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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 19, Iss. 23 — Nov. 7, 2011
  • pp: 22863–22873
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Wagon wheel fiber based multichannel plasmonic sensor

Yating Zhang, Chi Zhou, Li Xia, Xia Yu, and Deming Liu  »View Author Affiliations


Optics Express, Vol. 19, Issue 23, pp. 22863-22873 (2011)
http://dx.doi.org/10.1364/OE.19.022863


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Abstract

We proposed the first microstructured optical fiber based multichannel plasmonic sensor design. The large air holes can facilitate sample loading and can be modified to realize two functionalities, dual analyte sensing and self referencing. A theoretical analysis is carried out to simulate these two operation modes and study the influences of the structural variables on the sensor performance. In dual analyte detection, average sensitivity of 6.5 × 10−6 RIU for each channel can be achieved over a dynamic index range of 1.33 to 1.36. In self referencing operation, the capability of the proposed sensor in nullifying environmental noises has also been demonstrated.

© 2011 OSA

1. Introduction

Being highly sensitive to changes in the refractive index (RI) of the medium adjacent to metal, surface plasmon resonance (SPR) sensor has become a powerful tool for biochemical analysis without molecule labeling [1

1. J. Homola, S. Yee, and G. Gauglitz, “Surface plasmon resonance sensors: review,” Sens. Actuators B Chem. 54(1–2), 3–15 (1999). [CrossRef]

3

3. M. Skorobogatiy, “Microstructured and photonic bandgap fibers for applications in the resonant bio- and chemical sensors,” J. Sens. 2009, 524237 (2009).

]. Since most of early SPR sensors target single channel sensing, the lack of compensating ability makes them rather prone to suffer from noises in real world applications, including instrumental instability, temperature fluctuation and non-specific molecular interactions. Moreover, these single-channel SPR devices could no longer meet the increasing demand of multi analyte detection in many fields, such as clinical diagnostics, drug discovery, and environmental monitoring. In the past decades, multichannel SPR sensors attracted immense research interest and undergone boosting development, from bulky prism-coupled configurations to micron-scaled fiber structures [4

4. J. Homola, H. Vaisocherová, J. Dostálek, and M. Piliarik, “Multi-analyte surface plasmon resonance biosensing,” Methods 37(1), 26–36 (2005). [CrossRef] [PubMed]

9

9. Z. Zhang, P. Zhao, F. Sun, G. Xiao, and Y. Wu, “Self-referencing in optical-fiber surface plasmon resonance sensors,” IEEE Photon. Technol. Lett. 19(24), 1958–1960 (2007). [CrossRef]

]. In these sensors, multiple sensing channels with bio receptors for specific analyte detection and reference channels responding to non-specific effects are fabricated in parallel or in series. The optical fiber based multichannel sensors show great advantages in portability, higher integration and distributed remote sensing. However, to fabricate active elements, fiber jacket and cladding should be manually removed prior to gold coatings [7

7. W. Peng, S. Banerji, Y. C. Kim, and K. S. Booksh, “Investigation of dual-channel fiber-optic surface plasmon resonance sensing for biological applications,” Opt. Lett. 30(22), 2988–2990 (2005). [CrossRef] [PubMed]

,9

9. Z. Zhang, P. Zhao, F. Sun, G. Xiao, and Y. Wu, “Self-referencing in optical-fiber surface plasmon resonance sensors,” IEEE Photon. Technol. Lett. 19(24), 1958–1960 (2007). [CrossRef]

]. This process is easy to cause mechanical failures and add fabrication cost.

In recent years, a new type of SPR sensors based on microstructured optical fibers (MOFs) has been proposed [10

10. A. Hassani and M. Skorobogatiy, “Design criteria for microstructured optical fiber based surface plasmon resonance sensors,” J. Opt. Soc. Am. B 24(6), 1423–1429 (2007). [CrossRef]

15

15. M. Hautakorpi, M. Mattinen, and H. Ludvigsen, “Surface-plasmon-resonance sensor based on three-hole microstructured optical fiber,” Opt. Express 16(12), 8427–8432 (2008). [CrossRef] [PubMed]

]. Difficulty of surface polishing has been avoided. The porous fiber structures can offer natural channels for fluid packaging, which is of importance for measurement of volatile or toxic samples. Moreover, the micron-sized channels require small volume of sample and contribute to efficient interaction between the core mode and the sample. Till now, all these reported plasmonic sensors are developed for single analyte sensing. Multi analyte detection has not been implemented with a MOF based system, to our knowledge. In this paper, we present a novel multichannel plasmonic sensor designed with a modified wagon wheel (WW) fiber. A theoretical analysis is carried out to investigate capability of the proposed sensor in dual analyte detection. The functionality of self referencing to eliminate environmental noises will also be elucidated. After that the effects of several structural variables on the sensor performance will be discussed.

2. Dual analyte detection in WW fiber

WW fibers, with their unique features of notably large hole size and strong evanescent field, present an attractive platform for various sensing applications [15

15. M. Hautakorpi, M. Mattinen, and H. Ludvigsen, “Surface-plasmon-resonance sensor based on three-hole microstructured optical fiber,” Opt. Express 16(12), 8427–8432 (2008). [CrossRef] [PubMed]

17

17. S. C. Warren-Smith, S. Afshar V., and T. M. Monro, “Theoretical study of liquid-immersed exposed-core microstructured optical fibers for sensing,” Opt. Express 16(12), 9034–9045 (2008). [CrossRef] [PubMed]

]. A typical WW fiber based SPR sensor studied in Ref [15

15. M. Hautakorpi, M. Mattinen, and H. Ludvigsen, “Surface-plasmon-resonance sensor based on three-hole microstructured optical fiber,” Opt. Express 16(12), 8427–8432 (2008). [CrossRef] [PubMed]

]. is shown in Fig. 1(a)
Fig. 1 (a) WW fiber based SPR sensor with three uniformly coated air holes. (b) Modified WW fiber with a high index overlayer deposited on top of gold film in Channel 1. (c) Structural parameters of the proposed sensor.
. The silica fiber consists of three air holes, where cladding layers and gold layers are uniformly deposited in sequence. Since the size of the air holes can be tens of micrometers, fabrication of the multi-layered channel structures and the infiltration of sample into channels become easier. The cladding layers, with a refractive index slightly smaller than the fiber core, are introduced to tune the propagation loss of the sensor to a tolerable level. Polymers and down-doped silica are both candidates for the cladding layers. The gold layers, generally with a nanoscale thickness, can be coated via the technique of high-pressure microfluidic chemical deposition [18

18. P. J. A. Sazio, A. Amezcua-Correa, C. E. Finlayson, J. R. Hayes, T. J. Scheidemantel, N. F. Baril, B. R. Jackson, D. J. Won, F. Zhang, E. R. Margine, V. Gopalan, V. H. Crespi, and J. V. Badding, “Microstructured optical fibers as high-pressure microfluidic reactors,” Science 311(5767), 1583–1586 (2006). [CrossRef] [PubMed]

]. For such configuration, we will first investigate the feasibility of dual analyte detection. We consider an analyte RI profile of na1 in one of the metalized holes (denoted as Channel 1) and na2 in the other two holes (Channel 2&3). To solve the electromagnetic modes of the sensor, the finite element method (FEM) with perfectly matched layers (PML) is used. The fiber structural variables required for simulation are illustrated in Fig. 1(c). We fix the radius of core curvature to r = 4μm. Thicknesses of the core struts (ts), cladding layers (tcladding) and gold films (tAu) are 200nm, 1.25μm and 40nm, respectively. The refractive indices of the core and cladding are chosen to be 1.46 and 1.44, whilst the permittivity of gold is defined with the Drude formula [11

11. B. Gauvreau, A. Hassani, M. Fassi Fehri, A. Kabashin, and M. A. Skorobogatiy, “Photonic bandgap fiber-based Surface Plasmon Resonance sensors,” Opt. Express 15(18), 11413–11426 (2007). [CrossRef] [PubMed]

].

The decay in the power after a length of fiber (length of L) can be calculated with the formula
Pout=Pin[Txexp(αxL)+(1Tx)exp(αyL)]
(1)
where Pin and Pout denote the input and output power, respectively. The attenuation constants of the two polarized core modes, αx and αy, are defined as follows [15

15. M. Hautakorpi, M. Mattinen, and H. Ludvigsen, “Surface-plasmon-resonance sensor based on three-hole microstructured optical fiber,” Opt. Express 16(12), 8427–8432 (2008). [CrossRef] [PubMed]

].

α=4πλ0Im(neff)
(2)

Tx is the power percentage of the x-polarized mode at input end. It covers a range of 0 to 1.

Figure 3
Fig. 3 Tx-dependent power transmission spectrum. na1 = 1.33 and na2 = 1.36. L = 1mm.
shows the power transmission spectrum at various values of Tx. The sensor length L is assumed to be 1mm. The two transmission dips, from left to right, respectively represent the resonance in Channel 1 and that in Channel 2 and 3. Note that the dip positions remain unaffected by the change of Tx. It is because the resonance wavelengths depend only on the respective analyte refractive indices. The value of Tx determines the incident power distribution between the two polarized modes and thus the amplitude of resonance signals from different channels. As Tx increases, an upward shift of the left dip and a downward shift of the right one are clearly seen, implying a coupling suppression in Channel 1 and a coupling enhancement in Channel 2 and 3. When Tx rises to 0.6, the left dip becomes too small to identify. In the rest of the paper, the value of Tx is chosen to be 0 for two reasons. (i) Signals from different channels are clearly visible. (ii) Only half of the fiber (cut along the symmetry axis of the RI profile) needs to be computed thanks to the particular incident polarization (linear polarization along the symmetry axis).

To verify the capability of the metalized fiber in dual analyte sensing, na1 is fixed at 1.33 whilst na2 is changed from 1.36 to 1.33. As shown in Fig. 4(a)
Fig. 4 Dual analyte operation of the metalized fiber (a) without and (b) with a high RI overlayer. na1 = 1.33. na2 changes from 1.36 to 1.33. noverlayer = 1.42, toverlayer = 70nm and L = 1mm.
, a decrease in na2 results in a blue resonance shift for Channel 2 and 3. Particularly, when na2 is reduced to a value close to na1, for instance, na2 = 1.34, the two resonance dips merge to form a single dip. Ideally, a single dip appears only when all channels contain the same analyte (na1 = na2 = 1.33). The particular scenario where na2 = 1.34 indicates that the sensor fails to distinguish between analytes when there is a small analyte RI difference.

This problem can be solved by adding a high RI polymer layer on top of the gold surface in Channel 1, as illustrated in Fig. 1(b). The thickness and RI of the overlayer are set to noverlayer = 1.42 and toverlayer = 70nm, respectively. The spectral responses of the modified fiber are presented in Fig. 4(b) to compare with Fig. 4(a). Note the difference that when na1 = na2 = 1.33, the resonance dip of Channel 1 splits from that of Channel 2 and 3 and moves to a longer wavelength at 648nm. A spectral separation of 75nm is obtained between the two dips. As na2 increases, the dip of Channel 2 and 3 shifts towards the red without affecting that of Channel 1, which shows excellent independence between channels. However, the maximum analyte RI in Channel 2 and 3 is determined by the resonance wavelength of Channel 1, which can be vindicated from the observation that the left dip merges into the right one when na2 rises to 1.36.

The RI and thickness of the overlayer play a dominant role in tuning the upper limit of the analyte RI in Channel 2 and 3. It is exemplified in Fig. 5
Fig. 5 Dependence of the transmission responses on the (a) RI and (b) thickness of the overlayer. na1 = 1.33 and na2 = 1.36. L = 1mm. In figure (a), toverlayer = 70nm and noverlayer varies from 1.42 to 1.45. In figure (b), noverlayer = 1.44 and toverlayer varies from 60nm to 80nm.
, where an operation range of 1.33 to 1.36 is desired for each channel. We just consider the case where the two dips are the closest in the spectra (na1 = 1.33, na2 = 1.36). In Fig. 5(a), the thickness of the overlayer is kept at 70nm whilst the RI of the overlayer is varied from 1.42 to 1.45. The dip of Channel 1 shifts from 648nm to 679nm, accompanied with an improved discriminative property of the sensor. In Fig. 5(b), we fix the RI of the overlayer at 1.44 whilst change the thickness from 60nm to 80nm. It can be found that a thicker overlayer also contributes to a larger separation between the two dips. There are more than one pairs of noverlayer and toverlayer to ensure a RI range of 1.33 to 1.36. Here we take noverlayer = 1.44 and toverlayer = 80nm as an example to study the sensing performance in dual analyte detection.

Figure 6(a)
Fig. 6 (a) Calibration relations for dual analyte sensing over a RI range of 1.33 to 1.36. (b) Responses for the self referencing operation. noverlayer = 1.44, toverlayer = 80nm and L = 1mm. na1 changes from 1.33 to 1.335 whilst na2 changes from 1.34 to 1.35
presents the mapping of resonance wavelength vs analyte RI for each channel. Both responses follow an approximately linear relationship, with a slope of 1535nm/RIU for Channel 1 and 1550nm/RIU for Channel 2 and 3. By assuming a demodulation resolution of 0.01nm of the spectrometer, the corresponding sensitivities are 6.51 × 10−6 RIU and 6.45 × 10−6 RIU, respectively.

The capability of the proposed device in dual analyte sensing yields two applications. One is tracking two different analytes in one liquid sample, where each analyte is captured by a functional material deposited in the corresponding channel. The other application is sensing two different liquid samples simultaneously with the technique of selective filling [20

20. B. T. Kuhlmey, B. J. Eggleton, and D. K. C. Wu, “Fluid-filled solid-core photonic bandgap fibers,” J. Lightwave Technol. 27(11), 1617–1630 (2009). [CrossRef]

].

3. Self referencing operation in WW fiber

The proposed sensor can also be used as a self referencing device, where Channel 1 is chosen as the sensing channel, and Channel 2 and 3 both work as reference channels. The reference channels are usually coated with receptors responding to the non-target molecule in the sample. To simulate the self referencing capability of the sensor, we roughly assume a RI change of 0.01 (from 1.34 to 1.35) in the reference channels due to the variation of the non-target molecule captured by the receptors (i.e. concentration variation), and a change of 0.005 in the overall RI of the sample (from 1.33 to 1.335) in the sensing channel. The operation of the sensor is shown in Fig. 6(b), where the structural variables used for simulation are identical with those in Section 2. Both the referencing signal and the sensing signal appear as absorption dips. The variation of the non-target molecule, which causes a red shift of the referencing signal, also contributes to the shift of the sensing resonance. Hence, instead of a single resonance wavelength, the spectral separation of the two dips (λsep) can be used to nullify the non-specific effects. In addition, the two resonance share almost the same instrumental noise and thermal instability, λsep also helps to eliminate external influences. In this operation mode, the sensitivity of the sensor could be calculated as
S(λ)=|ΔλsepΔna|
(3)
where na is RI of the analyte of interest in the sample.

4. Influences of structural variables

In Section 2 and 3, the design principle of the proposed sensor with both functionalities of dual analyte detection and self referencing is presented. In the following part, we will discuss the influences of several structural variables on the sensor performance. Since there has already been a detailed analysis on the effects of the gold layer thickness and RI of the core material [15

15. M. Hautakorpi, M. Mattinen, and H. Ludvigsen, “Surface-plasmon-resonance sensor based on three-hole microstructured optical fiber,” Opt. Express 16(12), 8427–8432 (2008). [CrossRef] [PubMed]

], we mainly focus on the thickness of the cladding layer, the sensor length and the sensing layers which are usually required for bio and chemical detection.

4.1 Cladding layer

4.2 Sensor length

Sensor length is another factor that influences the sensor response. Large sensor length can facilitate the process of fabrication and ensures sufficient interaction with sample. Figure 7(b) shows the transmission properties of our proposed sensor with different lengths, where a cladding thickness of 1.5μm is chosen for a relatively low propagation loss. The sensor length changes from 1mm to 7mm. Note that the amplitude of resonance signal increases as the sensor length is increased while the resonance wavelength remains unaffected. It means one can extend the sensor length for higher signal to noise ratio (SNR) whilst maintain the sensitivity. If the propagation loss of the sensor is further reduced, for example, by increasing the cladding thickness or the core-cladding index contrast [15

15. M. Hautakorpi, M. Mattinen, and H. Ludvigsen, “Surface-plasmon-resonance sensor based on three-hole microstructured optical fiber,” Opt. Express 16(12), 8427–8432 (2008). [CrossRef] [PubMed]

], we believe that the sensor length can be extended to a centimeter scale.

4.3 Sensing layer

As is mentioned in Section 2 and 3, for capturing different analytes of interest in one liquid sample, as well as for self-referencing, channels in the proposed sensor are usually surface-functionalized with a layer of sensing material. To find out the effects of the structural variables of the sensing layer, including RI and thickness, on the sensor performance, we take into account the scenario of dual-analyte operation and add two types of sensing layers, sensing layer 1 and 2, respectively in the upper channel and the lower two channels in Fig. 1(b). A detection analyte RI range of 1.33~1.34 in each channel is assumed to evaluate the sensitivity. The structural parameters used for analysis are the same as those in Section 2.

Figure 8(a)
Fig. 8 (a) Dependence of channel sensitivity on the RI of the sensing layer. Red curve: tsen2 = 60nm, tsen1 = 60nm and nsen1 = 1.38. Black curve: tsen1 = 60nm, tsen2 = 60nm and nsen2 = 1.36; (b) Variation of channel sensitivity with thickness of the sensing layer. Red curve: nsen2 = 1.36, tsen1 = 60nm, nsen1 = 1.38. Black curve: nsen2 = 1.36, tsen2 = 60nm, nsen1 = 1.38.
shows the dependence of the channel sensitivity on the RI of the respective sensing layer, where the thicknesses of both sensing layers (tsen1 and tsen2) are fixed at 60nm. We rationally choose a RI range of 1.36 to 1.46 for the sensing layers, which covers a large variety of materials, like polymers and most biomolecules [6

6. E. K. Akowuah, T. Gorman, S. Haxha, and J. V. Oliver, “Dual channel planar waveguide surface plasmon resonance biosensor for an aqueous environment,” Opt. Express 18(24), 24412–24422 (2010). [CrossRef] [PubMed]

,14

14. A. Hassani and M. Skorobogatiy, “Photonic crystal fiber-based plasmonic sensors for the detection of biolayer thickness,” J. Opt. Soc. Am. B 26(8), 1550–1557 (2009). [CrossRef]

]. Each curve in the figure is calculated by changing the RI of one sensing layer whilst maintaining a constant RI for the other sensing layer. For the analysis of sensing layer 1, the RI of sensing layer 2 is kept at 1.36 (nsen2 = 1.36) and the analysis for sensing layer 2 is carried out by assuming nsen1 = 1.38. From Fig. 8(a), one can observe that with the increasing RI of sensing layer, each curve shows an increase in the channel sensitivity. This rising trend is attributed to the shift of the resonance dip in the transmission spectrum to a longer wavelength, where a higher sensitivity can be attained [9

9. Z. Zhang, P. Zhao, F. Sun, G. Xiao, and Y. Wu, “Self-referencing in optical-fiber surface plasmon resonance sensors,” IEEE Photon. Technol. Lett. 19(24), 1958–1960 (2007). [CrossRef]

,13

13. Y. Zhang, L. Xia, C. Zhou, X. Yu, H. Liu, D. Liu, and Y. Zhang, “Microstructured fiber based plasmonic index sensor with optimized accuracy and calibration relation in large dynamic range,” Opt. Commun. 284(18), 4161–4166 (2011). [CrossRef]

]. For the sensing layers in Channel 2 and 3, note that there is an upper RI limit at 1.44. It is because when nsen2 exceeds this value, the resonance dip for the lower two channels moves towards the red and mixes with that of the upper channel, leading to a failure of dual-analyte sensing. Additionally, the sensitivity of Channel 2 and 3 shows a much steeper variation as compared to that of Channel 1 with the same RI change of the sensing layer, which indicates a better capability of sensing layer 2 in tuning the channel sensitivity. As there is a perfect inter-channel independence in our proposed sensor, the channel sensitivity should be unaffected by the RI and thickness of the sensing layer from other channels. However, the upper limit of RI for the sensing layer in Channel 2 and 3 depends on the resonance wavelength of Channel 1, which is strongly influenced by the RI and thickness of the sensing layer in Channel 1.

Figure 8(b) illustrates the channel sensitivity as a function of the thickness of the sensing layer. The thicknesses of sensing layer 1 and 2 both cover a range from 0 to 140nm. nsen1 and nsen2 are set to 1.38 and 1.36 respectively. tsen2 is fixed at 60nm to compute the curve for Channel 1. Curve for Channel 2 and 3 is obtained by assuming a constant value of 60nm for tsen1. From the starting point of the upper curve, we find that the sensitivity of Channel 1 without a sensing layer is 1400nm/RIU over an analyte RI range of 1.33 to 1.34, which is smaller than that shown in Section 2 (1535nm/RIU). A similar case can also be found when tsen2 = 0. It is because the calibration relations shown in Fig. 6(a) are not exactly linear, the channel sensitivities given in Section 2 are both average values over a larger analyte RI range (1.33~1.36). For each curve, a sharp decrease in the channel sensitivity is observed as the thickness of sensing layer increases, which is consistent with the results from Ref [21

21. Y. Yuan, L. Ding, and Z. Guo, “Numerical investigation for SPR-based optical fiber sensor,” Sens. Actuators B Chem. 157(1), 240–245 (2011). [CrossRef]

]. It can be readily understood from the fact that a thicker sensing layer causes a larger distance of the sample from the gold surface, where the plasmon field reaches its maximum value. As a result, the reduced overlap between surface plasmons and the sample lowers the channel sensitivity. If an analyte RI change of 2 × 10−5 RIU is detected, thickness of sensing layer 1 and sensing layer 2 should be smaller than 140nm and 100nm respectively to ensure sufficient sensitivity (with a spectrometer resolution of 0.01nm).

It is worth mentioning that in Fig. 8(a) (or (b)), where tsen1 (or nsen1) and tsen2 (or nsen2) are constant, one can find out many pairs of nsen1 (or tsen1) and nsen2 (or tsen2) that ensure equal sensitivity for each channel by drawing a series of horizontal lines across the two curves and locating the points of intersection. It is of significance for the self referencing operation. Because the RI change due to the variation of non-target analyte in the sample will induce equal resonance shift for the sensing channel and the reference channels. The influence of non-specific analyte can be completely eliminated.

Table 1

Tab. 1. Characteristics of compact dual-analyte SPR sensors

table-icon
View This Table
compares the performance of our proposed sensor and the other reported compact SPR sensors used for dual-analyte detection. Three parameters are taken into account, including the length of sensing element, detection RI range and sensitivity. As is seen from the table, dual-analyte fiber SPR sensors have longer sensing elements than the planar waveguide based sensor. The longer element length in the fiber based sensors can facilitate fabrication and sample loading, and is advantageous for monitoring weakly absorbing analytes. Another strength of our sensor lies in a much higher sensitivity over a larger detection RI range, as compared to the other two types of fiber based sensors, which helps meet a wider range of sensing demands. With an overall consideration of the above three parameters, our proposed sensor shows a better performance.

Finally, we remark the fabrication feasibility of the cladding layer and overlayer in our design. As mentioned in Section 2, both doped silica and polymer can be used as the material of cladding layers. Ref [22

22. S. Kim, Y. Jung, K. Oh, J. Kobelke, K. Schuster, and J. Kirchhof, “Defect and lattice structure for air-silica index-guiding holey fibers,” Opt. Lett. 31(2), 164–166 (2006). [CrossRef] [PubMed]

]. reported a holey fiber with P2O5-F codoped silica ring layers inside the air holes. The doped silica layers were fabricated using the conventional stack-and-draw technique, where doped silica layers were added into the preform and then drawn together with the fiber material at a high temperature. This method can be applied in our design to produce silica cladding layers. Sol-gel is a common approach to synthesize thin polymer films. For example, in Ref [23

23. Y. Abe, Y. W. Shi, Y. Matsuura, and M. Miyagi, “Flexible small-bore hollow fibers with an inner polymer coating,” Opt. Lett. 25(3), 150–152 (2000). [CrossRef] [PubMed]

], a liquid-phase polymer coating technique has been used for polymer film deposition on top of silver layer in hollow glass fibers. A polymer solution is injected into the holes and polymer films are formed by continuously heating the fibers. In our design, the cladding layers and the overlayer are possible be fabricated with the same technique by replacing the polymer solutions.

5. Conclusion

The first MOF based multichannel plasmonic sensor was proposed in this paper. The large sized air holes in the WW fiber could facilitate the sample loading and the fabrication of multiple layers. The proposed sensor can be configured to implement two functionalities, dual analyte sensing and self referencing. The dual analyte operation is not only suitable for the measurement of two analytes in one liquid sample, but also lends itself to simultaneous detection of two different liquid samples. With structural optimization, average sensitivity of 6.5 × 10−6 RIU for all channels has been achieved over a RI range of 1.33 to 1.36 in the dual-analyte mode, which is higher than other reported compact dual-analyte plasmonic sensors. The self referencing operation has also been demonstrated to enable elimination of external influences, including variation of non-target analytes, temperature fluctuation and instrumental noises.\

Acknowledgments

This work is supported by the International Cooperation Project between China and Singapore (No. 2009DFA12640 and A*STAR SERC grant 0921450031), Natural Science Foundation of Hubei Province (No. 2009CDA129) and partly supported by Professional Talent & Innovation Fund (0109182929, 0124182015, Huazhong University of Science & Technology).

References and links

1.

J. Homola, S. Yee, and G. Gauglitz, “Surface plasmon resonance sensors: review,” Sens. Actuators B Chem. 54(1–2), 3–15 (1999). [CrossRef]

2.

A. K. Sharma, R. Jha, and B. D. Gupta, “Fiber-optic sensor based on surface plasmon resonance: A comprehensive review,” IEEE Sens. J. 7(8), 1118–1129 (2007). [CrossRef]

3.

M. Skorobogatiy, “Microstructured and photonic bandgap fibers for applications in the resonant bio- and chemical sensors,” J. Sens. 2009, 524237 (2009).

4.

J. Homola, H. Vaisocherová, J. Dostálek, and M. Piliarik, “Multi-analyte surface plasmon resonance biosensing,” Methods 37(1), 26–36 (2005). [CrossRef] [PubMed]

5.

X. D. Hoa, A. G. Kirk, and M. Tabrizian, “Towards integrated and sensitive surface plasmon resonance biosensors: a review of recent progress,” Biosens. Bioelectron. 23(2), 151–160 (2007). [CrossRef] [PubMed]

6.

E. K. Akowuah, T. Gorman, S. Haxha, and J. V. Oliver, “Dual channel planar waveguide surface plasmon resonance biosensor for an aqueous environment,” Opt. Express 18(24), 24412–24422 (2010). [CrossRef] [PubMed]

7.

W. Peng, S. Banerji, Y. C. Kim, and K. S. Booksh, “Investigation of dual-channel fiber-optic surface plasmon resonance sensing for biological applications,” Opt. Lett. 30(22), 2988–2990 (2005). [CrossRef] [PubMed]

8.

B. Špačková, M. Piliarik, P. Kvasnička, C. Themistos, M. Rajarajan, and J. Homola, “Novel concept of multi-channel fiber optic surface plasmon resonance sensor,” Sens. Actuators B Chem. 139(1), 199–203 (2009). [CrossRef]

9.

Z. Zhang, P. Zhao, F. Sun, G. Xiao, and Y. Wu, “Self-referencing in optical-fiber surface plasmon resonance sensors,” IEEE Photon. Technol. Lett. 19(24), 1958–1960 (2007). [CrossRef]

10.

A. Hassani and M. Skorobogatiy, “Design criteria for microstructured optical fiber based surface plasmon resonance sensors,” J. Opt. Soc. Am. B 24(6), 1423–1429 (2007). [CrossRef]

11.

B. Gauvreau, A. Hassani, M. Fassi Fehri, A. Kabashin, and M. A. Skorobogatiy, “Photonic bandgap fiber-based Surface Plasmon Resonance sensors,” Opt. Express 15(18), 11413–11426 (2007). [CrossRef] [PubMed]

12.

X. Yu, Y. Zhang, S. Pan, P. Shum, M. Yan, Y. Leviatan, and C. Li, “A selectively coated photonic crystal fiber based surface plasmon resonance sensor,” J. Opt. 12(1), 015005 (2010). [CrossRef]

13.

Y. Zhang, L. Xia, C. Zhou, X. Yu, H. Liu, D. Liu, and Y. Zhang, “Microstructured fiber based plasmonic index sensor with optimized accuracy and calibration relation in large dynamic range,” Opt. Commun. 284(18), 4161–4166 (2011). [CrossRef]

14.

A. Hassani and M. Skorobogatiy, “Photonic crystal fiber-based plasmonic sensors for the detection of biolayer thickness,” J. Opt. Soc. Am. B 26(8), 1550–1557 (2009). [CrossRef]

15.

M. Hautakorpi, M. Mattinen, and H. Ludvigsen, “Surface-plasmon-resonance sensor based on three-hole microstructured optical fiber,” Opt. Express 16(12), 8427–8432 (2008). [CrossRef] [PubMed]

16.

S. V Afshar , Y. Ruan, S. C. Warren-Smith, and T. M. Monro, “Enhanced fluorescence sensing using microstructured optical fibers: a comparison of forward and backward collection modes,” Opt. Lett. 33(13), 1473–1475 (2008). [CrossRef] [PubMed]

17.

S. C. Warren-Smith, S. Afshar V., and T. M. Monro, “Theoretical study of liquid-immersed exposed-core microstructured optical fibers for sensing,” Opt. Express 16(12), 9034–9045 (2008). [CrossRef] [PubMed]

18.

P. J. A. Sazio, A. Amezcua-Correa, C. E. Finlayson, J. R. Hayes, T. J. Scheidemantel, N. F. Baril, B. R. Jackson, D. J. Won, F. Zhang, E. R. Margine, V. Gopalan, V. H. Crespi, and J. V. Badding, “Microstructured optical fibers as high-pressure microfluidic reactors,” Science 311(5767), 1583–1586 (2006). [CrossRef] [PubMed]

19.

X. Yu, S. Zhang, Y. Zhang, H. P. Ho, P. Shum, H. Liu, and D. Liu, “An efficient approach for investigating surface plasmon resonance in asymmetric optical fibers based on birefringence analysis,” Opt. Express 18(17), 17950–17957 (2010). [CrossRef] [PubMed]

20.

B. T. Kuhlmey, B. J. Eggleton, and D. K. C. Wu, “Fluid-filled solid-core photonic bandgap fibers,” J. Lightwave Technol. 27(11), 1617–1630 (2009). [CrossRef]

21.

Y. Yuan, L. Ding, and Z. Guo, “Numerical investigation for SPR-based optical fiber sensor,” Sens. Actuators B Chem. 157(1), 240–245 (2011). [CrossRef]

22.

S. Kim, Y. Jung, K. Oh, J. Kobelke, K. Schuster, and J. Kirchhof, “Defect and lattice structure for air-silica index-guiding holey fibers,” Opt. Lett. 31(2), 164–166 (2006). [CrossRef] [PubMed]

23.

Y. Abe, Y. W. Shi, Y. Matsuura, and M. Miyagi, “Flexible small-bore hollow fibers with an inner polymer coating,” Opt. Lett. 25(3), 150–152 (2000). [CrossRef] [PubMed]

OCIS Codes
(060.2280) Fiber optics and optical communications : Fiber design and fabrication
(060.2370) Fiber optics and optical communications : Fiber optics sensors
(240.6680) Optics at surfaces : Surface plasmons
(060.4005) Fiber optics and optical communications : Microstructured fibers

ToC Category:
Sensors

History
Original Manuscript: July 25, 2011
Revised Manuscript: October 7, 2011
Manuscript Accepted: October 11, 2011
Published: October 27, 2011

Citation
Yating Zhang, Chi Zhou, Li Xia, Xia Yu, and Deming Liu, "Wagon wheel fiber based multichannel plasmonic sensor," Opt. Express 19, 22863-22873 (2011)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-19-23-22863


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References

  1. J. Homola, S. Yee, and G. Gauglitz, “Surface plasmon resonance sensors: review,” Sens. Actuators B Chem.54(1–2), 3–15 (1999). [CrossRef]
  2. A. K. Sharma, R. Jha, and B. D. Gupta, “Fiber-optic sensor based on surface plasmon resonance: A comprehensive review,” IEEE Sens. J.7(8), 1118–1129 (2007). [CrossRef]
  3. M. Skorobogatiy, “Microstructured and photonic bandgap fibers for applications in the resonant bio- and chemical sensors,” J. Sens.2009, 524237 (2009).
  4. J. Homola, H. Vaisocherová, J. Dostálek, and M. Piliarik, “Multi-analyte surface plasmon resonance biosensing,” Methods37(1), 26–36 (2005). [CrossRef] [PubMed]
  5. X. D. Hoa, A. G. Kirk, and M. Tabrizian, “Towards integrated and sensitive surface plasmon resonance biosensors: a review of recent progress,” Biosens. Bioelectron.23(2), 151–160 (2007). [CrossRef] [PubMed]
  6. E. K. Akowuah, T. Gorman, S. Haxha, and J. V. Oliver, “Dual channel planar waveguide surface plasmon resonance biosensor for an aqueous environment,” Opt. Express18(24), 24412–24422 (2010). [CrossRef] [PubMed]
  7. W. Peng, S. Banerji, Y. C. Kim, and K. S. Booksh, “Investigation of dual-channel fiber-optic surface plasmon resonance sensing for biological applications,” Opt. Lett.30(22), 2988–2990 (2005). [CrossRef] [PubMed]
  8. B. Špačková, M. Piliarik, P. Kvasnička, C. Themistos, M. Rajarajan, and J. Homola, “Novel concept of multi-channel fiber optic surface plasmon resonance sensor,” Sens. Actuators B Chem.139(1), 199–203 (2009). [CrossRef]
  9. Z. Zhang, P. Zhao, F. Sun, G. Xiao, and Y. Wu, “Self-referencing in optical-fiber surface plasmon resonance sensors,” IEEE Photon. Technol. Lett.19(24), 1958–1960 (2007). [CrossRef]
  10. A. Hassani and M. Skorobogatiy, “Design criteria for microstructured optical fiber based surface plasmon resonance sensors,” J. Opt. Soc. Am. B24(6), 1423–1429 (2007). [CrossRef]
  11. B. Gauvreau, A. Hassani, M. Fassi Fehri, A. Kabashin, and M. A. Skorobogatiy, “Photonic bandgap fiber-based Surface Plasmon Resonance sensors,” Opt. Express15(18), 11413–11426 (2007). [CrossRef] [PubMed]
  12. X. Yu, Y. Zhang, S. Pan, P. Shum, M. Yan, Y. Leviatan, and C. Li, “A selectively coated photonic crystal fiber based surface plasmon resonance sensor,” J. Opt.12(1), 015005 (2010). [CrossRef]
  13. Y. Zhang, L. Xia, C. Zhou, X. Yu, H. Liu, D. Liu, and Y. Zhang, “Microstructured fiber based plasmonic index sensor with optimized accuracy and calibration relation in large dynamic range,” Opt. Commun.284(18), 4161–4166 (2011). [CrossRef]
  14. A. Hassani and M. Skorobogatiy, “Photonic crystal fiber-based plasmonic sensors for the detection of biolayer thickness,” J. Opt. Soc. Am. B26(8), 1550–1557 (2009). [CrossRef]
  15. M. Hautakorpi, M. Mattinen, and H. Ludvigsen, “Surface-plasmon-resonance sensor based on three-hole microstructured optical fiber,” Opt. Express16(12), 8427–8432 (2008). [CrossRef] [PubMed]
  16. S. V Afshar , Y. Ruan, S. C. Warren-Smith, and T. M. Monro, “Enhanced fluorescence sensing using microstructured optical fibers: a comparison of forward and backward collection modes,” Opt. Lett.33(13), 1473–1475 (2008). [CrossRef] [PubMed]
  17. S. C. Warren-Smith, S. Afshar V., and T. M. Monro, “Theoretical study of liquid-immersed exposed-core microstructured optical fibers for sensing,” Opt. Express16(12), 9034–9045 (2008). [CrossRef] [PubMed]
  18. P. J. A. Sazio, A. Amezcua-Correa, C. E. Finlayson, J. R. Hayes, T. J. Scheidemantel, N. F. Baril, B. R. Jackson, D. J. Won, F. Zhang, E. R. Margine, V. Gopalan, V. H. Crespi, and J. V. Badding, “Microstructured optical fibers as high-pressure microfluidic reactors,” Science311(5767), 1583–1586 (2006). [CrossRef] [PubMed]
  19. X. Yu, S. Zhang, Y. Zhang, H. P. Ho, P. Shum, H. Liu, and D. Liu, “An efficient approach for investigating surface plasmon resonance in asymmetric optical fibers based on birefringence analysis,” Opt. Express18(17), 17950–17957 (2010). [CrossRef] [PubMed]
  20. B. T. Kuhlmey, B. J. Eggleton, and D. K. C. Wu, “Fluid-filled solid-core photonic bandgap fibers,” J. Lightwave Technol.27(11), 1617–1630 (2009). [CrossRef]
  21. Y. Yuan, L. Ding, and Z. Guo, “Numerical investigation for SPR-based optical fiber sensor,” Sens. Actuators B Chem.157(1), 240–245 (2011). [CrossRef]
  22. S. Kim, Y. Jung, K. Oh, J. Kobelke, K. Schuster, and J. Kirchhof, “Defect and lattice structure for air-silica index-guiding holey fibers,” Opt. Lett.31(2), 164–166 (2006). [CrossRef] [PubMed]
  23. Y. Abe, Y. W. Shi, Y. Matsuura, and M. Miyagi, “Flexible small-bore hollow fibers with an inner polymer coating,” Opt. Lett.25(3), 150–152 (2000). [CrossRef] [PubMed]

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