Abstract

We propose and develop an intensity-detection-based refractive-index (RI) sensor for low-cost, rapid RI sensing. The sensor is composed of a polymer bent ridge waveguide (BRWG) structure on a low-cost glass substrate and is integrated with a microfluidic channel. Different-RI solutions flowing through the BRWG sensing region induce output optical power variations caused by optical bend losses, enabling simple and real-time RI detection. Additionally, the sensors are fabricated using rapid and cost-effective vacuum-less processes, attaining the low cost and high throughput required for mass production. A good RI solution of 5.31 10−4 × RIU−1 is achieved from the RI experiments. This study demonstrates mass-producible and compact RI sensors for rapid and sensitive chemical analysis and biomedical sensing.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Optofluidic refractometry-based sensors have received continuously growing interest for flow analysis because of their unique advantages including simple structure, high sensitivity, and label-free detection [1–5 ]. Optofluidic refractometry-based sensors can measure tiny changes in refractive index (RI) on the surface of the sensing region, thereby facilitating the monitoring of molecular interactions and chemical reactions for a wide range of applications including biomedical detection, chemical analysis, and food safety. Over the past few decades, there have been considerable efforts to develop different types of optofluidic RI sensors, including prism sensors [6–8 ], Mach-Zender interferometers sensors [9–11 ], surface plasmon resonance (SPR) sensors [12–15 ], fiber optic nanoplasmonic sensors [16–19 ], waveguide (WG) sensors [20–22 ], photonic crystal (PhC) sensors [23–26 ], and guided-mode-resonance (GMR) sensors [27–33 ]. While high-performance RI detection has been demonstrated in these technologies, there are unfortunately several limitations that should be overcome before commercializing RI sensors for practical applications. First, the fabrication process of the RI sensors usually involves complicated and time-consuming fabrication steps such as E-beam lithography, vacuum thin-film deposition, and dry etching processes, leading to a long production time. Ideally, simple and high-throughput fabrication methods should be adapted for successful mass-production. Second, the optical signal readout systems usually require precise components and instruments, such as high-resolution spectrometers, high-stability lasers, and high-precision rotation and/or translation stages, making the entire detection system bulky and costly. A simple, compact, and cost-effective detection system is more favorable to develop portable detection systems for on-site applications. Third, the detection mechanisms are usually based on wavelength-resolved and angular-resolved approaches, which require a high-cost and bulky setup or complicated post-processes to obtain the RI of the solution, thus lacking the capacity for compact or real-time RI detection systems, respectively. Therefore, strong demands exist for developing simple RI sensors and compact detection systems that meet the above requirements for practical applications.

Optical bend loss is a well-known phenomenon in optical WGs [34, 35]. As light propagates from a straight WG segment to a bent WG segment, the radiation loss from the cladding layer can result in significant optical loss, leading to attenuations in the output light intensity. The magnitude of bend loss is strongly dependent on the field distribution of the guided modes supported in the WG, which is a function of the RIs of the core and cladding materials. In this study, we propose and develop a WG-type optofluidic RI sensor for rapid and low-cost RI sensing. The proposed WG-type sensor consists of a polymer bent ridge waveguide (BRWG) structure as the sensing region and is integrated with a microfluidic module. As the sample solutions are injected into the sensor, the variations in RI on the surface of the BRWG sensing region modifies the optical bend loss, leading to changes in the output light intensity of the WG. By monitoring the variations in output light intensity, sensitive and real-time RI detection can be achieved. In addition, the polymer WG RI sensors are fabricated by simple vacuum-less processes using spin-coating and optical lithography, providing the unique advantages of high throughput and low cost that are necessary for mass-production. The detection system employs a low-cost, high-stability light-emitting diode (LED) as the light source and a photodetector (PD) as the optical receiver to precisely record the output light power, thereby enabling simple and real-time RI detection. We show the real-time characterization of the sensing system, and an RI solution of 5.31 × 10−4 RIU−1 is experimentally achieved. Numerical simulations are performed to investigate the geometry effects of the BRWG structures on the sensing performance of the sensors. These results demonstrate a new mass-producible and rapid RI sensor for a wide range of applications in chemical analysis and label-free biomedical detection.

2. Sensor design and sensing Principle

Figure 1 shows a schematic of our designed RI sensor based on BRWG structures. The sensor employs low-cost glass as the low-RI substrate. High-RI UV-sensitive SU-8 polymer with a thickness of t is adopted as the WG material. The SU-8 WG consists of a (1) linearly tapered WG structure, (2) BRWG structure with a bending angle of θ, and (3) output straight WG. The linearly tapered structure is designed to have a large input width of win for creating a larger input cross-section to match the beam size of the input light from the far field so the coupling loss between the WG and input light can be reduced. In addition, the width of the tapered WG varies linearly from win to w with a tapering angle of < 5° to ensure a negligible coupling loss in the tapered WG [36]. The sensor is also integrated with a polymer microfluidic module, in which the fluidic channel is centered in the BRWG sensing region to allow for the interaction of the light and the flow. In addition, the use of a microfluidic module also considerably improves the stability and measuring accuracy of the RI sensor, thereby enhancing the sensor performance. As the coupled light with an intensity of Iin propagates through the BRWG sensing region, a significant bend loss can occur as a result of partial light power leaking to the cladding layer because of the geometry variation. On the other hand, the rest of optical power is coupled to the output straight WG (Iout). The magnitude of the optical bend loss is very sensitive to the RI variation in the cladding layer of the WG, i.e., the injected solution. Therefore, injecting solutions with different RIs into the sensor will modify the optical bend loss, thereby resulting in variations in the output light intensity. As a result, a change in the RI of the solution can be transferred to a corresponding variation of output light intensity. By monitoring the intensity of the output light, the RI of the solution can be accurately determined.

 figure: Fig. 1

Fig. 1 Schematics of the proposed optofluidic waveguide refractive-index sensor, consisting of a tapered waveguide structure, bent waveguide sensing region, an output straight waveguide section, and microfluidic module.

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3. Fabrication of waveguide refractive-index sensors

The optofluidic WG RI sensors were fabricated by vacuum-less fabrication processes, as shown in Fig. 2. Low-cost glass was used as the substrate for the RI sensors. After the cleaning process of the glass substrates, UV-light-sensitive SU-8 polymer (Microchem Co., USA) was spin-coated on the glass substrates. After soft-baking for thermal curing, the SU-8 polymer layer was irradiated through a hard mask with UV light to define the WG patterns. With the hard-baking process, the exposed SU-8 polymer WG was cross-linked and solidified. Subsequently, the ridge WG structures were created by removing the unexposed SU-8 polymer region with the development process. To handle the fluidic sample solutions, cyclic olefin copolymer (COC) microfluidic modules with a fluidic channel of 32 mm (length) × 3 mm (width) ×0.2 mm (height) were prepared by injection-molded techniques [29] and were then connected by two flexible tubes. The sensor was then completed by binding the microfluidic module to the fabricated SU-8 WG chip. The fabrication processes of the sensors do not involve time-consuming vacuum processes, indicating significantly reduced production time. In addition, the estimated cost of the waveguide RI sensors is less than one USD per chip, which is much lower than these of optofluidic RI sensors (typically tens of USD or higher). These unique advantages make the waveguide RI sensors highly suitable for high-throughput mass-production.

 figure: Fig. 2

Fig. 2 Fabrication processes of the optofluidic waveguide refractive-index sensors integrated with microfluidic module.

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4. Characterization of waveguide refractive-index sensors

Figure 3 presents the characterization results of the fabricated WG sensor with a bending angle of θ = 10°. Figure 3(a) shows the topography of the fabricated SU-8 WG structure on glass measured by white light interferometry (WLI). (To carry out the WLI experiments, a 30-nm-thick Au film was sputtered on the tested SU-8 WG.) A clear ridge structure with good side-wall quality is observed. Figure 3(b) presents the spatial profile of the ridge WG structure along the X-direction. The results show a very smooth surface of the fabricated SU-8 ridge WG. In addition, the side walls of the SU-8 ridge WG are almost perfectly vertical, showing the good structural quality of the fabricated ridge WG. The width and height of the straight WG were measured to be w = 500 µm and t = 24 µm, respectively. Figure 3(c) shows a top-view optical image of the straight SU-8 WG on the glass coupled with green light. Clearly, the coupled light propagates and is well-confined in the SU-8 ridge WG, exhibiting clear waveguiding behavior in the polymer ridge WG. Figure 3(d) displays an optical image of the fabricated optofluidic WG RI sensors. These results demonstrate the good quality of the fabricated optofluidic WG RI sensors using high-throughput and vacuum-less fabrication processes.

 figure: Fig. 3

Fig. 3 Characterization of the polymer waveguide refractive-index sensors. (a) Measured topography of the SU8 straight waveguide structure, exhibiting a sharp and clear ridge structure. (b) Surface morphology of the waveguide along the X-direction, showing a flat surface of the SU-8 ridge waveguide. (c) Optical image of the SU-8 straight waveguide coupled with green light source, showing clear waveguiding behavior. (d) Optical image of the fabricated sensor chip.

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Figure 4 illustrates the optical detection system for the fabricated WG RI sensors. A commercially available, low-cost, 532-nm green LED was used as the light source. Compared to lasers, LEDs feature superior power stability, which is crucial for intensity-detection-based RI sensing systems to achieve better RI resolutions [31]. To further enhance the signal-to-noise ratio, a lock-in technique was employed by driving and directly modulating the LED using a 1-kHz square wave with a 50% duty cycle by a homemade LED driver. The emitted light was collimated using a lens and then coupled to the facet of the tapered WG of the sensor chip via a 20× objective. The WG sensor chip was mounted on a three-axis translation stage to finely adjust the position of the chip to couple the incident light into the WG with minimal coupling losses. The transmitted light through the WG chip was filtered by an adjustable iris, focused by a lens, and then directed to a Si PD to convert the output light intensity into photocurrent. Subsequently, the generated photocurrent was amplified by a homemade current amplifier with a band-pass filter, followed by an analog-to-digital converter to transform the analog signals to digital signals. Finally, the digital signals were recorded in real-time by a computer and demodulated using a lock-in program. Without bulky and costly components, this detection system provides the unique advantages of compactness and low cost required for practical applications.

 figure: Fig. 4

Fig. 4 Schematic of the transmission measurement system for the fabricated optofluidic waveguide refractive-index sensors.

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To characterize the RI sensing performance of the developed sensing system, RI experiments were performed using deionized (DI) water and sucrose solutions with different concentrations that have RIs in the range of n = 1.333–1.373, which is of particular interest for biological assays. DI water was first injected into the sensor chip as the blank solution. After the DI water fully filled the BRWG sensing region and the output light intensity reached a steady-state, sucrose solutions with different RIs were then successively injected into the sensor and the process was finished by a final injection of DI water. The intensity of the transmitted light (I) was synchronously recorded using the data acquisition system described above. Figure 5(a) shows the normalized real-time optical responses of the RI sensors obtained from the RI experiments, where the blank signal (I0) is obtained by averaging the output light intensity measured from the DI water, as shown in the first part of the real-time data labeled as n = 1.333. In addition, the noise of the system (σ) is determined from the standard deviation of the output light intensity measured from the blank solution. The results indicate a good power stability of σ = 0.021%, which is attributed to the excellent power stability and signal acquisition and processing system used in this study, thereby outperforming the value of σ ≈ 0.1% in the optofluidic RI sensing systems that use lasers as the light source [29, 32]. As the RI of the sample solution increases, we observe a clear decrease in the output light intensity due to the increasing optical loss in the BRWG sensing region. From the real-time optical responses, the average output light intensities at each RI resolution (Iave) are extracted, and the normalized average output light intensity (Iave/I0) as a function of the solution’s RI is presented in Fig. 5(b). From the results, the normalized sensitivity (Sn) and sensor RI resolution (Rs), which represents the minimal detectable change in the RI of the solution, can be evaluated as [29, 31]

Sn=ddn[Iavg(n)I0]
Rs=|σSn|

 figure: Fig. 5

Fig. 5 (a) Real-time responses of the sensing system for solutions with different refractive indices (RIs). The inset shows the emission spectrum of the LED light source. (b) Calibration curves of the normalized average intensities and the RI of the sample solutions. The mean values and error bars (represents the standard deviation) are obtained from six experiments using three different RI sensor chips.

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Fitting the experimental results by a linear function gives the normalized sensitivity of the RI sensor Sn = 0.392 ± 0.02 RIU−1. In addition, a linear correction coefficient of R2= 0.98955 is obtained, demonstrating the excellent linearity of this sensor over a wide dynamic RI range of 0.04 RIU. Together with the noise level of the system, the RI solution of this sensing system is determined to be Rs = 5.31 × 10−4 RIU−1. These results demonstrate the feasibility of low-cost and rapid RI sensing using the proposed WG sensors. The RI resolution and the linear range achieved in this work can already be applied in chemical analysis and biomedical detection. In addition, we note that the RI resolution of this sensor is of the same order of magnitude or comparable to existing optofluidic RI sensing systems, such as prism coupler detection systems (Rs = 3 × 10−5 RIU−1) [7], PhC-based biosensing systems (Rs = 1 10−5 × RIU−1) [24], interferometers (Rs = 1.2 × 10−4 RIU−1) [10], and intensity-detection-based GMR systems (Rs = 5.4 × 10−5 RIU−1) [32]. Further improvements in the RI resolution of the proposed sensor are possible by optimizing the BRWG structure, which will be discussed later. In addition, compared to these existing optofluidic RI sensing systems, our WG RI sensors do not involve any time-consuming vacuum fabrication processes, offering unique advantages for high-throughput industrial production for disposable bio-medical and other RI sensing applications.

5. Numerical simulations and discussion

To gain deeper understanding of the sensing capacity of the proposed RI sensor, 3D finite-element-method (FEM) analysis were performed for the BRWG structure. Because the sensitivity of the WG RI sensor are highly dependent on the WG geometry, here the transmittance for the BRWG structure was calculated as a function of the WG width w and the bending angle θ to study their effects on the sensitivity. The thickness of the SU-8 WG is t = 24 µm. The RIs of the materials at λ = 532 nm are n g = 1.45 for the low-RI glass substrate and n w = 1.57 for the high-RI SU-8 polymer WG. The RI of the solution was varied from 1.333 to 1.373. For excitation, a TE-polarized plane wave, in which the electric field vector is along the X-direction, with a wavelength of λ = 532 nm was used as the light source to launch light into the WG. The simulation domain was terminated with perfectly matched layers (PMLs) to completely absorb the outward waves, and the field distribution was then calculated. Power monitor layers located at the input WG and the output straight WG were employed to record the input light intensity (Iin) and light intensity transmitted through the BRWG structure (Iout), respectively. The transmittance (T ) of the BRWG structure was then determined as

T(n)=Iout(n)Iin

Figure 6(a) shows an example of the power distribution for the fundamental mode of the SU-8 WG exposed to DI water (n = 1.333), where the WG width and thickness are w = 10 µm and t = 24 µm, respectively. It can be clearly seen that the light is well-confined in the SU-8 WG region because of the large RI difference between the SU-8 WG and glass substrate as well as the solution. Figure 6(b) shows the normalized electric field distribution for the BRWG structure exposed to DI water (n = 1.333) with different bending angles for the case of w = 1 µm. As the guided light propagates through the bent WG section, the electric field leaks out to the cladding layer (the solution) due to the WG geometry variation, yielding optical bend losses. As the bending angle θ is increased, the light leakage from the WG becomes more significantly, leading to higher optical bend losses and thus larger attenuations in the output light intensity.

 figure: Fig. 6

Fig. 6 (a) Simulated intensity distribution of the fundamental mode for the waveguide structure with w = 10 µm and t = 24 µm, clearly showing optical confinement of light. (b) Simulated normalized field distribution of the bent ridge waveguide structures for different bending angles. The waveguide width and thickness are w = 1 µm and t = 24 µm, respectively.

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Figure 7(a) shows an example of the calculated transmittances as a function of the solution’s RI for the BRWG structure with w = 1 µm for different bending angles. For each bending angle, the transmittance decreases with increasing RI of the solution. This observation is attributed to the increased bend loss caused by the reduced RI difference between the SU-8 WG and cladding layer (the solution). The simulation results confirm that the variations in the RI of the solution can be translated into changes in the output light intensity, therefore enabling rapid intensity-detection-based RI sensing. In addition, as the bending angle increases, the transmittance also decreases because of the increased optical bend loss, as shown in Fig. 6(b). From the calculated transmittance data, we calculate the normalized transmittances T/T0, where T0 refers to the transmittance for the case of DI water (n = 1.333); the results are depicted in Fig. 7(b). For each bending angle, the normalized transmittance decreases with increasing RI of the solution. In addition, the slope becomes larger with increasing bending angle. From the normalized transmittance data, the normalized sensitivity can be calculated as

Sn=ddn[T(n)T0]

 figure: Fig. 7

Fig. 7 (a) Calculated transmittances and (b) normalized transmittances as a function of refractive index of the solution for the bent ridge waveguide structure with different bending angles. (c) Calculated normalized sensitivity as a function of waveguide width and bending angle. The normalized sensitivity is more pronounced for narrow waveguide width and larger bending angle.

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Figure 7(c) shows the calculated normalized sensitivity as a function of the WG width w and the bending angle θ. For a bending angle of θ = 10°, a normalized sensitivity of Sn = 0.03 RIU−1 is obtained for w = 10 µm. As the WG width decreases, the normalized sensitivity increases quickly to Sn = 0.394 RIU−1 for w = 1 µm, corresponding to a 13.1 × enhancement. This enhancement is attributed to the increased evanescent wave in the cladding layer that can increase the interaction between the solution and the electromagnetic field, especially when the WG width approaches the wavelength of the light source. On the other hand, it is also observed that increasing the bending angle can also enhance the normalized sensitivity because the lower transmittance caused by the increased optical bend loss, as shown in Fig. 7(a). These analysis indicates that the enhancement of normalized sensitivity is more pronounced in narrower WG widths and larger bending angles. A remarkable sensitivity of Sn = 4.081 RIU−1 is achievable for w = 1 µm and θ = 25°. However, increasing the bending angle also leads to reduced transmittances, as shown in Fig. 7(a), thereby weakening the output light intensity. This may lead to insufficient output light intensity and degrade the signal-to-noise ratio (noise of the system), thereby limiting the RI sensing performance. Therefore there may be a tread-off between the normalized sensitivity and the noise of the system to achieve highest RI sensing performance for the proposed optofluidic WG RI sensor. We believe that the RI sensing resolution can be further improved by optimizing the BRWG structures to enable low-cost, sensitive, and rapid chemical analysis and label-free biomedical detection.

Having demonstrated a good RI resolution over a wide dynamic range of 0.04 RIU, we finally discuss key parameters affecting the sensing performance of the developed WG RI sensors. A key factor for the precision of the WG RI sensors is their reproducibility. Because the WG RI sensors are fabricated using simple and mass-producible spin-coating and optical lithography techniques, high reproducibility can be achieved. The RI experiments indicated that the relative standard deviation of RI determination was only 0.21%, confirming the precision and reproducibility of the WG RI sensors. For the accuracy of the WG RI sensors, the linear dynamic range is a dominant factor. In RI experiments, the WG RI sensors exhibited a wide linear dynamic range of 0.04 RIU with an excellent linear correction coefficient of R2 = 0.98955, indicating their good accuracy. Another crucial parameter is the diagnosis speed. For RI sensing, because the developed WG RI sensors based on intensity-detection can achieve real-time continuous monitoring without complex postprocessing, the RI of the solution can be determined rapidly. For bio-medical sensing, the concentration of a specific analyte can also be determined rapidly after biomolecules are bound on the WG surface and bimolecular interactions reach a steady state. Therefore, the total diagnosis time for our WG RI sensors is dominated by the response time of bimolecular interactions, which is typically 10–20 min. Therefore, our WG RI sensors are expected to achieve rapid bio-medical detection for critical applications. Lastly, another vital difference between the existing optofluidic RI sensing systems and our WG RI sensing system is the light source. In many existing optofluidic RI sensing systems, monochromatic lasers of a specific wavelength are used as the light source. Therefore, the RI of a solution at this specific wavelength can be measured. However, in our sensing system, we employed an LED as the light source which has a relatively broader emission spectrum. As shown in the inset of Fig. 5(a), the LED had a spectral emission range approximately from 510 to 570 nm. As a result, the measured RI is actually the weighed value of the RI for the sample solution in the LED’s spectral emission range, and the dispersion effects of the sample solution may be crucial. To evaluate dispersion effects in our WG RI sensing system, we calculate the wavelength-dependent RI of the DI water at a temperature of 293 K, which can be described by [37]

n(λ)=1.3199+6878λ21.132×109λ4+1.11×1014λ6
where λ is the wavelength of light in units of nm. Using Eq. (5), we determine that the RI of the DI water decreases from 1.33592 at 510 nm to 1.33358 at 570 nm, which is a 1.75% decrease. Therefore, dispersion effects did not significantly affect our RI sensing results. However, if a sample solution or analyte displays strong dispersion effects in the spectral range of the LED light source, a more careful calibration should be performed to improve the accuracy of RI determination using this WG RI sensing system.

6. Conclusion

In this study, we demonstrate an intensity-detection-based polymer RI sensor. The sensing area consists of a bent ridge structure that allows the changes in RI of the solution injected onto its surface to be converted into variations in output light intensity. In addition, the sensors are fabricated using simple, rapid, and vacuum-less processes, offering the unique advantages of high throughput and low cost required for mass production. The detection system employs a low-cost, high-stability LED as the light source and a PD as the optical receiver, making the system cost-effective and compact, suitable for portable detection systems. The RI experiments demonstrate that a good RI resolution of Rs = 5.31 × 10−4 RIU−1 over a wide range of 0.04 RIU can be achieved. Numerical simulation results suggest that the sensitivity of the sensor can be considerably enhanced by optimizing the waveguide width and the bending angle for high-performance RI sensing. With the mass-producible sensor chips, compact detection system, and good RI resolution, we believe this RI detection system is promising for low-cost, rapid, and on-site chemical analysis and biomedical detection.

Funding

Ministry of Science and Technology of Taiwan (MOST) (MOST 104-2221-E-194-037-MY2, MOST 105-2113-M-194-009-MY3, and MOST 106-3114-E-194-001).

Acknowledgments

The authors acknowledge Prof. Wen-Hsin Hsieh at CCU for many insightful discussions. The authors also thank Chu-Tung Yeh and Jian Zhi Huang at CCU for assistance in the experiments.

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31. Y.-C. Lin, W.-H. Hsieh, L.-K. Chau, and G.-E. Chang, “Intensity-detection-based guided-mode-resonance optofluidic biosensing system for rapid, low-cost, label-free detection,” Sens. Actuators B: Chem. 250, 659–666 (2017). [CrossRef]  

32. S.-F. Lin, F.-C. Chang, Z.-H. Chen, C.-M. Wang, T.-H. Yang, W.-Y. Chen, and J.-Y. Chang, “A polarization control system for intensity-resolved guided mode resonance sensors,” Sensors 14, 5198–5206 (2014). [CrossRef]   [PubMed]  

33. P. G. Hermannsson, K. T. Sørensen, C. Vannahme, C. L. Smith, J. J. Klein, M.-M. Russew, G. Grützner, and A. Kristensen, “All-polymer photonic crystal slab sensor,” Opt. Express 23, 16529–16539 (2015). [CrossRef]   [PubMed]  

34. S. L. Chuang, Physics of Photonic Devices (Wiley, 2009), 2nd ed.

35. G. T. Reed and A. P. Knights, Silicon Photonics: An Introduction (Wiley, 2004). [CrossRef]  

36. Y. Fu, T. Ye, W. Tang, and T. Chu, “Efficient adiabatic silicon-on-insulator waveguide taper,” Photon. Res. 2, A41–A44 (2014). [CrossRef]  

37. A. N. Bashkatov and E. A. Genina, “Water Refractive Index in Dependence on Temperature and Wavelength: a Simple Approximation,” Proc. SPIE 5068, 393–395. (2003). [CrossRef]  

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2017 (1)

Y.-C. Lin, W.-H. Hsieh, L.-K. Chau, and G.-E. Chang, “Intensity-detection-based guided-mode-resonance optofluidic biosensing system for rapid, low-cost, label-free detection,” Sens. Actuators B: Chem. 250, 659–666 (2017).
[Crossref]

2016 (5)

C.-W. Wu, C.-Y. Chiang, C.-H. Chen, C.-S. Chiang, C.-T. Wang, and L.-K. Chau, “Self-referencing fiber optic particle plasmon resonance sensing system for real-time biological monitoring,” Talanta 146, 291–298 (2016).
[Crossref]

G. Liang, Z. Luo, K. Liu, Y. Wang, J. Dai, and Y. Duan, “Fiber optic surface plasmon resonance based biosensor technique: Fabrication, advancement, and application,” Crit. Rev. Anal. Chem. 46, 213–223 (2016).
[Crossref] [PubMed]

M. Puiu and C. Bala, “SPR and SPR imaging: Recent trends in developing nanodevices for detection and real-time monitoring of biomolecular events,” Sensors 16, 870 (2016).
[Crossref]

V. Toccafondo and C. J. Oton, “Robust and low-cost interrogation technique for integrated photonic biochemical sensors based on mach-zehnder interferometers,” Photon. Res. 4, 57–60 (2016).
[Crossref]

P. Singh, “SPR biosensors: Historical perspectives and current challenges,” Sens. Actuators B: Chem. 229, 110–130 (2016).
[Crossref]

2015 (4)

H. H. Nguyen, J. Park, S. Kang, and M. Kim, “Surface plasmon resonance: A versatile technique for biosensor applications,” Sensors 15, 10481–10510 (2015).
[Crossref] [PubMed]

P. G. Hermannsson, K. T. Sørensen, C. Vannahme, C. L. Smith, J. J. Klein, M.-M. Russew, G. Grützner, and A. Kristensen, “All-polymer photonic crystal slab sensor,” Opt. Express 23, 16529–16539 (2015).
[Crossref] [PubMed]

H.-Y. Li, W.-C. Hsu, K.-C. Liu, Y.-L. Chen, L.-K. Chau, S. Hsieh, and W.-H. Hsieh, “A low cost, label-free biosensor based on a novel double-sided grating waveguide coupler with sub-surface cavities,” Sens. Actuators B: Chem. 206, 371–380 (2015).
[Crossref]

Y.-F. Ku, H.-Y. Li, W.-H. Hsieh, L.-K. Chau, and G.-E. Chang, “Enhanced sensitivity in injection-molded guided-mode-resonance sensors via low-index cavity layers,” Opt. Express 23, 14850–14859 (2015).
[Crossref] [PubMed]

2014 (2)

Y. Fu, T. Ye, W. Tang, and T. Chu, “Efficient adiabatic silicon-on-insulator waveguide taper,” Photon. Res. 2, A41–A44 (2014).
[Crossref]

S.-F. Lin, F.-C. Chang, Z.-H. Chen, C.-M. Wang, T.-H. Yang, W.-Y. Chen, and J.-Y. Chang, “A polarization control system for intensity-resolved guided mode resonance sensors,” Sensors 14, 5198–5206 (2014).
[Crossref] [PubMed]

2013 (1)

2012 (2)

S. Surdo, S. Merlo, F. Carpignano, L. M. Strambini, C. Trono, A. Giannetti, F. Baldini, and G. Barillaro, “Optofluidic microsystems with integrated vertical one-dimensional photonic crystals for chemical analysis,” Lab Chip 12, 4403–4415 (2012).
[Crossref] [PubMed]

S. Dante, D. Duval, B. Sepúlveda, A. B. González-Guerrero, J. R. Sendra, and L. M. Lechuga, “All-optical phase modulation for integrated interferometric biosensors,” Opt. Express 20, 7195–7205 (2012).
[Crossref] [PubMed]

2011 (5)

M. I. Lapsley, I.-K. Chiang, Y. B. Zheng, X. Ding, X. Mao, and T. J. Huang, “A single-layer, planar, optofluidic mach-zehnder interferometer for label-free detection,” Lab Chip 11, 1795–1800 (2011).
[Crossref] [PubMed]

L. Malic, M. G. Sandros, and M. Tabrizian, “Designed biointerface using near-infrared quantum dots for ultrasensitive surface plasmon resonance imaging biosensors,” Anal. Chem. 83, 5222–5229 (2011).
[Crossref] [PubMed]

W.-T. Hsu, W.-H. Hsieh, S.-F. Cheng, C.-P. Jen, C.-C. Wu, C.-H. Li, C.-Y. Lee, W.-Y. Li, L.-K. Chau, C.-Y. Chiang, and S.-R. Lyu, “Integration of fiber optic-particle plasmon resonance biosensor with microfluidic chip,” Anal. Chim. Acta 697, 75–82 (2011).
[Crossref] [PubMed]

H. Schmidt and A. R. Hawkins, “The photonic integration of non-solid media using optofluidics,” Nat. Photon. 5, 598–604 (2011).
[Crossref]

X. Fan and I. M. White, “Optofluidic microsystems for chemical and biological analysis,” Nat. Photon. 5, 591–597 (2011).
[Crossref]

2010 (3)

2009 (1)

A. D. Falco, L. O’Faolain, and T. F. Krauss, “Chemical sensing in slotted photonic crystal heterostructure cavities,” Appl. Phys. Lett. 94, 063503 (2009).
[Crossref]

2008 (2)

D. Erickson, S. Mandal, A. H. J. Yang, and B. Cordovez, “Nanobiosensors: optofluidic, electrical and mechanical approaches to biomolecular detection at the nanoscale,” Microfluid. Nanofluid. 4, 33–52 (2008).
[Crossref] [PubMed]

W. Zhang, N. Ganesh, I. D. Block, and B. T. Cunningham, “High sensitivity photonic crystal biosensor incorporating nanorod structures for enhanced surface area,” Sens. Actuators B: Chem. 131, 279–284 (2008).
[Crossref]

2007 (1)

2006 (3)

L.-K. Chau, Y.-F. Lin, S.-F. Cheng, and T.-J. Lin, “Fiber-optic chemical and biochemical probes based on localized surface plasmon resonance,” Sens. Actuators B: Chem. 113, 100–105 (2006).
[Crossref]

D. Psaltis, S. R. Quake, and C. Yang, “Developing optofluidic technology through the fusion of microfluidics and optics,” Nature 442, 381–386 (2006).
[Crossref] [PubMed]

P. Domachuk, I. C. M. Littler, M. Cronin-Golomb, and B. J. Eggleton, “Compact resonant integrated microfluidic refractometer,” Appl. Phys. Lett. 88, 093513 (2006).
[Crossref]

2004 (1)

C. S. Burke, L. Polerecky, and B. D. MacCraith, “Design and fabrication of enhanced polymer waveguide platforms for absorption-based optical chemical sensors,” Meas. Sci. Technol. 15, 1140 (2004).
[Crossref]

2003 (1)

A. N. Bashkatov and E. A. Genina, “Water Refractive Index in Dependence on Temperature and Wavelength: a Simple Approximation,” Proc. SPIE 5068, 393–395. (2003).
[Crossref]

2002 (2)

J. Voros, J. Ramsden, G. Csucs, I. Szendro, S. D. Paul, M. Textor, and N. Spencer, “Optical grating coupler biosensors,” Biomaterials 23, 3699–3710 (2002).
[Crossref] [PubMed]

Z.-M. Qi, N. Matsuda, J. H. Santos, A. Takatsu, and K. Kato, “Prism-coupled multimode waveguide refractometer,” Opt. Lett. 27, 689–691 (2002).
[Crossref]

2000 (1)

Agarwal, A.

Aitchison, J. S.

Bahrami, F.

Bala, C.

M. Puiu and C. Bala, “SPR and SPR imaging: Recent trends in developing nanodevices for detection and real-time monitoring of biomolecular events,” Sensors 16, 870 (2016).
[Crossref]

Baldini, F.

S. Surdo, S. Merlo, F. Carpignano, L. M. Strambini, C. Trono, A. Giannetti, F. Baldini, and G. Barillaro, “Optofluidic microsystems with integrated vertical one-dimensional photonic crystals for chemical analysis,” Lab Chip 12, 4403–4415 (2012).
[Crossref] [PubMed]

Barillaro, G.

S. Surdo, S. Merlo, F. Carpignano, L. M. Strambini, C. Trono, A. Giannetti, F. Baldini, and G. Barillaro, “Optofluidic microsystems with integrated vertical one-dimensional photonic crystals for chemical analysis,” Lab Chip 12, 4403–4415 (2012).
[Crossref] [PubMed]

Bashkatov, A. N.

A. N. Bashkatov and E. A. Genina, “Water Refractive Index in Dependence on Temperature and Wavelength: a Simple Approximation,” Proc. SPIE 5068, 393–395. (2003).
[Crossref]

Block, I. D.

W. Zhang, N. Ganesh, I. D. Block, and B. T. Cunningham, “High sensitivity photonic crystal biosensor incorporating nanorod structures for enhanced surface area,” Sens. Actuators B: Chem. 131, 279–284 (2008).
[Crossref]

Bog, U.

Burke, C. S.

C. S. Burke, L. Polerecky, and B. D. MacCraith, “Design and fabrication of enhanced polymer waveguide platforms for absorption-based optical chemical sensors,” Meas. Sci. Technol. 15, 1140 (2004).
[Crossref]

Carlie, N.

Carpignano, F.

S. Surdo, S. Merlo, F. Carpignano, L. M. Strambini, C. Trono, A. Giannetti, F. Baldini, and G. Barillaro, “Optofluidic microsystems with integrated vertical one-dimensional photonic crystals for chemical analysis,” Lab Chip 12, 4403–4415 (2012).
[Crossref] [PubMed]

Chang, F.-C.

S.-F. Lin, F.-C. Chang, Z.-H. Chen, C.-M. Wang, T.-H. Yang, W.-Y. Chen, and J.-Y. Chang, “A polarization control system for intensity-resolved guided mode resonance sensors,” Sensors 14, 5198–5206 (2014).
[Crossref] [PubMed]

Chang, G.-E.

Y.-C. Lin, W.-H. Hsieh, L.-K. Chau, and G.-E. Chang, “Intensity-detection-based guided-mode-resonance optofluidic biosensing system for rapid, low-cost, label-free detection,” Sens. Actuators B: Chem. 250, 659–666 (2017).
[Crossref]

Y.-F. Ku, H.-Y. Li, W.-H. Hsieh, L.-K. Chau, and G.-E. Chang, “Enhanced sensitivity in injection-molded guided-mode-resonance sensors via low-index cavity layers,” Opt. Express 23, 14850–14859 (2015).
[Crossref] [PubMed]

Chang, J.-Y.

S.-F. Lin, F.-C. Chang, Z.-H. Chen, C.-M. Wang, T.-H. Yang, W.-Y. Chen, and J.-Y. Chang, “A polarization control system for intensity-resolved guided mode resonance sensors,” Sensors 14, 5198–5206 (2014).
[Crossref] [PubMed]

Chau, L.-K.

Y.-C. Lin, W.-H. Hsieh, L.-K. Chau, and G.-E. Chang, “Intensity-detection-based guided-mode-resonance optofluidic biosensing system for rapid, low-cost, label-free detection,” Sens. Actuators B: Chem. 250, 659–666 (2017).
[Crossref]

C.-W. Wu, C.-Y. Chiang, C.-H. Chen, C.-S. Chiang, C.-T. Wang, and L.-K. Chau, “Self-referencing fiber optic particle plasmon resonance sensing system for real-time biological monitoring,” Talanta 146, 291–298 (2016).
[Crossref]

Y.-F. Ku, H.-Y. Li, W.-H. Hsieh, L.-K. Chau, and G.-E. Chang, “Enhanced sensitivity in injection-molded guided-mode-resonance sensors via low-index cavity layers,” Opt. Express 23, 14850–14859 (2015).
[Crossref] [PubMed]

H.-Y. Li, W.-C. Hsu, K.-C. Liu, Y.-L. Chen, L.-K. Chau, S. Hsieh, and W.-H. Hsieh, “A low cost, label-free biosensor based on a novel double-sided grating waveguide coupler with sub-surface cavities,” Sens. Actuators B: Chem. 206, 371–380 (2015).
[Crossref]

W.-T. Hsu, W.-H. Hsieh, S.-F. Cheng, C.-P. Jen, C.-C. Wu, C.-H. Li, C.-Y. Lee, W.-Y. Li, L.-K. Chau, C.-Y. Chiang, and S.-R. Lyu, “Integration of fiber optic-particle plasmon resonance biosensor with microfluidic chip,” Anal. Chim. Acta 697, 75–82 (2011).
[Crossref] [PubMed]

L.-K. Chau, Y.-F. Lin, S.-F. Cheng, and T.-J. Lin, “Fiber-optic chemical and biochemical probes based on localized surface plasmon resonance,” Sens. Actuators B: Chem. 113, 100–105 (2006).
[Crossref]

Chen, C.-H.

C.-W. Wu, C.-Y. Chiang, C.-H. Chen, C.-S. Chiang, C.-T. Wang, and L.-K. Chau, “Self-referencing fiber optic particle plasmon resonance sensing system for real-time biological monitoring,” Talanta 146, 291–298 (2016).
[Crossref]

Chen, W.-Y.

S.-F. Lin, F.-C. Chang, Z.-H. Chen, C.-M. Wang, T.-H. Yang, W.-Y. Chen, and J.-Y. Chang, “A polarization control system for intensity-resolved guided mode resonance sensors,” Sensors 14, 5198–5206 (2014).
[Crossref] [PubMed]

Chen, Y.-L.

H.-Y. Li, W.-C. Hsu, K.-C. Liu, Y.-L. Chen, L.-K. Chau, S. Hsieh, and W.-H. Hsieh, “A low cost, label-free biosensor based on a novel double-sided grating waveguide coupler with sub-surface cavities,” Sens. Actuators B: Chem. 206, 371–380 (2015).
[Crossref]

Chen, Z.-H.

S.-F. Lin, F.-C. Chang, Z.-H. Chen, C.-M. Wang, T.-H. Yang, W.-Y. Chen, and J.-Y. Chang, “A polarization control system for intensity-resolved guided mode resonance sensors,” Sensors 14, 5198–5206 (2014).
[Crossref] [PubMed]

Cheng, S.-F.

W.-T. Hsu, W.-H. Hsieh, S.-F. Cheng, C.-P. Jen, C.-C. Wu, C.-H. Li, C.-Y. Lee, W.-Y. Li, L.-K. Chau, C.-Y. Chiang, and S.-R. Lyu, “Integration of fiber optic-particle plasmon resonance biosensor with microfluidic chip,” Anal. Chim. Acta 697, 75–82 (2011).
[Crossref] [PubMed]

L.-K. Chau, Y.-F. Lin, S.-F. Cheng, and T.-J. Lin, “Fiber-optic chemical and biochemical probes based on localized surface plasmon resonance,” Sens. Actuators B: Chem. 113, 100–105 (2006).
[Crossref]

Chiang, C.-S.

C.-W. Wu, C.-Y. Chiang, C.-H. Chen, C.-S. Chiang, C.-T. Wang, and L.-K. Chau, “Self-referencing fiber optic particle plasmon resonance sensing system for real-time biological monitoring,” Talanta 146, 291–298 (2016).
[Crossref]

Chiang, C.-Y.

C.-W. Wu, C.-Y. Chiang, C.-H. Chen, C.-S. Chiang, C.-T. Wang, and L.-K. Chau, “Self-referencing fiber optic particle plasmon resonance sensing system for real-time biological monitoring,” Talanta 146, 291–298 (2016).
[Crossref]

W.-T. Hsu, W.-H. Hsieh, S.-F. Cheng, C.-P. Jen, C.-C. Wu, C.-H. Li, C.-Y. Lee, W.-Y. Li, L.-K. Chau, C.-Y. Chiang, and S.-R. Lyu, “Integration of fiber optic-particle plasmon resonance biosensor with microfluidic chip,” Anal. Chim. Acta 697, 75–82 (2011).
[Crossref] [PubMed]

Chiang, I.-K.

M. I. Lapsley, I.-K. Chiang, Y. B. Zheng, X. Ding, X. Mao, and T. J. Huang, “A single-layer, planar, optofluidic mach-zehnder interferometer for label-free detection,” Lab Chip 11, 1795–1800 (2011).
[Crossref] [PubMed]

Chu, T.

Chuang, S. L.

S. L. Chuang, Physics of Photonic Devices (Wiley, 2009), 2nd ed.

Cordovez, B.

D. Erickson, S. Mandal, A. H. J. Yang, and B. Cordovez, “Nanobiosensors: optofluidic, electrical and mechanical approaches to biomolecular detection at the nanoscale,” Microfluid. Nanofluid. 4, 33–52 (2008).
[Crossref] [PubMed]

Cronin-Golomb, M.

P. Domachuk, I. C. M. Littler, M. Cronin-Golomb, and B. J. Eggleton, “Compact resonant integrated microfluidic refractometer,” Appl. Phys. Lett. 88, 093513 (2006).
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M. I. Lapsley, I.-K. Chiang, Y. B. Zheng, X. Ding, X. Mao, and T. J. Huang, “A single-layer, planar, optofluidic mach-zehnder interferometer for label-free detection,” Lab Chip 11, 1795–1800 (2011).
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P. Domachuk, I. C. M. Littler, M. Cronin-Golomb, and B. J. Eggleton, “Compact resonant integrated microfluidic refractometer,” Appl. Phys. Lett. 88, 093513 (2006).
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G. Liang, Z. Luo, K. Liu, Y. Wang, J. Dai, and Y. Duan, “Fiber optic surface plasmon resonance based biosensor technique: Fabrication, advancement, and application,” Crit. Rev. Anal. Chem. 46, 213–223 (2016).
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Eggleton, B. J.

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D. Erickson, S. Mandal, A. H. J. Yang, and B. Cordovez, “Nanobiosensors: optofluidic, electrical and mechanical approaches to biomolecular detection at the nanoscale,” Microfluid. Nanofluid. 4, 33–52 (2008).
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A. D. Falco, L. O’Faolain, and T. F. Krauss, “Chemical sensing in slotted photonic crystal heterostructure cavities,” Appl. Phys. Lett. 94, 063503 (2009).
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X. Fan and I. M. White, “Optofluidic microsystems for chemical and biological analysis,” Nat. Photon. 5, 591–597 (2011).
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W. Zhang, N. Ganesh, I. D. Block, and B. T. Cunningham, “High sensitivity photonic crystal biosensor incorporating nanorod structures for enhanced surface area,” Sens. Actuators B: Chem. 131, 279–284 (2008).
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Y.-C. Lin, W.-H. Hsieh, L.-K. Chau, and G.-E. Chang, “Intensity-detection-based guided-mode-resonance optofluidic biosensing system for rapid, low-cost, label-free detection,” Sens. Actuators B: Chem. 250, 659–666 (2017).
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Jen, C.-P.

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Ku, Y.-F.

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Y.-F. Ku, H.-Y. Li, W.-H. Hsieh, L.-K. Chau, and G.-E. Chang, “Enhanced sensitivity in injection-molded guided-mode-resonance sensors via low-index cavity layers,” Opt. Express 23, 14850–14859 (2015).
[Crossref] [PubMed]

H.-Y. Li, W.-C. Hsu, K.-C. Liu, Y.-L. Chen, L.-K. Chau, S. Hsieh, and W.-H. Hsieh, “A low cost, label-free biosensor based on a novel double-sided grating waveguide coupler with sub-surface cavities,” Sens. Actuators B: Chem. 206, 371–380 (2015).
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W.-T. Hsu, W.-H. Hsieh, S.-F. Cheng, C.-P. Jen, C.-C. Wu, C.-H. Li, C.-Y. Lee, W.-Y. Li, L.-K. Chau, C.-Y. Chiang, and S.-R. Lyu, “Integration of fiber optic-particle plasmon resonance biosensor with microfluidic chip,” Anal. Chim. Acta 697, 75–82 (2011).
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G. Liang, Z. Luo, K. Liu, Y. Wang, J. Dai, and Y. Duan, “Fiber optic surface plasmon resonance based biosensor technique: Fabrication, advancement, and application,” Crit. Rev. Anal. Chem. 46, 213–223 (2016).
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S.-F. Lin, F.-C. Chang, Z.-H. Chen, C.-M. Wang, T.-H. Yang, W.-Y. Chen, and J.-Y. Chang, “A polarization control system for intensity-resolved guided mode resonance sensors,” Sensors 14, 5198–5206 (2014).
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L.-K. Chau, Y.-F. Lin, S.-F. Cheng, and T.-J. Lin, “Fiber-optic chemical and biochemical probes based on localized surface plasmon resonance,” Sens. Actuators B: Chem. 113, 100–105 (2006).
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Y.-C. Lin, W.-H. Hsieh, L.-K. Chau, and G.-E. Chang, “Intensity-detection-based guided-mode-resonance optofluidic biosensing system for rapid, low-cost, label-free detection,” Sens. Actuators B: Chem. 250, 659–666 (2017).
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L.-K. Chau, Y.-F. Lin, S.-F. Cheng, and T.-J. Lin, “Fiber-optic chemical and biochemical probes based on localized surface plasmon resonance,” Sens. Actuators B: Chem. 113, 100–105 (2006).
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P. Domachuk, I. C. M. Littler, M. Cronin-Golomb, and B. J. Eggleton, “Compact resonant integrated microfluidic refractometer,” Appl. Phys. Lett. 88, 093513 (2006).
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G. Liang, Z. Luo, K. Liu, Y. Wang, J. Dai, and Y. Duan, “Fiber optic surface plasmon resonance based biosensor technique: Fabrication, advancement, and application,” Crit. Rev. Anal. Chem. 46, 213–223 (2016).
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Liu, K.-C.

H.-Y. Li, W.-C. Hsu, K.-C. Liu, Y.-L. Chen, L.-K. Chau, S. Hsieh, and W.-H. Hsieh, “A low cost, label-free biosensor based on a novel double-sided grating waveguide coupler with sub-surface cavities,” Sens. Actuators B: Chem. 206, 371–380 (2015).
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G. Liang, Z. Luo, K. Liu, Y. Wang, J. Dai, and Y. Duan, “Fiber optic surface plasmon resonance based biosensor technique: Fabrication, advancement, and application,” Crit. Rev. Anal. Chem. 46, 213–223 (2016).
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W.-T. Hsu, W.-H. Hsieh, S.-F. Cheng, C.-P. Jen, C.-C. Wu, C.-H. Li, C.-Y. Lee, W.-Y. Li, L.-K. Chau, C.-Y. Chiang, and S.-R. Lyu, “Integration of fiber optic-particle plasmon resonance biosensor with microfluidic chip,” Anal. Chim. Acta 697, 75–82 (2011).
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D. Erickson, S. Mandal, A. H. J. Yang, and B. Cordovez, “Nanobiosensors: optofluidic, electrical and mechanical approaches to biomolecular detection at the nanoscale,” Microfluid. Nanofluid. 4, 33–52 (2008).
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Mao, X.

M. I. Lapsley, I.-K. Chiang, Y. B. Zheng, X. Ding, X. Mao, and T. J. Huang, “A single-layer, planar, optofluidic mach-zehnder interferometer for label-free detection,” Lab Chip 11, 1795–1800 (2011).
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Matsuda, N.

Merlo, S.

S. Surdo, S. Merlo, F. Carpignano, L. M. Strambini, C. Trono, A. Giannetti, F. Baldini, and G. Barillaro, “Optofluidic microsystems with integrated vertical one-dimensional photonic crystals for chemical analysis,” Lab Chip 12, 4403–4415 (2012).
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Mojahedi, M.

Nazirizadeh, Y.

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H. H. Nguyen, J. Park, S. Kang, and M. Kim, “Surface plasmon resonance: A versatile technique for biosensor applications,” Sensors 15, 10481–10510 (2015).
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A. D. Falco, L. O’Faolain, and T. F. Krauss, “Chemical sensing in slotted photonic crystal heterostructure cavities,” Appl. Phys. Lett. 94, 063503 (2009).
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Oton, C. J.

Park, J.

H. H. Nguyen, J. Park, S. Kang, and M. Kim, “Surface plasmon resonance: A versatile technique for biosensor applications,” Sensors 15, 10481–10510 (2015).
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J. Voros, J. Ramsden, G. Csucs, I. Szendro, S. D. Paul, M. Textor, and N. Spencer, “Optical grating coupler biosensors,” Biomaterials 23, 3699–3710 (2002).
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Polerecky, L.

C. S. Burke, L. Polerecky, and B. D. MacCraith, “Design and fabrication of enhanced polymer waveguide platforms for absorption-based optical chemical sensors,” Meas. Sci. Technol. 15, 1140 (2004).
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J. Voros, J. Ramsden, G. Csucs, I. Szendro, S. D. Paul, M. Textor, and N. Spencer, “Optical grating coupler biosensors,” Biomaterials 23, 3699–3710 (2002).
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G. T. Reed and A. P. Knights, Silicon Photonics: An Introduction (Wiley, 2004).
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R. Robelek and J. Wegener, “Label-free and time-resolved measurements of cell volume changes by surface plasmon resonance (SPR) spectroscopy,” Biosens. Bioelectron. 25, 1221–1224 (2010).
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Sandros, M. G.

L. Malic, M. G. Sandros, and M. Tabrizian, “Designed biointerface using near-infrared quantum dots for ultrasensitive surface plasmon resonance imaging biosensors,” Anal. Chem. 83, 5222–5229 (2011).
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J. Voros, J. Ramsden, G. Csucs, I. Szendro, S. D. Paul, M. Textor, and N. Spencer, “Optical grating coupler biosensors,” Biomaterials 23, 3699–3710 (2002).
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S. Surdo, S. Merlo, F. Carpignano, L. M. Strambini, C. Trono, A. Giannetti, F. Baldini, and G. Barillaro, “Optofluidic microsystems with integrated vertical one-dimensional photonic crystals for chemical analysis,” Lab Chip 12, 4403–4415 (2012).
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S. Surdo, S. Merlo, F. Carpignano, L. M. Strambini, C. Trono, A. Giannetti, F. Baldini, and G. Barillaro, “Optofluidic microsystems with integrated vertical one-dimensional photonic crystals for chemical analysis,” Lab Chip 12, 4403–4415 (2012).
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J. Voros, J. Ramsden, G. Csucs, I. Szendro, S. D. Paul, M. Textor, and N. Spencer, “Optical grating coupler biosensors,” Biomaterials 23, 3699–3710 (2002).
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L. Malic, M. G. Sandros, and M. Tabrizian, “Designed biointerface using near-infrared quantum dots for ultrasensitive surface plasmon resonance imaging biosensors,” Anal. Chem. 83, 5222–5229 (2011).
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Tang, W.

Tarasov, V.

Textor, M.

J. Voros, J. Ramsden, G. Csucs, I. Szendro, S. D. Paul, M. Textor, and N. Spencer, “Optical grating coupler biosensors,” Biomaterials 23, 3699–3710 (2002).
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Toccafondo, V.

Trono, C.

S. Surdo, S. Merlo, F. Carpignano, L. M. Strambini, C. Trono, A. Giannetti, F. Baldini, and G. Barillaro, “Optofluidic microsystems with integrated vertical one-dimensional photonic crystals for chemical analysis,” Lab Chip 12, 4403–4415 (2012).
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Voros, J.

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[Crossref] [PubMed]

Wang, C.-T.

C.-W. Wu, C.-Y. Chiang, C.-H. Chen, C.-S. Chiang, C.-T. Wang, and L.-K. Chau, “Self-referencing fiber optic particle plasmon resonance sensing system for real-time biological monitoring,” Talanta 146, 291–298 (2016).
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G. Liang, Z. Luo, K. Liu, Y. Wang, J. Dai, and Y. Duan, “Fiber optic surface plasmon resonance based biosensor technique: Fabrication, advancement, and application,” Crit. Rev. Anal. Chem. 46, 213–223 (2016).
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R. Robelek and J. Wegener, “Label-free and time-resolved measurements of cell volume changes by surface plasmon resonance (SPR) spectroscopy,” Biosens. Bioelectron. 25, 1221–1224 (2010).
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X. Fan and I. M. White, “Optofluidic microsystems for chemical and biological analysis,” Nat. Photon. 5, 591–597 (2011).
[Crossref]

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W.-T. Hsu, W.-H. Hsieh, S.-F. Cheng, C.-P. Jen, C.-C. Wu, C.-H. Li, C.-Y. Lee, W.-Y. Li, L.-K. Chau, C.-Y. Chiang, and S.-R. Lyu, “Integration of fiber optic-particle plasmon resonance biosensor with microfluidic chip,” Anal. Chim. Acta 697, 75–82 (2011).
[Crossref] [PubMed]

Wu, C.-W.

C.-W. Wu, C.-Y. Chiang, C.-H. Chen, C.-S. Chiang, C.-T. Wang, and L.-K. Chau, “Self-referencing fiber optic particle plasmon resonance sensing system for real-time biological monitoring,” Talanta 146, 291–298 (2016).
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Yamaguchi, I.

Yamamoto, M.

Yang, A. H. J.

D. Erickson, S. Mandal, A. H. J. Yang, and B. Cordovez, “Nanobiosensors: optofluidic, electrical and mechanical approaches to biomolecular detection at the nanoscale,” Microfluid. Nanofluid. 4, 33–52 (2008).
[Crossref] [PubMed]

Yang, C.

D. Psaltis, S. R. Quake, and C. Yang, “Developing optofluidic technology through the fusion of microfluidics and optics,” Nature 442, 381–386 (2006).
[Crossref] [PubMed]

Yang, T.-H.

S.-F. Lin, F.-C. Chang, Z.-H. Chen, C.-M. Wang, T.-H. Yang, W.-Y. Chen, and J.-Y. Chang, “A polarization control system for intensity-resolved guided mode resonance sensors,” Sensors 14, 5198–5206 (2014).
[Crossref] [PubMed]

Ye, T.

Zhang, H.

Zhang, W.

W. Zhang, N. Ganesh, I. D. Block, and B. T. Cunningham, “High sensitivity photonic crystal biosensor incorporating nanorod structures for enhanced surface area,” Sens. Actuators B: Chem. 131, 279–284 (2008).
[Crossref]

Zheng, Y. B.

M. I. Lapsley, I.-K. Chiang, Y. B. Zheng, X. Ding, X. Mao, and T. J. Huang, “A single-layer, planar, optofluidic mach-zehnder interferometer for label-free detection,” Lab Chip 11, 1795–1800 (2011).
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Anal. Chem. (1)

L. Malic, M. G. Sandros, and M. Tabrizian, “Designed biointerface using near-infrared quantum dots for ultrasensitive surface plasmon resonance imaging biosensors,” Anal. Chem. 83, 5222–5229 (2011).
[Crossref] [PubMed]

Anal. Chim. Acta (1)

W.-T. Hsu, W.-H. Hsieh, S.-F. Cheng, C.-P. Jen, C.-C. Wu, C.-H. Li, C.-Y. Lee, W.-Y. Li, L.-K. Chau, C.-Y. Chiang, and S.-R. Lyu, “Integration of fiber optic-particle plasmon resonance biosensor with microfluidic chip,” Anal. Chim. Acta 697, 75–82 (2011).
[Crossref] [PubMed]

Appl. Phys. Lett. (2)

P. Domachuk, I. C. M. Littler, M. Cronin-Golomb, and B. J. Eggleton, “Compact resonant integrated microfluidic refractometer,” Appl. Phys. Lett. 88, 093513 (2006).
[Crossref]

A. D. Falco, L. O’Faolain, and T. F. Krauss, “Chemical sensing in slotted photonic crystal heterostructure cavities,” Appl. Phys. Lett. 94, 063503 (2009).
[Crossref]

Biomaterials (1)

J. Voros, J. Ramsden, G. Csucs, I. Szendro, S. D. Paul, M. Textor, and N. Spencer, “Optical grating coupler biosensors,” Biomaterials 23, 3699–3710 (2002).
[Crossref] [PubMed]

Biosens. Bioelectron. (1)

R. Robelek and J. Wegener, “Label-free and time-resolved measurements of cell volume changes by surface plasmon resonance (SPR) spectroscopy,” Biosens. Bioelectron. 25, 1221–1224 (2010).
[Crossref]

Crit. Rev. Anal. Chem. (1)

G. Liang, Z. Luo, K. Liu, Y. Wang, J. Dai, and Y. Duan, “Fiber optic surface plasmon resonance based biosensor technique: Fabrication, advancement, and application,” Crit. Rev. Anal. Chem. 46, 213–223 (2016).
[Crossref] [PubMed]

J. Opt. Soc. Am. A (1)

Lab Chip (2)

M. I. Lapsley, I.-K. Chiang, Y. B. Zheng, X. Ding, X. Mao, and T. J. Huang, “A single-layer, planar, optofluidic mach-zehnder interferometer for label-free detection,” Lab Chip 11, 1795–1800 (2011).
[Crossref] [PubMed]

S. Surdo, S. Merlo, F. Carpignano, L. M. Strambini, C. Trono, A. Giannetti, F. Baldini, and G. Barillaro, “Optofluidic microsystems with integrated vertical one-dimensional photonic crystals for chemical analysis,” Lab Chip 12, 4403–4415 (2012).
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Meas. Sci. Technol. (1)

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Figures (7)

Fig. 1
Fig. 1 Schematics of the proposed optofluidic waveguide refractive-index sensor, consisting of a tapered waveguide structure, bent waveguide sensing region, an output straight waveguide section, and microfluidic module.
Fig. 2
Fig. 2 Fabrication processes of the optofluidic waveguide refractive-index sensors integrated with microfluidic module.
Fig. 3
Fig. 3 Characterization of the polymer waveguide refractive-index sensors. (a) Measured topography of the SU8 straight waveguide structure, exhibiting a sharp and clear ridge structure. (b) Surface morphology of the waveguide along the X-direction, showing a flat surface of the SU-8 ridge waveguide. (c) Optical image of the SU-8 straight waveguide coupled with green light source, showing clear waveguiding behavior. (d) Optical image of the fabricated sensor chip.
Fig. 4
Fig. 4 Schematic of the transmission measurement system for the fabricated optofluidic waveguide refractive-index sensors.
Fig. 5
Fig. 5 (a) Real-time responses of the sensing system for solutions with different refractive indices (RIs). The inset shows the emission spectrum of the LED light source. (b) Calibration curves of the normalized average intensities and the RI of the sample solutions. The mean values and error bars (represents the standard deviation) are obtained from six experiments using three different RI sensor chips.
Fig. 6
Fig. 6 (a) Simulated intensity distribution of the fundamental mode for the waveguide structure with w = 10 µm and t = 24 µm, clearly showing optical confinement of light. (b) Simulated normalized field distribution of the bent ridge waveguide structures for different bending angles. The waveguide width and thickness are w = 1 µm and t = 24 µm, respectively.
Fig. 7
Fig. 7 (a) Calculated transmittances and (b) normalized transmittances as a function of refractive index of the solution for the bent ridge waveguide structure with different bending angles. (c) Calculated normalized sensitivity as a function of waveguide width and bending angle. The normalized sensitivity is more pronounced for narrow waveguide width and larger bending angle.

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

S n = d d n [ I avg ( n ) I 0 ]
R s = | σ S n |
T ( n ) = I out ( n ) I in
S n = d d n [ T ( n ) T 0 ]
n ( λ ) = 1.3199 + 6878 λ 2 1.132 × 10 9 λ 4 + 1.11 × 10 14 λ 6

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