Citation

Alzamil, Ahmed and Khalil, Muhammad Amir and Yong, Wong Hin and Alenezi, Abdulmajeed M. and Alawad, Mohamad A. and Maash, Abdulwadoud A. and Soliman, Mohamed S. and Hussain, Riaz and Islam Khan, Mohammad Tariqul (2026) Design and performance evaluation of a multi-band metamaterial absorber for oil quality sensing. Journal of Science: Advanced Materials and Devices, 11 (1). p. 101081. ISSN 24682179

Text Design and performance evaluation of a multi-band metamaterial absorber for oil quality sensing.pdf - Published Version Restricted to Repository staff only Download (7MB)

Abstract

This study presents a highly efficient metamaterial (MTM) absorber designed for precise sensing applications, particularly for distinguishing edible oils based on their dielectric properties. Utilising a compact maze-shaped structure comprising a copper resonator and a Rogers 5880 substrate, the absorber achieves near-perfect (> 99 %) absorption efficiency across the 2–5 GHz frequency range. The absorber’s geometric parameters were investigated in detail, revealing significant improvements in multi-band performance and resonance tuning with incremental increases in the resonator’s complexity. Comprehensive simulations conducted using CST Micro wave Studio and validated through equivalent circuit modelling demonstrated strong agreement, establishing a robust design methodology. Experimental verification confirmed the absorber’s sensitivity, demonstrating clear differentiation among mustard, coconut, and sunflower oils through distinct resonance-frequency shifts attrib utable to their dielectric constants. The sensor achieved an exceptional quality factor (Q = 170), high sensitivity (0.85 GHz per dielectric unit), and superior absorption performance, positioning it as a promising candidate for industrial applications in quality control and food safety

Item Type:	Article
Uncontrolled Keywords:	Absorber
Subjects:	T Technology > TJ Mechanical Engineering and Machinery > TJ1040-1119 Machinery exclusive of prime movers
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	06 Jan 2026 07:56
Last Modified:	06 Jan 2026 07:56
URII:	http://shdl.mmu.edu.my/id/eprint/15137

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