Constructing Fe2O3 nanoparticles in nitrogen-doped carbon materials to enhance the electrochemical sensing performance of Pb2+ and Cd2+

Wu, Shiya; Lyu, Renliang; Xiong, Wei; Xing, Xiujing; Li, Hao

doi:10.1039/d3dt01664a

Dalton Trans.

2023

DOI: 10.1039/d3dt01664a

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Constructing Fe₂O₃ nanoparticles in nitrogen-doped carbon materials to enhance the electrochemical sensing performance of Pb²⁺ and Cd²⁺

Shiya Wu,

Renliang Lyu,

Wei Xiong

et al.

Abstract: N-doped carbon material is known for its high conductivity, rich N content, and high adsorption activity. When combined with Fe2O3 to form nanocomposites, it can improve the conductivity of Fe2O3...

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2024

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Cited by 4 publications

References 58 publications

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Development of MOF-derived Co3O4 microspheres composed of fiber stacks for simultaneous electrochemical detection of Pb2+ and Cu2+

Guo,

Li,

Xing

et al. 2024

Microchim Acta

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As an ideal transition metal oxide, Co3O4 is a P-type semiconductor with excellent electrical conductivity, non-toxicity and low cost. This work reports the successful construction of Co3O4 materials derived from metal-organic frameworks (MOFs) using a surfactant micelle template-solvothermal method. The modified electrodes are investigated for their ability to electrochemically detect Pb2+ and Cu2+ in aqueous environments. By adjusting the mass ratios of alkaline modifiers, the morphological microstructures of Co3O4-X exhibit a transition from distinctive microspheres composed of fiber stacks to rods. The results indicate that Co3O4-1(NH4F/CO(NH2)2 = 1:0) has a distinctive microsphere structure composed of stacked fibers, unlike the other two materials. Co3O4-1/GCE is used as the active material of the modified electrode, it shows the largest peak response currents to Pb2+ and Cu2+, and efficiently detects Pb2+ and Cu2+ in the aqueous environment individually and simultaneously. The linear response range of Co3O4-1/GCE for the simultaneous detection of Pb2+ and Cu2+ is 0.5–1.5 μM, with the limits of detection (LOD, S/N = 3) are 9.77 nM and 14.97 nM, respectively. The material exhibits a favorable electrochemical response, via a distinctive Co3O4-1 microsphere structure composed of stacked fibers. This structure enhances the number of active adsorption sites on the material, thereby facilitating the adsorption of heavy metal ions (HMIs). The presence of oxygen vacancies (OV) can also facilitate the adsorption of ions. The Co3O4-1/GCE electrode also exhibits excellent anti-interference ability, stability, and repeatability. This is of great practical significance for detecting Pb2+ and Cu2+ in real water samples and provides a new approach for developing high-performance metal oxide electrochemical sensors derived from MOFs. Graphical abstract

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Development of MOF-derived Co3O4 microspheres composed of fiber stacks for simultaneous electrochemical detection of Pb2+ and Cu2+

Guo,

Li,

Xing

et al. 2024

Microchim Acta

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A novel electrode for simultaneous detection of multiple heavy metal ions without pre-enrichment in food samples

Gao,

Yin,

Wang

et al. 2024

Food Chemistry

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A novel prediction approach driven by graph representation learning for heavy metal concentrations

Hao,

Li,

et al. 2024

Science of The Total Environment

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Constructing Fe₂O₃ nanoparticles in nitrogen-doped carbon materials to enhance the electrochemical sensing performance of Pb²⁺ and Cd²⁺

Abstract: N-doped carbon material is known for its high conductivity, rich N content, and high adsorption activity. When combined with Fe2O3 to form nanocomposites, it can improve the conductivity of Fe2O3...

Cited by 4 publications

References 58 publications

Development of MOF-derived Co3O4 microspheres composed of fiber stacks for simultaneous electrochemical detection of Pb2+ and Cu2+

Development of MOF-derived Co3O4 microspheres composed of fiber stacks for simultaneous electrochemical detection of Pb2+ and Cu2+

A novel electrode for simultaneous detection of multiple heavy metal ions without pre-enrichment in food samples

A novel prediction approach driven by graph representation learning for heavy metal concentrations

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