A compact microwave biosensor reaches 600 MHz/RIU sensitivity, offering promising label-free detection for biological-equivalent media and future point-of-care diagnostic systems with further development and validation.
A newly developed microwave metamaterial biosensor uses a compact microstrip architecture for label-free blood and haemoglobin analysis. The sensor achieved a maximum simulated sensitivity of 600 MHz per refractive index unit (MHz/RIU), highlighting its potential for rapid biological sensing.
The device uses a surface-resonant microstrip architecture containing parallel rectangular resonators. Researchers designed the geometry to avoid multilayer complexity and used a Rogers RT5880 lossy dielectric substrate. Photolithography was used to pattern and etch the conductor layer, while ADS helped refine dimensional parameters and CST Microwave Studio was used to extract effective electromagnetic properties through S-parameter retrieval.
Performance evaluations showed an optimal resonance frequency of approximately 2.1 GHz. Between 2 and 2.5 GHz, the sensor displayed epsilon-near-zero behaviour, enhancing electromagnetic field localisation and producing a dipole-like electric field pattern along its central axis. Silver and graphene were identified as conductive materials with high transmittance, while silver resonators were used with blood- and haemoglobin-equivalent analytes.
Testing across refractive indices of 1.33–1.43 produced a smooth, monotonic decrease in resonance frequency as refractive index increased. The modelled response showed transmission dips of approximately −60 dB, low reflection around the dominant resonance, a quality factor of 22.46 and a maximum figure of merit of 5.34.
The proposed approach could support label-free characterisation of complex biological fluids and, with further development, portable point-of-care diagnostic systems. The technology could enable real-time, non-invasive monitoring outside centralised laboratories, although further validation with sophisticated clinical samples is needed before applications such as chronic disease monitoring can be established.
