Colorimetric Sensor Arrays for Antioxidant Discrimination Based on

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Colorimetric Sensor Array for Antioxidant Discrimination Based on the Inhibition of Oxidation Reaction Between 3,3’,5,5’-Tetramethylbenzidine and Hydrogen Peroxide Xin Li, Caiyun Kong, and Zhengbo Chen ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.8b18548 • Publication Date (Web): 08 Feb 2019 Downloaded from http://pubs.acs.org on February 8, 2019

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ACS Applied Materials & Interfaces

Colorimetric Sensor Array for Antioxidant Discrimination Based on the Inhibition of Oxidation Reaction Between 3,3’,5,5’-Tetramethylbenzidine and Hydrogen Peroxide Xin Li,1 Caiyun Kong,1 and Zhengbo Chen1* 1Department

of Chemistry, Capital Normal University, Beijing, 100048, China E-mail: [email protected]

ABSTRACT：The discrimination of antioxidants is of great significance due to their essential roles in various biological processes and many diseases. Compared with the traditional lock-key sensing mode for single target detection at a time, sensor arrays can discriminate various antioxidants simultaneously. Nanomaterial-based sensor arrays have shown great promise for antioxidant discrimination, however, as far as it’s known none of them have been reported for discriminating antioxidants based on catalytic reaction of intrinsic peroxidase-like activity of two-dimensional nanomaterials. To fill the gap, we herein unveil a colorimetric (e.g., UV-vis absorption) approach for antioxidant discrimination based on three nanomaterials (graphene oxide (GO), molybdenum disulfide (MoS2), and tungsten disulfide (WS2))-catalyzed 3,3’,5,5’-tetramethylbenzidine (TMB)-hydrogen peroxide (H2O2) reaction system. In this sensor array, the antioxidants inhibit the reaction between TMB and H2O2, resulting in different colorimetric response patterns. The obtained patterns for five antioxidants, including ascorbic acid (AA), cysteine (Cys), melatonin (MT), uric acid (UA), and glutathione (GSH), at the 60 nM level, were successfully discriminated using linear discriminant analysis (LDA)

both in buffer and serum samples.

KEYWORDS ： Peroxidase-like activity; Linear discriminant analysis; 3,3’,5,5’-tetramethylbenzidine; Colorimetric sensor array; Antioxidant discrimination

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1. Introduction Antioxidants are molecules that can scavenge free radical reactions. Antioxidants in foods and biological fluids play a key role in the prevention and treatment of premature aging and diseases such as coronary artery disease,1 cancer,2 Parkinson's disease,3 Alzheimers’ dementia4 in the human body. Consider five antioxidants (ascorbic acid (AA), cysteine (Cys), melatonin (MT), uric acid (UA), and glutathione (GSH)), for example, AA deficiency will causes scurvy.5 GSH plays a crucial role in metabolic process, gene regulation, cancer cell death, and intracellular signal transduction.6 The abnormal level of Cys causes some diseases, such as edema, growth retardation, and skin lesion.7 High concentrations of UA lead to gout and Lesch-Nyhan syndrome.8 MT helps regulate sleep, and a low concentration of MT can cause various cancers.9 Therefore, to satisfy the requirement of medical diagnosis, an effective and sensitive detection of antioxidants is highly desirable. In recent years, plenty of colorimetric sensing systems for detection of targets have been established due to their important advantages, such as excellent sensitivity, good repeatability, low cost, simplicity of experimental operation, and allowance for naked-eye observation.10-13 In recent years, colorimetric approaches have been employed to detect individual antioxidant.14-19 However, these individual sensors were designed for detecting only a single antioxidant at a time, which do not allow high-through-put detection. In such a case, colorimetric sensor arrays have emerged as a powerful tool for discrimination of multiple antioxidants simultaneously. Sensor arrays termed “chemical noses/tongues” consisting of multiple sensing receptors can give cross-reactive signals to each target without the problem of selectivity.20-26 Herein, we report a single-channel colorimetric sensor array for sensitive discrimination of antioxidants based on nanozymes-catalyzed 3,3’,5,5’-tetramethylbenzidine (TMB)-H2O2 reaction system. In the colorimetric sensor array, the high peroxidase-like activities of a series of nanozymes (GO, MoS2, and WS2) (Fig. 1) were employed for catalyzing the reaction between TMB and H2O2. In the presence of target antioxidants (AA, Cys, MT, UA, and GSH), the reaction between TMB and H2O2 ACS Paragon Plus Environment

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is inhibited by leading to the oxidation behaviors of TMB to varying degrees, and giving various colorimetric response change. The variation of the colorimetric response change as fingerprint can be utilized to discriminate these antioxidants at a low level of concentration (60 nM), even in serum samples. (Figure 1)

2. Experimental section 2.1. Reagents Graphene oxide (GO), molybdenum sulfide (MoS2), and tungsten disulfide (WS2) were purchased from Beijing Dk Nano technology Co., LTD (China). 3,3',5,5'-tetramethybenzidine (TMB) and hydrogen peroxide (30 wt%) were obtained from Aladdin (Shanghai, China). Ascorbic acid (AA), cysteine (Cys), melatonin (MT), uric acid (UA), and glutathione (GSH) were purchased from Sangon Biotechnology Co. Ltd. (Shanghai, China). All other reagents were of analytical reagent grade. Ultrapure water was provided by a Direct-Q3 system and used as a solvent in all experiments throughout the study. 2.2. Instrumentation The Ultraviolet-visible (UV-vis) spectra were obtained by an UV-2550 Spectrophotometer (Shimadzu Corporation). The morphology and size of GO, MoS2, and WS2 were characterized by transmission electron microscope (TEM H-7650, Hitachi, Japan). Ultrapure water (18.2 MΩ/cm) produced by a Milli-Q Integral system (Millipore) was used in all experiments. 2.3. The preparation of nanosheet dispersion (GO, MoS2, and WS2) First, put 0.64 g solid GO, MoS2, and WS2 powder in 10 mL deionized water, respectively. Subsequently, ultrasound treatment was performed for 12 hours with cell disintegrator. The gray-black supernatant and black precipitate produced by ultrasonic treatment were collected. The above process was repeated at least three times. The collected clear liquids are used as nanosheet (GO, MoS2, and WS2) reserve liquids. 2.4. The discrimination of antioxidants ACS Paragon Plus Environment

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Antioxidant discrimination was performed as follows: firstly, single nanosheet dispersion (GO, MoS2, and WS2, 20 μL, 20%), 160 μL of 60 nM antioxidant (final concentration), and 160 μL of 2 mM H2O2 (final concentration) were mixed in 400 μL Britton-Robinson (BR) buffer (pH 3.43) solution. After incubation for 20 min at room temperature, 160 μL of 25 mM TMB (final concentration) was added to the above solution for 1 h of reaction. Subsequently, the absorbance values of the mixture at 450 and 650 nm were recorded, respectively, for discriminating different antioxidants. Prior to measurement, the serum obtained from the General Hospital of the People’s Liberation Army (Beijing, China) was filtered with a 0.22 μM nitrate cellulose membrane, and then diluted 100 times with Britton-Robinson buffer solution (pH 3.43). The raw data matrix was processed using classical linear discriminant analysis (LDA) in SPSS (version 11.03).

3. Results and discussion 3.1. Determination principle Scheme 1 illustrates the assay principle of the sensor array toward target antioxidants. Without target antioxidants, three kinds of nanozymes peroxidase-like activity all can catalyze TMB-H2O2 reaction system into a luminescent derivative with the absorption band at 650 nm, resulting in a light-green color. In contrast, in the presence of target antioxidants, these antioxidants can inhibit the catalytic activity of these nanomaterials, thereby slowing down the catalytic ability of GO/WS2/MoS2 toward TMB/ H2O2, leading to decreased color change to varying degrees depending on of diverse antioxidant abilities of different antioxidants. For each antioxidant, the sensor array generates a unique colorimetric response pattern that can be further differentiated via using LDA. (Scheme 1) 3.2. Optimization of sensing conditions The discrimination performance of the sensor array was strongly dependent on the relevant experimental parameters including solution pH, TMB concentration, H2O2 concentration, and incubation time. Thus, their effects on the absorbance of oxidized TMB should be investigated. The effect of solution pH ranging from 2.73 to 8.04 on the absorbance of oxidized TMB in the absence and ACS Paragon Plus Environment

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presence of antioxidants is shown in Fig. S1. When pH was 3.43, the absorbance reached the maximum. TMB and H2O2 play an important role in the color change of the solution. Therefore, the concentrations of TMB and H2O2 were studied (Fig. S2 and Fig. S3). As shown in Fig. S2, it was observed that the absorbance increased with the increase of TMB concentration from 0 to 25 mM, and decreased when it was beyond TMB concentration of 25 mM. Therefore, 25 mM TMB was selected for the following work. As shown in Fig. S3, absorbance increased with the increasing concentration of H2O2. Considering the two aspects of solution color and economy saving, 2 M H2O2 was chosen for the further experiments. The incubation time of antioxidants was also important to the response signal, as shown in Fig. S4, with the increase of incubation time from 0 to 20 min, the absorbance increased. However, beyond 20 min, the absorbance began to decrease. Thus, 20 min was the optimal incubation time. 3.3. Antioxidant discrimination by the sensor array Since the different antioxidants have distinct diversity in terms of inhibition of the reaction between TMB and H2O2, colorimetric approach-based sensor array for discrimination of antioxidants (here 5 antioxidants were selected as an example, Table S1) was established. Antioxidants at the same concentration (50, 60, 100, and 160 nM) were incubated with the three nanozymes (GO, MoS2, and WS2) for a period of time in the presence of TMB and H2O2, respectively. As representative, the absorbance responses of various nanozymes-TMB-H2O2 system to the different thiols with the same concentration are shown in Fig. 2(A-C) and Table S2-S5. We employed the parameter (K0-K), where K and K0 were the absorption of oxTMB solution (K=OD650 nm/ OD450 nm) in the presence and absence of the target antioxidants, respectively, to construct the colorimetric response patterns (or “fingerprint maps”) for 60 nM antioxidants (Fig. 2D). To further generate the colorimetric response pattern of the sensor array against the five antioxidants, five replicates of colorimetric response patterns of the sensor array toward antioxidants were recorded, and then subjected to linear discriminate analysis (LDA) to convert the training matrix (3 nanozymes × 5 antioxidants × 8 replicates) into three canonical scores (Fig. 2E), and the first three discrimination factors were utilized to generate three-dimensional (3D) canonical score plots. As revealed in Fig. 2F, five different antioxidants were correctly assigned to their ACS Paragon Plus Environment

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respective groups without cross influences, suggesting the successful discrimination of the five antioxidants with an identification accuracy of 100%. To obtain the limit of detection of the sensor array for antioxidant discrimination, we tested the antioxidants at the concentrations of 50, 60, 100, and 160 nM, respectively. Five antioxidants at the three concentrations (60, 100, and 160 nM) were clustered into different groups and all groups were separated from each other (Fig. 3(B-D)). Whereas the antioxidant groups at the concentration of 50 nM exhibited obvious overlap (Fig. 3A), indicating that our sensor array can identify antioxidants at a concentration as low as 60 nM. The jackknifed classification matrix exhibits that the classification accuracy of antioxidants was enhanced from 75% for the response signal from GO to 97.5% for that from GO and WS2, to 100% for that from GO, MoS2 and WS2 (Fig. 4). (Figure 2) (Figure 3) (Figure 4) To evaluate the sensitivity of the sensor array, GSH and MT as representative compounds were used to determine the array’s analytical performances (Table S6-S7). Because factor 2 in the LDA plots was less than 23%, it is acceptable to employ factor 1 to correlate the concentrations of GSH and MT. As illustrated in Fig. 5(A,C), six groups of GSH and MT samples were located explicitly in six isolated clusters, respectively. The linear ranges of GSH and MT were 60-3000 nM, and 12-300 nM, respectively (Fig. 5(B,D)). (Figure 5) 3.4. Discrimination of mixtures of antioxidants After successful discrimination of different concentrations of antioxidants, the performance of the sensor assay was further demonstrated for the discrimination of antioxidant mixtures with various molar ratios. To perform even more stringent test of the sensor array, eleven binary mixtures of GSH and MT (i.e., GSH 100%-MT 0%, GSH 90%-MT 10%, GSH 80%-MT 20%, GSH 70%-MT 30%, GSH 60%-MT 40%, GSH 50%-MT 50%, GSH 40%-MT 60%, GSH 30%-MT 70%, GSH 20%-MT 80%, ACS Paragon Plus Environment

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GSH 10%-MT 90%, and GSH 0%-MT 100%), eleven binary mixtures of MT and UA (i.e., MT 100%-UA 0%, MT 90%-UA 10%, MT 80%-UA 20%, MT 70%-UA 30%, MT 60%-UA 40%, MT 50%-UA 50%, MT 40%-UA 60%, MT 30%-UA 70%, MT 20%-UA 80%, MT 10%-UA 90%, and MT 0%-UA 100%), and nine mixtures of GSH, MT, and UA (i.e., GSH 100%-MT 0%-UA 0%, GSH 60%-MT 20%-UA 20%, GSH 40%-MT 40%-UA 20%, GSH 40%-MT 20%-UA 40%, GSH 20%-MT 60%-UA 20%, GSH 20%-MT 40%-UA 40%, GSH 20%-MT 20%-UA 60%, GSH 0%-MT 100%-UA 0%, and GSH 0%-MT 0%-UA 100%) were analyzed. Here, three kinds of binary or ternary mixtures of the antioxidants were randomly selected (Table S8-S10). As shown in Fig. 6, these antioxidant mixtures can be well distinguished from each other, indicating the potential ability of the sensor array to detect samples with complex composition. (Figure 6) 3.5. Real sample detection In order to further explore the practical application of the sensing array towards antioxidants, the antioxidants in human serum samples were evaluated. As is known, serum contains not only electrolytes and proteins but also phosphates and different carboxylate ions, which is likely to cause a unique array’s response pattern. To eliminate the matrix effect, the human serum was diluted 100-fold with Britton-Robinson buffer solution (pH 3.43). Then, the five antioxidants at 60 nM were spiked into the diluted serum samples, respectively. The colorimetric response patterns of the sensor array toward the serum and five antioxidants in the presence of serum are shown in Fig. 7 and Table S11, the serum itself generated a unique array’s response, and the five antioxidants distinguished from each other. Meanwhile, the selectivity of the sensor array was investigated. Other serum antioxidants such as α-tocopherol, β-carotene and bilirubin (60 nM). These serum antioxidants can also generate array’s responses, however, they were clearly separated from the five antioxidants (AA, Cys, MT, UA, and GSH). In addition, the detection results and the recoveries for the spiked samples were given in Table S12. It can be seen that the recovery for the spiked samples was between 96.75% and 100.04%. These results confirmed that this sensor array works well in real sample with complex matrix. ACS Paragon Plus Environment

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(Figure 7)

4. Conclusion In summary, we have developed an effective colorimetric sensor array for sensitive discrimination of antioxidants, which takes advantage of three kinds of nanomaterials (GO, MoS2, WS2) with peroxidase-like activity to catalyze TMB-H2O2 reaction system. The sensor array was challenged against five antioxidants with a low concentration of 60 nM, and with the help of 3D canonical score plots of the responses, the sensor array was able to successfully discriminate all five antioxidants. Moreover, the sensor array can also discriminate different concentrations of antioxidants and antioxidant mixtures. Importantly, the sensor array also works well in antioxidant discrimination as low as 60 nM in real serum samples, which makes the sensor array hold a great promise for medical diagnosis. Supporting information UV-vis absorption spectra of oxidized TMB solutions; The training matrix of the colorimetric response patterns against 5 antioxidants and the mixture. These experimental data is available free of charge via http://pubs.acs.org.

Acknowledgements This study was supported by Scientific Research Project of Beijing Educational Committee (Grant No. KM201710028009), Youth Innovative Research Team of Capital Normal University, and Capacity Building for Sci-Tech Innovation-Fundamental Scientific Research Funds (025185305000/195).

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Scheme Captions Scheme 1 Schematic illustration of the colorimetric sensor array for absorbance pattern discrimination of antioxidants. Fig. 1 TEM images of (A) GO, (B) MoS2, and (C) WS2. Fig. 2 UV-vis absorption spectra of TMB solution in the presence of (A) GO (B) MoS2 (C) WS2 nanozymes with the same concentration (60 nM). (D) UV-vis absorbance response patterns of the sensor array toward 60 nM antioxidants (UA, MT, GSH, Cys, and AA). (E) Canonical score plot for the discrimination of the five antioxidants at 60 nM based on the sensor array. (F) Canonical score plots for the first three factors of relative absorbance change (K0-K) pattern analyzed by LDA with the sensor array toward 60 nM antioxidants. Fig. 3 Canonical score plot for UV-vis absorption response patterns obtained with the sensor array toward different concentrations of antioxidants: (A) 50 nM, (B) 60 nM, (C) 100 nM, (D) 160 nM. Fig. 4 Jackknifed classification matrix obtained using LDA for three nanomaterials for the 5 antioxidants at 60 nM. Fig. 5 The LDA plots for (A) GSH at different concentrations (60, 120, 240, 300, 600, and 3000 nM), and (C) MT at various concentrations (12, 30, 60, 120, 240, and 300 nM). Plot of the discriminant factor 1 versus the logarithm of (B) GSH concentration, and (D) MT concentration. Fig. 6 Canonical score plots for the first three factors of absorbance response patterns obtained against (A) the mixture of GSH and MT, as well as pure GSH and MT, (B) the mixture of MT and UA, as well as pure MT and UA, and (C) the mixture of GSH, MT, and UA, as well as pure GSH, MT, and UA. Fig. 7 (A) Relative absorbance change (K0-K) patterns of the sensor array against antioxidants at 60 nM as an average of five parallel measurements. (B) Canonical score plots for the first three factors of relative absorbance change (K0-K) pattern analyzed by LDA.


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Scheme 1


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Fig. 1


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Fig. 2


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Fig. 3


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Fig. 4


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Fig. 5


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Fig. 6


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Fig. 7


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TOC FIGURE


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Colorimetric Sensor Arrays for Antioxidant Discrimination Based on

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