The paper proposes an ultrahigh sensitivity microwave sensor for retrieving the complex permittivity of the liquid sample. The proposed sensor originally evolves from the complementary split-ring resonator-based sensor, which confines the electric field in the slot of the resonator. A complementary electric-LC resonator structure is applied to substitute the CSRR due to its excellent ability of concentrating the electric field. To further improve the field confinement, a meander slot is embedded in the central arm of the resonator. Then, two etched complementary split-ring resonators are added on both sides of the central meander arm of the modified complementary electric-LC resonator. A polydimethylsiloxane microfluidic channel substrate is placed on the meander arm, and a mixture of water-ethanol is injected. The variations in the resonant frequency and peak attenuation are used to retrieve the complex permittivity of the injected liquid sample. The developed sensors are fabricated and tested, and a good agreement is observed between measured data and reference values. The peak sensitivities in retrieving the relative permittivity achieve 2.5% and 2.71% with the proposed sensors, which are much higher than previously reported designs.

Microwave microfluidic sensors have been employed for dielectric characterization of different liquids. Intuitively, the microfluidic channel plays a vital role in determining the sensor performance. In this article, for the first time, numerical optimization design of microfluidic channel route is carried out with the aim of improving the sensor sensitivity. Two swarm intelligence algorithms, i.e., particle-ant colony optimization algorithm and wolf colony algorithm, are implemented for the route optimization. Through the developed optimization procedure, the sensor sensitivity of the original design can be increased significantly. Several prototypes of optimized sensors are fabricated and tested, and they exhibit good capability in retrieving the liquid properties. In comparison with original complementary split-ring resonator-based sensor with a sensitivity of 0.308% for water measurement, the optimized sensor achieves a high sensitivity value of 0.55%, i.e., the sensor sensitivity is increased by 78.6% after optimization. The developed methodology can also be used in other designs, such as series LC -based sensor, whose sensitivity can be improved by about 50%. It is demonstrated that the developed methodology possesses good automatic optimization ability and universality for the optimal design of microwave microfluidic sensors.

This study proposes an ultrahigh-sensitivity split-ring resonator-based microwave sensor for retrieving the complex permittivity of liquid samples. An interdigital capacitor structure was used to expand the sensing area and the sensitivity. A defected ground structure and A parallel dual split-ring resonator were introduced to improve the quality factor. A polydimethylsiloxane microfluidic channel substrate was placed above the interdigital capacitor structure. The channel route coincided with the interdigital gap to fully utilize the strong electric field. Ethanol–water solutions with varying ethanol fractions were injected into the channel as the testing liquid. It was demonstrated that the variation in resonant frequency can be used to retrieve the dielectric properties of liquid samples. The proposed sensor used a small liquid volume of ~0.68 μL and provided values in good agreement with the reference data.

The paper proposes a temperature-compensated sensor for microfluidic application. The sensor consists of a splitter/combiner section with two symmetrical complementary split-ring resonators (CSRR). A polydimethylsiloxane substrate is attached with the microfluidic channel aligned to the meander slot of the CSRR. A split-ring resonator (SRR) with peripheral circuit is added to detect environmental temperature. The water-methanol mixtures with various water fractions are measured at temperature ranging from 20°C to 50°C. The environmental temperature is firstly measured by the resonant frequency shift produced by SRR. Then, the variations in the resonant frequency and quality factor of the differential splitter/combiner section, as well as the temperature, are used as the input data for training the neural network. In comparison with previous designs, the proposed sensor is capable of not only suppressing the influences of environmental factors, but also retrieving the permittivity and loss tangent of liquid sample at specific temperature, thereby identifying the liquid accurately.