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Fluorescence Based Platform to Discriminate Protein Using Carbon Quantum Dots
Author(s) -
Carneiro Cruz Antônio Alvernes,
Freire Rafael Melo,
Froelich Deise Beatriz,
Alves de Lima Ari Clesius,
Muniz André Rodrigues,
Ferreira Odair Pastor,
Fechine Pierre Basílio Almeida
Publication year - 2019
Publication title -
chemistryselect
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 34
ISSN - 2365-6549
DOI - 10.1002/slct.201901014
Subject(s) - graphene , fluorescence , raman spectroscopy , biomolecule , carbon fibers , fourier transform infrared spectroscopy , nanoparticle , materials science , chemistry , myoglobin , bovine serum albumin , nanotechnology , analytical chemistry (journal) , chemical engineering , chromatography , organic chemistry , physics , optics , engineering , quantum mechanics , composite number , composite material
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=Fe 3+ , Cu 2+ or Ni 2+ ) 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.