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The Complete Biosensor | OMICS International
ISSN: 2155-6210
Journal of Biosensors & Bioelectronics

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The Complete Biosensor

Robert Magnusson*

Department of Electrical Engineering, University of Texas at Arlington, Resonant Sensors Incorporated, Arlington, Texas, USA

*Corresponding Author:
Robert Magnusson
Department of Electrical Engineering
University of Texas at Arlington
Resonant Sensors Incorporated
Arlington, Texas, USA
E-mail: [email protected]

Received Date: May 20, 2013; Accepted Date: May 21, 2013; Published Date: May 22, 2013

Citation: Magnusson R (2013) The Complete Biosensor. J Biosens Bioelectron 4:e120. doi: 10.4172/2155-6210.1000e120

Copyright: © 2013 Magnusson R. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Design and fabrication of nanostructured sensors based on optical, electrical, or mechanical principles has advanced tremendously in the last decade. This is due in part to progress in numerical design tools, computational power and nanofabrication capability. Across the globe, there is growing interest in advanced sensor technologies for diverse applications in homeland security, biomedicine, drug development, food safety and environmental monitoring. These sensor systems must be portable and cost-effective, while providing rapid response with high sensitivity, reliability and minimal false-reading counts. Most biosensor technologies currently available employ fluorescent or absorption labeling to register a specific bio molecular reaction. For reasons of expense and expediency, there is increasing demand for improved sensor techniques that do not require labeling. In this editorial, we briefly discuss advanced guided-mode resonance (GMR) biosensor technology meeting these demands. Since these sensors can be designed to resonate in multiple modes, complete information about a bio reaction can be extracted, including the bio layer thickness, bio layer refractive index, and the change in the background solution refractive index. Thus, justifiably, we refer to this sensor as the complete biosensor.

The resonance frequency of the GMR device varies as any of its structural parameters change. In a bio molecular binding event, an attaching bio layer alters the effective thickness of the resonant layer affecting the resonance wavelength. This is shown schematically in figure 1. The wavelength change can be monitored with a spectrum analyzer in real time to quantify the binding dynamics. A wide variety of sensor geometries, materials and system architectures can be implemented.


Figure 1: A guided-mode resonance sensor schematic.

To provide a historical perspective, in 1992, Magnusson and Wang [1] suggested application of the GMR effect to sensors and disclosed GMR filters that were tunable on variation in resonance structure parameters, including thickness and refractive index [2]. Wawro et al. [3] presented new GMR biosensor embodiments, as well as new applications of these sensors when integrated with optical fibers [3]. The use of modal and polarization diversity for multi parametric biosensors is a particularly interesting aspect of this technology [4].

Applying the GMR concept, we have developed and verified a new foundational methodology for biosensing that is robust against false readings. We employ modal and polarization-based parametric discrimination. Our resonant sensors are designed to support two or more leaky optical modes in the spectral band of interest. These modes can be directly excited with a beam of unpolarized light as they resonate in their respective polarization states. This property provides enriched data sets that can be used to calibrate simultaneously for variations such as temperature or sample background density, in the same sensor element, thus increasing detection accuracy and reducing probability of false readings. Concurrent, co-localized data acquisition via such polarization and modal diversity eliminates errors associated with the use of separate reference sites. Our sensors can be arrayed into highdensity (~ 10,000 sensors/cm2) platforms that are easily interrogated with a single beam of light. They are extremely economic in fabrication and amenable to mass production. This important sensor technology will find increasing application, for example, in medical diagnostics and drug development.

Applying this concept, figure 2 shows an example set of resonance peaks for a typical sensor. All four peaks can be monitored conveniently in real time using a spectrum analyzer (Figure 2).


Figure 2: Computed resonance spectra for a representative dielectric GMR sensor operating in an aqueous environment. Two resonance peaks associated with the lowest waveguide modes for each polarization state appear.

In an example experiment, we monitor the TE0 and TM0 peaks at an identical physical location on the sensor surface. The objective is to determine the refractive index of the bio layer and background in a single measurement. We assume the thickness of the bio layer is ~ 20 nm, based on knowledge of the binding molecular species. Figure 3 shows the measured spectral shifts for both polarization states as a function of time for the detection of the biomarker protein calreticulin. The capture layer is a specific monoclonal IgG antibody (anti-calreticulin) that is covalently bound to the sensor surface. By back fitting this dualpeak response into our rigorous electromagnetic analysis codes, we distinguish the resonance shift contributions due to the targeted bio reaction from the background bulk-index changes (which occur, for example, due to thermal drift). This is accomplished by calculating and mapping the TE and TM resonance peak shifts over a range of added bio layer and background refractive indices. The resulting numerical look-up table is applied to match the corresponding detection layer and background index when the two resonance peak shifts are known. Figure 4 illustrates the resulting resonance shift contributions due to surface changes and background variations.


Figure 3: Measured biolayer temporal dynamics for a 68 nM solution of calreticulin binding to anti-calreticulin in a PBS solution. We record both polarization states for the lowest waveguide modes.


Figure 4: Refractive indices of the biolayer and background determined by backfitting the results in figure 3 to a numerical model. The background refractive index remains constant during this particular reaction.

In conclusion, the guided-mode resonance biosensor is complete in the sense that it can be used to find all unknowns in a bio reaction as a function of time. The example provided demonstrates determination of two parameters for calreticulin binding to a matched antibody. By fitting to a model of the known sensor with rigorous electromagnetic computations, we quantify the variations in the layer refractive index and in conclusion, the guided-mode resonance biosensor is complete in the sense that it can be used to find all unknowns in a bio reaction as a function of time. The example provided demonstrates determination of two parameters for calreticulin binding to a matched antibody. By fitting to a model of the known sensor with rigorous electromagnetic computations, we quantify the variations in the layer refractive index, and show that the background is stable. By monitoring an additional peak due to a third resonating mode, the bio layer thickness could also be found.


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