Plasmonics
GST and MXene-Based Highly Sensitive Refractive Index Sensor with Gold Gratings Resonator Operating for Infrared Region
Rahul Gupta
1
,
R. P. Dwivedi
1
,
Zen A Sbeah
2
,
Vishal Sorathiya
2
,
Abdullah Alwabli
3
,
Ahmad Alghamdi
4
,
Osama S. Faragallah
5
2
Faculty of Engineering and Technology, Parul Institute of Engineering and Technology, Parul University, Vadodara, India
|
Publication type: Journal Article
Publication date: 2024-09-09
Journal:
Plasmonics
scimago Q3
SJR: 0.437
CiteScore: 5.9
Impact factor: 3.3
ISSN: 15571955, 15571963
Abstract
This paper presents a plasmonic metamaterial sensor utilizing gold resonator gratings with different radii for the cylindrical gratings. The sensor is simulated using the finite element method (FEM) in the infrared wavelength range of 0.7 to 2.5 µm. The sensor structure consists of six layers, with the gold resonator on the top, beneath it a Ge–Sb–Te (GST) substrate sandwiched between two silicon (Si) substrates and then a MXene substrate sandwiched between two SiO2 substrates. The design exhibits distinct reflectance characteristics across the proposed range, which is suitable for different sensing applications. A comparison is made between the two states of GST (amorphous and crystalline) to investigate the sensitivity of the device. Geometrical parameters, including the height of GST and Si, are optimized, changing the oblique incident of light, and three types of comparisons are conducted. Firstly, a sensitivity comparison is made between this work and previously published research. Secondly, a quality factor and figure of merit comparison is performed. Lastly, a sensitivity comparison is made between different sensing techniques and the technique employed in this work. After optimizing the design parameters, the device demonstrates the highest detection sensitivity, yielding results of sensitivity equal to 800 nm /RIU. The proposed design-based metamaterial can be utilized as a lab-on-chip sensor.
Found
Are you a researcher?
Create a profile to get free access to personal recommendations for colleagues and new articles.