Journal Highlight: Fluorescence based platform to discriminate protein using carbon quantum dots

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  • Published: Jun 12, 2019
  • Author: spectroscopyNOW
  • Channels: UV/Vis Spectroscopy
thumbnail image: Journal Highlight: Fluorescence based platform to discriminate protein using carbon quantum dots

A theoretical and experimental study based on carbon quantum dots and metallic ions has been devised for distinguishing proteins by their fluorescence patterns and supported by pattern recognition using linear discriminant analysis.

Cruz, A.A.C., Freire, R.M., Froelich, D.B. et al. (2019). Fluorescence based platform to discriminate protein using carbon quantum dots. ChemistrySelect 4 (19): 5619-5627

Abstract: There is an urgent demand to develop a cheap, fast and robust methodology to sense proteins, since these biomolecules are often used as biomarker responsible for diagnosing of some diseases, such as cancer. In this regard, we report a theoretical and experimental study, as well as a cheap and effective ‘chemical‐nose’ strategy based on carbon quantum dots (CQDs) and metallic cations (M) to discriminate proteins at concentration as low as 50 nM. Thus, the CQDs were firstly synthesized through citric acid thermolysis and their characteristics were fully investigated by UV‐Vis absorption, fluorescence, infrared (FTIR), XPS and Raman spectroscopies and atomic force microscopy (AFM). These results pointed out for quasi‐spherical CQDs with diameters in the range of 1.2‐7 nm, presence of stacked graphitic layers and oxygenated functional groups, as well as disordered carbon. Based on the structural and morphological features, computational simulations were carried out to obtain a better understanding of the atomic structure. Our results evidenced a carbon‐based nanoparticle formed by stacked graphene nanoflakes containing defects due to the presence of functional groups within the graphene layers. Afterwards, a 'tongue'‐based approach was developed by using three distinct CQDs – M (M=Fe3+, Cu2+ or Ni2+) ensembles, which allowed us to acquire different and reproducible fluorescence patterns for four proteins (bovine serum albumin, hemoglobin, myoglobin and cytochrome C) at 50 nM. Subsequently, the pattern recognition was performed using linear discriminant analysis and 36 samples were correctly identified affording 100% of accuracy.

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