A novel non-enzymatic electrochemiluminescence sensor for the detection of glucose based on the competitive reaction between glucose and phenoxy dextran for concanavalin A binding sites

Electrochimica Acta 180 (2015) 471–478

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A novel non-enzymatic electrochemiluminescence sensor for the detection of glucose based on the competitive reaction between glucose and phenoxy dextran for concanavalin A binding sites Yu Fana , Xingrong Tanb , Xiaofang Liua , Xin Oua , Shihong Chena,* , Shaping Weia,* a Key Laboratory of Luminescent and Real-Time Analytical Chemistry (Southwest University), Ministry of Education, College of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China b Department of Endocrinology, 9 th People’s Hospital of Chongqing, Chongqing 400700, China

A R T I C L E I N F O

A B S T R A C T

Article history: Received 5 February 2015 Received in revised form 21 July 2015 Accepted 29 August 2015 Available online 1 September 2015

A non-enzymatic electrochemiluminescence (ECL) sensor based on the competitive reaction between glucose and phenoxy dextran (DexP) for concanavalin A (ConA) binding sites was constructed for detecting glucose. The hybrid of graphitic carbon nitride nanosheet and 3,4,9,10-perylenetetracarboxylic acid (g-C3N4-PTCA) as signal probe was modified onto the glass carbon electrode (GCE) for immobilizing DexP through p-p interaction. Then, ConA was incubated onto the electrode via the specific binding between ConA and DexP. When the modified electrode was immersed into glucose solution, glucose would compete with DexP for ConA. With the increase in the concentration of glucose, the more ConA was taken away from the electrode, resulting in a gradual increase in ECL intensity, thus, achieving the determination of glucose. The linear range was 1.0
10105.2
105 M and the detection limit was 4.0
1011 M (S/N = 3). Moreover, this sensor possessed high sensitivity, good stability and reproducibility, indicating a promising development of non-enzymatic ECL glucose sensor. ã 2015 Elsevier Ltd. All rights reserved.

Keywords: Electrochemiluminescence Non-enzymatic sensor Graphitic carbon nitride 3,4,9,10-perylenetetracarboxylic acid Competitive reaction

1. Introduction Glucose is the major energy source in cellular metabolism, and high levels of glucose in the blood may increase the risk of diabetes to induce kidney failure or heart disease. Thus, the detection of glucose has attracted more attention in clinical, biological and chemical samples, and plays an increasingly important role in the improvement of life quality [1–5]. Several methods have been used for glucose measurements, such as high-performance liquid chromatography (HPLC) [6,7], spectrophotometry [8], polarimetry [9], gas chromatography [10], and electrochemistry [11–13]. Since Clark and Lyons reported the first enzyme electrode in 1962 [14], enzyme biosensors have been widely used in the detection of glucose due to their high sensitivity, rapid response, and low expense. However, as is well-known, the enzyme is a sensitive guy to the temperature, pH, toxic chemicals and humidity. For example, glucose oxidase would quickly lose activity below pH 2 and above pH 8, and temperature above 40  C also could cause damages [15]. So the poor stability and low

* Corresponding author: Tel.: +86 23 68253172; fax: +86 23 68253172. E-mail addresses: [email protected] (S. Chen), [email protected] (S. Wei) . http://dx.doi.org/10.1016/j.electacta.2015.08.153 0013-4686/ ã 2015 Elsevier Ltd. All rights reserved.

reproducibility are the common drawbacks of the enzyme sensors. Luckily, non-enzymatic sensors can overcome above limitations of enzyme sensors, thus have attracted much attention. For example, Ci et al. established an enzyme-free amperometric glucose sensor based on nickel oxide hollow microspheres for electrocatalytic oxidation of glucose in alkaline solution [16]. But the detection in alkaline solution would hinder the universal applications of the sensor in human blood. Additionally, non-enzymatic glucose sensors based on the competitive binding reaction of glucoseConA-dextran system have also been investigated. Cella et al. constructed a chemiresistive affinity biosensor based on the change of conductance in electrochemical channels since the blocking effect by biomacromolecules (ConA) could be effectively prevented after the glucose competed with DexP for ConA [17]. Such a glucose sensor exhibited a high sensitivity in the picomolar range and an exquisite selectivity. Huang et al. constructed an onoff switchable electrochemical biosensor which performed a gradual decrease of the negative charge on the sensor surface after the glucose displaced ConA from the electrode due to the pHsensitive of ConA [18]. Among various non-enzymatic glucose detection methods, few works have focused on the non-enzymatic ECL sensor. Chen et al. built a non-enzymatic ECL sensor for glucose by using palladium nanoparticles supported on functional carbon nanotubes [19].

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Traditional ECL luminophores such as luminol [20,21], Ru (bpy)32+ [22] and quantum dots (CdS, CdSe, CdTe and ZnS) [23] have been well studied. Compared with above ECL sensing materials, graphitic carbon nitride (g-C3N4), as a worthy extension of carbon in nanomaterial applications, has attracted a lot of attention due to its unique structure and properties [24,25]. Since first reported [26], ultrathin g-C3N4 nanosheet, is widely used in photochemical or electrochemical catalysis in recent years due to its non-toxic, low cost, high thermal and chemical stability, easy functionalization and good solubility [27,28]. For example, Cheng et al. constructed an ECL sensor based on ultrathin g-C3N4 nanosheet for the detection of Cu2+ [29]. Additionally, 3,4,9,10perylenetetracarboxylic acid (PTCA), as an organic perylene dye with a flat p system, has good membrane-forming property. Furthermore, it also can enhance the ECL signal of peroxydisulfate (S2O82) emission [30,31]. Considering the excellent characteristics of g-C3N4 nanosheet and PTCA, in this work, the hybrid of g-C3N4 nanosheet and PTCA as signal probe was prepared and modified onto the GCE for immobilizing DexP through p–p interaction. Then ConA was modified onto the electrode via the special binding between ConA and DexP. When the electrode was immersed into glucose solution (pH 7.4), the glucose would compete with DexP for ConA binding sites due to the fact that glucose displays a higher binding affinity for ConA than DexP [32,33]. After the competitive reaction occurs, ConA would be taken away from the electrode, thus the hindering effect from ConA incubated on the electrode would be weakened, resulting in an increase in ECL intensity (Scheme 1). The possible electrochemical reaction of g-C3N4 and coreactant S2O82 on the electrode are as follows [29]: g-C3N4 + e ! g-C3N4

S2O82 + e ! SO42 + SO4

g-C3N4 + SO4 !g-C3N4* + SO42 and/or g-C3N4 + SO4 ! g-C3N4+ + SO42

g-C3N4+ + g-C3N4 ! g-C3N4* + g-C3N4 finally, g-C3N4* ! g-C3N4 + hn Simply, an electron from working electrode is injected into gC3N4 to produce the g-C3N4, and the S2O82 is electro-reduced to SO42 and SO4. Subsequently, the strong oxidant SO4 reacts with the g-C3N4 to produce the excited state g-C3N4 (g-C3N4*) via electron transfer from g-C3N4 to SO4. Or SO4 oxidizes g-C3N4 to g-C3N4+, and g-C3N4+ reacts with g-C3N4 to produce the ground state and excited state g-C3N4. Finally, g-C3N4* decays back to the ground state g-C3N4, generating ECL emission. The integration of g-C3N4-PTCA as excellent ECL signal probe and the competitive binding reaction of glucose-ConA-dextran system would provide a superior choice to fabricate a non-enzymatic ECL glucose sensor. 2. Materials and methods 2.1. Chemicals Melamine (2,4,6-triamino-1,3,5-trazine, 99%) was obtained from Aladdin Ltd. (Shanghai, China). 1,2-epoxy-3-phenoxypropane (Epoxy) and Tween20 were obtained from Sigma Chemical Co. (St. Louis, MO, USA). Glucose, maltose and lactose were purchased from Acros Organic (Beijing, China). 3,4,9,10-Perylenete-tracarboxylic dianhydride (C24H8O6, PTCDA) was bought from Lian Gang Dyestuff Chemical Industry Co. Ltd. (Liaoning, China). The stock solution of glucose was prepared with double distilled water. Uric acid (UA), ascorbic acid (AA) and dopamine (DA) were purchased from Chemical Reagent Co. (Chongqing, China). Histidine, aspartic acid, and serine were purchased from Kangda Amino Acid (Shanghai, China). Dextran was purchased from Sinopharm Chemical Reagent Co. (Shanghai, China). Phenoxy dextran (DexP) was synthesized according to the literature [34]. Phosphate buffered saline (PBS) solutions (0.10 M) with various pH were prepared using 0.10 M K2HPO4 and 0.10 M KH2PO4. The supporting electrolyte was 0.10 M KCl. Concanavalin A (3 mg/mL) was prepared in 0.10 M PBS (pH 7.4) with 0.50 mM CaCl2 and 0.50 mM MnCl2 to activate ConA conformation. Human serum samples were obtained from the Ninth People’s Hospital (Chongqing, China). IgG was obtained from Beijing Biosynthesis

Scheme 1. Illustration of the fabrication process and the detection principle of the glucose sensor.

Y. Fan et al. / Electrochimica Acta 180 (2015) 471–478

Biotechnology Co. Ltd. (Beijing, China). Albumin was purchased from Hualan Biotechnology Co. Ltd. (Chongqing, China). All the other chemical reagents was of analytical grade and used from the convenient way. Double distilled water was employed throughout the experiments. 2.2. Apparatus

473

remove the remaining unexfoliated bulk g-C3N4 powder. The liquid supernatant was dried to get the g-C3N4 nanosheet. 2.4. Preparation of the sensor Before modification, a GCE was polished with 0.3 and 0.05 mm alumina slurry, and washed ultrasonically in ethanol and double distilled water, respectively, then dried at room temperature. 1 mg g-C3N4 nanosheet and 1 mg PTCA were dispersed in 2 mL water with vigorous ultrasound for 8 h to get a pink dispersion. 10 mL dispersion of g-C3N4-PTCA was dropped onto the preprocessed GCE and dried at ambient environment. Next, the modified electrode was incubated with 10 mL as-prepared phenoxy dextran (DexP) solution for 1 h, and then Tween20 (0.1%, 10 mL) was used to block possible remaining active sites of g-C3N4-PTCA to eliminate non-specific binding effect. Last, 10 mL 3 mg/mL ConA solution was dropped over the electrode for 40 min. The obtained sensor was stored at 4  C in a refrigerator for future use. The fabrication process of the sensor was illustrated in Scheme 1.

The ECL emission was measured with a model MPI-E electrochemiluminescence analyzer (Xi’an Remax Electronic Science & Technology Co. Ltd. Xi’an, China) with the voltage of the photomultiplier tube (PMT) set at 600 V in the process of detection. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) measurements were carried out with a CHI 600D electrochemical work station (Shanghai CH Instruments, China). A common three electrode system was used with a modified glassy carbon electrode (GCE, F = 4.0 mm) as working electrode, a platinum wire as the auxiliary and a saturated calomel electrode (SCE) as reference electrode for electrochemical experiments, or Ag/AgCl as reference electrode for ECL experiments. The Fouriertransform infrared (FT-IR) spectroscopy was recorded on a Nexus 670 FT-IR spectrophotometer using a KBr pellets (Nicolet Instruments). The crystal structure of the samples was studied using Powder X-ray diffraction (XRD) (Purkinje General Instrument XD3) with Cu Ka radiation (l = 0.15406 nm). Scanning electron microscopy (SEM) was carried out on the S-4800 (Hitachi Instruments Co., Japan). The ultraviolet-visible (UV–vis) absorption was recorded with an UV-2450 spectrophotometer (Shimadzu, Japan).

After the sensor was incubated in glucose solution for 5 min and then washed carefully with double distilled water, the ECL measurement was performed in 3 mL 0.10 M PBS (pH 7.4) containing 0.10 M K2S2O8 at room temperature. The measurement was based on the change of the ECL signal (DI = ItI0). Here, It and I0 are the ECL intensity at the sensor incubated with and without glucose, respectively.

2.3. Synthesis of g-C3N4 nanosheet

3. Result and discussion

The g-C3N4 nanosheet was prepared following the previously reported literature [35] with slight modification. In brief, 20 g melamine powder was put into an alumina crucible with a cover and then heated at 600  C for 2 h with a ramp of 3  C/min, and then cooled to room temperature to generate yellow bulk g-C 3N4 powder. Subsequently, 250 mg of bulk g-C3N4 powder was dispersed in 250 mL water with ultrasound for 10 h. After that, the obtained suspension was centrifuged at 5000 rpm for 5 min to

3.1. Characterization of the synthesized nanomaterials

2.5. Experimental measurements

The morphologies and microstructures of g-C3N4 nanosheet, PTCA and g-C3N4-PTCA hybrid were investigated using SEM. As seen in the SEM image of g-C3N4 nanosheet (Fig. 1A), the layered and platelet-like structure was observed, which was consistent with the reported literature [36]. Fig. 1B presents the SEM image of PTCA. As expect, the irregularly quadrate-shaped structure was

Fig. 1. SEM images of (A) g-C3N4 nanosheet, (B) PTCA and (C) g-C3N4-PTCA hybrid.

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observed, demonstrating the synthesis of PTCA. For g-C3N4-PTCA hybrid (Fig. 1C), both the lamellar structure of g-C3N4 nanosheet and the irregularly quadrate-shaped structure of PTCA were clearly observed. And the g-C3N4 nanosheet was coated on the surface of PTCA, indicating the successful preparation of g-C3N4-PTCA hybrid through p-p interaction. Fourier-transform infrared (FT-IR) was performed to confirm the combination of g-C3N4 nanosheet with PTCA, and the FT-IR spectra of g-C3N4 nanosheet, PTCA and g-C3N4-PTCA are shown in Fig. 2A. As seen from curve a, typical absorption peaks for g-C3N4 nanosheet were observed at 810 cm1 for out-of-plane bending modes of the rings, 1646 cm1 and 1576 cm1 for C(sp2)=N, 1319 cm1 for the C(sp2)-N stretching modes in a graphite-type structure [35]. For the FT-IR spectrum of PTCA (curve b), the absorption peaks at 3123 cm1 for C-H stretch, 1773 cm1 for C=O stretch, 1594 cm1 for C=C stretch were clearly observed [30,37,38]. Above characteristic peaks of both g-C3N4 nanosheet and PTCA were observed in the spectrum of the g-C3N4-PTCA hybrid (curve c), indicating the successful functionalization of g-C3N4 nanosheet with PTCA. X-ray diffraction (XRD) patterns of a series of materials are presented in Fig. 2B. One feature diffraction peak of g-C3N4 nanosheet (curve a) was observed at 27.34 (002), indicating the successful synthesis of g-C3N4 nanosheet [35]. Since PTCA was prepared from PTCDA, PTCA may exhibit similar diffraction peaks to PTCDA. As expected, PTCA (curve b) displays four distinct diffraction peaks at 9.04 (011), 12.06 (012), 24.42 (024) and 27.18 (102), respectively, which were consistent with those of PTCDA reported by Karl et al. [39]. Curve c presents the diffraction peaks of g-C3N4-PTCA hybrid. Obviously, typical diffraction peaks of both g-C3N4 nanosheet and PTCA could be observed at g-C3N4PTCA hybrid. At around 27, the diffraction peak of g-C3N4 nanosheet and PTCA had overlapped. The UV–vis absorption spectroscopy of different materials was investigated and the results are shown in Fig. 2C. g-C3N4 nanosheet (curve a) showed an absorption spectrum at 320 nm, which was

consistent with that of reported g-C3N4 nanosheet [27]. Meanwhile, one typical absorption peak of PTCA attributed to the perylene core p-p* transition was observed at 513 nm (curve b), and another strong absorption peak was at 224 nm [31]. However, the characteristic absorption peak of g-C3N4 nanosheet at 320 nm was red shifted to a higher wavelength of 325 nm in the spectrum of g-C3N4-PTCA hybrid (curve c), which was due to the p-p stacking interaction between the g-C3N4 nanosheet and PTCA. This result further demonstrated the successful functionalization of gC3N4 nanosheet with PTCA. 3.2. ECL, CV and EIS characterization of the sensor ECL measurement is used to characterize the changes of the electrode behavior after each modified step. Fig. 3A displays the ECL signals at different modified electrodes in 0.10 M PBS (pH 7.4) containing 0.10 M K2S2O8 by cycling the potential between 1.30 and 0.0 V at a scan rate of 300 mV/s. As shown, compared with the bare electrode (curve a), a strong ECL signal was observed at the g-C3N4-PTCA/GCE (curve b). When DexP was modified onto the electrode by p-p stacking interactions between g-C3N4-PTCA and DexP, the ECL signal decreased (curve c). Upon immobilizing Tween20 onto the modified electrode to block any naked/bare sites on the g-C3N4-PTCA, the ECL intensity further decreased (curve d). With the modification of ConA through the specific binding between ConA and DexP, the ECL emissions further decreased obviously (curve e). Fig. 3B depicts the cyclic voltammograms (CVs) of different modified electrodes in 0.10 M PBS (pH 7.0) containing [Fe(CN)6]4/ 3 . Compared with the bare electrode (curve a), the redox peak currents at the g-C3N4-PTCA/GCE decreased (curve b), indicating that the g-C3N4-PTCA had been immobilized on the electrode surface. When DexP, Tween20 and ConA were successively modified onto the electrode (curve c to e), the redox peak currents continuously decreased, which was due to the fact that DexP, Tween20 and ConA protein on the electrode could hinder the

Fig. 2. (A) FT-IR spectra, (B) X-ray diffraction (XRD) patterns and (C) UV–vis spectra of g-C3N4 nanosheet (a), PTCA (b), and g-C3N4-PTCA hybrid (c).

Y. Fan et al. / Electrochimica Acta 180 (2015) 471–478

7500

A

b

475

A

c

ECL intensity/a.u.

ECL intensity/a.u.

15000

d

10000

5000

e a

240

4

0.04

6

Time/s

8000

ECL intensity/a.u.

a e

0

-120

0.0

0.3

0.12

4000

5

0.6

6

pH

7

8

9

C 7500

C ECL intensity/a.u.

-Z''im/ohm

0.10

6000

E/V 600

0.08

B

-240 -0.3

0.06

Concentration of K2S2O8/M

B

120

I/μA

4500

3000

0 2

6000

400

200

a

6000

4500

e

0

0

2

4

6

8

Time/min 0

700

1400

2100

Z're/ohm Fig. 3. (A) ECL responses in PBS (pH 7.4) containing 0.10 M K2S2O8, (B) CVs and (C) EIS in 5.0 mM K3[Fe(CN)6]/K4[Fe(CN)6] at the bare GCE (a), g-C3N4-PTCA/GCE (b), DexP/g-C3N4-PTCA/GCE (c), Tween20/DexP/g-C3N4-PTCA/GCE (d), and ConA/ Tween20/DexP/g-C3N4-PTCA/GCE (e). Scan rate of 300 mV/s for ECL and 100 mV/ s for CV measurements.

4/3

electron transfer of redox probe [Fe(CN)6] . The CV characterization demonstrated the successful modification of the sensor. EIS was used to characterize the assembly process of the sensor. The semicircle diameter in the impedance spectrum equals to the electron transfer resistance (Ret), which controls the electron transfer kinetics of the redox probe at the electrode interface. As seen from Fig. 3C, compared with the bare GCE (curve a), an obviously increased Ret was discovered at the g-C3N4-PTCA modified GCE (curve b), ascribing to the poor conductivity of gC3N4-PTCA. When DexP, Tween20, and ConA were successively modified onto the electrode, continuous increases in Ret were observed (curve c to e), which was due to the fact that Dexp, Tween20 and ConA modified onto the electrode could hinder the electron transfer between the redox probe [Fe(CN)6]4/3 and the electrode. The EIS results are in accordance with those of the CV characterization, further confirming the successful modification of the electrode.

Fig. 4. (A) Effect of K2S2O8 concentration on the ECL responses in PBS (pH 7.4). (B) Effect of pH of PBS on the ECL responses in the presence of 0.10 M K2S2O8. (C) Effect of incubating time on the ECL responses in PBS (pH 7.4) containing 0.10 M K2S2O8. Scan rate: 300 mV/s. Scan voltage: -1.30 V0.0 V.

3.3. Optimation of measurement conditions In order to optimize the performance of the proposed sensor, the influence of the concentration of K2S2O8, pH of PBS and incubation time of glucose on the ECL response was investigated. Fig. 4A depicts the influence of K2S2O8 concentration (0.050.12 M) on the ECL intensity of the sensor incubated with 6.0
107 M glucose. With increasing the concentration of K2S2O8 from 0.05 to 0.10 M, the intensity of the ECL signal distinctly increased. When the concentration of S2O82 increased to 0.10 M, the ECL intensity reached a relatively stable platform. Thus, 0.10 M was selected as the optimal concentration of K2S2O8 in the further work. The influence of pH was studied at the sensor incubated with 6.0
107 M glucose. As shown in Fig. 4B, the maximum ECL intensity was obtained at pH 7.0. Taking into account the possible application of the sensor, pH 7.4, the physiological pH, was selected in the further work. In order to investigate the influence of incubation time of glucose on the ECL response, the sensor was incubated with

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Y. Fan et al. / Electrochimica Acta 180 (2015) 471–478

Table 1 Comparison of different non-enzymatic glucose sensors. Electrodea

Methods

Linear range/M

Detection limit/M

Ref.

NiO-HMSa /GCE ConA/Tween20/DexP/SWNTsb/GEc ConA/Tween20/DexP/GOd /GE PdNPs-FCNTse/Nafion-GCE Pd@Cysf-C60/GCE CuONPs-CNFsg /GCE ConA/Tween20/DexP/g-C3N4-PTCA/GCE

Amperometry Chemiresist EIS ECL Amperometry Amperometry ECL

1.67
106–6.87
103 1.0
1012–1.0
109 5.0
106–9.0
103 5.0
107–4.0
105 2.5
106–1.0
103 5.0
107–1.1
102 1.0
1010–5.2
105

5.3
107 – 3.4
107 9.0
108 1.0
106 2.0
107 4.0
1011

[16] [17] [18] [19] [44] [45] This work

c d e f g

NiO-HMS: nickel oxide hollow microspheres. SWNTs: single-walled carbon nanotubes. GE: gold electrode. GO: grapheme oxide. PdNPs-FCNTs: palladium nanoparticles-functional carbon nanotubes. Cys: l-Cysteine. CuONPs-CNFs: cupric oxide nanoparticles-carbon nanofibers.

6.0
107 M glucose for different time. As seen from Fig. 4C, the ECL signal increased from 1 to 5 min. After the incubation time increased to 5 min, only a slight change in the ECL intensity was observed. Hence, 5 min was chosen as the best time of incubating glucose in this experiment. 3.4. Performance of the sensor 3.4.1. Calibration curve The determination of the glucose was carried out using the sensor by ECL under the optimized experimental conditions. As seen from Fig. 5, the ECL intensity increased with the increase in glucose concentration. The corresponding calibration curve of the change of ECL intensity (DI) versus the logarithm of glucose concentration is shown in the Inset of Fig. 5. The linear range was from 1.0
1010 to 5.2
105 M with a detection limit of 4.0
1011 M. The linear regression equation was DI = 10981.54 + 1013.47 log c, and the correlation coefficient of R = 0.991. The details of the comparison between our glucose sensor and other non-enzymatic sensors are provided in Table 1. As seen from the Table 1, our non-enzymatic ECL sensor in this work exhibited a better performance than most reported works in terms of linear range and detection limit. The reasonable explanation is as follows. First, g-C3N4 oneself exhibited an excellent ECL behavior and can generate a strong ECL intensity in cathodic potentials. Second, a signal-on electrochemiluminescence sensor with gC3N4-PTCA as signal probe was fabricated for detecting glucose in this work. Such a signal-on assay would be preferable on the improvement of the assay sensitivity than signal-off assays. Third,

9000

ΔI=10981.54+1013.47logc

R=0.991

j

2000

3000

0

0

-10

-8 -6 logc/M

-4

0 4.0

5

10

15

Time/day 7500

B

5000

2500

0 0

30

60

90

Time/s Fig. 6. (A) Long-term storage stability and (B) operational stability of the sensor incubated with 6.0
107 M glucose in PBS (pH 7.4) containing 0.10 M K2S2O8.

4

3000

0

Possible interferences

a

3.2

6000

Table 2 Interference experiment with the sensor.

4000

6000

A

the competitive binding reaction of glucose-ConA-dextran system may endow our prepared sensor with excellent performance such as high sensitivity and wide linearity since Cella et al. have reported a chemiresistive affinity biosensor for glucose detection

ΔI/a.u.

ECL intensity/a.u.

6000

9000

ECL intensity/a.u.

b

ECL intensity/a.u.

a

4.8

5.6

Time/s Fig. 5. ECL responses of the sensor to 0.0 (a), 1.0
1010 (b), 6.0
1010 (c), 1.6
109 (d), 1.2
108 (e), 6.2
108 (f), 1.6
107 (g), 6.6
107 (h), 1.7
106 (i), and 5.2
105 M (j) glucose in PBS (pH 7.4) containing 0.10 M K2S2O8. Scan rate: 300 mV/s. Scan voltage: 1.30 V0.0 V. Inset showed the linear calibration curve.

albumin (5.5
10 mg/mL) ascorbic acid (0.10 mM) dopamine (0.10 mM) tyrosine (0.10 mM) lactose (0.10 mM) serine (1 mg/mL) histidine (1 mg/mL)

DI ratioa 1.13 0.94 0.95 1.06 1.06 0.93 0.96

Possible interferences 4

IgG (1.7
10 mg/mL) citrate (0.10 mM) uric acid (0.10 mM) maltose (0.10 mM) fructose (0.10 mM) aspartic acid (1 mg/mL)

DI ratioa 1.11 1.06 0.93 1.07 1.05 1.05

a DI = ItI0; It is the ECL intensity of the sensor incubated with the glucose or the mixture of glucose and interfering substance, and I0 is the ECL intensity in absence of both glucose and interfering substance. The DI ratios were calculated by testing the DI of the sensor incubated with the mixture of an interfering substance and 4.5
108 M glucose, and comparing it with that in the case of the incubation with glucose alone.

Y. Fan et al. / Electrochimica Acta 180 (2015) 471–478

477

Table 3 Determination of glucose in blood serum samples. Serum sample

Know value /mM

Determined valuea /mM

R.S.D (%)

Relative error (%)

1 2 3

4.4 4.7 5.2

5.70.3 6.00.3 6.40.4

5.3 5.0 6.3

29.5 27.7 23.1

a

Mean  SD, n = 3.

with a high sensitivity in the picomolar range using competitive reaction of glucose-ConA-dextran system [17]. Additionally, the ECL technique oneself exhibited high sensitivity. 3.4.2. Stability and reproducibility of the sensor Fig. 6A shows the long-term storage stability of our proposed sensor. The ECL response of the sensor towards 6.0
107 M glucose was tested every day. After 15 days, the ECL intensity decreased 19.3%, demonstrating that the sensor possessed good stability. Fig. 6B reveals the operational stability of the sensor under 10 successive measurements, and a relative standard deviation (R. S.D) of 1.1% was obtained. The reproducibility of the proposed sensor was also investigated, and the R.S.D was 4.1% for five sensors prepared under the same condition. 3.4.3. Selectivity of the sensor Selectivity is an important criterion for the sensors. Interference experiments were performed with possible interferences including the major proteins in the blood serum, amino acid and other glycosides in the case of 4.5
108 M glucose. The results were listed in Table 2. As seen from Table 2, the uric acid, ascorbic acid, tyrosine, maltose, dapamine, lactose, serine, histidine, aspartic acid and fructose did not cause an obvious interference on the determination of 4.5
108 M glucose. However, albumin and IgG in physiological concentration showed a certain amount of interference for the determination of glucose. The reasonable explanation may be as follows. First, as the most abundant type of immunoglobumin, IgG shows an affinity to the ConA [40–41]. As the most abundant protein in plasma, human albumin serum is also glycated by glucose. When the sensors were immersed into the incubation solution concluding glucose and protein (IgG or albumin), both the glucose and protein would compete with DexP for ConA binding sites of the sensor, thus resulting in interference. Obviously, the glucose displays a significantly higher binding affinity for ConA than IgG and albumin. 3.4.4. Application of the sensor The analytical reliability and potential application of the sensor were investigated. The analysis of real samples with 1000 times diluted serum samples was performed using our prepared sensor. As seen from Table 3, the results obtained at our sensors were higher than those in traditionally clinical testing. Relative errors in the range of 20%30% were observed. As we can see from Table 2, albumin and IgG in human blood serum have some influence. Additionally, other more protein in human blood serum may also cause a certain amount of interference, thus resulting in a total deviation of 20%30% for the determination of glucose in human blood. This may be a limitation which must be taken into consideration in the practical application of our sensor. Perhaps, our sensors could be used for detecting the glucose in human blood through calibrating the results with above deviation. Additionally, it was found that albumin and IgG did not exhibit an obvious interference towards the determination of the glucose when their concentration was lower than physiological concentration in

human blood. Thus, our proposed sensor would exhibit potential applications for detecting lower concentrations glucose in unconventional body fluids such as tears, saliva and urine, and intracellular glucose at the single-cell level in metabolomic studies due to the absence of proteins or only presence of low concentration proteins in these cases [42–43]. 4. Conclusions In this work, a novel non-enzymatic ECL sensor for glucose detection was fabricated employing the competition reaction between glucose and dextran for ConA binding sites. With the gC3N4-PTCA hybrid as ECL signal probe, our prepared sensor exhibited a lower detection limit and wider linear range compared with other most glucose sensor. Furthermore, this sensor possessed the simplicity of preparation, high sensitivity, good stability and reproducibility. The integration of g-C3N4-PTCA hybrid as ECL signal probe and the competition reaction between glucose and dextran for ConA binding sites would provide a promising platform for non-enzymatic ECL glucose sensor. Acknowledgments This work was financially supported by the NNSF of China (21075100,21275119, 21105081), Ministry of Education of China (Project 708073), Research Fund for the Doctoral Program of Higher Education (RFDP) (20110182120010), Natural Science Foundation of Chongqing City (CSTC-2011BA7003, CSTC2014JCYJA20005, CSTC-2010BB4121), Science and Technology Commission of Beibei (2012-27), Medical Scientific Research Projects of Health Bureau of Chongqing (2012-2-286), and the Fundamental Research Funds for the Central Universities (XDJK2013A008, XDJK2013A027, XDJK2014A012), China. References [1] D.W. Schmidtke, A. Heller, Accuracy of the one-point in vivo calibration of wired glucose oxidase electrodes implanted in jugular veins of rats in periods of rapid rise and decline of the glucose concentration, Anal. Chem. 70 (1998) 2149–2155. [2] I. Amato, Race quickens for non-stick blood monitoring technology, Science 258 (1992) 892–893. [3] M. Ben-Moshe, V.L. Alexeev, S.A. Asher, Fast responsive crystalline colloidal array photonic crystal glucose sensors, Anal. Chem. 78 (2006) 5149–5157. [4] J. Castillo, S. Gáspár, S. Leth, M. Niculescu, A. Mortari, I. Bontidean, V. Soukharev, S.A. Dorneanu, A.D. Ryabov, E. Csöregi, Biosensors for life quality design, development and applications, Sensor. Actuat. B: Chem. 102 (2004) 179–194. [5] Y. Tian, Y. Liu, W.P. Wang, X. Zhang, W. Peng, CuO nanoparticles on sulfurdoped graphene for nonenzymatic glucose sensing, Electrochim. Acta 156 (2015) 244–251. [6] G.R. Guile, P.M. Rudd, D.R. Wing, S.B. Prime, R.A. Dwek, A rapid high-resolution high-performance liquid chromatographic method for separating glycan mixtures and analyzing oligosaccharide profiles, Anal. Biochem. 240 (1996) 210–226.  ska, P. Szefer, Simultaneous separation and [7] M. Grembecka, A. Lebiedzin determination of erythritol, xylitol, sorbitol, mannitol, maltitol, fructose, glucose, sucrose and maltose in food products by high performance liquid chromatography coupled to charged aerosol detector, Microchem. J. 117 (2014) 77–82. [8] Q. Chang, H.Q. Tang, Optical determination of glucose and hydrogen peroxide using a nanocomposite prepared from glucose oxidase and magnetite nanoparticles immobilized on graphene oxide, Microchim. Acta 181 (2014) 527–534.

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