Graphene BioFET sensors for SARS-CoV-2 detection: a multiscale simulation approach

Area of Science:

• Nanotechnology
• Biophysics
• Computational Science

Background:

• Biological Field-Effect Transistors (BioFETs) show potential for detecting ions and molecules.
• Two-dimensional (2D) materials enhance BioFET performance and enable new applications.
• Fast, reliable, and affordable SARS-CoV-2 detection is crucial during the pandemic.

Purpose of the Study:

• To develop a comprehensive computational approach for understanding BioFET sensor mechanisms.
• To investigate the impact of molecular interactions on sensor sensitivity.
• To guide the design of improved 2D material-based biosensors.

Main Methods:

• A multiscale simulation platform combining atomic and mesoscopic levels.
• Detailed modeling of receptor charge distribution and reconfiguration upon target binding.
• Analysis of sensitivity changes in the transduction mechanism.

Main Results:

• The computational model captures the charge dynamics of receptor-analyte interactions.
• Simulations reveal how molecular binding influences the sensor's electrical signal.
• The approach provides insights into the sensitivity of 2D material-based BioFETs.

Conclusions:

• The multiscale platform offers in-depth understanding of BioFET sensing mechanisms.
• Computational insights can drive the development of more sensitive and specific biosensors.
• This work supports the exploration of novel 2D materials and receptors for disease detection.