Enhanced Malaria Detection Using a Hybrid Borophene-Based Terahertz Biosensor with Random Forest Regression Analysis

This study presents a terahertz biosensor design for early-stage malaria detection, integrating borophene, black phosphorus, and graphene in a multi-resonator configuration. The sensor operates in the 0.1–10 THz range and achieves a maximum sensitivity of 700 GHzRIU⁻¹ with a quality factor exceeding 10.5. The design incorporates rectangular resonators coated with borophene, a square ring resonator coated with black phosphorus, and a circular resonator coated with graphene, all fabricated on a SiO₂ substrate. Random Forest Regression analysis of the sensor's performance demonstrates exceptional prediction accuracy with R² values ranging from 93 to 100% across varying incident angles and graphene chemical potentials. The sensor exhibits distinct transmission characteristics for different stages of malaria infection, with frequency shifts of 40 GHz and 30 GHz observed in two separate detection bands. This high-performance biosensor platform represents a significant advancement in rapid, label-free malaria diagnosis.