Citation

Hamza, Musa N. and Islam Khan, Mohammad Tariqul and Koziel, Slawomir and Lavadiya, Sunil and Din, Iftikhar ud and Sanches, Bruno and Naqvi, Syeda Iffat and Panda, Abinash and Naser Moghadasi, Mohammad and Virdee, Bal and Farmani, Ali and Livreri, Patrizia and Islam, Md. Shabiul and Abbasi, Nisar Ahmad and Alibakhshikenari, Mohammad (2026) Precision Sensing at Selected Spectrum: A Double-Negative Low-Cost Metamaterial Sensor with Enhanced Directionality for Biomedical Applications. IEEE Sensors Journal. p. 1. ISSN 1530-437X

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Abstract

This paper presents a novel, lowcost, compact sensor that integrates a doublenegative (DNG) metamaterial with an artificial magnetic conductor (AMC)-backed microstrip patch antenna, specifically optimized for highprecision biomedical applications in the 2–4 GHz range. Unlike prior designs, the proposed sensor leverages the simultaneous negative permittivity and permeability of the DNG structure together with the AMC layer to achieve enhanced gain, directionality, and sensitivity—key performance factors for GHz-range diagnostics. Fabricated on a cost-effective FR4 substrate with a compact footprint of 50 × 50 mm², the sensor is well-suited for scalable, real-world deployment. Full-wave simulations confirm negative refractive index characteristics within the 2.0–2.7 GHz band, and a peak gain of 7.6 dBi is achieved, surpassing comparable designs. Experimental validation in an anechoic chamber shows excellent agreement with simulation results. Benchmarking demonstrates that the sensor outperforms state-of-the-art counterparts in gain, size, and directional response. These features make it a strong candidate for biomedical applications such as wearable health monitoring, portable medical imaging, and non-invasive diagnostics including early-stage brain abnormality detection.

Item Type:	Article
Uncontrolled Keywords:	Metamaterials
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7871 Electronics--Materials
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Apr 2026 07:57
Last Modified:	02 Apr 2026 07:57
URII:	http://shdl.mmu.edu.my/id/eprint/15675

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