Smart graphene-enhanced ceramic material refractive index sensor simulation design developed for highly sensitive...

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Materials Science

Background:

• Early detection of breast cancer is crucial for effective treatment.
• Surface Plasmon Resonance (SPR) biosensors offer high sensitivity for biological analyte detection.
• Graphene integration enhances biosensor performance due to its unique properties.

Purpose of the Study:

• To propose and investigate a novel Graphene Octagonal Cylinder-Shaped Surface Plasmon Resonance (GOCSPR) biosensor.
• To evaluate the biosensor's efficacy in detecting specific breast cancer cell lines (MCF-7 and MDA-MB-231).
• To optimize the biosensor design using machine learning for enhanced detection efficiency.

Main Methods:

• Fabrication of a biosensor with an octagonal gold structure and a cylindrical silver structure, integrated with graphene.
• Utilizing the surface plasmon resonance (SPR) technique for sensing.
• Employing a Linear Regression machine learning algorithm for design optimization and analysis of layer height and cylinder radius.

Main Results:

• Achieved high sensitivity rates of 714.28 nm/RIU for MCF-7 and 785.71 nm/RIU for MDA-MB-231 cells.
• The GOCSPR biosensor exhibited good quality factors (560 for MCF-7, 557 for MDA-MB-231).
• Optimization using Linear Regression confirmed design parameters with high R² values.

Conclusions:

• The developed GOCSPR biosensor is a highly efficient platform for detecting breast cancer cells.
• The novel design incorporating graphene, gold, and silver shows significant potential for medical applications in cancer diagnostics.
• The study highlights the successful application of machine learning in optimizing biosensor performance.