhancing the sensing performance. This study emphasizes the important role of different oxidation states of Fe in the electrochemical sensing of HMIs and provides a new idea for the design and preparation of high-performance metal oxide nanosensors for real-time environmental detection.
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