2D Materials for Surface Plasmon Resonance-based Sensors

2021 ◽  
Author(s):  
Sanjeev Kumar Raghuwanshi ◽  
Santosh Kumar ◽  
Yadvendra Singh
Keyword(s):  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
2D Materials

scholarly journals Surface Plasmonic Sensors: Sensing Mechanism and Recent Applications

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5262
Author(s):  
Qilin Duan ◽  
Yineng Liu ◽  
Shanshan Chang ◽  
Huanyang Chen ◽  
Jin-hui Chen
Keyword(s):  
Surface Plasmon Resonance ◽  
Optical Fiber ◽  
Surface Plasmon ◽  
Optical Sensors ◽  
Plasmon Resonance ◽  
2D Materials ◽  
Localized Surface Plasmon ◽  
Plasmonic Sensors ◽  
Hyperbolic Dispersion ◽  
Challenges And Opportunities

Surface plasmonic sensors have been widely used in biology, chemistry, and environment monitoring. These sensors exhibit extraordinary sensitivity based on surface plasmon resonance (SPR) or localized surface plasmon resonance (LSPR) effects, and they have found commercial applications. In this review, we present recent progress in the field of surface plasmonic sensors, mainly in the configurations of planar metastructures and optical-fiber waveguides. In the metastructure platform, the optical sensors based on LSPR, hyperbolic dispersion, Fano resonance, and two-dimensional (2D) materials integration are introduced. The optical-fiber sensors integrated with LSPR/SPR structures and 2D materials are summarized. We also introduce the recent advances in quantum plasmonic sensing beyond the classical shot noise limit. The challenges and opportunities in this field are discussed.

scholarly journals A Multi-Objective Optimization of 2D Materials Modified Surface Plasmon Resonance (SPR) Based Sensors: An NSGA II Approach

2021 ◽  
Vol 11 (10) ◽  
pp. 4353
Author(s):  
Pericle Varasteanu ◽  
Mihaela Kusko
Keyword(s):  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
2D Materials ◽  
Semiconductor Film ◽  
Nsga Ii ◽  
Multi Objective ◽  
At Resonance ◽  
Sensor Structure ◽  
Thin Semiconductor Film

Modifying the structure of surface plasmon resonance based sensors by adding 2D materials has been proven to considerably enhance the sensor’s sensitivity in comparison to a traditional three layer configuration. Moreover, a thin semiconductor film placed on top of the metallic layer and stacked together with 2D materials enhances even more sensitivity, but at the cost of worsening the plasmonic couplic strength at resonance (minimum level of reflectivity) and broadening the response. With each supplementary layer added, the complexity of optimizing the performance increases due to the extended parameter space of the sensor. This study focused on overcoming these difficulties in the design process of sensors by employing a multi-objective genetic algorithm (NSGA II) alongside a transfer matrix method (TMM) and, at the same time, optimizing the sensitivity to full width at half maximum (FWHM), and the reflectivity level at a resonance for a four layer sensor structure. Firstly, the thin semiconductor’s refractive index was optimized to obtain the maximum achievable sensitivity with a narrow FWHM and a reflectivity level at a resonance of almost zero. Secondly, it was shown that refractive indices of barium titanate (BaTiO3) and silicon (Si) are the closest to the optimal indices for the silver—graphene/WS2 and MoS2 modified structures, respectively. Sensitivities up to 302 deg/RIU were achieved by Ag–BaTIO3–graphene/WS2 configurations with an FWHM smaller than 8 deg and a reflectivity level less than 0.5% at resonance.

2D Materials for Gas and Biosensing Applications

2021 ◽  
pp. 69-106
Keyword(s):  
Thin Films ◽  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
2D Materials ◽  
Silicon Layer ◽  
Detection Accuracy ◽  
Surface Plasmon Resonance Sensor ◽  
Resonance Sensor ◽  
Toxic Gas

This chapter discusses the unique and novel properties of 2D materials useful for toxic gas and biosensing applications. The work presented in this chapter mainly focuses on latest research done on 2D materials related to toxic gas and biosensing for surface plasmon resonance based sensors. Here, we proposed a surface plasmon resonance sensor utilizing P3OT thin films which can sense different concentration of NO2 gas. The performance of proposed design is evaluated by calculating sensitivity, detection accuracy and quality factor, with and without use of silicon layer. Sensitivity of proposed sensor increases by using silicon.

scholarly journals Sensitivity Enhancement of a Surface Plasmon Resonance with Tin Selenide (SnSe) Allotropes

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 173 ◽  
Author(s):  
Xiaoyu Dai ◽  
Yanzhao Liang ◽  
Yuting Zhao ◽  
Shuaiwen Gan ◽  
Yue Jia ◽  
...  
Keyword(s):  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
2D Materials ◽  
Biochemical Sensors ◽  
2D Material ◽  
Tin Selenide ◽  
Spr Sensor ◽  
Different Types ◽  
Sensor Properties

Single layers of tin selenide (SnSe), which have a similar structure as graphene and phosphorene, also show excellent optoelectronic properties, and have received much attention as a two-dimensional (2D) material beyond other 2D material family members. Surface plasmon resonance (SPR) sensors based on three monolayer SnSe allotropes are investigated with the transfer matrix method. The simulated results have indicated that the proposed SnSe-containing biochemical sensors are suitable to detect different types of analytes. Compared with the conventional Ag-only film biochemical sensor whose sensitivity is 116°/RIU, the sensitivities of these SnSe-based biochemical sensors containing α-SnSe, δ-SnSe, ε-SnSe, were obviously increased to 178°/RIU, 156°/RIU and 154°/RIU, respectively. The diverse biosensor sensitivities achieved with these three SnSe allotropes suggest that these 2D materials can adjust SPR sensor properties.

scholarly journals Sensitivity Enhancement of Two-Dimensional Materials Based on Genetic Optimization in Surface Plasmon Resonance

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1198 ◽  
Author(s):  
Guo Xia ◽  
Cuixia Zhou ◽  
Shiqun Jin ◽  
Chan Huang ◽  
Jinyu Xing ◽  
...  
Keyword(s):  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Metal Film ◽  
2D Materials ◽  
Optimization Method ◽  
Sensitivity Enhancement ◽  
Optimal Number ◽  
Two Dimensional ◽  
Two Dimensional Materials

Sensitivity is an important performance index for evaluating surface plasmon resonance (SPR) biosensors. Sensitivity enhancement has always been a hot topic. It is found that the different refractive indices of samples require different combinations of prism and metal film for better sensitivity. Furthermore, the sensitivity can be enhanced by coating two-dimensional (2D) materials with appropriate layers on the metal film. At this time, it is necessary to choose the best film configuration to enhance sensitivity. With the emergence of more and more 2D materials, selecting the best configuration manually is becoming more complicated. Compared with the traditional manual method of selecting materials and layers, this paper proposes an optimization method based on a genetic algorithm to quickly and effectively find the optimal film configuration that enhances sensitivity. By using this method, not only can the optimal number of layers of 2D materials be determined quickly, but also the optimal configuration can be conveniently found when many materials are available. The maximum sensitivity can reach 400°/RIU after optimization. The method provided application value for the relevant researchers seeking to enhance sensitivity.

scholarly journals Sensitivity Improvement of a Surface Plasmon Resonance Sensor Based on Two-Dimensional Materials Hybrid Structure in Visible Region: A Theoretical Study

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2445 ◽  
Author(s):  
Zhining Lin ◽  
Shujing Chen ◽  
Chengyou Lin
Keyword(s):  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
2D Materials ◽  
Incident Light ◽  
Visible Region ◽  
Hybrid Structure ◽  
Two Dimensional ◽  
Spr Sensor ◽  
Sensitivity Improvement

In this paper, we propose a surface plasmon resonance (SPR) sensor based on two-dimensional (2D) materials (graphene, MoS2, WS2 and WSe2) hybrid structure, and theoretically investigate its sensitivity improvement in the visible region. The thickness of metal (Au, Ag or Cu) and the layer number of each 2D material are optimized using genetic algorithms to obtain the highest sensitivity for a specific wavelength of incident light. Then, the sensitivities of proposed SPR sensors with different metal films at various wavelengths are compared. An Ag-based SPR sensor exhibits a higher sensitivity than an Au- or Cu-based one at most wavelengths in the visible region. In addition, the sensitivity of the proposed SPR sensor varies obviously with the wavelength of incident light, and shows a maximum value of 159, 194 or 155°/RIU for Au, Ag or Cu, respectively. It is demonstrated that the sensitivity of the SPR sensor based on 2D materials’ hybrid structure can be further improved by optimizing the wavelength of incident light.

Comparison of sensitivity of the refractometric methods of frustrated total internal reflection and surface plasmon resonance

2020 ◽  
pp. 44-49
Author(s):  
I. N. Pavlov
Keyword(s):  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Total Internal Reflection ◽  
Resonance Method ◽  
Internal Reflection ◽  
Optical Methods ◽  
Thin Boundary Layer ◽  
Experimental Conditions ◽  
Frustrated Total Internal Reflection

Two optical methods, namely surface plasmon resonance imaging and frustrated total internal reflection, are described in the paper in terms of comparing their sensitivity to change of refractive index of a thin boundary layer of an investigated medium. It is shown that, despite the fact that the theoretically calculated sensitivity is higher for the frustrated total internal reflection method, and the fact that usually in practice the surface plasmon resonance method, on the contrary, is considered more sensitive, under the same experimental conditions both methods show a similar result.

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