2026

Experimentally validated dual-band GHz metamaterial perfect absorber biosensor with negative-index response and AI-assisted electromagnetic analysis for breast cancer dielectric discrimination.

Biosensors & bioelectronics Hamza MN, Islam MT, Koziel S, Alibakhshikenari M, Virdee B, Mariyanayagam D, Lavadiya S, Din IU, Sanches B, Naqvi SI, Panda A, Farmani A, Mezache Z, Shamsan ZA, Farmani H, Ghafourivayghan M, Chaudhary MA, Naser-Moghadasi M, Islam MS
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组成图示

示意图生成中

传感器类型

检测对象

检测原理

检测灵敏度

效应效果

传感器的构成

中文摘要

英文摘要

A dual-band GHz metamaterial absorber (MPA) biosensor is presented as a proof-of-concept platform for dielectric-sensitive electromagnetic sensing integrated with AI-assisted broadband spectral analysis. The proposed three-layer copper-FR-4-copper structure exhibits near-unity absorption at 6.0 GHz and 9.1 GHz and demonstrates engineered negative-index behavior, which enhances electromagnetic field confinement and sensitivity to dielectric perturbations. The biosensor was fabricated using standard PCB technology and experimentally validated through free-space microwave measurements, showing excellent agreement with simulations and absorption exceeding 92.5% and 99.8% at the two resonant frequencies. For dielectric-sensitive analysis, the sensor response was evaluated under simulation-based loading conditions using low-loss COC/COP coverslip layers and literature-reported electromagnetic properties representative of healthy-like and malignant-like breast environments. Distinct broadband spectral perturbations were observed across the 5-10 GHz frequency range, demonstrating sensitivity to dielectric-property variations. Sensor performance was assessed using quality factor, sensitivity, and figure-of-merit metrics. An AI-assisted spectral interpretation framework was developed using distance- and correlation-based analysis of the broadband electromagnetic response. The results demonstrate that the proposed platform combines dual-band high-Q absorption, engineered triple-negative electromagnetic characteristics, and AI-assisted spectral analysis within a unified microwave biosensing framework. No biological samples were evaluated in this study; therefore, the reported results should be interpreted as simulation-based dielectric-loading analyses rather than validated cancer-detection outcomes.

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