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<p>Lead in drinking water is a major public health issue, in view of this there is a great demand for sensitive and reliable detection technology. Quantum Dots are currently being extensively investigated as sensing probe. They have a strong interaction with heavy metal ions resulting in changes in optical or electrochemical signals. Various QDs like Graphene QDs, carbon QDs, oxygen based QDs, and doped semiconductor QDs have shown to be highly sensitive and selective for Pb2+ ions and can be used effectively in different water samples. This review compares the different QDs based on their starting materials, detection methods and performance. Detection methods are also compared based on their sensitivity selectivity and Quantum yield. Recently QD based lead sensors have been integrated with the internet of things and artificial intelligence. These technologies enable cheaper and more precise detection, even in complex samples. Machine learning algorithms such as Random Forests, SVMs and CNNs are also used to enhance detection accuracy. They also enable automated or real time detection. This review highlights these advances and ethical issues surrounding them. These technologies demonstrate great potential for sustainable and low-cost water detection and promotion of public health.</p> Graphical Abstract <p></p>

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Quantum Dots for Fluorescence-Based Lead Detection: Advancing IoT-Enabled Smart Water Monitoring Systems

  • 期刊论文

  • 发表日期：

    2026年2月28日

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  • Manju Nath Mishra,
  • Satanand Mishra,
  • Shivani Pandey,
  • Tanmay Sardar

摘要

Lead in drinking water is a major public health issue, in view of this there is a great demand for sensitive and reliable detection technology. Quantum Dots are currently being extensively investigated as sensing probe. They have a strong interaction with heavy metal ions resulting in changes in optical or electrochemical signals. Various QDs like Graphene QDs, carbon QDs, oxygen based QDs, and doped semiconductor QDs have shown to be highly sensitive and selective for Pb2+ ions and can be used effectively in different water samples. This review compares the different QDs based on their starting materials, detection methods and performance. Detection methods are also compared based on their sensitivity selectivity and Quantum yield. Recently QD based lead sensors have been integrated with the internet of things and artificial intelligence. These technologies enable cheaper and more precise detection, even in complex samples. Machine learning algorithms such as Random Forests, SVMs and CNNs are also used to enhance detection accuracy. They also enable automated or real time detection. This review highlights these advances and ethical issues surrounding them. These technologies demonstrate great potential for sustainable and low-cost water detection and promotion of public health.

Graphical Abstract