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<p>In this work, we present a rapid, label free, non-ionization method for identifying early-stage cervical cancer using a metamaterial (MM) biosensor composed unit cell with two resonant frequencies 3.8014&#xa0;GHz and 12.219&#xa0;GHz. These frequencies alter in response to variations in the dielectric characteristics of the tissue, making it possible to distinguish between the refractive indices of normal and malignant tissues. The biosensor’s maximal sensitivity, within the refractive index (RI) range of 1.1 to 1.6, is 0.8&#xa0;GHz/RIU at low frequency and 2.4&#xa0;GHz/RIU at high frequency. This method is faster than direct tissue analysis and doesn’t need labelling. Additionally, compared to single-frequency biosensors, dual-frequency detection offers higher accuracy. This method might also be modified for different forms of cancer by comparing the biological features of malignant and normal tissues. Results show that this approach effectively detects structural and compositional changes in biological tissues, demonstrating that its potential for tissue analysis applications.</p>

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Detection of Cervical Cancers via Dual-Band Metamaterial Biosensor: A Simulation Study

  • 期刊论文

  • 发表日期：

    2026年1月31日

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  • Zienab EL-Wasif

摘要

In this work, we present a rapid, label free, non-ionization method for identifying early-stage cervical cancer using a metamaterial (MM) biosensor composed unit cell with two resonant frequencies 3.8014 GHz and 12.219 GHz. These frequencies alter in response to variations in the dielectric characteristics of the tissue, making it possible to distinguish between the refractive indices of normal and malignant tissues. The biosensor’s maximal sensitivity, within the refractive index (RI) range of 1.1 to 1.6, is 0.8 GHz/RIU at low frequency and 2.4 GHz/RIU at high frequency. This method is faster than direct tissue analysis and doesn’t need labelling. Additionally, compared to single-frequency biosensors, dual-frequency detection offers higher accuracy. This method might also be modified for different forms of cancer by comparing the biological features of malignant and normal tissues. Results show that this approach effectively detects structural and compositional changes in biological tissues, demonstrating that its potential for tissue analysis applications.