The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Highly efficient SnS-decorated Bi2O3 nanosheets for simultaneous electrochemical detection and removal of Cd(II) and Pb(II)
Abstract
Abstract The sensitive detection and efficient removal of heavy metal ions with high toxicity and mobility are of great importance for the environmental monitoring and control. Here, a novel SnS-decorated Bi2O3 nanosheets heterostructure was synthesized via a facile hydro/solvothermal strategy. The as-prepared SnS-Bi2O3 nanocomposites presented impressive merits of the high surface area, mesoporous channels and fast electron transfer for the electrochemical reactions. And the synergistic interaction between SnS and Bi2O3 significantly improved the “deposition-stripping” process involving electron transfer, mass diffusion and phase transition. With the unique characteristics, the SnS-Bi2O3 nanocomposites exhibited improved sensitivity and robust stability on the electrochemical detection of Cd(II) and Pb(II) with low LODs of 1.50 nM and 1.40 nM, respectively, and were successfully applied on the detection of Cd(II) and Pb(II) in tap water and lake water samples. Furthermore, the electrochemical removal performance towards Cd(II) and Pb(II) was achieved as well. These SnS-Bi2O3 nanocomposites could serve as an efficient platform on the detection and removal of heavy metal ions in environmental electrochemistry.
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“The cathodes made of carbon-based or sulfur mixture with different ratios in acidic conditions are suitable for removing Hg 2+ , Cd 2+ , Pb 2+ , and Cu 2+ from wastewater 100 .”
From Removal of heavy metal ions from wastewater: a comprehensive and critical review (2021) · cites this paper as [100]
Citations
Cites
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Removal of heavy metal ions from wastewater: a comprehensive and critical review
Qasem, N. A. A., Mohammed, R. H., & Lawal, D. U. (2021) · npj Clean Water · doi:10.1038/s41545-021-00127-0 · Cited by 2,225
Metadata
- Authors
- Jin, W., Fu, Y., Hu, M., Wang, S., & Liu, Z.
- Affiliations
- Jiangnan University (CN)
- Anhui University (CN)
- Corresponding author
- Jin, Wei
- Liu, Zhonggang
- Venue
- Journal of Electroanalytical Chemistry
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 856
- Pages
- 113744
- ISSN
- 1572-6657 (ISSN-L)
- OpenAlex
- W2992701768
- Cited by
- 93
- References
- 52
- Citations by year
- 2026: 5 · 2025: 19 · 2024: 14 · 2023: 15 · 2022: 15 · 2021: 14 · 2020: 10
- FWCI
- 4.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Locations
- Journal of Electroanalytical Chemistry (publishedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (JUSRP21936)
- National Natural Science Foundation of China (201904001, 51604253)
- Topics
- Physical Sciences › Chemistry › Electrochemistry › Electrochemical Analysis and Applications
- Physical Sciences › Chemistry › Analytical Chemistry › Electrochemical sensors and biosensors
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Electrocatalysts for Energy Conversion
- Keywords
- cadmium determination, lead ion detection, heavy metal removal, heterostructures, hydro(solvo)thermal synthesis, water pollution monitoring, tap water, lake water
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- CoChat. (2026). Citation check: Highly efficient SnS-decorated Bi2O3 nanosheets for simultaneous electrochemical detection and removal of Cd(II) and Pb(II). https://app.cochat.ai/papers/10.1016/j.jelechem.2019.113744
- The paper
- Jin, W., Fu, Y., Hu, M., Wang, S., & Liu, Z. (2020). Highly efficient SnS-decorated Bi2O3 nanosheets for simultaneous electrochemical detection and removal of Cd(II) and Pb(II). Journal of Electroanalytical Chemistry. https://doi.org/10.1016/j.jelechem.2019.113744
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.