Construction and Evaluation of a Self-Calibrating Multiresponse and Multifunctional Graphene Biosensor

Area of Science:

• Materials Science
• Nanotechnology
• Biomedical Engineering

Background:

• Graphene-based biosensors often rely on single signal types, necessitating external calibration.
• Existing biosensors face limitations in accuracy and specificity for complex biological samples.

Purpose of the Study:

• To develop a nonenzymatic graphene biosensor capable of detecting glucose and Escherichia coli using multiple signal outputs.
• To create a self-calibrating biosensor system that enhances accuracy and specificity through integrated signal monitoring.

Main Methods:

• Functionalization of graphene sheets with dopamine-functionalized polyethylene glycol and 2,5-thiophenediylbisboronic acid via nitrene cycloaddition and condensation reactions.
• Conjugation of mannose onto the biosensor surface to improve specificity for E. coli detection.
• Utilizing fluorescence, UV absorption, and electrochemical signals for simultaneous and cross-validated detection.

Main Results:

• The multivalent interactions significantly enhanced fluorescence and UV absorption signals.
• Functionalized graphene sheets exhibited measurable electrochemical signal changes.
• Mannose conjugation improved the magnitude and specificity of signals for E. coli detection in complex media.
• The integrated multi-signal system demonstrated real-time self-calibration capabilities.

Conclusions:

• The developed graphene biosensor effectively detects glucose and E. coli with high accuracy and specificity.
• The multi-signal, self-calibrating approach overcomes limitations of single-signal biosensors.
• This versatile platform holds potential for detecting various biosystems in diverse applications.