Carbon Dots and Quantum Dots for Fluorescent Sensing and Imaging

5,872 papers · previously filed under “Materials Chemistry”

Carbon Dots and Quantum Dots for Fluorescent Sensing and Imaging

This research community develops carbon-based nanomaterials, primarily carbon dots and quantum dots, to create fluorescent probes for detecting specific chemicals and for biological imaging.

The work centers on the synthesis of nitrogen-doped carbon dots and graphene quantum dots, often using green synthesis methods. These materials are engineered to exhibit ratiometric fluorescence, allowing for sensitive and selective detection of targets such as tetracycline antibiotics, heavy metal ions, and other small molecules. The recurring focus is on creating highly sensitive fluorescent sensors and probes that can function in complex environments, including food samples and biological systems. While the core material is carbon, the research frequently involves co-doping with elements like nitrogen, sulfur, or boron to tune optical properties. Applications range from analytical chemistry, where the goal is precise quantification of analytes, to biomedical imaging, where the dots serve as low-toxicity alternatives to traditional inorganic quantum dots.

The community’s output is most concentrated in research related to Nitrogen, Cadmium, and Carbon. Nitrogen research accounts for 1.1% of the element’s total literature, while Cadmium and Carbon each represent 1.3% and 1.1% of their respective fields. Nitrogen also contributes the highest number of papers to this group, with 754 entries.

There are 5,872 papers in this community, with the most frequent publication venues being Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Microchemical Journal, and Carbon.

Recent work continues to focus on the application of these materials in food safety, such as detecting residual antibiotics like ofloxacin and cefadroxil, and in biomedical contexts, including ophthalmology and drug delivery. Newer studies also explore the integration of machine learning to optimize the synthesis of solid-state emitting carbon dots and the development of dual-mode sensors that combine colorimetric and fluorometric detection to overcome cross-interference.

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