Implementing neural network approach to create carbon-based optical nanosensor of heavy metal ions in liquid media

Sarmanova, Olga; Laptinskiy, Kirill; Burikov, Sergey; Chugreeva, Galina N.; Dolenko, Tatiana A.

doi:10.1016/j.saa.2022.122003

Cited by 4 publications

(6 citation statements)

References 30 publications

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“…The signals from multiple materials were also reported by Sarmanova et al [88], who looked at fluorescence signals generated via carbon dots in the presence of Cu 2+ , Ni 2+ , Cr 3+ and NO − 3 ions in an aqueous solution. The use of one and dimensional CNN machine learning tools enabled the simultaneous determination of the metal ions Cu 2+ , Ni 2+ and Cr 3+ with a root mean squared error of 0.28 mM, 0.79 mM and 0.24 mM, respectively.…”

Section: Luminescent Sensorssupporting

confidence: 64%

“…Selectively designed nanosensors analysing samples with analogous materials that may generate confounding signals [88]. This may make these more traditionally designed nanosensors function as originally envisaged.…”

Section: Discussionmentioning

confidence: 99%

“…The use of machine learning for the design and application of nanosensors has been regularly reported in the literature [84][85][86][87][88][89][90][91][92][93][94]. In short, machine learning techniques are utilised alongside large quantities of data and serve to aid in determining patterns that would not be readily apparent without such tools.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Arellano Vidal,

Govan

2024

Agronomy

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Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector.

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Section: Luminescent Sensorssupporting

confidence: 64%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Arellano Vidal,

Govan

2024

Agronomy

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“…Many studies are being conducted on the influence of certain substances or environmental parameters on the intensity of CDs’ PL. It has been found that different substances can both increase and decrease CDs’ PL. − Like this, the influence of different ions manifests in different ways in CDs solutions in different solvents. The authors have found that the photoluminescence intensity of CDs obtained from citric acid significantly decreases in the presence of Al 3+ , Hg 2+ , Pb 2+ , Ni 2+ , Cu 2+ , Cr 3+ , Co 2+ and Fe 3+ ions in PBS, Britton-Robbinson and NaAc-HAc solvents to varying degrees.…”

Section: Introductionmentioning

confidence: 90%

“…One of the applications of such CDs’ properties is the sensorics of the environment . Sensors of pH and temperature, bioactive molecules, metal ions in liquid media , have been developed on the basis of CD.…”

Section: Introductionmentioning

confidence: 99%

Quenching of Photoluminescence of Carbon Dots by Metal Cations in Water: Estimation of Contributions of Different Mechanisms

Vervald,

Laptinskiy,

Chugreeva

et al. 2023

J. Phys. Chem. C

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All carbon nanoparticles with a developed surface exhibit photoluminescence (PL), the surface origin of which makes it possible to create environmental nanosensors based on it. The active development of sensors for metal ions based on the photoluminescence of carbon dots (CDs) has shown that the quenching of CDs’ photoluminescence can occur due to different mechanisms. However, the magnitude of their contributions to total PL quenching has not been studied. This article presents the results of studies of the mechanisms of photoluminescence quenching in aqueous solutions of carbon dots of hydrothermal synthesis by metal cations: Co2+, Cu2+, Mg2+, Ni2+, Pb2+, Zn2+, Al3+, Cr3+, and Fe3+. It was established that, for different ions, the following mechanisms make significant contributions to the quenching of the photoluminescence of carbon dots in water: the effect of an inner filter; static quenching due to changes in luminophores caused by (de)protonation of the surface groups of carbon dots with a change of pH value; dynamic quenching. Their contributions to the total quenching of photoluminescence of nanoparticles and the limits of detection were quantitatively determined. The conducted research can be used as an algorithm for the analysis of photoluminescence quenching of carbon nanoparticles by any ions in complex media.

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Decoding Fluorescence Excitation-Emission Matrices of Carbon Dots Aqueous Solutions with Convolutional Neural Networks to Create Multimodal Nanosensor of Metal Ions

Sarmanova,

Chugreeva,

Laptinskiy

et al. 2023

Moscow Univ. Phys.

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Implementing neural network approach to create carbon-based optical nanosensor of heavy metal ions in liquid media

Cited by 4 publications

References 30 publications

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Quenching of Photoluminescence of Carbon Dots by Metal Cations in Water: Estimation of Contributions of Different Mechanisms

Decoding Fluorescence Excitation-Emission Matrices of Carbon Dots Aqueous Solutions with Convolutional Neural Networks to Create Multimodal Nanosensor of Metal Ions

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