Optical Graphene-Based Biosensor for Nucleic Acid Detection; Influence of Graphene Functionalization and Ionic...

Area of Science:

• Biomedical Engineering
• Materials Science
• Analytical Chemistry

Background:

• Graphene-based biosensors offer promising platforms for nucleic acid detection.
• Key parameters like graphenic structure, dispersion, and ionic strength influence sensor performance.
• Optimizing these parameters is crucial for developing accurate and reliable nucleic acid detection methods.

Purpose of the Study:

• To investigate the interactions between a fluorescein-labeled DNA probe and target DNA on three different graphene materials.
• To determine the most suitable graphene platform and optimal conditions for nucleic acid detection.
• To evaluate the impact of ionic strength and graphene functionalization on detection sensitivity.

Main Methods:

• Characterization of graphene oxide (GO), carboxyl graphene (GO-COOH), and reduced graphene oxide with PEGylated amino groups (rGO-PEG-NH₂) using SEM and TEM.
• Fluorescence resonance energy transfer (FRET) was employed to study molecular interactions and the influence of ionic strength.
• Analysis of fluorescence intensity and anisotropy to assess quenching effects and target-induced signal changes.

Main Results:

• Graphene functionalization, dispersion, and concentration affected fluorescence quenching of the DNA probe.
• GO and GO-COOH exhibited higher quenching efficiencies for the fluorescein-labeled DNA probe.
• Addition of MgCl₂ or MgSO₄ enhanced quenching, with rGO-PEG-NH₂ showing the most significant signal decrease (4.1-fold) upon target DNA binding at 10 mM salt concentration.

Conclusions:

• Reduced graphene oxide functionalized with PEGylated amino groups (rGO-PEG-NH₂) combined with MgCl₂ and fluorescence anisotropy provides an advantageous platform for nucleic acid detection.
• This optimized combination allows for accurate target DNA detection and guides future rational design of biosensors.
• Understanding graphene-material interactions and optimizing assay conditions are critical for advancing biosensor technology.