The advantages conferred by the physical, optical and electrochemical properties of graphene-based nanomaterials have contributed to the current variety of ultrasensitive and selective biosensor devices. In this review, we present the points of view on the intrinsic properties of graphene and its surface engineering concerned with the transduction mechanisms in biosensing applications. We explain practical synthesis techniques along with prospective properties of the graphene-based materials, which include the pristine graphene and functionalized graphene (i.e., graphene oxide (GO), reduced graphene oxide (RGO) and graphene quantum dot (GQD). The biosensing mechanisms based on the utilization of the charge interactions with biomolecules and/or nanoparticle interactions and sensing platforms are also discussed, and the importance of surface functionalization in recent up-to-date biosensors for biological and medical applications.

Surface plasmon Resonance (SPR) has recently been of interest for label-free voltage sensing. Several SPR structures have been proposed. However, making a quantitative cross-platform comparison for these structures is not straightforward due to (1) different SPR measurement mechanisms; (2) different electrolytic solution and concentration in the measurement; and (3) different levels of external applied potential. Here, we propose a quantitative approach to make a direct quantitative comparison across different SPR structures, different electrolytic solutions and different SPR measurement mechanisms. There are two structures employed as example in this theoretical study including uniform plasmonic gold sensor and bimetallic layered structure consisting of uniform silver layer (Ag) coated by uniform gold layer (Ag). The cross-platform comparison was carried by several performance parameters including sensitivity (S), full width half maximum (FWHM) and figure of merit (FoM). We also discuss how the SPR measurement mechanisms enhance the performance parameters and how the bimetallic layer can be employed to enhance the FoM by a factor of 1.34 to 25 depending on the SPR detection mechanism.

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In this paper, we report a theoretical framework on the effect of multiple resonances inside the dielectric cavity of insulator-insulator-metal-insulator (IIMI)-based surface plasmon sensors. It has been very well established that the structure can support both long-range surface plasmon polaritons (LRSPP) and short-range surface plasmon polaritons (SRSPP). We found that the dielectric resonant cavity under certain conditions can be employed as a resonator to enhance the LRSPP properties. These conditions are: (1) the refractive index of the resonant cavity was greater than the refractive index of the sample layer and (2) when light propagated in the resonant cavity and was evanescent in the sample layer. We showed through the analytical calculation using Fresnel equations and rigorous coupled wave theory that the proposed structure with the mentioned conditions can extend the dynamic range of LRSPP excitation and enhance at least five times more plasmon intensity on the surface of the metal compared to the surface plasmon excited by the conventional Kretschmann configuration. It can enhance the dip sensitivity and the dynamic range in refractive index sensing without losing the sharpness of the LRSPP dip. We also showed that the interferometric modes in the cavity can be insensitive to the surface plasmon modes. This allowed a self-referenced surface plasmon resonance structure, in which the interferometric mode measured changes in the sensor structure and the enhanced LRSPP measured changes in the sample channel.

Angular scanning-based surface plasmon resonance measurement has been utilized in label-free sensing applications. However, the measurement accuracy and precision of the surface plasmon resonance measurements rely on an accurate measurement of the plasmonic angle. Several methods have been proposed and reported in the literature to measure the plasmonic angle, including polynomial curve fitting, image processing, and image averaging. For intensity detection, the precision limit of the SPR is around 10–5 RIU to 10–6 RIU. Here, we propose a deep learning-based method to locate the plasmonic angle to enhance plasmonic angle detection without needing sophisticated post-processing, optical instrumentation, and polynomial curve fitting methods. The proposed deep learning has been developed based on a simple convolutional neural network architecture and trained using simulated reflectance spectra with shot noise and speckle noise added to generalize the training dataset. The proposed network has been validated in an experimental setup measuring air and nitrogen gas refractive indices at different concentrations. The measurement precision recovered from the experimental reflectance images is 4.23 × 10–6 RIU for the proposed artificial intelligence-based method compared to 7.03 × 10–6 RIU for the cubic polynomial curve fitting and 5.59 × 10–6 RIU for 2-dimensional contour fitting using Horner's method.

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