Vol. 39, issue 04, article # 6
Copy the reference to clipboard
Abstract:
The paper presents the results of using carbon dots as luminescent nanosensors of heavy metal ions in aqueous media. The application of machine learning methods to the photoluminescence spectra of nanoparticles in multicomponent aqueous salt solutions made it possible to simultaneously determine the concentrations of desired substances. The comparative analysis of the quality of solving the inverse problem by different neural networks was carried out. Comparison of the results of using neural networks and X-ray fluorescence analysis for determining the ionic composition of industrial process media showed that the accuracy of the developed nanosensor fully meets the requirements for monitoring and controlling the composition of waste and process water.
Keywords:
carbon dot, nanosensor, photoluminescence, machine learning method, neural network, perceptron, recurrent neural network
Figures:
References:
1. Ochieng E.Z., Lalah J.O., Wandiga S.O. Analysis of heavy metals in water and surface sediment in five rift valley lakes in Kenya for assessment of recent increase in anthropogenic activities // Bull. Environ. Contamination Toxicol. 2007. V. 79, N 5. P. 570–576. DOI: 10.1007/s00128-007-9286-4.
2. Bhuyan Md.S., Haider S.M.B., Meraj G., Bakar M.A., Islam Md.T., Kunda M. Siddique Md.A.B., Ali M.M., Mustary S., Mojumder I.A., Bhat M.A. Assessment of heavy metal contamination in beach sediments of Eastern St. Martin’s Island, Bangladesh: Implications for environmental and human health risks // Water. 2023. V. 15, N 13. P. 2494. DOI: 10.3390/w15132494.
3. Dong Y., Gao Z., Di J., Wang D., Yang Z., Wang Y., Xie Z. Study on the effectiveness of sulfate reducing bacteria to remove heavy metals (Fe, Mn, Cu, Cr) in acid mine drainage // Sustainability. 2023. V. 15, N 6. P. 5486. DOI: 10.3390/su15065486.
4. Mahey S., Kumar R., Sharma M., Kumar V., Bhardwaj R. A critical review on toxicity of cobalt and its bioremediation strategies // Appl. Sci. 2020. V. 2, N 7. DOI: 10.1007/s42452-020-3020-9.
5. Kadadou D., Tizani L., Alsafar H., Hasan S.W. Analytical methods for determining environmental contaminants of concern in water and wastewater // MethodsX. 2024. V. 12, N 102582. DOI: 10.1016/j.mex.2024.102582.
6. Long X., Li R., Xiang J., Wu S., Wang J. Ultrabright carbon dots as a fluorescent nano sensor for Pb2+ detection // RSC Adv. 2022. V. 12, N 37. P. 24390–24396. DOI: 10.1039/d2ra03591j.
7. Dolenko S.A., Laptinskiy K.A., Korepanova A.A., Burikov S.A., Dolenko T.A. Intelligent control of the synthesis of luminescent carbon dots with the desired photoluminescence quantum yield using machine learning // Opt. Memory Neural Networks. 2025. V. 34, N 1. P. 18–29. DOI: 10.3103/s1060992x24700887.
8. Szapoczka W.K., Olla C., Carucci C., Truskewycz A.L., Skodvin T., Salis A., Carbonaro C.M., Holst B., Thomas P.J. Ratiometric fluorescent pH sensing with carbon dots: Fluorescence mapping across pH levels for potential underwater applications // Nanomaterials. 2024. V. 14, N 17. P. 1434. DOI: 10.3390/nano14171434.
9. Sarmanova O.E., Laptinskiy K.A., Khmeleva M.Yu., Burikov S.A., Dolenko S.A., Tomskaya A.E., Dolenko T.A. Development of the fluorescent carbon nanosensor for pH and temperature of liquid media with artificial neural networks // Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 2021. V. 258, N 119861. DOI: 10.1016/j.saa.2021.119861.
10. Döring A., Qiu Y., Rogach A.L. Improving the accuracy of carbon dot temperature sensing using multi-dimensional machine learning // ACS Appl. Nano Materials. 2024. V. 7, N 2. P. 2258–2269. DOI: 10.1021/acsanm.3c05688.
11. Vervald A.M., Laptinskiy K.A., Chugreeva G.N., Burikov S.A., Dolenko T.A. Quenching of photoluminescence of carbon dots by metal cations in water: Estimation of contributions of different mechanisms // J. Phys. Chem. C. 2023. V. 127, N 44. P. 21617–21628. DOI: 10.1021/acs.jpcc.3c05231.
12. Biomass derived carbon quantum dots synthesized via a continuous hydrothermal flow process: Pat. WO2021130501A1. Great Britain. Kellici S., Baragau А. (01.07.2021).
13. Carbon dot fluorescent probe based high sensitive and high selective method for detecting trace silver nano particles in water and/or environment: Pat. CN106053408A. China. (26.10.2016).
14. Laptinskii K.A., Burikov S.A., Verval'd A.M., Gus'kov A.A., Plastinin I.V., Sarmanova O.E., Utegenova L.S., Dolenko T.A. Opredelenie soderjaniya vrednykh primesei v vode s pomoshch'yu lazernoi spektroskopii kombinatsionnogo rasseyaniya i metodov mashinnogo obucheniya // Optika atmosf. i okeana. 2024. V. 37, N 4. P. 287–293. DOI: 10.15372/AOO20240404.
15. Laptinskii K.A., Burikov S.A., Sarmanova O.E., Verval'd A.M., Utegenova L.S., Plastinin I.V., Dolenko T.A. Diagnostika vrednykh primesei v vodnykh sredakh s pomoshch'yu spektroskopicheskikh metodov i algoritmov mashinnogo obucheniya // Opt. i spektroskop. 2023. V. 131, N 6. P. 810. DOI: 10.21883/os.2023.06.55915.106-23.
16. Dolenko S.A. I.G. Persiantsev’s Scientific School at the Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics: History of development and overview of key works // Pattern Recognit. Image Anal. 2023. V. 33, N 4. P. 1564–1586. DOI: 10.1134/s1054661823040132.
17. Lou X.-T., Zhan L., Chen B.-B. Recent progress of carbon dots in fluorescence sensing // Inorganics. 2025. V. 13, N 8. P. 256. DOI: 10.3390/inorganics13080256.
18. Daniel S. Characterization of carbon dots // Carbon Dots in Analytical Chemistry. Amsterdam: Elsevier, 2023. 348 p. DOI: 10.1016/b978-0-323-98350-1.00015-3.
19. Lakovich Dj. Osnovy fluorestsentnoi spektroskopii. M.: Mir, 1986. 496 p.
20. Kurdekar A., Chunduri L.A.A., Bulagonda E.P., Haleyurgirisetty M.K., Kamisetti V., Hewlett I.K. Comparative performance evaluation of carbon dot-based paper immunoassay on Whatman filter paper and nitrocellulose paper in the detection of HIV infection // Microfluid. Nanofluid. 2016. V. 20, N 7. DOI: 10.1007/s10404-016-1763-9.
21. Zhang M., Long X., Ma Y., Wu S. Re-discussion on the essence of the ultra-bright fluorescent carbon dots synthesized by citric acid and ethylenediamine // Opt. Mater. 2023. V. 135, N 113311. DOI: 10.1016/j.optmat.2022.113311.
22. Zhang W., Kasun L.C., Wang Q.J., Zheng Y., Lin Z. A review of machine learning for near-infrared spectroscopy // Sensors. 2022. V. 22, N 24. P. 9764. DOI: 10.3390/s22249764.
23. Penfold T., Watson L., Middleton C., David T., Verma S., Pope T., Kaczmarek J., Rankine C. Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy // Mach. Learn.: Sci. Technol. 2024. V. 5, N 2. P. 021001. DOI: 10.1088/2632-2153/ad5074.
24. Wu X., Zhao Z., Tian R., Niu Y., Gao S., Liu H. Total synchronous fluorescence spectroscopy coupled with deep learning to rapidly identify the authenticity of sesame oil // Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 2021. V. 244. P. 118841. DOI: 10.1016/j. saa.2020.118841.
25. Zhang X., Zhang Y., Yang X., Wang Z., Liu X. Biochemical oxygen demand prediction based on three-dimensional fluorescence spectroscopy and machine learning // Sensors.2025. V. 25, N 3. P. 711. DOI: 10.3390/s25030711.