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

Bo Bo Han1, Shobhit K Patel2, Ashish Baldania3

  • 1Department of Information Communication and Technology, Marwadi University, Rajkot 360003, India.

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Summary

This study introduces a novel graphene-based biosensor for breast cancer cell detection using surface plasmon resonance (SPR). The Graphene Octagonal Cylinder-Shaped SPR (GOCSPR) biosensor demonstrates high sensitivity for identifying MCF-7 and MDA-MB-231 cancer cells.

Keywords:
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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.