Electrochemiluminescence resonance energy transfer between graphene quantum dots and gold nanoparticles for DNA damage detection
Abstract
Bright blue luminescent graphene quantum dots (GQDs) with major graphitic structured nanocrystals and a photoluminescence (PL) quantum yield of 15.5% were synthesized and used to monitor DNA damage. The GQDs were prepared by ultraviolet irradiation without using a chemical agent. The as-prepared GQDs showed excitation-dependent PL and stable electrochemiluminescence (ECL) behaviors. Gold nanoparticles (AuNPs) were linked with a probe of single-stranded DNA (cp53 ssDNA) to form AuNPs-ssDNA. The ECL signal of the GQDs could be quenched by non-covalent binding of the AuNPs-ssDNA to the GQDs, due to the occurrence of an electrochemiluminescence resonance energy transfer between the GQDs and the AuNPs. When AuNPs-ssDNA was then hybridized with target p53 DNA to form AuNPs-dsDNA, the non-covalent interaction between the GQDs and the ds-DNA weakened and the ECL of the GQDs recovered. This engendered an ECL sensor for the detection of target p53 ssDNA, with a detection limit of 13 nM. The resultant ECL sensor could be used for DNA damage detection based on its different bonding ability to damaged target p53 ssDNA and cp53 ssDNA linked AuNPs. The presented method could be expanded to the development of other ECL biosensors, for the quantification of nucleic acids, single nucleotide polymorphisms or other aptamer-specific biomolecules.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.