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<p>The use of metamaterials and graphene improves the performance of many photonic structures. Here we have introduced one sensor which is having improved sensitivity for the detection of cancer which is utilizing graphene and metamaterials. We have used the COMSOL Multiphysics simulator for simulating sensor designs. The improvement in the design is recorded using the observed absorptance results for the different biomolecules. The highest sensitivity of 1250&#xa0;nm/RIU is obtained using this optimized design. The optimization regarding the manuscript structure is carried out and the results for the absorptance for all these parameters are also presented in this paper. The E-field analysis of the structure for the different points of the investigated spectrum is also observed and it matches well with the obtained absorption results which are used for enhanced sensing purposes. Machine learning optimization is applied to improve the sensitivity of the sensor. The identified optimal sensor can be utilized in rapid cancer-detecting devices, enhancing biomedical applications. Its efficiency in identifying cancerous cells enables faster diagnosis and early intervention, potentially improving patient outcomes. This sensor technology holds significant promise for advancing diagnostic tools in the medical and healthcare fields. The observed optimum detecting sensor can be used in fast cancer-detecting devices for biomedical applications.</p>

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Graphene-Based Machine Learning–Optimized Surface Plasmon Resonance Biosensor Design for Skin Cancer Detection

  • 期刊论文

  • 发表日期：

    2025年1月9日

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  • Nithya S.,
  • Jayanthi Muthuswamy,
  • Osamah Alsalman,
  • Shobhit K. Patel

摘要

The use of metamaterials and graphene improves the performance of many photonic structures. Here we have introduced one sensor which is having improved sensitivity for the detection of cancer which is utilizing graphene and metamaterials. We have used the COMSOL Multiphysics simulator for simulating sensor designs. The improvement in the design is recorded using the observed absorptance results for the different biomolecules. The highest sensitivity of 1250 nm/RIU is obtained using this optimized design. The optimization regarding the manuscript structure is carried out and the results for the absorptance for all these parameters are also presented in this paper. The E-field analysis of the structure for the different points of the investigated spectrum is also observed and it matches well with the obtained absorption results which are used for enhanced sensing purposes. Machine learning optimization is applied to improve the sensitivity of the sensor. The identified optimal sensor can be utilized in rapid cancer-detecting devices, enhancing biomedical applications. Its efficiency in identifying cancerous cells enables faster diagnosis and early intervention, potentially improving patient outcomes. This sensor technology holds significant promise for advancing diagnostic tools in the medical and healthcare fields. The observed optimum detecting sensor can be used in fast cancer-detecting devices for biomedical applications.