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Article

Surface Plasmon Resonance Sensor Based on Polypyrrole–Chitosan–BaFe2O4 Nanocomposite Layer to Detect the Sugar

by
Amir Reza Sadrolhosseini
1,*,
Pooria Moozarm Nia
2,
Mahmoud Naseri
3,
Ahmad Mohammadi
4,
Yap Wing Fen
5,*,
Suhidi Shafie
1 and
Halimah Mohamed Kamari
5
1
Functional Device Laboratory, Institute of Advanced Technology, Universiti Putra Malaysia (UPM), Selangor 43400, Malaysia
2
Advance Material Research Group, Center of Hydrogen Energy, Institute of Future Energy, Universiti Technology Malaysia, Kuala Lumpur 58000, Malaysia
3
Department of Physics, Faculty of Science, Malayer University, Malayer 65719-95863, Iran
4
Department of Electrical and computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin 34517-45346, Iran
5
Department of Physics, Faculty of Science, Universiti Putra Malaysia (UPM), Selangor 43400, Malaysia
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2855; https://doi.org/10.3390/app10082855
Submission received: 22 August 2019 / Revised: 28 September 2019 / Accepted: 30 September 2019 / Published: 20 April 2020

Abstract

:
The surface plasmon resonance sensor was used to detect and measure low concentrations of sugar. A polypyrrole–chitosan–BaFe2O4 nanocomposite layer was prepared to improve the surface of the gold layer for the detection of glucose, fructose, and sucrose using the surface plasmon resonance technique. The polypyrrole–chitosan–BaFe2O4 was synthesized using the electrodeposition method in different thicknesses. The functional group, crystal structure, and morphology of the layer were investigated with Fourier transform infrared spectroscopy, X-ray diffraction technique, and field emission electron microscopy. Consequently, the BaFe2O4 was scattered on the surface of the polymer, and the affinity of polypyrrole–chitosan–BaFe2O4 to bond with glucose is higher than that for the other sugars. The sensor limit was 0.005 ppm.

1. Introduction

In the last decade, many researchers focused on the detection and measurement of the low concentration of sugar such as glucose, fructose, and sucrose [1]. Sugars are found in the blood, food, and beverages. Numerous methods have been used to measure glucose, fructose, and sucrose levels, including colorimetric [2], infrared [3], Raman [4], and electrochemistry spectroscopy [5]. Other analytical methods such as gas chromatography and mass spectroscopy were considered to investigate sugar levels in human and natural products with a limit in the range of mM to µM [6]. The optical method is an interesting technique to evaluate chemical and biochemical properties [7]. Hence, the detection and measurement of low concentrations of glucose, fructose, and sucrose were carried out using a laser interferometer [8] Ultraviolet-visible (UV-Vis) absorption spectroscopy, fluorescence spectroscopy, the optical polarization properties of medium and heterodyne polarimetry [9,10], reflected THz radiation [11], and a surface plasmon resonance sensor [12,13,14]. Recently, glucose biosensors have been presented based on enzymatic catalysts in three versions of enzymatic glucose biosensors [15]. The limitation of these sugar biosensors depends on the performance of these sensors—that is, the inherent instability [16] and the sensitivity of these type of biosensors depends on pH, the temperature of samples, and concentration of oxygen [17,18].
The ratio of signal to noise and immobilization of sugar to the sensing layer are the significant parameters to design the application of glucose, fructose, and sucrose sensors [19,20]. Therefore, the nanoparticles (Au, Ag, Fe2O3, and ZnO) and conductive polymer (polypyrrole) were used to increase and improve the ratio of signal to noise and immobilization in sugar sensors. Polypyrrole has the potential of electron transfer, because the electron transfer between sugar and the sensing layer is slow [1]. Hence, the coupling of nanoparticles and polypyrrole can improve the electron transfer in the procedure of sugar detection (sensor) [1,16], and it can also enhance the selectivity and sensitivity of the sensor.
Moreover, the surface plasmon resonance (SPR) sensor is a versatile and accurate method to detect the chemical materials and sugars. The SPR phenomenon occurs at the interface of two mediums with opposite dielectric signs. Normally, the gold layer is used for the SPR sensors, and the sensing layer has been coated on the surface of the gold layer to improve the sensitivity and selectivity of the sensor. Recently, the nanocomposite layers have been used for the sensing layer. The gold nanoparticle, carbon-based nanostructure [1], and magnetic nanoparticles were used to improve the sensitivity of the sugar sensor. Magnetic nanoparticles have some advantages, such as high catalytic efficiency, good stability, and monodispersion [2]. Hence, many researchers used the magnetic nanoparticles such as ZnFe2O4 [2], Fe3O4 [21], and polypyrrole–chitosan/Fe2O3 nanocomposite [16] to detect glucose and other sugars; the detection limit was in the range of 1 to 3 µM, and the ratio of signal to noise was about 3. Consequently, the magnetic nanoparticles are a considerable nanostructure to design the sugar sensor.
In this study, a polypyrrole–chitosan–BaFe2O4 (PPy-Chi-BaFe2O4) nanocomposite layer was prepared using the electrochemical method. The prepared layer was characterized using the field emission scanning electron microscopy (FE-SEM), the X-ray diffraction (XRD), and the Fourier transform infrared spectroscopy (FT-IR). The characterized layer was used to detect the glucose, fructose, and sucrose using the surface plasmon resonance technique.

2. Materials and Methods

2.1. Materials

To prepare the PPy–Chi–BaFe2O4, commercial chemical components were used. Iron nitrate (Fe (NO3)3.9H2O) and barium nitrate (Ca(NO3)2.6H2O), polyvinyl alcohol (PVA, MW = 31,000 g/mol), glucose, sucrose, fructose, chitosan (medium molecular weight (75–85% deacetylated), lithium perchlorate, and potassium dihydrogen phosphate were purchased from Sigma Aldrich company. The materials were in high quality, and had high purity of about 99%. The saturated calomel electrodes (SCE) was used as a reference electrode from BASi Company.

2.2. Preparation of Polypyrrole–Chitosan–BaFe2O4

2.2.1. Synthesis of BaFe2O4

To synthesize the PPy–Chi–BaFe2O4 nanocomposite layer, a PVA/BaFe2O4 nanocomposite was prepared using the thermal treatment method [22,23,24]. Ba(NO3)2 H2O, Fe(NO3)3 H2O, DDW, and polyvinyl alcohol (PVA) were used to precursors, solvent, and capping agent, respectively [24].
First, 3.5 g of PVA was dissolved in 100 mL of DDW at 353 K. After that, 0.2 mmol Fe (NO3)3 H2O and 0.1 mmol Ca(NO3)2 H2 (Fe:Ba=2:1) were added into the PVA solution, and the mixture was stirred constantly for 1.5 h, and a clear solution was obtained. The clear mixture was heated at 362 K for 24 h to remove the solvent (deionized distilled water, or DDW), and the solid BaFe2O4 was reminded. The product was grounded in a mortar to obtain the uniform powder, and it was heated at 773 K for crystallization of the nanocrystal and decomposition of the PVA. The final powder was used to prepare the PPy–Chi–BaFe2O4.

2.2.2. Synthesis of PPy–Chi–BaFe2O4 Composite Layer

The PPy–Chi–BaFe2O4 was synthesized using the electrodeposition method. The gold-coated glass slide was prepared using a sputtering coating method, and the pyrrole was polymerized on the surface of the gold layer in the presence of chitosan and BaFe2O4 using the cyclic voltammetry method.
To prepare the electrolyte solution for the electrodeposition of PPy–Chi–BaFe2O4, 0.2 g of chitosan powder was dissolved in 50 ml aqueous solution of 0.3M oxalic acid, and it was stirred 4 h prior to its use for the preparation of the layer. The monomer solution contains mixed electrolytes in a 0.1 M phosphate buffer solution (pH 7.2), 0.1 M pyrrole monomer, 0.1 M LiClO4 as a dopant, 0.1 M chitosan, and 0.05 g of BaFe2O4. The electrolyte was stirred during electrodeposition to make sure there is no precipitation. The working electrode, reference electrode, and counter electrode were a gold-coated glass slide, saturated calomel electrodes (SCE), and platinum electrode, respectively. The polymerization of pyrrole was carried out at the potential range of 0 V to 0.8 V in the presence of chitosan and BaFe2O4 nanoparticles at different cycles from 1 to 50. The prepared samples were put in the oven for 2 h to dry the layers. The PPy–Chi–BaFe2O4 composite layers were characterized using the FT-IR spectrometer (model: NEXUS), XRD spectrometer (WITec, Alpha 300R with Shimadzu diffractometer: model XRD6000 and Cu, Ka (0.154 nm)), and the FE-SEM (NOVA NANOSEM 230)

2.3. Surface Plasmon Resonance Setup

The SPR setup based on the Kretschmann configuration was used to measure the low concentration of glucose, fructose, and sucrose. Figure 1 shows the SPR setup with a high index prism based on angular modulation, and the variation of laser beam intensity was detected at a different angle using a silicon photodetector.
The PPy–Chi–BaFe2O4 composite layer was deposited on the gold side of the glass slide, and it was attached to a high index prism (SF52, Foctek) using the index matching gel (F-IMF-105, Newport, USA) [12,24,25]. The prism was placed on a precision rotation stage, and the fluid holder was contacted to the sensing layer using an O-ring. The glucose, fructose, and sucrose were contacted separately to the sensing layer. The rotation stage was rotated up to 14° at increments of 0.01°. The intensity of the laser beam and the angle of rotation were registered when the rotation stage was stopped momentarily.
The experiment was repeated separately for each sugar about 10 times. The SPR signal was analyzed using the Fresnel’s theory based on the matrix method for a multi-layer system [26].

2.4. Preparation of Sugar Solution

First, 0.1 g of glucose, sucrose, and fructose were separately used to prepare the high concentration of sugar solution in 100 ml of the deionized distilled water (DDW). Other concentrations of the sugar solution, including 0.005 ppm (0.0005 mg/dL), 0.05 ppm (0.005 mg/dL), 0.5 ppm (0.05 mg/dL), 5 ppm (0.5 mg/dL), 15 ppm (1.5 mg/dL), and 25 ppm (2.5 mg/dL), were systematically prepared by dissolving the sugars solution in the DDW.
In this research, glucose, fructose, and sucrose (three sugars) in six concentrations were used to do the experiment, and the SPR experiment was repeated 10 times for each concentration of sugar.

3. Results and Discussion

Figure 2 shows the FT-IR spectrum, XRD analysis result, and FE-SEM image. Figure 2a shows the FT-IR spectrum; the main peaks of the FT-IR spectrum appeared at 3257.85, 2930.02, 1630.1, 1525.39, 1428.81, 1283.03, 1021.22, 964.53, 673.36, and 617.05 cm-1. The peaks at 3257.85, 1525.39, and 1428.81 cm-1 related to the stretching vibrations of N–H, C–N, and C–C in the pyrrole ring, respectively [27,28]. The peak centered at 2930.02 cm-1 corresponded to the asymmetric vibration of CH2, and the peaks that appeared at 1283.03, 1021.22, and 964.53 cm-1 were assigned to the C–H deformation, C–O–C, and the C–N stretching vibration of PPy, respectively. Moreover, the peaks located at 3257.85 and 2930.02 cm-1 corresponded to N–H, while the vibration of CH2 of the chitosan chain overlapped on the vibration of N=H and CH2 in polypyrrole [28]. The peaks at 1630.1, 673.36, and 617.05 cm-1 presented a vibration of C=O in the amid band, N–H out of the plane, and O–H out of the plane in chitosan [29].
Figure 2b shows the XRD spectrum of the PPy–Chi–BaFe2O4 nanocomposite layer. Following the literature, the XRD spectrum of PPy–Chi appears at 21.4° [29], which explains the PPy–Chi that formed in the amorphous form in the thin layer. The main peak of the XRD spectrum for BaFe2O4 appeared at 23.3°, 24.4°, 34.23°, 37.1°, 39.5°, 45.2°, 48.1°, and 59.7°, which related to reflection plane of (110), (200), (112), (202), (420), (104), (202), and (120), respectively. They confirm that BaFe2O4 has a face-centered cubic structure [30]. The XRD pattern of PPy–Chi–BaFe2O4 confirms that the BaFe2O4 also formed in the composite layer in the cubic structure. During the electrodeposition process, monomer molecules and metal ions at the electrode–electrolyte interface deposited on the surface of the working electrode by reaching the proper applied potential, and the mechanism is as follows [28]:
Chi Py + + Ba + + e ( applied potential ) Chi PPy Ba
Figure 2 shows the FE-SEM image of the PPy–Chi–BaFe2O4 nanocomposite layer. The PPy–Chi–BaFe2O4 composite layer was formed during electro-polymerization of pyrrole in the presence of chitosan, LiClO4 (as a dopant), and BaFe2O4 nanoparticles, and the chitosan agglomerated the polypyrrole and BaFe2O4 nanoparticles during the electro-polymerization of pyrrole. Finally, the PPy–Chi–BaFe2O4 composite layer was formed on the surface of the gold layer [16]. BaFe2O4 nanoparticles (NPs) were scattered on the surface of layer, and it impressed the morphology of the PPy–Chi–BaFe2O4 nanocomposite layer [31].
The essential charges for organizing the PPy were the electrons and anions that LiClO4 and BaFe2O4 provided them. Therefore, the role of LiClO4 and BaFe2O4 were as the electron and anions charges to create the counterbalance of the PPy–Chi–BaFe2O4 composite layer. Hence, the polymer electropolymerized at the surface of the working electrode, containing BaFe2O4 nanoparticles [32].
The gold/PPy–Chi–BaFe2O4 system layer was prepared in different thickness. The thickness of the gold layer was 48.3 nm, and the thickness of the PPy–Chi–BaFe2O4 composite layers was tested using a profilometer (AMBIOS, XP-200) in the range of 3 ± 1 nm to 94 ± 1 nm, and Figure 3a shows that the thickness of the layers increased as the cycle increases. The system layer was coated on the microscope glass slide, and it was attached to the prism using index matching gel. The SPR signal was separately registered for each thickness in the presence of DDW (n = 1.3323). Figure 3b,c shows the SPR results to test the refractive index of the layers and baseline. The refractive index of the layer was achieved by analysis of the SPR signal using Fresnel’s theory for the multilayer system when the refractive index of the gold layer was 0.237 + 3.335i.
Fresnel’s theory predicts the reflection coefficient (r) from dielectric and metal medium such as the gold layer. The matrix form that was presented in ref. [33,34] is used for the multilayer system. The reflectance (R) is equal to r × r. If the refractive index (n) and accurate thickness (t) of layers are unknown, they can obtain from the minimum root square of the difference between the theory and experimental value of reflectance as follows [26,27]:
Ψ = θ [ R T h e o r y ( θ , n , t ) R E x p ( θ , n , t ) ]
where RTheory and RExp are the theoretical and experimental value of reflectance, respectively.
According to the SPR signals analysis, the accurate thickness of layers was in the range of 3.6 nm to 94.2 nm, and the refractive index of the PPy–Chi–BaFe2O4 was in the range of 1.67571 + 0.139i to 1.57072 + 0.164i. Figure 3c shows the SPR signal at the baseline when the baseline was obtained at 53.948 using the PPy–Chi–BaFe2O4 layer with 7.3 nm of thickness.
The variation of real (n) and imaginary (k) parts of the refractive index were demonstrated in Figure 4a,b, respectively. Consequently, the real (n) part of the refractive index decreased, and the imaginary (k) part of the PPy–Chi–BaFe2O4 increased when the thickness of the layer increased. The pertinent parameters were listed in Table 1.
The PPy–Chi–BaFe2O4 with 7.3 nm thickness was used to measure and detect the low concentration of glucose, sucrose, and fructose. The experiment was separately carried out in the presence of 0.005 ppm (0.0005 mg/dL), 0.05 ppm (0.005 mg/dL), 0.5 ppm (0.05 mg/dL), 5 ppm (0.5 mg/dL), 15 ppm (1.5 mg/dL), and 25 ppm (2.5 mg/dL) of glucose, sucrose, and fructose. The SPR signals were registered, and the variation of resonance angle shift was achieved during 420 s. Figure 5a–c shows the resonance angle shift with time (sensorgram) for the glucose, fructose, and sucrose, respectively. As a result, the experimental values fit well with the first order Langmuir formula ( Δ θ = Δ θ s a t ( 1 exp ( k a t ) ) ) [35,36].
The concentration of sugars that attached the sensing layer was tested using UV-vis spectroscopy. The PPy–Chi–BaFe2O4 sensing layer was separately immersed in the glucose, fructose, and sucrose solutions in the concentration of 15 ppm. The concentrations of glucose, fructose, and sucrose solution were measured before and after the experiment. The degree of adsorption was calculated from [37]:
Π = 100 × C i C f C i
where C i and C f were the initial and final concentration of the sugar. Figure 6a1,a2,b1,b2,c1,c2 shows the UV-visible spectra of the glucose, fructose, and sucrose in the concentrations of 2 ppm (0.2 mg/dL), 6 ppm (0.6 mg/dL), 15 ppm (1.5 mg/dL), 20 ppm (2 mg/dL), and 50 ppm (5 mg/dL). The main peak occurred at about 280 nm for them. The intensity peaks were driven for each concentration of the glucose, fructose, and sucrose. Figure 6a3, b3, c3 shows the variation of peak intensity with each concentration of the sugar as a calibration curve. Figure 6a2,b2,c2 show the UV-vis spectra of the sugar solutions after they contacted with the sensing layer and the concentration of glucose, fructose, and sucrose after contact with the PPy–Chi–BaFe2O4 sensing layer was achieved from the calibration curve. As a result, the concentration of glucose was lower than that of the other sugars, and it means that the affinity of the PPy–Chi–BaFe2O4 nanocomposite layer to the adsorption of the glucose is much higher than that of the other sugars. The concentration value of the sugar and the degree of adsorption are listed in Table 2.
Magnetic nanoparticles and chitosan can be used for sugar sensor application, because they contribute to transfer the electron between a receptor (sensing layer) and sugar [1,2,16]. The BaFe2O4 NPs can enhance the electron transfer between glucose, fructose, and sucrose with the sensing layer. The molecule of glucose contains one hydroxyl group (OH) and one hydroxymethyl group (CH2OH) at the edge of the molecule plane, and the hydroxyl group can interact with the BaFe2O4 -NPs in the sensing layer. The difference between glucose and fructose is in the number of hydroxyl groups at the edge of molecule planes so that fructose contains two hydroxymethyl groups (CH2OH) at the edge of molecule plane, and they cause the space moment between fructose and BaFe2O4 NPs. Moreover, the number of hydroxyl groups (OH) in glucose is higher than those in fructose. Therefore, the tendency of glucose to exchange the electron with a sensing layer is higher than fructose [2,38]. The molecule of sucrose is a combination of glucose and fructose, and it is very heavy and stable. Hence, the tendency of sucrose to exchange the electron with BaFe2O4 NPs is very weak. Consequently, the degree of adsorption for glucose is higher than the degree of adsorption of fructose and sucrose. Therefore, the PPy–Chi–BaFe2O4 nanocomposite layer is sensitive to bind the low concentration of the glucose.

4. Conclusions

The PPy–Chi–BaFe2O4 nanocomposite layer was prepared using electrodeposition technique. The cyclic voltammetry method was used in a different cycle to control the thickness of the layer, which was in the range of 3.6 nm to 94.2 nm. The refractive indices of the PPy–Chi–BaFe2O4 composite layers were measured using the SPR method, and they ranged from 1.67571 + 0.139i to 1.57072 + 0.164i, and the resonance angle shifted from 53.126° to 68.131°. The PPy–Chi–BaFe2O4 nanocomposite layer was used to detect the sugar. As a result, the affinity of the sensing layer to bind the glucose was higher than that of sucrose and fructose, and the limit sensor was about 0.005 ppm (0.0005 mg/dL). Consequently, PPy–Chi–BaFe2O4 nanocomposite layer tends to interact with sugar, and it can adsorb the glucose well.

Author Contributions

A.R.S., P.M.N., M.N., and Y.W.F. S.S.; methodology, A.R.S., M.N., A.M.; software, A.R.S. and Y.W.F., M.N., A.M.; formal analysis, A.R.S., P.M.N.; investigation, A.R.S.; data curation, A.R.S.; writing—original draft preparation, A.R.S., P.M.N., M.N., and A.M., H.M.K.; writing—review and editing, A.R.S.; project administration, A.R.S., S.S.; funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Geran Putra Berimpak, Universiti Putra Malaysia.

Acknowledgments

The authors acknowledge funding from the Universiti Putra Malaysia, under Geran Putra Berimpak (UPM/800-3/31/1GPB/2019/9674700) and the Institute of Advanced Technology (ITMA) UPM to provide the analytical facilities.

Conflicts of Interest

The authors declare no conflict of interest

References

  1. Taguchi, M.; Ptitsyn, A.; McLamore, E.S.; Claussen, J.C. 2014 Nanomaterial-mediated Biosensors for Monitoring Glucose. J. Diabetes Sci. Technol. 2014, 8, 403–411. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Su, L.; Feng, J.; Zhou, X.; Ren, C.; Li, H.; Chen, X. Colorimetric Detection of Urine Glucose Based ZnFe2O4 Magnetic Nanoparticles. Anal. Chem. 2012, 84, 5753–5758. [Google Scholar] [CrossRef] [PubMed]
  3. Yadava, J.; Rani, A.; Singh, V.; Murari, B.M. Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy. Biomed. Signal Process. Control. 2015, 18, 214–227. [Google Scholar] [CrossRef]
  4. Pandey, R.; Paidi, S.K.; Valdez, T.A.; Zhang, C.; Spegazzini, N.; Dasari, R.R.; Barman, I. Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy. Acc. Chem. Res. 2017, 50, 264–272. [Google Scholar] [CrossRef] [Green Version]
  5. Nor, N.M.; Razak, K.A.; Lockman, Z. Physical and Electrochemical Properties of Iron Oxide Nanoparticles-modified Electrode for Amperometric Glucose Detection. Electrochim. Acta 2017, 248, 160–168. [Google Scholar]
  6. Wahjudi, P.N.; Patterson, M.E.; Lim, S.; Yee, J.K.; Mao, C.S.; Paul, L. Measurement of Glucose and Fructose in Clinical Samples Using Gas Chromatography/Mass Spectrometry. Clin. Biochem. 2010, 43, 198–207. [Google Scholar] [CrossRef] [Green Version]
  7. Damborský, P.; Švitel, J.; Katrlík, J. Optical biosensors. Essays Biochem. 2016, 60, 91–100. [Google Scholar]
  8. Yeh, Y.-L. Real-time measurement of glucose concentration and average refractive index using a laser interferometer. Opt. Laser. Eng. 2008, 46, 666–670. [Google Scholar] [CrossRef]
  9. Yu-Lung, L.; Tsung-Chih, Y.A. Polarimetric glucose sensor using a liquid crystal polarization modulator driven by a sinusoidal signal. Opt. Commun. 2006, 259, 40–48. [Google Scholar]
  10. Chien, C.; Wen-Chuan, K.; Tung-Sheng, H.; Hui-Kang, T.A. Phase sensitive optical rotation measurement in a scattered chiral medium using a Zeeman laser. Opt. Commun. 2004, 203, 259–266. [Google Scholar]
  11. Torii, T.; Chiba, H.; Tanabe, T.; Oyama, Y. Measurements of glucose concentration in aqueous solutions using reflected THz radiation for applications to a novel sub-THz radiation non-invasive blood sugar measurement method. Digit. Health 2017, 3, 1–5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Sadrolhosseini, A.R.; Rashid, S.A.; Jamaludin, N.; Noor, A.S.M.; Isloor, A.M. Surface plasmon resonance sensor using polypyrrole-chitosan/graphene quantum dots layer for detection of sugar. Mater. Res. Express 2019, 6, 075028. [Google Scholar] [CrossRef]
  13. Kun Huang, C.; Cheng Chih, H.; Chin, C.D. 2003 Interferometric optical sensor for measuring glucose concentration. Appl. Opt. 2003, 42, 5774–5776. [Google Scholar]
  14. Kolomenskii, A.A.; Gershon, P.D.; Schuessler, H.A. Sensitivity and detection limit of concentration and adsorption measurements by laser-induced surface plasmon resonance. Appl. Opt. 1997, 36, 6539–6547. [Google Scholar] [CrossRef]
  15. Toghill, K.E.; Compton, R.G. Electrochemical non-enzymatic glucose sensors: A perspective and an evaluation. Int. J. Electrochem. Sci. 2010, 5, 1246–1301. [Google Scholar]
  16. Amir AL-Mokaram, A.M.A.; Yahya, R.; Abdi, M.; Ekramul Mahmud, H.N.M. One-step electrochemical deposition of Polypyrrole–Chitosan–Iron oxide nanocomposite films for non-enzymatic glucose biosensor. Mater. Lett. 2016, 183, 90–93. [Google Scholar] [CrossRef]
  17. Karyakin, A.A.; Vuki, M.; Lukachova, L.V.; Karyakina, E.E.; Orlov, A.V.; Karpachova, G.P.; Wang, J. Processible polyaniline as an advanced potentiometric pH transducer. Appl. Biosens. Anal. Chem. 1999, 71, 2534–2540. [Google Scholar] [CrossRef]
  18. Ramanavicius, A.; Kausaite, A.; Ramanaviciene, A.; Acaite, J.; Malinauskas, A. 2006 Redox enzyme–glucose oxidase–initiated synthesis of polypyrrole. Synth. Met. 2006, 156, 409–413. [Google Scholar] [CrossRef]
  19. Portaccio, M.; Lepore, M.; Della Ventura, B.; Stoilova, O.; Manolova, N.; Rashkov, I.; Mita, D.G. Fiber-optic glucose biosensor based on glucose oxidase immobilised in a silica gel matrix. J. Sol-Gel Sci. Technol. 2009, 50, 437–448. [Google Scholar] [CrossRef]
  20. Salimi, A.; Compton, R.G.; Hallaj, R. Glucose biosensor prepared by glucose oxidase encapsulated sol-gel and carbonnanotube-modified basal plane pyrolytic graphite electrode. Anal. Biochem. 2004, 333, 49–56. [Google Scholar] [CrossRef]
  21. Wei, H.; Wang, E. Fe3O4 Magnetic Nanoparticles as Peroxidase Mimetics and Their Applications in H2O2 and Glucose Detection. Anal. Chem. 2008, 80, 2250–2254. [Google Scholar] [CrossRef] [PubMed]
  22. Naseri, M.; Naderi, E.; Sadrolhosseini, A.R. Effect of Phase Transformation on Physical and Biological Properties of PVA/CaFe2O4 Nanocomposite. Fiber Polym. 2016, 17, 1667–1674. [Google Scholar] [CrossRef]
  23. Candeia, R.A.; Souza, M.A.F.; Bernardi, M.I.B.; Maestrelli, S.C.; Santos, I.M.G.; Souza, A.G.; Longo, E. Monoferrite BaFe2O4 applied as ceramic pigment. Ceram. Int. 2007, 33, 521–525. [Google Scholar] [CrossRef]
  24. Sadrolhosseini, A.R.; Naseri, M.; Rashid, S.A. Polypyrrole-chitosan/nickel-ferrite nanoparticle composite layer for detecting heavy metal ions using surface plasmon resonance technique. Opt. Laser Technol. 2017, 93, 216–223. [Google Scholar] [CrossRef]
  25. Sadrolhosseini, A.R.; Naseri, M.; Kamari, H.M. Surface plasmon resonance sensor for detecting of arsenic in aqueous solution using polypyrrole-chitosan-cobalt ferrite nanoparticles composite layer. Opt. Commun. 2017, 383, 132–137. [Google Scholar] [CrossRef]
  26. Sadrolhosseini, A.R.; Noor, A.S.M.; Maarof, M.M. Application of Surface Plasmon Resonance Based on Metal Nanoparticle. In Plasmonics—Principles and Applications; Kim, K.Y., Ed.; IntecOpen: London, UK, 2012; Volume 10, pp. 253–282. [Google Scholar]
  27. Jang, J.; Oh, J.H. Fabrication of a Highly Transparent Conductive Thin Film from Polypyrrole/Poly(methyl methacrylate) Core/Shell Nanospheres. Adv. Funct. Mater. 2005, 15, 494–502. [Google Scholar] [CrossRef]
  28. Moozarm Nia, P.; Lorestani, F.; Meng, W.P.; Alias, Y. A novel non-enzymatic H2O2 sensor based on polypyrrole nanofibers–silver nanoparticles decorated reduced graphene oxide nano composites. Appl. Surf. Sci. 2015, 332, 648–656. [Google Scholar] [CrossRef]
  29. Ruhi, G.; Modi, O.P.; Dhawan, S.K. Chitosan-polypyrrole-SiO2 composite coatings with advanced anticorrosive properties. Synth. Met. 2015, 200, 24–39. [Google Scholar] [CrossRef]
  30. Mandizadeh, S.; Salavati-Niasari, M.; Sadri, M. Hydrothermal synthesis, characterization and magnetic properties of BaFe2O4 nanostructure as a photocatalytic oxidative desulfurization of dibenzothiophene. Sep. Purif. Technol. 2017, 175, 399–405. [Google Scholar] [CrossRef] [Green Version]
  31. Utami, R.S.; Puspasari, I.; Shyuan, L.K.; Mohamed, A.B.; Alva, S. Effect of Process Parameters on the Synthesis of Polypyrrole by the Taguchi Method. MJAS 2016, 20, 660–669. [Google Scholar] [CrossRef]
  32. Moozarm Nia, P.; Meng, W.P.; Alias, Y. One-Step Electrodeposition of Polypyrrole-Copper Nano Particles for H2O2 Detection. J. Electrochem. Soci. 2016, 163, B8–B14. [Google Scholar]
  33. Sharma, K.K. Optics: Principles and Applications; Academic Press: CA, USA, 2006; pp. 120–180. [Google Scholar]
  34. Homola, J. Surface Plasmon Resonance Based Sensors; Springer: Berlin/Heidelberg, Germany, 2006; pp. 80–110. [Google Scholar]
  35. Beketov, G.V.; Shirshov, Y.M.; Shynkarenko, O.V.; Chegel, V.I. Surface plasmon resonance spectroscopy: Prospects of superstrate refractive index variation for separate extraction of molecular layer parameters. Sens. Actuators B 1998, 48, 432–438. [Google Scholar] [CrossRef]
  36. Shishehbore, M.R.; Afkhami, A.; Bagheri, H. Salicylic acid functionalized silica coated magnetite nanoparticles for solid phase extraction and preconcentration of some heavy metal ions from various real samples. Chem. Cent. J. 2011, 5, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Sun, M.; Li, P.; Jin, X.; Ju, X.; Yan, W.; Yuan, J.; Xing, C. Heavy metal adsorption onto graphene oxide, amino group on magnetic nanoadsorbents and application for detection of Pb(II) by strip sensor. Food Agric. Immunol. 2018, 29, 1053–1073. [Google Scholar] [CrossRef] [Green Version]
  38. Zhang, W.; Li, X.; Zou, R.; Wu, H.; Shi, H.; Yu, S.; Liu, Y. Multifunctional glucose biosensors from Fe3O4 nanoparticles modified chitosan/graphene nanocomposites. Sci. Rep. 2015, 5, 11129. [Google Scholar] [CrossRef] [Green Version]
Figure 1. The surface plasmon resonance (SPR) setup to test the PPy–Chi–BaFe2O4 sensing layer .
Figure 1. The surface plasmon resonance (SPR) setup to test the PPy–Chi–BaFe2O4 sensing layer .
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Figure 2. (a) The Fourier transform infrared spectroscopy (FT-IR) spectrum, (b) XRD result, and (c) FE-SEM image.
Figure 2. (a) The Fourier transform infrared spectroscopy (FT-IR) spectrum, (b) XRD result, and (c) FE-SEM image.
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Figure 3. (a) Variation in the thickness of layer with increasing the cycle; (b) The SPR signal related to obtaining the refractive index of the PPy–Chi–BaFe2O4 composite layer in the different thickness; (c) The SPR signal at the baseline.
Figure 3. (a) Variation in the thickness of layer with increasing the cycle; (b) The SPR signal related to obtaining the refractive index of the PPy–Chi–BaFe2O4 composite layer in the different thickness; (c) The SPR signal at the baseline.
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Figure 4. Variation of (a) real (n) and (b) imaginary (k) parts of the refractive index of PPy–Chi–BaFe2O4.
Figure 4. Variation of (a) real (n) and (b) imaginary (k) parts of the refractive index of PPy–Chi–BaFe2O4.
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Figure 5. The variation of the resonance angle shift with time-related to (a) glucose, (b) fructose, and (c) sucrose. (d) Variation of the resonance angle shift with the concentration of the sugar.
Figure 5. The variation of the resonance angle shift with time-related to (a) glucose, (b) fructose, and (c) sucrose. (d) Variation of the resonance angle shift with the concentration of the sugar.
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Figure 6. (a1,a2) The UV-vis spectra of the different concentration of glucose (b1,b2) The UV-vis spectra of the different concentration of fructose, (c1,c2) The UV-vis spectra of the different concentrations of sucrose. The calibration curve for (a3) glucose, (b3) fructose, and (c3) sucrose.
Figure 6. (a1,a2) The UV-vis spectra of the different concentration of glucose (b1,b2) The UV-vis spectra of the different concentration of fructose, (c1,c2) The UV-vis spectra of the different concentrations of sucrose. The calibration curve for (a3) glucose, (b3) fructose, and (c3) sucrose.
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Table 1. The thickness, refractive index, resonance angle, and reflectance of SPR signal related to PPy–Chi–BaFe2O4 sensing layer
Table 1. The thickness, refractive index, resonance angle, and reflectance of SPR signal related to PPy–Chi–BaFe2O4 sensing layer
SampleThickness
(nm)
(Profilometer)
Thickness (nm)
(SPR Analysis)
Refractive IndexResonance AngleReflectance
133.61.67571 + 0.139i53.126°0.045
277.31.66191 + 0.141i53.948°0.088
31818.51.64183 + 0.144i56.668°0.216
49494.21.57072 + 0.164i68.131°0.658
Table 2. The concentrations of the sugar before and after adsorption with the sensing layer and the degree of adsorption of the sensing layer.
Table 2. The concentrations of the sugar before and after adsorption with the sensing layer and the degree of adsorption of the sensing layer.
SampleCi (ppm)Cf(ppm)Degree of Adsorption (Π)
Glucose15 (1.5 mg/dL)1.05 (0.105 mg/dL)93%
Fructose15 (1.5 mg/dL)2.7 (0.27 mg/dL)82%
Sucrose15 (1.5 mg/dL)3.6 (0.27 mg/dL)76%

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Sadrolhosseini, A.R.; Moozarm Nia, P.; Naseri, M.; Mohammadi, A.; Wing Fen, Y.; Shafie, S.; Kamari, H.M. Surface Plasmon Resonance Sensor Based on Polypyrrole–Chitosan–BaFe2O4 Nanocomposite Layer to Detect the Sugar. Appl. Sci. 2020, 10, 2855. https://doi.org/10.3390/app10082855

AMA Style

Sadrolhosseini AR, Moozarm Nia P, Naseri M, Mohammadi A, Wing Fen Y, Shafie S, Kamari HM. Surface Plasmon Resonance Sensor Based on Polypyrrole–Chitosan–BaFe2O4 Nanocomposite Layer to Detect the Sugar. Applied Sciences. 2020; 10(8):2855. https://doi.org/10.3390/app10082855

Chicago/Turabian Style

Sadrolhosseini, Amir Reza, Pooria Moozarm Nia, Mahmoud Naseri, Ahmad Mohammadi, Yap Wing Fen, Suhidi Shafie, and Halimah Mohamed Kamari. 2020. "Surface Plasmon Resonance Sensor Based on Polypyrrole–Chitosan–BaFe2O4 Nanocomposite Layer to Detect the Sugar" Applied Sciences 10, no. 8: 2855. https://doi.org/10.3390/app10082855

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