Label-Free Biomolecule Detection in Physiological Solutions With Enhanced Sensitivity Using Graphene Nanogrids FET...

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Artificial Intelligence

Background:

• Graphene nanogrid sensors show promise for Hepatitis B (Hep-B) surface antigen detection, but face challenges in quantifying low concentrations in serum due to non-specific binding and sensor variability.
• Existing methods struggle with significant overlap in sensitivity values between different Hep-B concentrations (0.1-100 fM) in real-world samples.

Purpose of the Study:

• To develop a novel method for accurate quantification of ultra-low concentrations of Hep-B surface antigen in serum.
• To overcome the limitations of existing biosensor quantification techniques for Hepatitis B detection.

Main Methods:

• Utilized a graphene nanogrid field-effect transistor biosensor operated in heterodyne mode (100 kHz–1 MHz) to mitigate Debye screening effects.
• Implemented a probabilistic neural network (PNN) for data analysis and quantification of Hep-B antigen concentrations.
• Compared PNN performance against polynomial fit and static neural network models.

Main Results:

• The PNN model achieved quantification error within 10% for Hep-B concentrations ranging from 0.1 to 100 fM in serum.
• This represents a substantial improvement over polynomial fit (77% error) and static neural networks (66% error).
• The proposed methodology reduced the detection limit for Hep-B in serum by over three orders of magnitude compared to current state-of-the-art sensors.

Conclusions:

• Probabilistic neural networks offer a robust solution for accurate quantification of ultra-low Hep-B concentrations in complex biological samples like serum.
• The developed graphene nanogrid biosensor system with PNN significantly enhances the sensitivity and reliability of Hepatitis B diagnostics.
• This approach paves the way for earlier and more precise detection of Hepatitis B, improving patient outcomes.