Electroless Deposition: A Nonelectroreduction Anodic Stripping Analysis Strategy for Heavy Metal Ions Using Nitrogen-Doped Graphdiynes/Carbon Nanotubes
In the past decades, the anodic stripping voltammetry (ASV) method has been the most commonly utilized electrochemical proposal for detecting trace amounts of heavy metal ions (HMIs). It involves two steps: HMI electroreduction and zerovalent metal reoxidation. However, it has been overlooked that the testing process could be simplified, made more efficient, and energy-saving by avoiding the electroreduction step. In this study, it is revealed that graphdiynes (GDY) as a remarkable material can induce the accumulation and self-reduction of Hg<sup>2+</sup> and Cu<sup>2+</sup> through a simple electroless deposition process, leading to the formation of zerovalent metals (Hg<sup>0</sup> and Cu<sup>0</sup>). Inspired by this property, a nonelectroreduction ASV (NSV) sensing strategy for HMI detection was then proposed innovatively. By synthesizing a nitrogen-doped GDY/carbon nanotubes (N-GDY/CNTs) nanohybrid as the electrode material, the introduction of CNTs aims to enhance the conductivity and prevent GDY aggregation, while N-doping further improves the sensing performance. The results showed that the N-GDY/CNTs-based NSV sensing strategy not only eliminates the related electroreduction process referring to the common ASV strategy but also enables sensitive detection of Cu<sup>2+</sup> and Hg<sup>2+</sup> with extremely low detection limits (0.67 nM for Cu<sup>2+</sup> and 0.33 nM for Hg<sup>2+</sup>). This indicates its great potential for wide applications in HMI testing.
Authors
- Gangbing Zhu (ORCID: https://orcid.org/0000-0003-2686-5961)
- Zhen Zhang (ORCID: https://orcid.org/0000-0003-4926-347X)
- Yinhui Yi (ORCID: https://orcid.org/0000-0001-5332-4189)
- Yifan Zhou (ORCID: https://orcid.org/0000-0002-9836-6355)
- Shuhao Zhang (ORCID: https://orcid.org/0000-0002-4240-3815)
Institutions
- Jiangsu University (CN)
- Water Technology (Australia) (AU)
Publication Details
- Journal
- Inorganic Chemistry
- Published
- 2025-05-07
- DOI
- https://doi.org/10.1021/acs.inorgchem.5c00714
- Citations
- 8
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 5.84