Optical sensors play an indispensable role in various applications, covering multiple fields from detecting nanofilms and nanoparticles to identifying biomolecules, viruses, and cells. By improving the performance of sensors, especially by enhancing the interaction between light and matter and increasing sensitivity, it is expected to achieve a series of breakthrough applications.
Metamaterial, as an artificial periodic structure, has subwavelength dimensions, and its response to electromagnetic waves can be controlled through structural design and material parameters. Metal metamaterials can concentrate strong electric field energy in a very small spatial range, thereby achieving strong interaction with analytes. Although terahertz (THz) sensors based on metal metamaterials have shown advantages in contactless and label free detection, there is still a gap of over two orders of magnitude in sensitivity and detection limits compared to existing mature biochemical diagnostic methods such as enzyme immunoassay. Existing research results indicate that optimized metamaterial sensors should have resonant modes with high local electric field enhancement factor, high quality factor (Q value), and smaller mode volume at the analyte location. However, so far, there is still a lack of a systematic and practical optimization strategy that can uniformly consider all performance parameters of metamaterial sensors for rational design (compared to trial and errormethods).
Another limitation of this type of sensor research is related to the performance evaluation parameters commonly used. The first one is the refractive index sensitivity (S), which represents the resonant frequency shift of the metamaterial structure caused by the unit refractive index change of the analyte (unit: GHz/RIU, where RIU represents the refractive index unit). The second one is the sensing
sensitivity value (FOM), which represents the ratio of the full width at half maximum (FWHM) of the power spectrum to S (in units of 1/RIU). The problem with these parameters is that they are only applicable when the type, location, and quantity of analyte are the same, that is, when the analyte is a fully covered dielectric film with the same thickness, the performance of the sensor can be accurately evaluated through the S and FOM parameters.
To solve the above problems, Associate Professor Cao Lei from Huazhong University of Science and Technology, together with Professor Hartmut G. Roskos' team from Frankfurt University in Germany, Professor Peter Haring Bol í var's team from Siegen University in Germany, and Professor Shihab Al Daffie's team from Eindhoven University of Technology in the Netherlands, designed and implemented a terahertz metamaterial sensor (ID esRR) with a cross finger structure based on dielectric perturbation theory, which can achieve high sensitivity and high-quality sensing of nanoscale thickness analytes. The relevant research results were published in the journal Photonics Research under the title "Interdigitated terahertz metamaterial sensors: design with the dielectric perturbation theory".
The research team uses simplified resonator dielectric perturbation theory to quantitatively guide the rational design of metamaterial sensors. By perfectly combining the cross finger structure with the electrically open ring metamaterial resonator (eSRR), a novel ID esRR metamaterial sensor is formed, effectively improving sensing sensitivity. Compared with the traditional eSRR structure, the fundamental mode resonance of the ID eSRR structure has a high Q value. In addition, it is obviously unreasonable to compare the performance of different sensors only based on two traditional performance parameters (S and FOM). Different sensors should be designed for different types of analytes, and different performance parameters should be adopted. For the medium analyte placed in a uniform thin film form on the sensor, the research team suggests normalizing the traditional FOM on the film thickness to obtain the third performance indicator, which is thickness normalized FOM (TN-FOM).
Source: Sensor Expert Network