Abstract
Pollution by heavy metals is threatening the environment and human health, yet there is a lack of a rapid methods to detect multiple metal ions. Here, we built a fluorescence sensor array based on carbon quantum dots to detect Cr6+, Fe3+, Fe2+, and Hg2+ in environmental samples. We added xylenol orange as the receptor to construct the sensor array under pH regulation. We also designed a SX-model by combining stepwise prediction and machine learning to assist the fluorescence sensor array in detecting single and mixed heavy metal ions in deionized water and real samples. Results show that the sensor array detects four heavy metal ions within a concentration range of 1–50 μM with an accuracy of 95%, and the sensor identifies binary mixed samples with an accuracy of 95%. In addition, metal ions occurring in 144 lake water samples were discriminated with 100% accuracy. Overall, the SX-model-assisted fluorescence sensor array is an efficient method for detecting heavy metal ions in environmental samples.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aceto M, Abollino O, Bruzzoniti MC, Mentasti E, Sarzanini C, Malandrino M (2002) Determination of metals in wine with atomic spectroscopy (flame-AAS, GF-AAS and ICP-AES); a review. Food Addit Contam 19:126–133. https://doi.org/10.1080/02652030110071336
Adhao R, Pachghare V (2020) Feature selection using principal component analysis and genetic algorithm. J Discrete Math Sci Cryptogr 23(2):595–602. https://doi.org/10.1080/09720529.2020.1729507
Ahmed G, Rosana B, Josefa A, Marta E (2014) Highly fluorescent carbon dots as nanoprobes for sensitive and selective determination of 4-nitrophenol in surface waters. Microchim Acta 182:51–59. https://doi.org/10.1007/s00604-014-1302-x
Barati A, Mojtaba S, Ai H (2016) Metal-ion-mediated fluorescent carbon dots for indirect detection of sulfide ions. Sens Actuators B Chem 230:289–297. https://doi.org/10.1016/j.snb.2016.02.075
Cai Y, Wang M, Liu M, Zhang J, Zhao Y (2022) A portable optical fiber sensing platform based on fluorescent carbon dots for real-time pH detection. Adv Mater Interfaces 9:2101633. https://doi.org/10.1002/admi.202101633
Cao L, Sahu P, Anilkumar C, Bunker J, Fernando A, Wang P, Guliants A, Tackett N (2011) Carbon nanoparticles as visible-light photocatalysts for efficient CO2 conversion and beyond. J Am Chem Soc 133:4754–4757. https://doi.org/10.1021/ja200804h
Chen S, Liu M-X, Yu Y-L, Wang J-H (2019) Room-temperature synthesis of fluorescent carbon-based nanoparticles and their application in multidimensional sensing. Sens Actuators B Chem. https://doi.org/10.1016/j.snb.2019.03.067
Chen S, Liu M-X, Yu Y-L, Wang J-H (2019) Room-temperature synthesis of fluorescent carbon-based nanoparticles and their application in multidimensional sensing. Sens Actuators B Chem 288:749–756. https://doi.org/10.1016/j.snb.2019.03.067
Demr DA (2021) Determining the levels of some trace elements and heavy metals (Cu, Mn, Mg, Fe, Zn, CO, Pb and CD) in the cancers of lip and oral cavity. Res Article 5(3):31–321. https://doi.org/10.21203/rs.3.rs-309806/v1
Gao G, Yao W, Hao R, Yang J, Wu F (2018) On-off-on fluorescent nanosensor for Fe3+ detection and cancer/normal cell differentiation via silicon-doped carbon quantum dots. Carbon 134:232–243. https://doi.org/10.1016/j.carbon.2018.02.063
Ge X, Mo Q, Wang G, Zhao Y, Wang S (2021) Determination of available and carbonate antimony (Sb) in soil by Atomic Fluorescence Spectrometry. E3S Web of Conf 236: 02033–02036. Doi: https://doi.org/10.1051/e3sconf/202123602033
Jing W, Lu Y, Yang G, Wang F, He L, Liu L (2017) Fluorescence sensor array based on amino acids-modulating quantum dots for the discrimination of metal ions. Anal Chim Acta. https://doi.org/10.1016/j.aca.2017.07.011
Köhn HF, Hubert LJ (2014) Hierarchical cluster analysis. Wiley, London, pp 1–13
Kuznetsova O, Timerbaev A (2021) Direct seawater analysis by high-resolution ICP-MS provides insights into toxic metal accumulation in marine sediments. At Spectrosc 42:85–90. https://doi.org/10.46770/AS.2021.005
Li LL, Zhao X, Tseng ML, Tan R (2020) Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm. J Clean Prod 242(1):118447–118441. https://doi.org/10.1002/9781118445112.stat02449.pub2
Liu Z, Zhang Y (2021) Credit evaluation with a data mining approach based on gradient boosting decision tree. J Phys: Conf Ser 1848:012034–012040. https://doi.org/10.1088/1742-6596/1848/1/012034
Mitchell L, Shen C, Timmins S, Park B (2021) A versatile fluorescent sensor array for platinum anticancer drug detection in biological fluids. ACS Sensors 6:1261–1269. https://doi.org/10.1021/acssensors.0c02553
Oliveira B, Abreu F (2021) Carbon quantum dots synthesis from waste and by-products: perspectives and challenges. Mater Lett 282:128764–128770. https://doi.org/10.1016/j.matlet.2020.128764
Sun C, Jiang X, Li B, Kong X (2021) Fluorescence behavior and mechanisms of Poly(ethylene glycol) and their applications in Fe3+ and Cr6+ detections, data encryption, and cell imaging. ACS Sust Chem Eng 37:5166–5178. https://doi.org/10.1021/acssuschemeng.1c00250
Wang Y, Peng N, Jiang S, Lu W, Li Z, Liu H, Lin J, Sun Y, Li Z (2018) Highly sensitive fluorometric determination of oxytetracycline based on carbon dots and Fe3O4 MNPs. Sens Actuators B Chem 254:1118–1124. https://doi.org/10.1016/j.snb.2017.07.182
Wang F, Na N, Ou Y (2021) Particle-in-a-frame gold nanomaterials with an interior nanogap-based sensor array for versatile analyte detection. Microchim Acta 184:187–193. https://doi.org/10.1039/D1CC01094H
Wu J, Li J, Qiu X, Liu Y (2020) A comparative analysis of multi-machine learning algorithms for data-driven RANS turbulence modelling. J Phys Conf Ser 1684:012043. https://doi.org/10.1088/1742-6596/1684/1/012043
Xu Z, Wang Z, Liu M (2020) Machine learning assisted dual-channel carbon quantum dots-based fluorescence sensor array for detection of tetracyclines. Spectrochim Acta Part A Mol Biomol Spectrosc 232:118147–118154. https://doi.org/10.1016/j.saa.2020.118147
Yan C, Qu Z, Wang J, Cao L, Han Q (2022) Microalgal bioremediation of heavy metal pollution in water: recent advances, challenges, and prospects. Chemosphere 286:131870–131882. https://doi.org/10.1016/j.chemosphere.2021.131870
Zhou X, Wang T, Zhao M, Wang P, Liu Y (2021) Chemical-tongue sensor array for determination of multiple metal ions based on trichromatic lanthanide-based nanomaterials. Sens Actuators B Chem 343:130107. https://doi.org/10.1016/j.snb.2021.130107
Zhu F, Gao J, Yang J, Ye N (2022) Neighborhood linear discriminant analysis. Pattern Recogn 123:108422. https://doi.org/10.1016/j.patcog.2021.108422
Acknowledgements
The writers gratefully acknowledge financial support from the Xinjiang Uygur Autonomous Region Natural Science Foundation of China (No. 2019D01C068); Xinjiang Uygur Autonomous Region Key Research and Development Projects (No. 2017B03017-5); (No. 2017B03017-3).
Author information
Authors and Affiliations
Contributions
Conceptualization, formal analysis, and writing of the original draft were performed by YL. Writing—review & editing, supervision, and project administration were performed by XW. The methodology and formal analysis was provided by JW. Conceptualization and methodology were provided by ZX, who also helped in writing the original draft and in general. Data were curated by HL. Writing—review and editing was performed by TY. Finally, formal analysis was conducted by QS.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Liu, Y., Chen, J., Xu, Z. et al. Detection of multiple metal ions in water with a fluorescence sensor based on carbon quantum dots assisted by stepwise prediction and machine learning. Environ Chem Lett 20, 3415–3420 (2022). https://doi.org/10.1007/s10311-022-01475-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10311-022-01475-0