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A Fluorescent Binary Ensemble based on Pyrene Derivative and SDS Assemblies as a Chemical Tongue for Discriminating Metal Ions and Brand Water Lijun Zhang, Xinyan Huang, Yuan Cao, Yunhong Xin, and Liping Ding ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.7b00634 • Publication Date (Web): 14 Nov 2017 Downloaded from http://pubs.acs.org on November 17, 2017

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A Fluorescent Binary Ensemble based on Pyrene Derivative and SDS Assemblies as a Chemical Tongue for Discriminating Metal Ions and Brand Water Lijun Zhang,1 Xinyan Huang,2 Yuan Cao,1 Yunhong Xin,2 Liping Ding*,1 1

Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, PR China; 2

School of Physics and Information Technology, Shaanxi Normal University, Xi’an 710062, PR China

ABSTRACT: Enormous effort has been put to the detection and recognition of various heavy metal ions due to their involvement in serious environmental pollution and many major diseases. The present work developed a single fluorescent sensor ensemble that can distinguish and identify a variety of heavy metal ions. A pyrene-based fluorophore (PB) containing a metal ion receptor group was specially designed and synthesized. Anionic surfactant SDS assemblies can effectively adjust its fluorescence behavior. The selected binary ensemble based on PB/SDS assemblies can exhibit multiple emission bands and provide wavelength-based cross-reactive responses to a series of metal ions to realize pattern recognition ability. The combination of surfactant assembly modulation and the receptor for metal ions empowers the present sensor ensemble strong discrimination power, which could well differentiate 13 metal ions, including Cu2+, Co2+, Ni2+, Cr3+, Hg2+, Fe3+, Zn2+, Cd2+, Al3+, Pb2+, Ca2+, Mg2+, and Ba2+. Moreover, this single sensing ensemble could be further applied for identifying different brands of drinking water.

KEYWORDS: fluorescent sensor, metal ion, SDS, pattern recognition, discrimination

The tremendous use and spread of heavy metal ions have caused serious environmental pollution.1-3 Heavy metal ions are also the source of many major diseases.4-5 Accurate and rapid detection and identification of heavy metal ions in aqueous solution are of great significance for food safety, ecological environment improvement and human health. Among a variety of reported methods for detecting metal ions, fluorescent sensors have gained enormous attention because of their strong merits lying in simplicity, sensitivity, selectivity, in vitro and/or vivo measurements, real time and online detection.6-9 However, the use of traditional lock-key type sensors with high selectivity can’t meet the needs of recognizing different metal ions or analysing complex samples containing mixed metal ions. Therefore, there is an urgent need to develop high-throughput detection methods that can not only recognize different metal ions but also effectively analyze complex samples. Inspired by the working principle of animals’ olfactory system, researchers have developed various fluorescent sensor arrays for realizing the identification of multi metal ions. Fluorescent sensor arrays are usually composed of a plenty of fluorescent sensing elements with interactive responses and can form characteristic fingerprints for specific analytes.10-12 Anzenbacher Jr. et al. reported using a sensor array containing 9 cross-reactive 8hydroxyquinoline-based sensing elements to realize discrimination of 10 metal ions.13 Chang and co-workers used

five small fluorophores connected to a metal chelator to compose a fluorescent array and realized identification of 7 different metal ions.14 Tan and co-workers developed a sensor array composed of four anionic conjugated polyelectrolytes (CPEs) with a common poly(p-pheynylene ethynylene) (PPE) backbone to provide pattern recognition for 8 metal ions.15 Lee et al. used an array of 47 offthe-shelf dyes and realized identification of 44 metal ions.16 However, traditional sensor arrays usually use a large number of sensor elements to achieve cross-reactive responses. Although they can provide good fingerprint recognition ability, but often consume large amount of samples, and encounter complex data collection issues due to the use of different fluorophores. Therefore, to avoid such issues, single discriminative fluorescent sensors based on multi-wavelength cross-reactive responses are developed. Using such sensors, the data acquisition for an analyte can be achieved through one-scan of the whole spectrum, and the fluorescence responses at different wavelengths can be combined to generate a recognition pattern for a specific analyte.17-20 However, single fluorescent sensing systems for discrimination of metal ions are still scarcely developed and in highly demanded.21 We have been interested in using surfactant assemblies modulating the fluorescence emission of a fluorophore with multiple emission bands to develop discriminative sensors.22-24 The variation of aggregation states of amphiphilic surfactant assemblies can well modulate the

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fluorescence emission of the encapsulated fluorophores.23, 25 Thus, analyte-induced aggregation changes of surfactant assemblies could be signalled by the fluorophores.26 The combination of fluorescence variations at different emission wavelengths of such single sensor ensembles could generate recognition fingerprint for the tested analytes. Based on this strategy, we have constructed two fluorescent binary ensembles capable of discriminating multiple metal ions. One is based on a bispyrene fluorophore with two pyrene moieties connected with a flexible polar spacer,23 the ensemble of which with SDS assemblies realized the discrimination of 6 metal ions including Fe3+, Co2+, Ni2+, Cu2+, Pb2+, and Hg2+. The other binary ensemble is based on a mono-pyrene fluorophore and SDS assemblies, which could differentiate 7 quenching metal ions.22 To further enhance the discrimination power of such binary ensemble sensing systems, we specially introduced a metal receptor unit to the pyrene fluorophore. The specially designed and synthesized fluorophore, bis(2picolyl)amine-modified pyrene derivative (PB), is shown in Scheme 1. As expected, anionic surfactant SDS can well modulate its fluorescence emission. Its ensemble with SDS assemblies displays strong discrimination ability and can well identify 13 different metal ions. Moreover, it could be further applied in monitoring the purity of purified water and identifying different brands of mineral water. The strong discrimination power of the present sensor ensemble could be due to the synergistic effect of the metal chelator’s binding ability and surfactant modulating effects on the photophysical properties of a probe with multiple emission bands. EXPERIMENTAL SECTION Chemicals and Instruments. 1,2-bis(2-aminoethoxy)ethane (EOA, 98%), sodium dodecyl sulfate (SDS, 99%) and 2-chloromethylpyridine hydrochloride (98%) were purchased from Sigma-Aldrich company and used as provided. Pyrene (Alfa, 98%) was recrystallized from ethyl alcohol before use. Pyrenesulfonyl chloride (PSC) was synthesized by adopting a literature method.27 Aniline (99.5%) and hexadecytrimethylammonium chloride (CTAC, 99%) were purchased from J&K scientific Ltd. All metal ions were used in nitrate salt (except HgCl2) and dissolved in water to obtain 0.25 mM stock solutions. Aqueous solutions were prepared from Milli-Q water (18.2 MΩ cm at 25 °C). We measured 1H NMR and 13C NMR spectra on a Bruker AV 400 MHz NMR spectrometer, and obtained the high resolution mass spectra (MS) on a Bruker maxis UHRTOF Mass Spectrometer (ESI positive mode). The FTIR spectra were recorded on a Fourier Transform Infrared Spectrometer (Vertex 70v, Bruker, Germany). UV−vis absorption spectra were recorded on a spectrophotometer (U3900, Hitachi). TEM image was measured by FEI Tecnai G2 F20 field transmission electron microscopy at 200 kV, and DLS tests were carried out on a Malvern Zetasizer

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Nano-ZS90 to detect the aggregate size distribution. Steady-state fluorescence measurements were performed at 25 °C on a FS5 fluorescence spectrometer (Edinburgh Instruments, UK) that uses 150 W xenon light as the excitation source. All samples were excited at 351 nm and the emission wavelength was fixed at 380 nm. The excitation and emission slits were fixed at 5.5 nm and 1.3 nm. Timeresolved fluorescence decays were conducted on a timecorrelated single photon-counting fluorescence spectrometer (FLS920, Edinburgh Instruments, UK) using a laser (343.4 nm) as the excitation source. Synthesis of Bis(2-picolyl)amine-Modified Pyrene Fluorophore. The synthesis process of the bis(2picolyl)amine-modified pyrene derivative, PB, is depicted in Scheme 1. The synthesis process and characterization data were described in detail as follows.

Scheme 1. Synthesis of bis(2-picolyl)amine-modified pyrene fluorophores, PB Compound Py-EOA was synthesized by adopting literature methods.28 The synthesis of compound BPA is as follows: To a solution of 2-chloromethylpyridine hydrochloride (3.94 g, 24 mmol) in H2O (3.0 mL), aniline (1.12 g, 12 mmol), 5 mol/L NaOH (6 mL), and CTAC (50 mg) were added under N2 protection. The mixture was stirred vigorously for 24h at r. t. After reaction, the mixture was extracted with CH2Cl2, and the organic solution was washed with H2O and dried over anhydrous Na2SO4. The crude product was obtained after removal of the organic solvent, and further purified by flash column (CH2Cl2/AcOEt=1/5, v/v) to yield the final pure product as beige solid (0.6 g, yield: 18.2%). 1H NMR (400 MHz, CDCl3, ppm): δ 8.58 (s, 2H), 7.63 (s, 2H), 7.21 (d, J = 43.7 Hz, 6H), 6.70 (s, 3H), 4.82 (s, 4H). MS (ESI, m/z) [M+H]+: Calcd for C18H17N3, 276.1493; found, 276.1507. Synthesis of BPAB is as follows: POCl3 (1.56 mL, 17 mmol) was added into an ice bath-cooled DMF solution (2 mL, 26 mmol) in portions over a period of 0.5 h. The solution was then stirred for 0.5 h. Compound BPA (0.60 g, 2.18 mmol) in DMF (1 mL) was added in portions in the above solution over a period of 20 min. The mixture was heated for 3 h at 80 oC and then poured into H2O (5 mL), and next neutralized to pH 6~8 with K2CO3 along with

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stirring. The mixture was extracted with CH2Cl2 and dried over anhydrous Na2SO4. Removal of the organic solvent gave the crude product, which was further purified by flash column (acetone/n-hexane=1/1, v/v) to give the pure product BPAB as yellow oil (185 mg, yield: 61%). 1H NMR (400 MHz, CDCl3, ppm): δ 9.73 (s, 1H), 8.62 (s, 2H), 7.68 (s, 4H), 7.23 (s, 4H), 6.80 (s, 2H), 4.92 (s, 4H). MS (ESI, m/z) [M+H]+: Calcd for C19H17N3O, 304.1444; found, 304.1443. Synthesis of PB is as follows: To a solution of BPAB (0.31 g, 1 mmol), Py-EOA (0.45 g, 1.1 mmol) in CH2Cl2 and MeOH (DCM-MeOH) mixture (3:1) (2 mL) was added. Then, anhydrous sodium sulfate (0.14 g, 1 mmol) was added. The suspension was stirred at r.t. overnight. Na2SO4 was filtered off and CH2Cl2 was removed. Then, methanol (1 mL) and NaBH4 (0.02 g, 0.5 mmol) was added to the obtained oil at 0 °C. After being stirred at r.t. for 45 min, the reaction was quenched by saturated NaHCO3 solution. MeOH was removed under reduced pressure. The aqueous phase was washed with DCM for 3 times. The combined organics were washed with brine and then dried over anhydrous Na2SO4. Removal of the organic solvent yielded the crude product, which was further purified by flash column (CH2Cl2/MeOH=8/1, v/v) to give the pure product as pale yellow solid (0.37 g, yield: 52.9%). 1H NMR (400 MHz, CDCl3, ppm): δ 9.04 (s, 1H), 8.66 (s, 1H), 8.56 (s, 2H), 8.18 (m, 7H), 7.58 (s, 2H), 7.20 (m, 6H), 6.67 (s, 2H), 4.77 (s, 4H), 3.95 (s, 2H), 3.67 (s, 2H), 3.45 (s, 6H), 3.08 (s, 2H), 2.89 (s, 2H). 13C NMR (101 MHz, CDCl3, ppm) δ 158.43, 149.70, 148.56, 136.89, 134.71, 130.91, 130.02, 126.99, 126.71, 125.15, 123.98, 123.76, 123.61, 122.09, 120.81, 112.76, 69.92, 69.39, 67.19, 57.20, 51.35, 46.12, 42.88. FTIR (KBr plate, cm-1): 3215 (–NH), 2923 (–CH2), 1664 (Ar C=N), 1589 (Ar C=C), 1247 (O=S=O), 1222 (–C–O– C). MS (ESI, m/z) [M+H]+: Calcd. for C41H41N5O4S, 700.2952; found, 700.2951. Sample Preparation. First, the aqueous stock solution of PB (2.5 mM) and that of SDS (40 mM) were prepared, where the necessary corresponding compounds were dissolved into methanol and water under sonication, respectively. Then, a series of SDS aqueous solutions at different concentrations were prepared by diluting SDS stock solution (40 mM) with water. The stock solutions of various metal ion salts (2.5 mM) were also prepared in water. All aqueous solutions were prepared using Milli-Q water (18.2 MΩ cm at 25 °C). Sensing Assays of Detecting Metal Ions. First, the stock solution of PB was mixed with the surfactant stock solution and diluted by water to obtain the final sensor solution containing 10 μM of PB and 5 mM of SDS, which were then sonicated for 30 min and allowed to stand overnight before use. For metal ion sensing assays, 2.5 mL of the sensor solution was added in a cuvette. The fluorescence emission spectrum of this blank sensor solution was scanned first and the fluorescence intensity at four typical wavelengths was recorded as I0. Next, the aqueous stock solutions of metal ions were gradually titrated into

the sensor solution, and the corresponding fluorescence intensity at the four wavelengths was recorded again and labeled as I. The logarithm data of the fluorescence variation (I/I0) were then calculated and used for pattern recognition and discrimination analysis. Sensing Assays of Detecting Brand Water. First, the sensor ensemble in Milli-Q water sample was taken as the blank, and the fluorescence emission intensity at the four wavelengths was regarded as I0. Then, the sensor ensemble in the tested water samples was prepared and the fluorescence intensity at the four wavelengths was taken as I. The test solution was prepared by diluting the mixture of PB stock solution and SDS stock solution with the tested brand water to contain PB at 10 μM and SDS at 5 mM, which was then sonicated for 30 min and allowed to stand overnight before use. The logarithm data of the fluorescence variation (I/I0) were then calculated and used for pattern recognition and discrimination analysis. 1 H NMR titration with metal ions. First, PB (2 mg, 2.8 µmol) was dissolved in 600 µL CD4O, and SDS (8 mg, 28 µmol) was dissolved in 160 µL CD4O (8 mg was selected as an appropriate amount for SDS after a series of try as it is close to saturation and causes no precipitation). Then, the two solutions were mixed and used for 1H NMR test in the absence of metal ions. Next, the CD4O solutions (30 µL) dissolving metal ions (2.8 µM) were prepared and titrated to the sensor solution at 0.3, 0.6, and 1 equiv. with PB. The 1H NMR spectra were measured again in the presence of different equiv. of metal ions. RESULTS AND DISCUSSION UV-vis Absorption and Fluorescence Emission of PB in Aqueous Solution. The basic photophysical properties of PB were evaluated by measuring its UV-vis absorption and steady-state fluorescence emission spectra in aqueous solution. As seen in Figure 1, the UV-vis absorption spectra of PB exhibit the typical pyrene absorption peaks at 268, 279, 351, and 379 nm. The molar absorption coefficient of PB at 351 nm was determined to be 2.4 (±0.01)×104 L mol-1 cm-1, with R2 value of 0.999 (Figure S1a, Supporting Information). The fluorescence emission spectrum of PB shows only monomer emission at 379 and 399 nm. This is true until the concentration of the probe increases to 60 μM. The further increase of PB concentration to 100 μM leads to gradual enhancement of excimer emission (Figure S1b, Supporting Information). Such results suggest that probe is well mono-molecularly dissolved in water at low concentration and has a good aqueous solubility. Moreover, the fluorescence quantum yield (Φ) of PB in water was determined to be 0.027.

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Figure 1. UV-vis absorption and steady-state fluorescence emission spectra of PB (10 μM) in water (λex = 351 nm). SDS Modulation Effect on Fluorescence Emission of PB in Water. As evidenced by our previous work, surfactant assemblies can well modulate the fluorescence emission of pyrene-based fluorophores.22-23, 29 Thus, the modulation effect of SDS assembles on the fluorescence emission of PB was evaluated in aqueous solutions. As shown in Figure 2a, SDS assemblies can also significantly adjust the fluorescence emission of the present probe. Clearly, along varying SDS concentration, the emission profile of PB changes remarkably. The presence of SDS assemblies can generate excimer emission and vary its ratio to monomer emission. The intensity ratio of excimer emission at 501 nm to monomer emission at 379 nm of PB in different SDS solutions were calculated and illustrated in Figure 2b. It can be seen that the ratio goes up along SDS concentration increasing from 1 to 5 mM, and then decreases in further concentrated SDS solutions. The highest ratio of excimer emission to monomer is found in 5 mM SDS solution. Therefore, 5 mM SDS was selected to incorporate the present fluorophore to construct a binary fluorescent ensemble for sensing metal ions because the ensemble exhibits apparent monomer-excimer co-emission and provides multiple emission signals. The TEM image of the binary ensemble was measured. As shown in Figure S2a (Supporting Information), the binary ensemble exists as round aggregates with a diameter at ca. 60~70 nm. The DLS measurements found a homogeneous size distribution of ca. 150 nm with a PDI value of 0.124 (Figure S2b, Supporting Information). Such dynamic aggregates could be further influenced by the added metal ions and cause various fluorescence variation. Thus, the fluorescence responses of the binary ensemble to a series of metal ions were measured. Sensing Behaviors to Multiple Metal Ions. The sensing behaviors of the sensor platform based on PB/SDS (10 μM /5 mM) to 13 metal ions were thoroughly examined. Very interestingly, the sensor ensemble exhibits three main different response modes to the 13 tested metal ions. namely Mg2+, Al3+, Ca2+, Cr3+, Fe3+, Co2+, Ni2+, Cu2+, Zn2+, Cd2+, Ba2+, Hg2+ and Pb2+.

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Figure 2. (a) Fluorescence emission spectra of PB (10 μM) in different concentrated SDS aqueous solutions (λex = 351 nm). (b) Fluorescence intensity ratio of excimer emission to monomer as a function of SDS concentration. The first mode is a turn-off response, where the fluorescence emission is gradually quenched along increasing metal ion concentration. This is observed for Cu2+ and Fe3+, which are shown in Figure 3a and Figure S3 (Supporting Information). The excimer emission can be effectively quenched to 2.26% of the original intensity by gradual addition of Cu2+ to 50 μM. Differently, the excimer emission is quenched to 16.35% of the original intensity by adding 160 μM Fe3+, suggesting a smaller quenching effect by Fe3+ compared to Cu2+. Moreover, the monomer quenching effect is considerably smaller than the excimer upon addition of both Cu2+ and Fe3+. The second mode is a ratiometric response, where an increased monomer emission is followed by a reduced excimer emission when metal ion concentration increases. This phenomenon is observed for the titration of 5 examined metal ions including Co2+, Ni2+, Cr3+, Al3+, and Ca2+, which are shown in Figure 3b and Figure S4a-d (Supporting Information). However, there is a big difference existing in the ratiometric responses to different metal ions. The monomer emission is greatly enhanced upon the proportional addition of Co2+, Ni2+ and Cr3+, at the same time, the excimer emission is significantly decreased. Differently, although the monomer emission is proportionally increased upon adding more Al3+ and Ca2+, the excimer emission does not decrease regularly. Such

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difference in the monomer and excimer responses is clearly illustrated in panel e and f of Figure S4 (Supporting Information). These results illustrate the sensor ensemble exhibits clear multiple-wavelength cross-reactive responses to the metal ions that induce the same response mode.

cimer are quite different from metal ion to metal ion (Figure S5f-g, Supporting Information). First, the enhancing extent is quite different. The ensemble sensor exhibits quite remarkable enhancing responses to Zn2+ and Cd2+, while the enhancing extent is much smaller to Pb2+, Mg2+, and Ba2+. Secondly, the enhancing effect to some metal ion is not unidirectional. The ensemble shows enhancing response to Hg2+ at low concentration but quenching effect at high concentration. Such results reveal again that the sensor ensemble exhibit clearly multiple-wavelength cross-reactive responses to these metal ions. The different fluorescence variations of the binary ensemble at monomer and excimer emission suggest that the emission at different wavelengths provides diverse responses toward different metal ions. Thus, the recognition patterns to different metal ions were produced through collection of the data of the fluorescence variation (lg(I/I0)) at four typical wavelengths (379, 399, 460 and 501 nm). These four wavelengths are representative for the pyrene emission at two monomer peaks, the distorted excimer, and the perfect excimer, respectively. As a consequence, the four fluorescence signals of the sensor are combined to form a specific recognition pattern for each metal ion and the results are displayed in Figure 4. The error bars represent the calculated standard deviation for three individual replicate measurements. It can be seen from the bar charts that the sensor can generate a distinct recognition pattern to the 13 metal ions, including Cu2+, Co2+ , Ni2+ Cr3+, Hg2+, Fe3+, Zn2+, Cd2+, Al3+, Pb2+, Ca2+, Mg2+, and Ba2+ no matter what concentration was measured. Such results indicate that the present sensor ensemble could provide multiple-wavelength cross-reactive responses to metal ions, where the fluorescence variation at a particular wavelength rather than a sensor element

Figure 3. Fluorescence spectra of PB/SDS (10 μM/5 mM) in aqueous solution upon addition of different metal ions: (a) Cu2+, (b) Co2+, and (c) Zn2+ ion. (λex = 351 nm) The third mode is a turn-on response, where both the monomer and excimer emission are gradually enhanced along increasing metal ion concentration. This phenomenon is observed for the titration of 6 examined metal ions including Zn2+, Cd2+, Pb2+, Mg2+, Ba2+, and Hg2+. The corresponding results are illustrated in Figure 3c and Figure S5 (Supporting Information). Once again, for this response mode, the fluorescence responses at monomer and ex-

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Figure 4. Four-signal recognition patterns of the sensor ensemble for 13 metal ions: (a) [Mn+] = 10 μM; (b) [Mn+] = 50 μM; and (c) [Mn+] = 100 μM. provides a cross-reactive signal. Thus, it significantly simplifies the data collection process and reduces the consumption of sensor samples. Discrimination Behaviors to Multiple Metal Ions. The cross-reactive responses indicate that the present sensor ensemble may be capable of differentiating all these tested metal ions. As a classical statistical technique, principal component analysis (PCA) has been widely applied for evaluating discrimination ability of the developed sensors. Usually, it estimates combinations of variables in multidimensional data sets and then characterizes groupings of objects (classification) within the sets.28, 30 This is achieved by calculating orthogonal eigenvectors (principal components, PCs) that lie in the direction of the maximum variance within that data. First, the discrimination of different metal ions at the same concentration was evaluated for the sensor ensemble. Three concentrations were used for measurements, namely, 10, 50, and 100 μM. The fluorescence responses to each metal ion at one particular concentration were measured repeatedly for 6 times, and the resulting fluorescence variation, lg(I/I0), at 379, 399, 460 and 501 nm were calculated and collected. Thus, a full set of fluorescence variation values were employed to build a matrix consisting of 1 sensor × 4 wavelengths × 13 metal ions × 6 replicates for PCA analysis. The resulting two dimensional PCA score plots are displayed in Figure 5a and Figure S6 (Supporting Information). At low concentration of 10 μM (Figure S6a), the clusters for Cu2+, Co2+, Ni2+, Cr3+, Hg2+ and Cd2+ are well separated from those for the other metal ions. However,

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the other 7 metal ions including Fe3+, Zn2+, Al3+, Mg2+, Pb2+, Ca2+ and Ba2+ are all overlapped together and difficult to be identified from each other. When these metal ions were measured at 50 μM, more types of metal ions are able to be separated and discriminated from one another. As seen from Figure 5a, 11 metal ions including Cu2+, Co2+, Ni2+, Cr3+, Hg2+, Fe3+, Zn2+, Cd2+, Al3+, Mg2+, and Ba2+ could be separately grouped in different clusters, and just the clusters for Pb2+ and Ca2+ are hard to be separated along PC1. For even higher concentrations at 100 μM, the PCA plots show much better separation and discrimination effect among the 13 metal ions (Figure S6b). Such results verify that the present sensor platform has strong discrimination ability of identifying the examined 13 metal ions, especially for recognizing higher concentration metal ions. We then measured the discrimination ability of the sensor platform to identify the same metal ion at different concentrations for quantitation analysis. The pattern responses of the sensor ensemble to three representative metal ions (Cu2+, Co2+ and Zn2+) at five different concentrations (5, 10, 20, 30, and 50 µM) were measured repeatedly for 6 times. The corresponding PCA plot is shown in Figure 5b. For both Zn2+ and Co2+, the same concentration for each individual metal ion is well grouped together and different concentration is well separated in different clusters, suggesting a possibility of quantitation analysis. As to Cu2+, only the cluster for the low concentration at 5 µM is well separated from the other concentrations. The clusters for higher concentration ranging from 10 to 50 µM are all overlapped. This could be due to the quenching effect by Cu2+ almost reaches to its maximum at 10 µM (Figure 3a). Differently, for Zn2+ and Co2+, the ensemble shows quite different fluorescence responses at the measured 5 concentrations (Figure 3b and 3c). Such results indicate that the metal ion at different concentration can be quantitatively measured by PCA plot as long as their fluorescence responses are different. Another thing should be noted is that the clusters for the three metal ions are all well separated from one another no matter what concentration for each metal ion is, suggesting again different metal ions could be differentiated by the sensor ensemble.

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water

Figure 5. Two dimensional PCA plots for PB/SDS (10 μM/5 mM) ensemble to (a) discriminate 13 metal ions at 50 µM and (b) identify three metal ions at different concentrations (5, 10, 20, 30, and 50 µM). Applications in Identifying Different Brands of Water. To check the practical application of the present sensor ensemble, its metal-ion discrimination ability was further used to identify 14 different types of drinking water (Table 1). It is known that different brand mineral water contains different composition of metal ions, where both the type and amount of metal ions are varying from brand to brand. Differently, purified drinking water should contain no metal ions. According to the crossreactive responses of the present sensor ensemble to different metal ions, it may on one hand differentiate purified drinking water from mineral water, and on the other hand discriminate among different brands of mineral water with respect to their metal ion composition. As shown in Table 1, 6 brands of purified water and 8 brands of mineral water (1 sample was tap water) were used for test. For measuring these different brands of drinking water, the sensor ensemble was prepared directly in these water samples. Then, their fluorescence emission was measured and compared with that measured in Milli-Q water to obtain the fluorescence variation at the four typical wavelengths. At r.t., the mineral water samples containing the sensor ensemble is not transparent due to the formation of strong aggregates. It can become total transparent when stored at 50 oC. Thus, the fluorescence emission of the sensor ensemble in the 14 drinking water samples was measured both at 25 oC and 50 oC, and the fluorescence variation data were used to evaluate the discrimination ability under both conditions.

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Icedew Chunyue 8210 Alpenwater Kunlunshan mineral water Nongfu Spring Haokuaihuo(Barrelled mineral water) Tap water

The fluorescence emission of the sensor ensemble in different water samples measured at 25 oC and 50 oC are illustrated in Figure 6a and Figure S7a, respectively. It can be seen that at both temperatures, the sensor ensemble exhibits different fluorescence emissions in different mineral water samples, but displays similar fluorescence emission in all the tested purified water samples (Insets of Figure 6a and Figure S7a). The recognition patterns to different drinking water samples were generated by collecting the data of the fluorescence variation at the four typical wavelengths. The resulting patterns obtained at 25 o C and 50 oC are illustrated in Figure 6b and Figure S7b, respectively. The error bars represent the calculated standard deviation for three individual replicate measurements. Evidently, it can be seen from the bar charts that the ensemble sensor can generate similar pattern responses to the 6 different brands of purified water and form a distinct recognition pattern to the 8 different brands of mineral water. This distinct pattern response suggests that the present sensor could not only differentiate purified water from mineral water, but also provide cross-reactive responses to different brands of mineral water and can discriminate them from one another.

Table 1. List of the 14 drinking water samples used in this study Type No. Brand

Purified water

Mineral

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C’estbon Aquafina Youyue Wahaha Wahaha Yangdao Wahaha (Crystal drilling water) Icedew

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Figure 6. (a) Fluorescence spectra of PB/SDS (10 μM/5 mM) in different drinking water (Inset: enlarged spectra tested in purified water samples); (b) Recognition patterns for different drinking water by collecting fluorescence quenching data at four selected wavelengths of the sensor; (c) Two dimensional PCA plot for PB/SDS (10 μM/5 mM) ensemble to discriminate 14 different brands of drinking water. (The brand name of each water sample is listed in Table 1, and the test is conducted at 25 oC.) Then, a full set of fluorescence values were used to build a matrix consisting of 1 sensor × 4 wavelengths × 14 drinking waters × 6 replicates for PCA analysis. The resulting two dimensional PCA score plots are displayed in Figure 6c and Figure S7c for the test at 25 and 50 °C. As can be seen, for both conditions, all the purified drinking water samples are clustered together and well separated from all the mineral water samples. This could be understood since there are no metal ions in purified water. As to the discrimination of mineral water, it can be seen from Figure 6c that different brands of mineral water could be grouped in well separated clusters except that the clusters of 8210 and Haokuaihuo (barrelled mineral water) are grouped together along PC1. These results imply that the present ensemble sensor has the ability to discriminate most of the examined brands of mineral water. To further validate the practical application of the present sensing system, the discrimination of brand mineral water from contaminated ones was measured. For this test, we randomly chose 2 brands of mineral water and two contaminated samples of each brand (Table S1). The contaminated samples were prepared by adding some extra metal ions in the brand water. As shown in Figure S8a (Supporting Information), the sensor ensemble exhibits quite different fluorescence emission profiles in different water samples. The fluorescence variation of the four typical wavelengths was collected by comparing the fluorescence intensity in each water sample to that in the Milli-Q water. The combination of the four fluorescence variation signals could also generate the recognition pattern for each sample (Figure S8b, Supporting Information). The PCA studies reveal that the present ensemble sensor could effectively differentiate the clean drinking water

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from the contaminated ones for brands of mineral water (Figure S8c, Supporting Information). Such results indicate that the present ensemble sensor could be used to monitor the quality of each brand of mineral water or to detect fake brand of mineral water. Sensing Mechanism Investigation. To understand the sensing mechanism of the present sensor ensemble, three particular experiments were conducted. First, 1H NMR spectroscopic titration of PB/SDS (1:10) with some metal ions including Fe3+, Co2+, Ni2+, Zn2+, and Cd2+ in CD4O was performed to investigate the interaction between the probe and metal ions (Figure 7 and Figure S9, Supporting Information). The five metal ions were chosen as representative of the three response modes (quenching mode: Fe3+; ratiometric mode: Co2+ and Ni2+; turn-on mode: Zn2+ and Cd2+). As seen from Figure 7, the gradual addition of Zn2+ causes apparent downfield shifts of the protons in the pyridine ring of the picolyl unit including Ha, Hb, and Hc. Their chemical shifts change from 8.56 to 8.77, from 7.66 to 8.17, and from 7.22 to 7.73, respectively. Moreover, the protons (Hd and He) in the benzene ring close to amine unit also exhibit clear downfield shifts from 7.13 to 7.25 for Hd, and from 6.67 to 6.91 for He, respectively. Such results suggest the picolyl moiety in the probe PB may take part in binding with Zn2+. The titration with Cd2+ induced similar chemical shifts of Ha to He as that observed for titration with Zn2+, suggesting Cd2+ has similar interaction with picolyl moiety (Figure S9a, Supporting Information). Upon titration of Fe3+, similar downfield shifts were observed for Ha, Hb, Hc, and Hd (Figure S9b, Supporting Information). Differently, the resonance of Hc is totally split into two triplet peaks upon addition of one equiv. Fe3+, which is more apparent than the change induced by one equiv. Zn2+. Moreover, the resonance of He exhibits slight upfield shift upon addition of Fe3+ rather than downfield shift as observed for titration of Zn2+. Upon titration of Co2+ and Ni2+, the chemical shifts of the protons Ha, Hb, Hc, Hd, and He show slight changes (Figure S9c and S9d, Supporting Information). Such different 1H NMR titration behaviors indicate clearly different responding mode metal ions have different interactions with the receptor unit, and prove that the picolyl moiety is involved in binding with some of the tested metal ions.

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Figure 7. Partial H NMR spectroscopic titration of sensor PB/SDS (1:10) with metal ion Zn2+ (0–1 equiv) in CD4O. The involvement of the picolyl unit as metal receptor in the sensing process was also confirmed by the following control experiment. The probe Py-EOA was used as a control compound since it has similar structure to PB but without the picolyl moiety. A similar sensing platform PyEOA/SDS (10 μM/5 mM) was constructed, and its sensing behaviors to the 13 metal ions were examined to evaluate the role of picolyl unit. By comparison, the control ensemble sensor exhibits either similar or smaller extent of fluorescence changes upon titration of the tested metal ions. As seen in Figure S10 (Supporting Information), the control sensor shows much smaller responses to 5 of the tested metal ions including Cu2+, Co2+, Cr3+, Zn2+, and Cd2+. It indicates that besides the modulation effect of surfactant assemblies, the metal receptor also plays an important role in the stronger discrimination ability of the ensemble sensor based on PB/SDS. We then conducted the time-resolved emission spectra (TRES) measurement to explore the nature of excimer of PB in the SDS assemblies, which can indicate the location of the probe in the assemblies.23 As shown in Figure 8, the spectrum profiles of the earlier time gates (0−2 and 2−5 ns) are mainly contributed by pyrene monomer emission. Along the time gate moving to longer time (after 5 ns), the excimer emission gradually contributes more to the profile. This result verifies that the excimer formation in SDS assemblies is actually via Birks’ scheme, and the nature of excimer is dynamic.31 Thus, the probe should stay in a more flexible microenvironment in SDS assemblies to form dynamic excimer, which is likely located at the surfactant assembly-water interface (Scheme 2). Such location can guarantee the probe is more sensitive to the metal ion induced aggregation changes of the surfactant assemblies and exhibit cross-reactive responses to the added metal ions.23

Figure 8. Time-resolved emission spectra of PB/SDS (10 μM/5 mM) (λex=343 nm; λem=501 nm). Based on the above results and discussion, a schematic cartoon was drawn to explain the observed cross-reactive responses of the present binary ensemble. As shown in Scheme 2, the assembling of PB in an appropriate SDS assembly can generate monomer-excimer co-emission. The binding of picolyl unit of PB with different metal ions and the electrostatic interaction between anionic SDS assemblies and cationic metal ions help to induce various fluorescence changes of the sensor ensemble. The combination of the cross-reactive responses at different wavelength then generates recognition patterns for different metal ions and realizes discrimination.

Scheme 2. Schematic representation of the discriminative sensing process of PB/SDS to various metal ions. CONCLUSIONS In summary, we developed a new fluorescent binary ensemble sensor based on a picolyl-modified pyrene derivative and SDS assemblies that is capable of discriminating multiple metal ions in aqueous solutions. The sensor ensemble displays multiple-wavelength cross-reactive responses to the tested metal ions. The combination of the fluorescence variation at four typical wavelengths could form specific recognition patterns for different metal ions. Moreover, PCA analysis demonstrates that the sensor system could differentiate and identify 13 metal ions, namely Cu2+, Co2+, Ni2+, Cr3+, Hg2+, Fe3+, Zn2+, Cd2+, Al3+, Pb2+, Ca2+, Mg2+, and Ba2+. The binary sensor ensemble could be further used to differentiate purified drinking water from mineral water as well as identify different

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brands of mineral water. 1H NMR titration and comparison of sensing behaviors with control binary ensemble reveal that the picolyl unit in the probe plays an important role in binding with metal ions and the strong discrimination behaviour of the sensor ensemble. Timeresolved emission spectra measurements find that the probe is more likely located at the assembly-water interface and is sensitive to the variation of the surfactant assemblies. The present work proves the introduction of analyte-binding receptor in the probe could enhance the discrimination ability of fluorescent binary ensembles based on surfactant modulation strategy.

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ASSOCIATED CONTENT

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Supporting Information. Uv-vis absorption and fluorescence emission of PB at different concentrations, TEM image and DLS data of PB/SDS ensemble, fluorescence titration of metal ions, PCA plots of discrimination of metal ions, discrimination of water at 50 °C, 1 2+ test with contaminated samples, H NMR titration with Cd , 3+ 2+ 2+ Fe , Co , and Ni , sensing behaviors of control sensor ensemble. “This material is available free of charge via the Internet at http://pubs.acs.org.”

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AUTHOR INFORMATION Corresponding Author * E-mail: [email protected]

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Author Contributions Lijun Zhang, Xinyan Huang, Yuan Cao, Yunhong Xin, Liping Ding

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Notes The authors declare no competing financial interest. (13)

ACKNOWLEDGMENT The authors acknowledge the financial support from National Natural Science Foundation of China (21573140), the Program of Introducing Talents of Discipline to Universities (B14041), Program for Changjiang Scholars and Innovative Research Team in Universities (IRT_14R33), and the Fundamental Research Funds for the Central Universities (GK201701004).

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