An Artificial Tongue Fluorescent Sensor Array for Identification and

Article pubs.acs.org/ac

An Artificial Tongue Fluorescent Sensor Array for Identification and Quantitation of Various Heavy Metal Ions Wang Xu,†,‡,¶ Changliang Ren,§,¶ Chai Lean Teoh,†,¶ Juanjuan Peng,† Shubhankar Haribhau Gadre,∥ Hyun-Woo Rhee,*,⊥ Chi-Lik Ken Lee,*,# and Young-Tae Chang*,†,∇ †

Department of Chemistry & MedChem Program of Life Sciences Institute, National University of Singapore, Singapore 117543 Singapore Peking Oxford Research Enterprise (SPORE), Environmental Research Institute (NERI), 5A Engineering Drive 1, #02-01, Singapore 117411 § Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Singapore 117602 ∥ Indian Institute of Technology Kanpur, Kalyanpur, Kanpur, Uttar Pradesh 208016, India ⊥ Department of Chemistry, School of Nano-Biosceince and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 689-798, Korea # Centre for Biomedical and Life Sciences, Technology Development Office, Singapore Polytechnic, Singapore 139651 ∇ Singapore Bioimaging Consortium, Agency for Science, Technology and Research (A*STAR), Singapore 138667 ‡

S Supporting Information *

ABSTRACT: Herein, a small-molecule fluorescent sensor array for rapid identification of seven heavy metal ions was designed and synthesized, with its sensing mechanism mimicking that of a tongue. The photoinduced electron transfer and intramolecular charge transfer mechanism result in combinatorial interactions between sensor array and heavy metal ions, which lead to diversified fluorescence wavelength shifts and emission intensity changes. Upon principle component analysis (PCA), this result renders clear identification of each heavy metal ion on a 3D spatial dispersion graph. Further exploration provides a concentrationdependent pattern, allowing both qualitative and quantitative measurements of heavy metal ions. On the basis of this information, a “safe-zone” concept was proposed, which provides rapid exclusion of versatile hazardous species from clean water samples based on toxicity characteristic leaching procedure standards. This type of small-molecule fluorescent sensor array could open a new avenue for multiple heavy metal ion detection and simplified water quality analysis.

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optical approach shows unique potential for high sensitivity and selectivity, cheap and easy handling instruments, and tunable emission range.20−24 The implementation of fluorescence sensor array techniques not only adopted all the merits of fluorescence, but also greatly augmented the feasibility of multitarget identification.25−28 Coupled with principle component analysis (PCA), sensor arrays can be an effective detection tool in sophisticated environments, such as in the analysis of toxic gases, environmental water pollutants, and illegal drug doping.29−32 Our group has demonstrated construction of sensor arrays for efficient identification of carbohydrates,33,34 metal ions,35,36 and environmental water samples.37 However, there is no reported sensor array that produces directly visible outputs for rapid on-site analyte detection.28,30,32 In this study, we accomplished the construction of a small-molecule fluorescent

eavy metal ion detection is a topic of intense research, due to their severe adverse impact on the environment and human health.1,2 Raised from both anthropological and natural sources, these species were discovered in soil, reservoir water, and ocean environment,3−5 where they accumulate in plants, meat, and other foods.6−8 Through these various absorption channels, these ions can gather in developing brains and disrupt protein/peptide secondary structures, leading to debilitating diseases.9−11 Specifically, local accumulation of Zn2+ in brains result in the rapid induction of Alzheimer amyloid structures.12,13 Due to their recalcitrant existence in environment and resistance toward common filtration-based removal methods, heavy metal ion pollution has haunted most developed countries.14 The harmful impact of heavy metal ions has urged wide research on their sensitive detection.15−19 Representative approaches range from spectroscopic detection, such as atomic absorption spectrophotometric methods and luminescent recombinant bacteria sensors, to newly developed absorptive stripping voltammetry measurements and DNA-based biosensors. Among these detection techniques, the fluorescence © XXXX American Chemical Society

Received: May 27, 2014 Accepted: August 14, 2014

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dx.doi.org/10.1021/ac501953z | Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistry

Article

Figure 1. Structures and sensing mechanism of the SGT sensor array. (a) Reaction motifs of the SGT sensor array. The upper row shows the fluorophores: P1 is picolinium, P2 is quinolinium, and P3 is BODIPY. The lower row shows the metal-chelating motifs: 1b is three-chelating, 2d is five-chelating, and 3d is seven-chelating. (b) Both intramolecular charge transfer (ICT) and photoinduced electron transfer (PET) in the sensors result in heavy metal ion induced changes in fluorescence intensity and emission maximum shift. (c) Structures of SGT1−5. The colors represent their observed fluorescence emission upon interaction with heavy metal ions.

expressed in parts per million (ppm) and coupling constants are reported as a J value in hertz (Hz). Spectroscopy was performed using a fluorimeter and UV/vis instrument (SpectraMax M2, Molecular Devices). Principal component analyses were performed using Pipeline Pilot Student Edition v6.1 and R-3.0.1, and principal component analyses graphs were visually obtained using Origin 9.0. Determination of the Dissociation Constants. The fluorescence emission spectra of SGT1−5 with seven different heavy metal ions were measured on a SpectraMax M2 plate reader. The heavy metal ion concentrations range from 0.1 to 90 μM, while the sensor concentration is fixed at 10 μM. The fluorescent titration curve was fitted to the standard equation using Graphpad Prism V5.0. The fluorescence intensities of each sensor−heavy metal ion pair was measured at their respective emission maxima with excitation at 365 nm. The bound fractions (X) of SGT sensors at each concentration were determined using the following equation

sensor arraydubbed the Singapore tongue (SGT)for rapid and sensitive detection of heavy metal ions. Fluorescent sensors that make up the SGT array have varying responses to different metal ions, and together, a unique fluorescence signature may be attributed to each metal ion. Using SGT coupled with PCA, we successfully achieved both quantitative and qualitative identification of seven different heavy metal ions, including Zn2+, Cu2+, Hg2+, Fe3+, Cr3+, Pb2+, and Cd2+. Moreover, we demonstrated rapid visual identification of seven heavy metal ions with simply a UV lamp, which can be easily adapted for on-site real-life metal analysis. On the basis of these responses, we constructed a “safe-zone” on the 3D dispersion graph, which allows immediate identification of any hazardous species that stay outside the safe-zone. Together, these results validate the application of SGT for rapid analysis of heavy metal ions and practical examination of water quality.

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EXPERIMENTAL SECTION Materials and Methods. All the chemicals were purchased from Sigma-Aldrich, Fluka, MERCK, Acros, and Alfa Aesar, and they were directly used without further purification. The heavy metal ions were produced from their respective nitrate salts. Normal phase column chromatography purification was carried using MERCK silica gel 60 (particle size 230−400 mesh, 0.040−0.063 mm). HPLC−MS was taken on an Agilent-1200 with a DAD detector and a single quadrupole mass spectrometer (6130 series). The analytical method, unless indicated, is as follows: A, H2O (0.1% HCOOH); B, CH3CN (0.1% HCOOH), gradient from 10 to 90% B in 10 min; C18 (2) Luna column (4.6 × 50 mm2, 3.5 μm particle size). 1H NMR and 13C NMR spectra were recorded on Bruker Avance 300 NMR and 500 NMR spectrometers, and chemical shifts are

X=

Fl − F0 Fsat − F0

(1)

where Fl and F0 are the fluorescence intensities of a given concentration of SGT sensors with and without heavy metal ions, respectively. Fsat is the fluorescence intensity at the same concentration of SGT sensors when fully bound. Fsat was determined by fluorescence titration at each concentration with a series of heavy metal ion concentrations. The results were plotted according to nonlinear fitting curve of the following equation F = F0 + B

(Fsat − F0)[SGT] (KD + [SGT])

(2)



Article

Figure 2. Typical fluorescence response of SGT sensor toward heavy metal ions. (a) Fluorescence responses of SGT1−5 (10 μM, diluted 100-fold from 1 mM DMSO solutions) to seven different metal ions (30 μM in PBS buffer at pH 7.0 prepared from nitrate salts). Ctrl is PBS buffer. The image is directly taken in a black box using a Canon EOS 550D digital single-lens reflex camera under the irradiation of a 365 nm UV lamp. Different colors are the intrinsic emission colors from different samples without further processing. (b) Fluorescence responses of SGT1−5 (10 μM) to seven different metal ions (30 μM in PBS buffer at pH 7.0) shown as a bar graph. The vertical axis represents fluorescence enhancement calculated from the respective emission maxima of each sensor−metal pair. (c) Upon Hg2+ activation, SGT1 displays changes in both emission intensity and emission maxima (580 to 545 nm). (d) Fluorescence emission intensity responses change with Hg2+ concentration (0−90 μM) at 545 nm. Inset is the linear range of the curve (0−10 μM).

P1, P2, and P3 and Supporting Information, part 1).42,43 On the other hand, N,N,N′,N′-tetrakis(2-pyridylmethyl)ethylenediamine (TPEN) and its derivatives were selected as metal chelators. These chelators consist of short alkylamine chains connecting several pyridyl rings that act as strong ligands to bind with transition metal ions (Figure 1a, lower row 1b, 2d, and 3d).44 Interaction of heavy metal ions to these metal chelating motifs could establish the styryl extended πconjugation, thus modulating the fluorescence of the main fluorophore via intramolecular charge transfer (ICT)45 and photoinduced electron transfer (PET)46,47 (Figure 1b). On the one hand, ICT usually occurs by company of adiabatic photoreactions that lead to two conformations of one molecule, with a difference in the dihedral angle between amino group and phenyl ring.48,49Therefore, ICT leads to a different subexcited level (charge transfer state) that will induce wavelength shift (Supporting Information, Figure S1).45,50 On the other hand, PET includes only transfer of electrons among established electron states between fluorophores and receptors.51 Therefore, PET leads to fluorescence intensity change, i.e., fluorescence turn-on/off (Supporting Information, Figure S2).52 Based on the fluorescence responses between sensors and metal ions, we have observed both wavelength shift and intensity change. Hence both ICT and PET are contributing to the fluorescence responses and therefore enhancing the differentiation among metal ions. In particular, picolinium dyes display environmentally sensitive properties exhibiting both hypsochromic and bathochromic shifts ranging between 530 and 600 nm upon chelation to heavy metal ions (Figure 1c, SGT1−3). The

where KD is the dissociation constant and [SGT] is the concentration of SGT sensors. Determination of Quantum Yield. Quantum yields were measured by dividing the integrated emission area of sensor fluorescent spectrum against the area of coumarin-153 in water excited at 365 nm (Φcoumarin‑153 = 0.12). Quantum yield was then calculated using eq 3, where F represents the integrated emission area of fluorescent spectrum, η representsthe refractive index of the solvent (same in this case), and Abs represents absorbance at excitation wavelength selected for standards and samples. Emission was integrated from 420 to 700 nm. ⎛ sample ⎞⎛ ηsample ⎞⎛ Abs reference ⎞ sample F Φsample = Φ ⎟ fluo fluo ⎜ reference ⎟⎜ reference ⎟⎜ ⎝F ⎠⎝ η ⎠⎝ Abssample ⎠

(3)

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RESULTS AND DISCUSSION Rational Construction of SGT Fluorescent Sensor Array. Rational construction of a styryl library was conducted to afford delicate selection of SGT members.38 The SGT sensor array consists of five fluorescent sensors that are generally made from a fluorophore connected to a metal chelator via a styryl motif (Figure 1a). These styryl motifs were synthesized via Knoevenagel condensation with the aromatic aldehyde of the metal chelator, where the coupling results in an extended πconjugation that red-shifts the emission wavelength of the fluorophore.39−41 Picoline, quinoline, and BODIPY moieties were selected as fluorophores to cover a broad range of emission wavelengths beyond 500 nm (Figure 1a, upper row C



Article

Figure 3. (a) PCA plot of the two primary principle components (PC1 and PC2, describing ca. 90% of total variance) for 40 samples (100 μL, 30 μM in PBS buffer, pH 7.0; seven heavy metal ions and one control, five trials each). The PCA plot is produced from the fluorescence responses of the SGT1−5 sensor array. On the right is the enlarged plot of the metals excluding Cu2+, Fe3+, Cr3+, and control. (b) 3D PCA plot of the three primary principle components.

quinolinium dye intrinsically fluoresces at 580 nm and can be shifted between 560 and 600 nm to render a higher resolution when interacting with metal ions (Figure 1c, SGT4). The BODIPY dye augments the array color by fluorescing at longer wavelengths between 600 and 630 nm, thus expanding the response range and differentiation capacity (Figure 1c, SGT5). In total, the diversified fluorescence emission (530 −630 nm, excited at 365 nm) should be attributed to the structural diversity of the sensor array: the more electronically conjugated products showed the longer wavelength emission. On the other hand, one important feature of the sensor array lies in the diversified response capacity, which is also achieved by TPEN derivatives. As shown in part 1 of the Supporting Information, moieties 1b, 2d, and 3d were synthesized from simple pyridine cores and afford diversified trap sizes, as well as different numbers of lone pair electrons participating in the chelation. This ensures that different sizes of traps could respond to different families of heavy metal ions and thus result in diversified fluorescence pattern changes (see Supporting Information, parts 3 and 4). Visualized Identification of Heavy Metal Ions. It is clear that all five sensors could respond to certain heavy metal ions in a perfect dose-dependent manner (Figure 2 and Supporting Information, part 4). Figure 2a exhibits that addition of Hg2+ could significantly enhance the emission intensity, as well as shifting the emission maxima. Figure 2b draws a fluorescence dose dependence curve that features a linear range of 0−10 μM, which allows accurate measurement of Hg2+ species in the

environment. The quantum yield of SGT1 is estimated to be 0.02, which increases to above 0.12 after interacting with Hg2+. Except for Cu2+ species, all other heavy metal ions induce a fluorescence increase of SGT sensors. One key advantage of this sensor array is its ability to serve as a rapid and sensitive on-site heavy metal ion detector. After careful analysis of the fluorescence responses of the SGT sensor array toward heavy metal ions, we pursued direct visualization of these responses to render a convenient and rapid method of detecting heavy metal ions (Figure 2c,d). Black Greiner 96-well plates were prefilled with the sensors row by row and were later treated with heavy metal ion solutions column by column. Hence, the whole preparation process can be partially highthroughput and easily applicable, even for nontrained hands. To facilitate common on-site usage, we fixed the excitation wavelength at 365 nm, which is the normal emission wavelength of a UV lamp. By irradiating the sensor−metal ion mixtures with UV lamp, we can observe that the sensors alone bear very weak fluorescence (SGT1−3) or negligible fluorescence (SGT4,5) (Figure 2c, Ctrl), which corresponds very well with the emission spectra and ensures much room for fluorescence change. The addition of different heavy metal ions clearly renders distinct features that enable direct identification of these species. Currently, the most state-of-the-art heavy metal ion detection methods require redundant machinery, sophisticated handling, and expensive instruments.16,18,53 However, these laboratorybased examinations are more and more lagging real environD



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Figure 4. (Left) 3D PCA dispersion plot of three primary principle components (PC1, PC2, and PC3, describing ca. 95% of total variance) of SGT−heavy metal ion (0.1−90 μM) fluorescence responses. Samples 1−21 are randomly prepared heavy metal ion samples (five trials each). Sample 1−3, Hg2+; sample 4−6, Zn2+; sample 7−9, Cd2+; sample 10−12, Cr3+; sample 13−15, Cu2+; sample 16−18, Fe3+; sample 19−21, Pb2+. Starshaped samples representing clean water are plotted together with seven heavy metal ions; brown dashed square is the safe-zone of clean water. (Right) Graph of enlarged safe-zone.

As is consistent with the fluorescence emission responses, Zn2+, Hg2+, Pb2+, and Cd2+ display much stronger and more versatile interactions with the sensors; hence, they localize separately and can easily be identified from the rest three heavy metal ions (Figure 3a). Among these four analytes, Hg2+, due to its highest fluorescence enhancement, mainly induced by SGT4 and SGT5, lies furthest away from the control sample, which is phosphate-buffered saline (PBS) solution. Zn2+, Pb2+, and Cd2+ stay relatively closer to each other on the 2D graph. To acquire an even clearer resolution of the heavy metal ion differentiation, we plotted the graph using three principle components occupying ca. 95% of the variance. Not surprisingly, on a 3D dispersion graph Zn2+, Pb2+, and Cd2+ show remarkable differentiation (Figure 3b). By enlarging the region of Cu2+, Fe3+, and Cr3+, we clearly observe that they can be differentiated and recognized, even with much lower fluorescence responses compared with the other four species. In total, we observe that within both 2D and 3D graph, all of the heavy metal ions can be consistently and clearly identified, which confirms our previous postulations that this sensor array can be applied for heavy metal ion differentiation. To further utilize the sensor array, we performed PCA analysis that incorporates all the concentration-based responses of heavy metal ions. Figure 4 shows responses that are composed of average PCA scores for each concentration of these metal ions. On the basis of this graph, it is possible to discriminate these heavy metal ions in a concentration range from 0.1 to 90 μM, which covers the typical metal ion pollution scope in wastewater according to toxicity characteristic leaching procedure (TCLP) standards.54 All data points from each single heavy metal ion were connected by lines of the same color to render a better display of the separation. Not surprisingly, all the connected lines originated from a similar point, at which the metal ion concentration is 0 μM. At lower metal ion concentrations, the data points stay closer to each. With the increase of concentrations, all seven heavy metal ions stretch toward different directions, indicating their diversified responses. One crucial goal of establishing such a concentrationdependent PCA graph is to prove that by utilizing such a sensor array, it is possible to identify anonymous heavy metal

mental analysis. SGT, on the other hand, provides an easy and delicate way of monitoring multiple heavy metal ions in real life. Just equipped with a UV lamp, we can readily tell the identity of the heavy metal ions by their signature fluorescence colors (Figure 2). Such a method could fundamentally accelerate environmental analysis process and to a large extent help environment protection. Instead of transferring environmental samples to laboratories and acquiring official reports using complicated instruments and time-consuming analysis, we can readily and instantly inspect water quality, i.e., whether it contains any heavy metal ions and, if so, which heavy metal ions are present. Since all these procedures may cost less than 5 min of sample preparation and very little expenditure as compared to instrumental analysis, we hold the faith that it can be a perfect substitute for current environmental analysis approaches. Multiple Heavy Metal Ion Quantitation and Safe-Zone Model. In advance of the visualized heavy metal ion detection approach, we could utilize data processing methods and transform complicated multidimensional data to a simple and readable format. To establish a clear metal identification model based on statistical multivariate processing, we performed principle component analysis for the fluorescence responses of sensor array against the heavy metal ions (Zn2+, Cu2+, Hg2+, Fe3+, Cr3+, Pb2+, and Cd2+), which contains plenty of differentiation information. The information is recorded from five channels: four of them represent the four colors that the spectra could cover [blue (480 nm), green (530 nm), and yellow (580 nm), red (630 nm)]; one more channel is recorded at their respective wavelengths of the highest emission intensity of each sensor−metal pair. The five channels and five sensors generate signal output in a multidimensional format (5 × 5 = 25 dimensions), which, after evaluation by PCA, results in clearly differentiated patterns with each clustering representing a type of heavy metal ion (Figure 3). Principle components derived from the PCA processing represent those parameters that mostly influence the analyte differentiation; hence, by plotting the PCA results as a 2D or 3D dispersion graph with the principle components as axis, we can easily identify the location of each heavy metal ion. E



Article

spectra of responsive SGT−heavy metal ion pairs, dissociation constants table of responsive SGT−heavy metal ion pairs, 3D dispersion graph of the PCA analyzed fluorescence responses, and Job’s plot of SGT−heavy metal ion pairs. This material is available free of charge via the Internet at http://pubs.acs.org.

ions quantitatively. To test our hypothesis, we prepared heavy metal ion solutions with random concentrations and labeled them as samples 1, 4, 7 and 10, 13, 16, and 19. These samples were further diluted to one-half (samples 2, 5, 8 and 11, 14, 17, and 20) and one-quarter (samples 3, 6, 9 and 12, 15, 18, and 21) of their original concentrations to help the identification. After measuring the fluorescence responses of these 21 samples, we combined them with our established standard fluorescence responses and performed PCA analysis. The results clearly indicates that sample 1 is Hg2+ with an approximate concentration of 50 μM. Samples 2 and 3 resides on the 25 and 12.5 μM position on the Hg2+ line, which further testifies our observation. Similarly, we have also derived from the graph that samples 4−6 belongs to the Zn2+ line, with concentrations equal to 10, 5, and 2.5 μM. The remaining 15 samples reside on their respective lines, which not only tells about their identities but also their quantity (Tables S2 and S3 and Figure S26 in the Supporting Information). Thus, based on the dose-dependent PCA analysis graph, we have successfully achieved quantitative identification of target heavy metal ion samples. Another advantage of the SGT sensor array is that it can differentiate clean and healthy samples from toxic materials. By testing known clean samples (mineral water and clean tap water) along with metal samples, we observed that these species, although containing various ions (Na+, K+, Cl−, PO43−, etc.) and small molecules (disinfectants, sugars, etc.), do not exhibit much variation on the 3D dispersion graph. Items that lie inside the safe-zone would be considered safe, while samples that lie outside the safe-zone display potential detriments on the basis of TCLP standards. By circling the tested clean water samples on the PCA graph, we create one safe-zone model that includes only the clean water substances. Moreover, our safezone theory is accumulative: the more samples we test, the more reliable our safe-zone can become. By expanding our sensor array and screening against more heavy metal ions, we could easily cover a broader range of analytes and establish more versatile and applicable safe-zone systems (Figure 4).

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Corresponding Authors

*H.W.R. e-mail: [email protected]. *C.-L.K.L. e-mail: [email protected]. *Y.-T.C. e-mail: [email protected]. Author Contributions ¶

The manuscript was written through contributions of all authors. W.X., C.R., and C.L.T. contributed equally to this work.

Notes

The authors declare no competing financial interests.

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ACKNOWLEDGMENTS The authors are very much grateful to the financial support from the Singapore Peking Oxford Research Enterprise (SPORE, COY-15-EWI-RCFSA/N197-1) and the Singapore Public Utilities Board (PUB) grant (TWQO/3110010).

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CONCLUSION In summary, we have developed a small-molecule fluorescent sensor array that exhibits spectroscopic responses toward heavy metal ions. The sensors respond with the analytes through the ICT mechanism, which not only changes the electron transfer efficiency but also increases the intramolecular conjugation, thus resulting in both emission wavelength shift and fluorescence intensity change. We further demonstrate that such combinatorial responses could mimic the function of flavor buds of human tongues and thus be applied to identify various heavy metal ions. The clearly visible sensing paradigm renders the sensor arrayof great value in rapid on-site environmental analysis. PCA analysis of these responses provides clear differentiation of the analytes on a 3D format and also leads to a quantitative identification of each heavy metal ion. Construction of a safe-zone based on this information could lead to a breakthrough in rapid sample analysis. Such a small-molecule sensor array could prove to be of great potential in environmental metal analysis.

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AUTHOR INFORMATION

ASSOCIATED CONTENT

S Supporting Information *

Complete synthetic schemes of SGT1−5, ICT and PET mechanism, normalized absorbance of SGT−heavy metal ion pairs, photography and dose-dependent fluorescence emission F



Article

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An Artificial Tongue Fluorescent Sensor Array for Identification and

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