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Malaria continues to pose a major global health threat, particularly in tropical and subtropical regions, necessitating rapid and accurate diagnostic solutions. Conventional methods such as microscopy and rapid diagnostic tests are limited by sensitivity, specificity, and accessibility. This study proposes a terahertz metasurface biosensor for label-free malaria detection, integrating hybrid graphene–copper architecture. Finite element simulations reveal high sensitivity (1000 GHz/RIU) across a refractive index range of 1.373–1.402 RIU, with multiple resonance modes spanning 0.4–0.85 THz, a minimum detection limit of 0.289, and a FWHM of 190 GHz. Sensor performance was further optimized by tuning graphene’s chemical potential, incident angle, and resonator geometry. A stacking ensemble machine learning model accurately predicted sensor responses, achieving up to 100% accuracy in classification tasks. The demonstrated binary encoding potential highlights applicability in multiplexed sensing and secure medical data encryption. Overall, the proposed sensor offers a compact, high-performance platform for rapid, non-invasive malaria diagnostics.
Wekalao et al. (Wed,) studied this question.