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IEEE photonics journal

Surface Plasmon Resonance (SPR) Sensor Based on Optimal Pre- and Post-Selection

Hailin Wang, Wenkang Li, Jianlin Huang, Rui Su, Ruilin Wang, Lan E Luo, Yuxia Zhang, Zhiyou Zhang, Quanxi Xue

Published Feb 4, 2019• 6 citations
Publisher

Abstract

A surface plasmon resonance (SPR) refractive index sensing technique based on the optimal pre-and post-selection (PPS) is put forward. The intensity contrast ratio (ICR) is introduced to estimate the precision of this sensing technique. Compared with the traditional sensing techniques, our method shows that the ICR can be improved by about two orders of magnitude with optimal pre-and post-selected states. It is also proved that our sensing technique has a large tolerance for the metal film thickness and the incident angle. Remarkably, the method greatly reduces the cost of experiments, which is significant for promoting the widespread applications of SPR sensors.

DOI
10.1109/jphot.2019.2896587

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A surface plasmon resonance (SPR) refractive index sensing technique based on the optimal pre-and post-selection (PPS) is put forward. The intensity contrast ratio (ICR) is introduced to estimate the precision of this sensing technique. Compared with the traditional sensing techniques, our method shows that the ICR can be improved by about two orders of magnitude with optimal pre-and post-selected states. It is also proved that our sensing technique has a large tolerance for the metal film thickness and the incident angle. Remarkably, the method greatly reduces the cost of experiments, which is significant for promoting the widespread applications of SPR sensors.
A nonlinear interferometer can be constructed by replacing the beam splitter in the Mach-Zehnder interferometer with four-wave mixing (FWM) process. Meanwhile, the conventional surface plasmon resonance (SPR) sensors can be extensively used to infer the information of refractive index of the sample to be measured via either angle demodulation technique or intensity demodulation technique. Combined with a single FWM process, a quantum SPR sensor has been realized, whose noise floor is reduced below standard quantum limit with sensitivity unobtainable with classical SPR sensor. Therefore, in this work we have theoretically proposed a nonlinear interferometric SPR sensor, in which a conventional SPR sensor is placed inside nonlinear interferometer, which is called as I-type nonlinear interferometric SPR sensor. We demonstrate that near resonance angle I-type nonlinear interferometric SPR sensor has the following advantages: its degree of intensity-difference squeezing, estimation precision ratio, and signal-noise-ratio are improved by the factors of 4.6 dB, 2.3 dB, and 4.6 dB respectively than that obtained with a quantum SPR sensor based on a single FWM process. In addition, the theoretical principle of this work can also be expanded to other types of sensing, such as bending, pressure, and temperature sensors based on a nonlinear interferometer.
We investigate the use of twin-mode quantum states of light with symmetric statistical features in their photon number for improving intensity-sensitive surface plasmon resonance (SPR) sensors. For this purpose, one of the modes is sent into a prism setup where the Kretschmann configuration is employed as a sensing platform and the analyte to be measured influences the SPR excitation conditions. This influence modifies the output state of light that is subsequently analyzed by an intensity-difference measurement scheme. We show that quantum noise reduction is achieved not only as a result of the sub-Poissonian statistical nature of a single mode, but also as a result of the nonclassical correlation of the photon number between the two modes. When combined with the high sensitivity of the SPR sensor, we show that the use of twin-mode quantum states of light notably enhances the estimation precision of the refractive index of an analyte. With this we are able to identify a clear strategy to further boost the performance of SPR sensors, which are already a mature technology in biochemical and medical sensing applications.
Surface plasmon resonance (SPR) sensors can reach the quantum noise limit of the optical readout field in various configurations. We demonstrate that two-mode intensity squeezed states produce a further enhancement in sensitivity compared with a classical optical readout when the quantum noise is used to transduce an SPR sensor signal in the Kretschmann configuration. The quantum noise reduction between the twin beams when incident at an angle away from the plasmonic resonance, combined with quantum noise resulting from quantum anticorrelations when on resonance, results in an effective SPR-mediated modulation that yields a measured sensitivity 5 dB better than that with a classical optical readout in this configuration. The theoretical potential of this technique points to resolving particle concentrations with more accuracy than is possible via classical approaches to optical transduction.
In this paper, we present a new kind of surface plasma resonance (SPR) sensor. It comprises three layers, an upper coupling prism, a middle metal layer, and a bottom semi-infinite two-level atom medium. Based on theoretical analysis on this SPR system, it is found that there are some new phenomena due to the coexistence of two resonant effects, i.e., the surface plasmon resonance and the resonant quantum transition between atomic energy levels. An interesting phenomenon is that the reflectivity of the incident light is very sensitive to the shift of atomic level induced by external field. This phenomenon is absent in ordinary SPR systems. Because the shift of atomic level is determined by external field, ultimately, the reflectivity of the incident light is sensitive to the external field. In this paper calculations are presented in the case where the external field is a magnetic field. It is found that the reflectivity of the incident light is very sensitive to the magnetic field. This property can be used for measuring a very weak magnetic field near surface of matter , thus it can be used for developing a new detection technology.
Abstract Our research aims to create a theoretical framework that may be used to improve phase-based surface plasmon resonance (SPR) biosensor precision beyond the shot noise limit by utilizing quantum states including squeezed states and NOON states. For this work, a two-mode phase sensing setup model is created. One of the two arms of the two mode model of this model has an integrated SPR system based on the Krestchmann arrangement. An experiment involving phase-based plasmonic biosensing is modeled using the two-mode setup. The state preparation, the biosensing component, and the measurement comprise the three main components of the model. The measurement varies depending on the particular input state. Quantum noise reduction for quantum states results from the combination of the sub-Poissonian statistical structure of a single mode and the non-classical correlation of the photon number between the two modes (entanglement). We show that when combined with the high sensitivity of the SPR sensor, the use of two-mode quantum states of light considerably increases the estimation precision of the refractive index of an analyte. Here, we use distilled water diluted BSA solutions at different concentrations that correlate to different refractive indices to reproduce a static phase-based SPR biosensor. Both lossless and lossy circumstances were taken into consideration for the model. The NOON state and the product coherent squeezed vacuum (PCSV) state are the two main quantum states taken into consideration in this work. The PCSV state exhibited the better limit of detection (LOD) among the states that we measured, suggesting that it is a promising candidate for the development of quantum biosensing systems. The effect of losses will require more investigation, but this work enables us to identify a precise course of action for enhancing the performance of the phase-based surface plasmon resonance sensor even further. Phase-based quantum SPR sensors have yet to be thoroughly examined, but intensity-based quantum SPR sensors have previously been extensively researched in the biochemical and medical sensing domains.
In this paper, we made a pioneering effort for the first time to implement Quantum Neural Network regressor model to predict the sensing behavior of Surface plasmon resonance (SPR) sensor and compared the performance of the proposed model with two traditional algorithms namely Support Vector Regressor (SVR) and Artificial Neural Network (ANN) regressor. The proposed trained quantum regressor model is crucial and efficient enough as it could be used to predict the trend of the target value that is confinement loss of the SPR biosensor.