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High-performance colorimetric detection of thiosulfate by using silver nanoparticles for smartphone-based analysis Chen Dong, Zhuqing Wang, Yujie Zhang, Xuehua Ma, M. Zubair Iqbal, Lijing Miao, Zhuangwei Zhou, Zheyu Shen, and Aiguo Wu ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.7b00257 • Publication Date (Web): 19 Jul 2017 Downloaded from http://pubs.acs.org on July 20, 2017

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High-performance colorimetric detection of thiosulfate by using silver nanoparticles for smartphone-based analysis

Chen Dong a, Zhuqing Wang b, Yujie Zhang a, Xuehua Ma a, M. Zubair Iqbal a, Lijing Miao a, Zhuangwei Zhou a, Zheyu Shen a, Aiguo Wu a*

a

Key Laboratory of Magnetic Materials and Devices & Key Laboratory of Additive Manufacturing

Materials of Zhejiang Province & Division of Functional Materials and Nanodevices, Ningbo Institute of Materials Technology& Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, China b

School of Chemistry and Chemical Engineering, Anqing Normal College, Anqing, Anhui 246001,

China

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ACS Paragon Plus Environment

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ABSTRACT Developing thiosulfate (S2O32-) sensors with silver nanoparticles (AgNPs) for analysis of aqueous solutions with the interference of other anions remains challenging. In this study, we propose a new strategy for excellent selective colorimetric detection of S2O32-. The non-morphological transition of AgNPs leading to a color change from yellow to brown is verified by UV-vis, TEM, DLS, SEM, and XPS analyses. The sensor exhibits high sensitivity with detection limits of 1.0 µM by naked-eye determination and 0.2 µM by UV-vis spectroscopy analysis. The linear relationship (R2=0.998) between the (A0-A)/A0 values and S2O32- concentrations from 0.2 µM to 2.0 µM indicates that the fabricated AgNPs-based colorimetric sensor can be employed for quantitative assay of S2O32-. Colorimetric responses are also monitored using the built-in camera of a smartphone. The sensor shows a linear response to S2O32- in 0–20.0 µM solutions under the optimized conditions and is thus more suitable for rapid on-site tests than other detection methods. A smartphone application (app) is downloaded under Android or IOS platforms to measure the RGB (red, green, blue) values of the colorimetric sensor after exposure to the analyte. Following data processing, the RGB values are converted into concentration values by using preloaded calibration curves. Confirmatory analysis indicates that the proposed S2O32- colorimetric sensor exhibits feasibility and sensitivity for S2O32- detection in real environmental samples.

Keywords: colorimetric sensor, silver nanoparticles, thiosulfate, RGB, smartphone-based analysis

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Over the past decades, studies on anion detection have exponentially increased because of the important roles of anions in nature, particularly as agricultural fertilizers and industrial raw materials, and their corresponding biological and environmental concerns. 1-3 Anions are involved in 4

chemical reactions involving enzyme synthesis,

ATP synthases,

5

and protein synthesis.

Thiosulfate (S2O32-) is an inorganic anion widely applied in various industries, such as textile, papermaking, leather, sewage disposal,

6

and pesticide. The chemical reactions of S2O32- can be

easily to generate toxic sulfide, hydrogen sulfide, sulfur dioxide, and other mixtures.

7, 8

The toxic

sulfide and its derivatives cause adverse physiological and biochemical conditions, such as heart and brain diseases and toxicity to living organisms.

9, 10

Thus, simple and rapid detection of S2O32-

in aquatic ecosystems has gained increasing interest. Currently, many methods or techniques with high selectivity and sensitivity can be used to detect S2O32-; these methods include titration, chromatography, (UV-vis),

12

and electrochemical measurements.

13

11

ultraviolet–visible spectroscopy

However, these techniques are time consuming

and require high operating costs and complicated sample preparation, which restrict their applications for on-site detection. Colorimetric sensors with noble metal nanoparticles have gained increasing research attention because of their distinct color variations associated with morphological changes. 14-19 These sensors based on gold nanoparticles (AuNPs) or silver nanoparticles (AgNPs) are easy and safe to handle, efficient, and inexpensive and do not require sophisticated instrumentation. Compared with AuNPs, AgNPs have been more widely used in colorimetric sensors for various analytes including metal ions,

20, 21

anions,

22, 23

small molecules,

24-27

DNA,

28

and proteins

29

because of its possess strong

surface plasmon resonance (SPR), highly stable dispersion, and catalytic activity.

30

The primary

detection strategy include triggering aggregation, 31 anti-aggregation, 32 etching, 33 or anti-etching 34 3


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of AgNPs. However, these existing strategy can only be used to control the morphological of AgNPs, resulting a color change, but the detection strategy based on non-morphological transition have not been studied. Most importantly, the study on fabrication of a colorimetric sensor for S2O32detection based on AgNPs is not familiar. Consequently, there is a great need to develop a new detection strategy for detection of S2O32- with excellent selectivity and high sensitivity. In this study, we propose a new strategy for high-performance colorimetric detection of S2O32(Scheme 1 and Scheme S1). We used tannic acid (TA) as stabilizer because it can be adsorbed onto the surface of AgNPs.

30

In the presence of S2O32-, a redox reaction occurs between the phenolic

hydroxyl of TA and S2O32-. Then the reaction of S2- (S2O32- is reduced by the phenolic hydroxyl group) and Ag+ generates a thin layer of Ag2S capped on the surface of AgNPs. Because of this mechanism, the non-morphological transition of AgNPs leading to an evident color change in the AgNPs dispersion from yellow to brown; as such, S2O32- could be detected rapidly and with excellent selectivity within 10 minutes. The other anions do not interfere on the selectivity. The limits of detection (LOD) are 1.0 µM by naked-eye determination and 0.2 µM by UV-vis spectroscopy analysis. Moreover, a user-friendly smartphone-based analytical method was developed to make our sensor accessible for on-site application. We downloaded the application (APP, such as colorassist) on the smartphone online. In the experiment, a sample containing S2O32was added to the AgNPs solution. An evident color change was observed and recorded by the APP, which could output the RGB values directly. According to Beer–Lambert Law, absorbance is directly proportional to the concentration of the absorbing material and the thickness of the absorbing layer when a beam of light is perpendicular to the uniform non-scattering light absorbing material. Hence, the RGB value can be directly converted into the absorbance of the solution to determine the concentration of S2O32-. 4


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EXPERIMENTAL SECTION Reagents and materials C76H52O46, AgNO3, HAuCl4.4H2O, NaCl, KCl, K2Cr2O7, Na2SO3, Na2SO4, NaNO3, NaHCO3, Na2C2O4, Na2CO3 Na2HPO4, NaH2PO4, KClO4, KBrO3, C2H3O2Na.3H2O, KMnO4, and Na3PO4 were purchased from Sinopharm Chemical Reagent Co. Ltd (Shanghai, China). Na4P2O7, Na2S2O3.5H2O, Na2S2O7, KBr, KI, NaNO2, Na2S, and Na2F were obtained from Aladdin Reagent Co. Ltd (Shanghai, China). Glassware was washed with aqua regia [HCl : HNO3 = 3 : 1 (V/V)] and rinsed with MilliQ water prior to use.

Apparatus Transmission electron microscopy (TEM) images were recorded from a JEOL 2100 instrument (JEOL, Japan) operated at 200.0 kV. Scanning electron microscopy (SEM) images were recorded from an S-4800 instrument (Hitachi, Japan). UV-vis spectroscopy analysis was performed using a T10CS instrument (Beijing Purkinje General Instrument Co., Ltd, China). The hydrodynamic size and zeta potential of particles were measured on Zetasizer Nano ZS instrumentation with a 633 nm laser (Malvern Instrument Ltd., UK). X-ray photoelectron spectroscopy (XPS) measurement was performed using an AXIS Ultra DLD instrument with Mg Kα radiation as X-ray source. The power was 96 W and the X-ray spot size was set to 700×300 µm. The pass energy of the XPS analyzer was set at 160eV and 20 eV. The step was 1.0eV and 0.5eV. The base pressure of the analysis chamber was better than 2.0×10-9 Torr. The XPS results were collected in binding energy forms and fitted using a nonlinear least-squares curve fitting program (CasaXPS software). For XPS analysis, 20 mL of the AgNPs and AgNPs dispersions containing 10 µM of S2O32- were lyophilized for 48h using 5


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vacuum freeze-drying equipment (LGJ-10). Photographs were taken using an Iphone 6 plus smartphone.

Synthesis of AgNPs AgNPs were prepared by reducing AgNO3 with TA in the presence of HAuCl4.4H2O. Briefly, aqueous solutions of AgNO3 (20.0 mM, 1.0 mL) and HAuCl4.4H2O (0.5 mM, 200 µL) were mixed with 98.0 mL of Milli-Q water and vigorously stirred at room temperature. The mixture was rapidly added with fresh TA solution (5.0 mM, 1.0 mL) under vigorous stirring. The reaction was kept for 30 min (the color of the solution turned into a vivid yellow). The AgNPs dispersion was stored at 4 °C until use.

Sensing detection of S2O32Sensing detection of S2O32- in aqueous solution was performed at room temperature. Typically, 200 µL of S2O32- aqueous solution of various concentrations were added to 800 µL of AgNPs-based colorimetric sensor. The mixture was maintained at room temperature for 1–30 min. Finally, color changes and SPR absorption spectra were recorded.

Selective detection of S2O32Other anions including CO32-, HCO3-, C2O42-, NO3-, NO2-, PO43-, P2O74-, HPO42-, H2PO4-, S2-, SO32-, SO42-, S2O32-, S2O72-, F-, Cl-, Br-, I-, ClO4-, BrO3-, CH3COO- (AC-), Cr2O72- and MnO4- were also tested in a similar manner to verify the selectivity of our colorimetric sensor based on AgNPs. The influence of the mixed anions on the selectivity of the sensor was also studied.

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Signal recording using a smartphone The colorimetric sensor was placed in a light box, illuminated with two LED lamps. The lamps were fixed on both sides of the top surface of the box to ensure homogeneous illumination, with the power of 3.0 W, color temperature of 5500 ± 200 K, and luminous flux range of 18000 lm to 19000 lm. The smartphone was placed in a holder at a fixed distance (20 cm) from our colorimetric system. Photographs were obtained using the built-in camera of the smartphone at the highest possible resolution. The app of colorassist was used to read the RGB values from the photographs. The RGB values were simultaneously transmitted through Wi-Fi network to the computer for calculation.

Application to the spiked real samples Real water samples of tap and pond water were obtained in our institution. The samples were first filtered through syringe filters with 0.2 µm membrane and directly spiked with standard S2O32solutions of different concentrations as stock solutions. The spiked samples were analyzed using our colorimetric sensor, smartphone based analysis method, or titration.

RESULTS AND DISCUSSION Formation mechanism of AgNPs The AgNPs formation process can be understood taking into account the interaction of AgNO3 with tannic acid (TA) in the presence of HAuCl4, in particular the fact that a single reagent plays multiple roles, as reducing and stabilzer agent (Scheme S1). Scheme S1(a) shows the importance of HAuCl4 as a catalyst in the synthesis of AgNPs. The standard potential of Au3+/Au0 is 1.498 V, which is higher than that of Ag+/Ag (0.7996 V), and this means that TA first reacts with HAuCl4 to produce gold nanoparticles (AuNPs). The presence of AuNPs provides a nucleation point for the formation of AgNPs, and its catalytic effect

35, 36

promotes the redox reaction between phenolic 7


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hydroxyl groups of TA and AgNO3 [Scheme S1(b)]. So that the generated AgNPs wrapped up on the surface of Au seeds, resulting AgNPs with uniform particle size.

Mechanism of the colorimetric sensor based on AgNPs for S2O32- detection The proposed mechanism of the colorimetric sensor based on AgNPs for S2O32- detection is shown in Scheme 1 and was verified by UV-vis, TEM, DLS, SEM, and XPS analyses. Figure S1 shows the UV-vis absorption spectra and image of the AgNPs-based colorimetric sensor under different conditions. The AgNPs dispersion is yellow and presents evident UV-vis absorption peak at 419 nm. The color of the AgNPs dispersion with S2O32- (10.0 µM) changes from yellow to brown, and the UV-vis absorption intensity significantly decreases. The redox reaction between the phenolic hydroxyl of TA and S2O32- could decrease the amount of TA on the surface of AgNPs, where Ag+ is partly exposed. The reaction of S2- (S2O32- is reduced by the phenolic hydroxyl group) and Ag+ generates Ag2S (Ksp = 6.3×10-50) capped on the surface of AgNPs; the morphology of AgNPs did not induce an obvious color change in the AgNPs dispersion from yellow to brown (Scheme 1 and Scheme S1). The TEM images of the AgNPs-based colorimetric sensor incubated with S2O32- at various concentrations, ranging from 0 to 20.0 µM. From Figure 1(a), it can be observed that the diameter of TA-AgNPs is around 18 nm and the nanopartices disperse well without aggregation. Figure 1(b) to Figure 1(h) shows that the morphology of AgNPs did not significantly change with the addition of various concentrations of S2O32-. Hence, the color change of the AgNPs solution was not induced by the morphology transition of the particles. The non-morphological transition of AgNPs observed in the TEM images was verified by the size distribution and Zeta potential of AgNPs under different conditions (Figure 2). It can be seen from Figure 2(a), the average hydrodynamic size of AgNPs is around 30-60 nm, which is slightly larger than the diameter obtained by TEM. The slightly larger hydrodynamic size value is due to the phenolic hydroxyl groups of TA on the surface of the particles. Regardless of the high or low S2O32- concentrations, the size of AgNPs did not change 8


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significantly. Figure 2 (b) depicts the zeta potential of the AgNPs incubated with S2O32- at various concentrations ranging from 0 to 20.0 µM. It can be seen that the zeta potential value is always negative and the range of change is slight. This is mainly because the existence of large amounts of phenolic hydroxyl groups of TA on the surface of the particles. The SEM images (Figure S2) confirmed the absence of significant changes in the size of AgNPs with or without S2O32-. These results are consistent with our proposed strategy for S2O32- detection (Scheme 1) and with the analysis of the UV-vis spectra and color change in the AgNPs dispersions (Figure S1). XPS spectra were used to characterize the binding energies of Ag 3d and S 2p. Figure 3(a) shows the Ag 3d spectra of AgNPs, AgNPs incubated with S2O32- and Ag2S. The peak for Ag 3d5/2 in the AgNPs occurred at 368.3 eV, revealing the existence of Ag+ ions besides Ag atoms.

37, 38

It is well

known that the surface of silver material has antibacterial properties, the mechanism is that the Ag atoms can be slowly oxidized by oxygen in the environment and then release free Ag+. The binding energy of Ag (0) 3d5/2 peak is 368.8 eV, after S2O32- was added into the solution, the 3d5/2 peak of Ag shifted to 368.3 eV, which is in good agreement with the bingding energy of Ag (І). Figure 3(b) shows the S 2p spectra of pure sodium thiosulfate and AgNPs in the presence of sodium thiosulfate (10.0 µM). Compared with the S 2p spectra of the pure sodium thiosulfate, the binding energy of S 2p3/2 at 161.9 eV and S 2p1/2 at 169.1 eV respectively shifted to 161.4 eV and 169.6 eV, indicating changes in the chemical state of the particle surface. 39 The peak position (Ag 3d5/2 at 368.4 eV and S 2p3/2 at 161.4 eV) are in good agreement with the reported values for Ag2S [Figure 3(a)].

40

In

addition, the final surface of the AgNPs is mostly Ag2S, considering Ksp[Ag2SO4] ˃ Ksp[Ag2S] ( Ksp[Ag2S]= 6.3 × 10-50, Ksp[Ag2SO4]=1.5 × 10-5). These results confirm the redox reaction between AgNPs and S2O32- in the aqueous solution (Scheme 1).

Optimization of experimental conditions The concentration of TA and HAuCl4, incubation time of AgNPs with anions, and pH of the AgNPs dispersion were optimized according to the sensing effect of the fabricated colorimetric 9


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sensor. Figure S3 shows the plot of A0/A (A is the absorbance value at 419 nm in the UV-vis spectra of the AgNPs-based colorimetric sensor incubated with 10.0 µM S2O32-, and A0 is the absorbance of the AgNPs-based colorimetric sensor without S2O32-) of the sensor with different TA concentrations. The optimal effect of S2O32- was detected when the TA concentration is 50.0 µM. The influence of HAuCl4 concentration in the AgNPs-based colorimetric sensor on sensing S2O32- was also studied. From the plot of A0/A of the AgNPs-based colorimetric sensor with different HAuCl4 concentrations (Figure S4), the optimal sensing effect of S2O32- was detected at 1.0 µM HAuCl4. The influence of pH on the sensing effect of the AgNPs-based colorimetric sensor on S2O32- was investigated by analyzing UV-vis absorption. Figure S5(a) shows the consistent absorbance of TA-AgNPs with pH 1.0 to 12.0. It can be seen that the colorimetric sensor was stable within pH 3.0 to 9.0. In addition, the acidulous dispersion of AgNPs (pH = 6.0) showed the optimal detection effect in the presence of S2O32- [10.0 µM; Figure S5(b)]. Hence, pH 6.0 was set as the optimal value for subsequent experiments. The influence of incubation period on the sensing effect of AgNPs-based colorimetric sensor for S2O32- (5.0 µM) was also investigated. Figure S6 shows that 8 min is sufficient to reach equilibrium. Therefore, all the experiments were performed within 8 min.

Selectivity of the S2O32- colorimetric sensor Under the optimized condition, the selectivity of the AgNPs-based colorimetric sensor for S2O32was evaluated through comparison with 22 anions (e.g., CO32-, HCO3-, C2O42-, NO3-, NO2-, PO43-, P2O74-, HPO42-, H2PO4-, S2-, SO32-, SO42-, S2O72-, F-, Cl-, Br-, I-, ClO4-, BrO3-, AC-, Cr2O72- and MnO4-). Figure 4(a) shows the image of the colorimetric sensor incubated with S2O32- or other anions (the concentration of each anion is 10.0 µM). Only the AgNPs dispersion with S2O32- is brown, and the other anions do not affect the color of the dispersion. Figure 4(c) shows the 10


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(A0-A)/A0 of the colorimetric sensor incubated with S2O32- or other anions. A and A0 are the absorbance value at 419 nm in the UV-vis absorption spectra of the colorimetric sensor incubated with or without S2O32-, respectively. The result shows the dramatic increase in the absorbance ratio ((A0-A)/A0) for S2O32-, whereas no obvious changes were detected for the other anions. The influence of other anions on the S2O32- sensing effect was investigated to verify the selectivity of the proposed AgNPs-based colorimetric sensor. Figure 4(b) shows the image of the colorimetric sensor incubated with S2O32- or with the mixture of S2O32- and the other anions (the concentration of each anion is 10.0 µM). The color of the AgNPs dispersion incubated with S2O32is similar to that of the dispersion incubated with the mixture of S2O32- and the other anions. Figure 4(d) shows the (A0-A)/A0 of the colorimetric sensor incubated with S2O32- or with the mixture of S2O32- and the other anions. A and A0 are the absorbance value at 419 nm in the UV-vis absorption spectra of the colorimetric sensor incubated with or without S2O32-, respectively. The result indicates that other anions have no influence on the S2O32- sensing effect by the AgNPs-based colorimetric sensor. Moreover, we also studied the influence of various metal ions on the S2O32sensing effect. Figure S7 shows the (A0-A)/A0 of the colorimetric sensor incubated with S2O32- or various metal ions. The inset of Figure S7 shows the image of the colorimetric sensor incubated with S2O32- or various metal ions. It can be seen that other metal ions do not interfere on the selectivity. Hence, the other anions or metal ions did not influence the S2O32- sensing effect of the AgNPs-based colorimetric sensor. In this work, we also studied the influence of potentially interfering anions and their tolerance ratios on S2O32- detection (Table S1). The detection of S2O32- exhibited high tolerance to the other anions, and the ionic strength did not affect the performance of the AgNPs-based colorimetric sensor. Therefore, the sensor exhibits excellent selectivity for S2O32-.

Sensitivity of the S2O32- colorimetric sensor Colorimetric responses and UV-vis spectra were employed to evaluate the sensitivity of the 11


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proposed AgNPs-based colorimetric sensor for S2O32-. Figure 5(a) shows the UV-vis absorption spectra of the AgNPs dispersions incubated with various concentrations of S2O32-. The SPR absorption intensity gradually decreased with increasing S2O32- concentration. The inset of Figure 5(a) shows the image of the AgNPs-based colorimetric sensor under the optimized conditions with various S2O32- concentrations (0.5-20.0 µM). When the S2O32- concentration is higher than 0.5 µM, the color of the AgNPs-based colorimetric sensor gradually changes from yellow to brown with increasing S2O32- concentration. The LOD values of our S2O32- colorimetric sensor are 1.0 µM by naked-eye determination and 0.2 µM by UV-vis spectroscopy analysis. Figure 5(b) shows the plot of (A0-A)/A0 of different AgNPs dispersions versus S2O32concentrations ranging from 0 to 20.0 µM (A and A0 are the absorbance of the AgNPs dispersions at 419 nm with or without S2O32-, respectively). The inset of Figure 5(b) shows the plot of (A0-A)/A0 versus S2O32- concentration ranging from 0.2 µM to 2.0 µM. A good linear relationship (R2=0.9987) was obtained between (A0-A)/A0 and S2O32- concentrations from 0.2 µM to 2.0 µM. Therefore, the proposed AgNPs-based colorimetric sensor can be used for quantitative assay of S2O32-. The LOD is 0.2 µM by UV-vis spectroscopy analysis, which is more simple and convenient than other detection methods, such as titration method.

Smartphone-based colorimetric read-out Several empirical formulas were evaluated to correlate the RGB values processed with the smartphone as shown in Figure S8. It can be seen that when the ratio of G/(R+G+B) was used, we can get the optimal linear relationship. Figure 6(a) shows the developed read-out method for detection of S2O32- by using the variations in the RGB values processed with the smartphone. Figure 6(b) shows the optimal linear relationship determined using the ratio of G/(R+G+B). By using this ratio, we obtained a calibration curve with the linearity of R2=0.9743 when the S2O32concentration ranges from 0 µM to 20.0 µM. We integrated the colorimetric sensor with a smartphone and then downloaded the APP (application, which is colorassist) on the smartphone 12


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online. The color of the solution changed when the sample containing S2O32- was added to the AgNPs-based colorimetric sensor. The changes were recorded using the APP, which could directly output the RGB values. Finally, the RGB values were simultaneously transmitted through Wi-Fi network to the computer for calculate the fitting parameters and the calibration plot [Figure 6(a)]]. Figure 6(c) shows the G/(R+G+B) of the colorimetric sensor incubated with S2O32- or other anions. The results show that the smartphone-based analysis method can distinguish S2O32- from the other anions. Signal readout by smartphone was employed to evaluate the sensitivity of the proposed smartphone-based analysis for S2O32-. We applied our colorimetric sensor versus S2O32concentrations ranging from 0 to 20.0 µM as examples to show the whole process and received results which are presented in Figure S9. Five times readout results shows the LOD value is 0.2 µM by smartphone-based analysis. For validation purposes, samples (n = 3) were analyzed using the smartphone and UV-vis analysis. Table 1 confirms no statistically significant differences in the average values at any S2O32concentration level. This finding demonstrates the excellent performance of the developed smartphone-based colorimetric readout method, which is cheaper and more portable, and accessible for in situ analysis of S2O32- than chromatographic techniques.

Application to the spiked real samples Usually, many real water samples contain lager amounts of macromolecules, cations, anions and organic compounds. These factors have a potential interference to our sensing system. For example, humic substances will interfere with the signal readout by smartphone because of its possess a brownish color; Excessive concentrations of cations or anions affect the surface charge of nanoparticles, which will induce the aggrregation of nanoparticles. Therefore, filtration or dilution of the real water samples is necessary. To evaluate the practicality of the AgNPs-based colorimetric sensor for on-site analysis and 13


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detection of S2O32-, we added a certain concentration of S2O32- to the real water samples, including tap and lake water, through standard addition method. The results analyzed by both methods are close, and the recoveries [calculated from: (observed value with S2O32- addition

_

observed value

without S2O32- addition)/added value ×100) are within 80.0% to 96.0% (Table 1). Hence, the proposed AgNPs-based colorimetric sensor exhibits potential for analysis of real environmental samples.

CONCLUSION We

propose

a

new

strategy

for

high-performance

colorimetric

detection

of

S2O32-

(non-morphological transition of AgNPs leading to a color change from yellow to brown). In the presence of S2O32-, a redox reaction occurs between the phenolic hydroxyl groups of TA and S2O32-, leading to decreased amount of TA on the surface of AgNPs, where Ag+ is partly exposed. The reaction of S2- (S2O32- is reduced by phenolic hydroxyl group) and Ag+ generates Ag2S capped on the surface of AgNPs. Under the optimized conditions, the selectivity of the AgNPs-based colorimetric sensor for S2O32- detection by the naked eye and UV-vis spectrum analysis is considered excellent compared with other anions or metal ions. The sensor exhibits high sensitivity with detection limits of 1.0 µM by naked-eye determination and 0.2 µM by UV-vis spectroscopy analysis. A good relationship (R2=0.998) between (A0-A)/A0 and S2O32- concentrations from 0.2 µM to 2.0 µM indicated that the sensor can be used for quantitative assay of S2O32-. Moreover, we integrated our AgNPs-based colorimetric sensor with a smartphone. The developed strategy is portable and accessible for in situ analysis and does not require advanced instruments. In addition, the results of S2O32- detection in real environmental samples indicate the feasibility and sensitivity of the proposed S2O32- colorimetric sensor.

ASSOCIATED CONTENT 14


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Supporting Information Supporting Information Available: The following files are available free of charge. Scheme S1. Reaction Mechanism of AgNPs with S2O32-. Figure S1: UV-vis spectra of the AgNPs dispersions incubated with or without 10.0 µM of S2O32-; Figure S2: SEM iamges of AgNPs at different conditions; Figure S3: Influence of TA concentration in the AgNPs-based colorimetric sensor on the sensing effect of S2O32-; Figure S4: Influence of HAuCl4 concentration in the AgNPs-based colorimetric sensor on the sensing effect of S2O32-; Figure S5: Influence of the pH value in the AgNPs-based colorimetric sensor; Figure S6: Influence of the incubation time between our colorimetric sensor and S2O32-; Figure S7: Selectivity of the AgNPs-based colorimetric sensor for S2O32- compared with the other metal ions; Figure S8: The ratio of R/(R+G+B) (a), G/(R+G+B) (b), B/(R+G+B) (c), R/G (d), R/B (e), and G/B (f) versus S2O32- concentration in the range of 0 to 20.0 µM. Figure S9: Sensitivity of the smartphone-based anslysis for S2O32- detection in the range of 0 to 20.0 µM. Table S1: Influence of potential interfering anions on the detection of S2O32-.

AUTHOR INFORMATION Corresponding Authors *E-mail: [email protected]. Phone: 0086-574-86685039. Fax: 0086-574-86685163. Notes The authors declare no competing financial interest.

ACKNOWLEDGEMENTS This work was supported by the Project for Science and Technology Service of Chinese Academy of Sciences (KFJ-SW-STS-172 & KFJ-EW-STS-016), the National Natural Science Foundation of China (U1607111), Hundred Talent Program of Chinese Academy of Sciences (2010-735), the aided program for Science and Technology Innovative Research Team of Ningbo Municipality (Grant No. 2014B82010, and 2015B11002), Natural Science Foundation of China (Grants Nos. 31128007), Natural Science Foundation of Ningbo (2016A610264, and 2016A610266) and Zhejiang Provincial Natural Science Foundation of China (Grant No. R5110230). 15


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Scheme 1. Mechanism of the AgNPs-based colorimetric sensor for S2O32- detection.

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Figure 1. TEM images of AgNPs incubated with S2O32- at various concentrations from 0 to 20.0 µM. (a) control; (b) 0.2 µM; (c) 0.5 µM; (d) 1.0 µM; (e) 2.0 µM; (f) 5.0 µM; (g) 10.0 µM; and (h) 20.0 µM.

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Figure 2. Size distribution (a) and Zeta potentials (b) of AgNPs incubated with S2O32- at various concentrations ranging from 0 to 20.0 µM.

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Figure 3. XPS spectra of AgNPs under different conditions. (a) Ag 3d of AgNPs, AgNPs incubated with 10.0 µM S2O32-, and Ag2S and (b) S 2p of sodium thiosulfate and AgNPs incubated with 10.0 µM S2O32-.

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Figure 4. Selectivity of the AgNPs-based colorimetric sensor for S2O32- compared with the other anions. (a) AgNPs-based colorimetric sensor incubated with 10.0 µM various anions; (b) AgNPs-based colorimetric sensor incubated with S2O32- (10.0 µM), or mixture of S2O32- (10.0 µM) and other anions (10.0 µM); (c) (A0-A)/A0 of the colorimetric sensor incubated with 10.0 µM of various anions; (d) (A0-A)/A0 of the colorimetric sensor incubated with S2O32- (10.0 µM) and other anions (10.0 µM). A and A0 are the absorbance at 419 nm in the UV-vis absorption spectra of the colorimetric sensor incubated with or without S2O32-, respectively

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Figure 5. Sensitivity of the AgNPs-based colorimetric sensor for S2O32- detection. (a) UV-vis absorption spectra of the AgNPs dispersions in the presence of various concentrations of S2O32-; the inset shows the image of the AgNPs-based colorimetric sensor containing various S2O32concentrations from 0 to 20.0 µM; (b) plot of (A0-A)/A0 as a function of S2O32- concentration ranging from 0 to 20.0 µM; A and A0 are the absorbance at 419 nm in the UV-vis absorption spectra of the colorimetric sensor incubated with or without S2O32-, respectively; the inset shows the plot of (A0-A)/A0 versus S2O32- concentration ranging from 0.2 to 2.0 µM.

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Figure 6. Integration of the AgNPs-based colorimetric sensor with a smartphone. (a) With the addition of samples containing S2O32- to the AgNPs-based colorimetric sensor, the detection results were recorded and transmitted to a server to instantly receive RGB data; (b) the ratio of G/(R+G+B) versus S2O32- concentration in the range of 0 to 20.0 µM; and (c) score plot using a smartphone based on the formula of G/(R+G+B) (each anion concentration is 10.0 µM ).

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Table 1. Detection of S2O32- in spiked real water samples by our AgNPs-based sensor or Titration method. Titration AgNPs-based Smartphone-based Added Recoverye b c d Sample method sensor (µM) analysis (µM) (µM)a (%) (µM) (mean±E, n=3) (mean±E, n=5) Tap water Lake water a

1.0

1.042±0.059

0.811±0.036

0.659±0.098

81.1

5.0

4.817±0.023

4.069±0.072

3.902±0.036

81.4

1.0

1.030±0.042±

0.796±0.060

0.713±0.105

79.6

4.883±0.017

4.774±0.044

3.978±0.128

95.5

5.0 2-

The added amount of S2O3 in the real water samples; The S2O32- concentration in the spiked real water samples determined by Titration method; c The S2O32- concentration in the spiked real water samples determined by our AgNPs-based sensor using UV-vis spectroscopy; d The S2O32- concentration in the spiked real water samples determined by AgNPs-based sensor using a smartphone e Calculated from the equation: (observed value with S2O32- addition – observed value without S2O32- addition) / added value×100.

b

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