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Bioinspired Copolymers Based Nose/Tongue-Mimic Chemosensor for Label-Free Fluorescent Pattern Discrimination of Metal Ions in Biofluids Zi-Yang Lin, Shi-Fan Xue, Zi-Han Chen, Xin-Yue Han, Guoyue Shi, and Min Zhang Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b01769 • Publication Date (Web): 04 Jun 2018 Downloaded from http://pubs.acs.org on June 4, 2018

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Analytical Chemistry

Bioinspired Copolymers Based Nose/Tongue-Mimic Chemosensor for Label-Free Fluorescent Pattern Discrimination of Metal Ions in Biofluids Zi-Yang Lin, Shi-Fan Xue, Zi-Han Chen, Xin-Yue Han, Guoyue Shi, Min Zhang* School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China. Phone & Tel: +86-21-54340042; Email: [email protected] ABSTRACT: There is a close correlation between body health and the level of biofluid-derived metal ions, which makes it an attractive model analyte for non-invasive health monitoring. The present work has developed a novel nose/tongue-mimic chemosensor array based on bioinspired polydopamaine/polyethyleneimine copolymers (PDA/PEIn) for label-free fluorescent determination of metal ions in biofluids. Three types of PDA/PEIn (PDA/PEI6, PDA/PEI18 and PDA/PEI48) were prepared by using different concentrations of PEI to construct the proposed sensor array, which would lead to unique fluorescence response patterns upon challenged with metal ions for their pattern discrimination. The results show that as few as 3 PDA/PEIn sensors can successfully realize the largescale sensitive detection of metal ions in biofluids. Moreover, we have demonstrated that PDA/PEIn sensors are qualified for lifetime-based pattern discrimination application. Furthermore, the sensors can distinguish between different concentrations of metal ions, as well as a mixture of different metal ions in biofluids, even the mixtures with different valence states. The method promises the simple, rapid, sensitive, and powerful discrimination of metal ions in accessible biofluids, showing the potential applications in the diagnosis of metal ion-involved diseases.

Introduction Metal ions play essential roles in biochemistry. Abnormal levels of metal ions in biofluids (e.g., urine, saliva and tears) can be potential indicator of several diseases.1,2 The detection of biofluid-derived metal ions such as Cu2+ and Fe3+ has gained significant interest in recent years, due to their uncommon contents in biofluids can be closely linked to various neurodegenerative diseases such as Alzheimer’s (AD), Huntington’s diseases (HD) and Parkinson’s (PD).3-5 In this regard, understanding the distribution and concentration of metal ions in biofluids is a central topic in bioanalytical chemistry, thus it is of great importance to monitor biofluid-relevant metal ions for biochemical researches. Currently, the standard assays for metal ions rely on instruments such as atomic absorption spectrometry (AAS), and inductively coupled plasma mass spectrometry (ICP-MS). Although these techniques can exhibit high selectivity and sensitivity, their applications are more or less hampered by limitations with respect to the high-cost and extensive sample pretreatment.6 To address this issue, fluorescent probes or sensors have gained more and more attention because of their advantages lying in sensitivity, selectivity, simplicity, real-time and onsite detection.7,8 However, most of traditional sensing methods often require receptors highly specific to the target ions (just as ‘lock and key’), seldom meet the need of recognizing various metal ions or discriminating complex samples containing mixed metal ions. Therefore, seeking facile, cost-effective and multiplex sensing methods is an urgent demand for largescale identification of metal ions in biofluids. A ‘chemical nose/tongue’ strategy provides another effective way to discriminate metal ions.9-16 In this approach, differential interactions of analytes with a receptor array can generate a response pattern which can be readily used for identification of analytes. Researchers have developed various kinds of pattern-based fluorescent sensor arrays for the detection of multiple metal ions17-18. For example, Chang et al. reported the discrimination of 7 metal ions by using a fluorescent sensor array composed by 5 small fluorophores

and a metal chelator.19 Lee et al. employed an array of 47 for the judgement of 44 metal ions.20 Despite these progresses, the pattern-based sensor arrays for massive applications are easily limited by the sophisticated operation (e.g. complicated synthesis or fluorophore-labeling, and complex data collection). Moreover, these methods have been rarely applied to sensing metal ions in biofluids. Considering this issue, much attention has been paid to developing a facile and cost-efficient pattern discrimination system for metal ions sensing. Inspired by the mussel adhesive formation, dopamine (DA) can be spontaneously oxidized and polymerized to form polydopamaine (PDA) under alkaline environment.21-23 Bioinspired PDA nanoparticles have been extensively utilized for a range of applications including surface modification, and metal deposition because of their unique physicochemical properties and excellent biocompatibility.24 Recently, the adaptation of PDA with fluorescence properties to the biochemical diagnostics has gained great research interest.25 Remarkable examples have been reported for facile preparation of PDA-based fluorescent nanoparticles.26 Moreover, Yildirim et al. reported a turn-on fluorescent array for the rapid sensing of DA via monitoring the generating fluorescence of PDA nanoparticles.27 It is revealed that there are many functional groups (e.g. catechol and imine) on the surface of PDA nanoparticles, which can gently couple with amine or thiol groups via Michael addition or Schiff base to form PDA-based copolymers for further applications.28,29 Polyethyleneimine (PEI, a commercially available amine-rich cationic polymer) would be thus polymerized with DA to sprout PDA/PEI copolymers with adjustable fluorescent properties by easily controlling their reaction conditions.24,30 The resultant fluorescent copolymers would have more functional groups for binding with various metal ions, accompanying with potential fluorescence responses for pattern discrimination of metal ions. To the best of our knowledge, there is no report concerning the pattern-based label-free discrimination of metal ions in biofluids utilizing PDA's intrinsic fluorescence properties.

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In our work, as a proof-of-concept study, three kinds of concentrations of PEI (6 mg/mL, 18 mg/mL and 48 mg/mL) were adopted to react with DA to form PDA/PEIn copolymers (PDA/PEI6, PDA/PEI18 and PDA/PEI48), and then the as-prepared PDA/PEIn were employed as a label-free fluorescent sensor array to identify metal ions in biofluids by pattern discrimination (Scheme 1). The selected PDA/PEIn sensors can exhibit different fluorescence intensities. Upon the presence of metal ions, they can show considerable varying fluorescent responses due to their diverse affinities towards metal ions. Principal component analysis (PCA) was used to analyze these fluorescent response patterns. Preliminary studies were performed in aqueous buffer. Using our sensor array, we have successfully discriminated 18 different metal ions, including Cu2+, Co2+, Cr6+, Ni2+, Fe2+, Fe3+, Zn2+, Mn2+, Ca2+, Mg2+, K+, Na+, Cd2+, Pb2+, Ag+, Hg2+, Cr3+ and Al3+. Additionally, all the 18 metal ions have been proved in a blind examination with an accuracy of 100%. Moreover, we applied this PDA/PEIn sensor array to detect metal ions in biofluids, and the results were well consisted with that of traditional ICP-AES method, confirming that the present assay has a promise in practical application with great accuracy and reliability.

Scheme 1. PDA/PEIn-based Chemosensor for Pattern Discrimination of Metal Ions. (a) Scheme diagram of the preparation of PDA/PEIn sensor array. (b) Mode of PDA/PEIn sensor array (PDA/PEI6, PDA/PEI18 and PDA/PEI48) for differentiating of metal ions by the pattern of fluorescence responses.

Experimental Section Chemicals. Dopamine hydrochloride (DA) was purchased from Sigma-Aldrich (St. Louis, MO). Polyethyleneimine (ca.30% in water) was obtained from Tokyo Chemical Industry Co. Ltd. The reagent-grade metal salts used in the work were purchased from Sinopharm Chemical Reagent Co. Ltd (Shanghai, China). Tris-HCl buffer (10 mM, pH 8.5) and Tris-HAc buffer (10 mM, pH 7.4) were prepared using metal-free reagents in in Milli-Q water. Artificial tears were purchased from URSAPHARM Arzneimittel GmbH (Germany). Artificial saliva consisted of the following components added to 50 mL water: 48 mg sodium chloride, 28 mg sodium phosphate dibasic, 28 mg monopotassium phosphate, 25 mg magnesium chloride, 124 mg potassium bicarbonate, 18 mg calcium chloride and 39 mg citric acid. The pH of the solution was adjusted to 6.7.31 Artificial urine solution contained 170 mM urea, 25 mM sodium bicarbonate, 1.1 mM lactic acid, 2.0 mM citric acid, 2.5 mM calcium chloride, 90 mM sodium chloride, 2.0 mM magnesium sulfate, 10 mM sodium sulfate, 7.0 mM potassium dihydrogen

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phosphate, 7.0 mM dipotassium hydrogen phosphate, and 25 mM ammonium chloride and all mixed in Milli-Q water, and the pH of the solution was adjusted to 6.0 by the addition of 1.0 M HCl.32 Artificial human sweat was prepared by mixing NaCl (1 g), NH4Cl (0.875 g), urea (0.25 g), lactate (0.75 g) and acetic acid (0.125 g) into distilled water, and the pH of solution was adjusted to 4.7.33 Apparatus. Fluorescence measurements were performed with a TECAN microplate reader (infinite M200 pro) using a 384-well black microplate. The excitation wavelength used was 386 nm for the emission spectra. UV-vis absorption spectroscopy was also recorded by the TECAN microplate reader using a 96-well transparent microplate. The fluorescence lifetime was characterized with an Edinburgh fluorescence spectrometer (FLS980). Transmission electron microscope (TEM) images were collected on a TEM (JEM-2011F) operated at 200 KV. The Fourier transform infrared spectroscopy (FTIR) spectra were obtained with a Nicolet optical bench (Nexus 670). Particles size distribution measured with a Malvern Zetasizer Nanoseries. Date analysis. SPSS 22.0 software (IBM) was used to process principal component analysis (PCA). GraphPad Prism 7.0 software was used to perform the data plotting. Each sample was repeated in quintuplicate. Preparation of PDA/PEIn sensor array. Different concentrations of PEI (6 mg/mL, 18 mg/mL, 48 mg/mL) were respectively dissolved in 6 mL Tris-HCl (10 mM, pH 8.5). 6 mg DA was added to the above solution. Then the reaction solution was magnetic stirred for 6 h at 25ºC under the air. The obtained purplish-red solution was freeze-drying after purified using dialysis tube (MWCO: 1000) and washed by Milli-Q water for three times. The obtained PDA/PEIn (PDA/PEI6, PDA/PEI18 and PDA/PEI48) were constructed as a sensor array, and their solutions were respectively diluted to 0.5 mg/mL. Fluorescent assay of metal ions by PDA/PEIn sensors. PDA/PEIn (PDA/PEI6, PDA/PEI18 and PDA/PEI48) were respectively dispersed in Tris-HAc buffer (10 mM, pH 7.4) as the sensor array. Then 10 µL various metal ions were mixed with 90 µL the above solutions, respectively. After 10 min at RT, the mixtures were added to the 384-well black microplate. The fluorescence change, which is defined as ((F0-F)/F0) where F0 and F are the fluorescence intensity of 536 nm in the absence and presence of metal ions, was used to measure the effect of various metal ions on the fluorescence of PDA/PEIn. Each metal ion was reacted with PDA/PEIn sensor array in five replicates. The raw data matrix (3 PDA/PEIn sensor array × 18 metal ions × 5 replicates) was got and processed by principal component analysis (PCA, a statistical analysis strategy). The heat map was obtained using GraphPad Prism 7.0 software based on the (F0-F)/F0 data, corresponding to the fluorescence patterns of PDA/PEIn sensor array toward analytes. For a blind examination, 10 µL of randomly selected metal ion was mixed with 90 µL PDA/PEIn sensor array, respectively, and fluorescent assays were conducted in the same way as described above.

Results and Discussion Design of PDA/PEIn-based pattern discrimination assay. PDA/PEIn was prepared via the copolymerization of DA and PEI in a one-pot format. As a control, DA was self-polymerized to form PDA in the absence of PEI. We measured the fluorescence emission spectra of PEI, PDA, and PDA/PEI18 (Figure 1a). PEI and PDA exhibited weak


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Analytical Chemistry fluorescence, while PDA/PEI18 displayed yellow-green image under a 365 UV lamp (inset of Figure 1a). The PDA/PEIn copolymer solutions showed a wide and strong emission peak at around 536 nm under the 386 nm UV radiation (Figure 1b), and their fluorescence were tunable and positively correlated with the concentration of PEI used. Furthermore, FTIR and TEM were also used to prove the successful synthesis of PDA/PEIn (Figure 1c and 1d). The TEM and DLS results of PDA and PDA/PEI18 indicated that the size of PDA aggregates can be significantly reduced by using PEI as the effector (Figure S1), which decreases the intermolecular coupling and then causes enhanced fluorescence of PDA/PEIn higher than that of PDA. The as-prepared PDA/PEIn copolymer can be readily employed as novel label-free fluorescent sensors for biochemical applications. As a proof of concept, PDA/PEI6, PDA/PEI18 and PDA/PEI48 were chosen as a label-free fluorescent sensor array for pattern discrimination of metal ions. Upon exposed with metal ions, due to the different binding affinity of the catechol groups and the PEI molecules towards metal ions, the selected PDA/PEIn sensors can show different fluorescence changes and provide distinct responses to a series of metal ions to realize pattern discrimination ability. For each metal ion, PDA/PEIn sensors generated a unique fluorescence response pattern, which can be further differentiated via the principal component analysis (PCA, a classical statistical technique) and would provide a novel sensory platform for the detection and discrimination of metal ions.

Figure 1. (a) UV-visible absorption and fluorescence spectra of PDA/PEI6, PDA/PEI18 and PDA/PEI48. (b) Fluorescence spectra of PEI, PDA and PDA/PEI18 (Inset: fluorescent images of PDA and PDA/PEI18). (c) FTIR spectra of PDA and PDA/PEI18; (d) TEM images of PDA (left) and PDA/PEI18 (right). Identification of metal ions. To test the discrimination power of our proposed PDA/PEIn sensor array, 18 metal ions, including Cu2+, Co2+, Cr6+, Ni2+, Fe2+, Fe3+, Zn2+, Mn2+, Ca2+, Mg2+, K+, Na+, Cd2+, Pb2+, Ag+, Hg2+, Cr3+ and Al3+ (each at 50 µM), were chosen as analytes. Preliminary studies were performed in buffer solution. For our study, each fluorescence response for a given metal ion was separately evaluated with 3 PDA/PEIn sensors (PDA/PEI6, PDA/PEI18 and PDA/PEI48), and resultant fluorescence changes were measured for generating pattern (F0-F)/F0 data and corresponding heat map (Figure 2a and 2b). Very interestingly, the PDA/PEIn sensors mainly exhibit three response modes (i.e., High, Medium and Small) to 18 metal ions according to the fluorescence changes

((F0-F)/F0). The fluorescence quenching was highly strong in the case of Cu2+, Co2+, Cr6+, and Ni2+ (High mode), whereas it was slightly weak in the presence of Fe2+ and Fe3+ (Medium mode), and the fluorescence changes to the rest of metal ions can be classified into Small mode (Figure 2a). Then, PCA was applied by calculating orthogonal eigenvectors (principal components, PC) that lie in the direction of the maximum variance within that data set to reduce the dimensionality of a data set for easier interpretion.34 Thus, the PCA concentrates the most significant characteristics (variance) of the data into a lower dimensional space. In our test, the PCA obtained from the (F0-F)/F0 data as mentioned above requires only 3 dimensions to describe 100% of the variance. For the PCA, a fluorescence response matrix consisting of 3 PDA/PEIn sensor array × 18 metal ions × 5 replicates was built to gain three canonical factors (99.075, 0.762, and 0.163%). The two most significant factors are plotted in 2D (Figure 2c). The fluorescence responses of PDA/PEIn sensors were strongly dependent on the property of metal ions. To be more specific, the catechol units and PEI moieties of PDA/PEIn possess diverse affinity towards metal ions. From the PCA, the 18 metal ions were separated into 3 kinds of areas, which is well consistent with the results of Figure 2a. What’s more, in each area, except the clusters for Mg2+ and Ca2+ are slightly overlapped together, those analytes generate distinct fluorescence patterns, could be separately grouped in different clusters. Such results confirm that the PDA/PEIn sensors, as nose/tongue-mimic chemosensor, have strong discrimination ability of detecting 18 metal ions.

Figure 2. (a) Fluorescence patterns of PDA/PEIn sensor array toward 18 metal ions (50 µM). (b) Heat map derived from fluorescence patterns of PDA/PEIn sensor array indicated. (c) Canonical score plots of PDA/PEIn sensor array against metal ions indicated. To further explore the possible mechanism involved in this system, the fluorescence lifetimes was also applied to characterize the interaction between PDA/PEIn sensors and metal ions. Six metal ions (Cu2+, Co2+, Fe2+, Fe3+, Cr3+ and Hg2+) were respectively chosen as model analytes from the three modes of High, Medium and Small. PDA/PEIn sensors challenged with Blank and the above six metal ions show unique changes in fluorescence lifetimes regarding different molecular interactions (Figure 3a-e). Besides, PCA was also used to realize a lifetime-based pattern discrimination using



PDA/PEIn sensor array, and lifetime-based pattern responses from these six metal ions were clearly differentiated, respectively (Figure 3f). As a result, our proposed PDA/PEIn-based pattern discrimination platform can be also used as a smart nose/tongue-mimic chemosensor for detecting metal ions with adequate recognizing powers in lifetime-based signal readout. Furthermore, to verify the efficiency of our sensor array, a blind examination was performed. All 18 samples were confirmed correctly using PDA/PEIn sensor array with 100% accuracy (Table S1).

Figure 3. Fluorescence lifetime spectra of (a) PDA/PEI6, (b) PDA/PEI18 and (c) PDA/PEI48 against 50 µM Cu2+, Co2+, Fe2+, Fe3+, Cr3+ and Hg2+, respectively. (d) Fluorescence lifetime (τ) response patterns of PDA-PEI sensor array toward Cu2+, Co2+, Fe2+, Fe3+, Cr3+ and Hg2+. (e) Heat map derived from the τ response patterns of PDA/PEIn sensor array toward Cu2+, Co2+, Fe2+, Fe3+, Cr3+ and Hg2+. (f) Canonical score plot of the τ response patterns indicated.

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To measure the sensitivity of PDA/PEIn sensor array, three metal ions (Cu2+, Fe3+ and Cr3+) representing High, Medium and Small mode were chosen to probe their analytical performances, respectively (Figure 4a-f, Figure S2-S4). We found that for each metal ion, the PDA plots for various concentrations were followed certain patterns, not random, and so can be discriminated from each other at many kinds of concentrations. Since PC2 (the second discriminant factor) was not exceeding 40%, PC1 (the first discriminant factor) can be simply used to quantify the concentrations of metal ions. As shown in Figure 4b, d, f, this PDA/PEIn sensor array was sensitive to detect certain concentrations of these metal ions, and the linear detection range of Cu2+ is 0.1-20 µM, Fe3+ is 0.1-40 µM and Cr3+ is 5-100 µM. After successful discrimination of metal ions in various concentration levels, we also detected the discrimination ability of PDA/PEIn sensor in mixtures. A series of mixtures of Cu2+ and Fe3+, Cu2+ and Cr3+, Fe3+ and Cr3+ with different molar ratios were tested for our study. These mixtures were fully separated from each other in PCA plots and properly arranged with the order of molar ratios (Figure S5-S7). In addition to that, the sensitivity of PDA/PEIn sensors to metal ions’ valence states were proved. From Figure S8, a mixture of two valence states of Cr (Cr3+ and Cr 6+) can be clearly distinguished from each other in the PCA plot, and Fe (Fe3+ and Fe2+) is also differentiated from each other using the PDA-PEI sensors (Figure S9). Those results demonstrated that this system would be potentially fit for analysis of complex composition, the PDA/PEIn sensors were still sensitive in mixtures. Metal ions sensing in biofluids is much more important than sensing metals in buffer solution. Biofluids, including saliva, tears, urine and sweat, play an important role in health. Body fluids can be analyzed in medical laboratory to find microbes, inflammation, cancers, etc. In medical contexts, it is a specimen taken for diagnostic examination or evaluation, and for identification of disease or condition. To check the practical application of the PDA/PEIn sensor array, we prepared a series of artificial biofluids, including tears, saliva, urine, and sweat. Then the three metal ions (Cu2+, Fe3+ and Cr3+) were spiked into these biofluids. Table 1 shows the detection of Cu2+ in five biofliuds using PDA/PEIn sensors, and Table S2-S3 illustrate the detection of Fe3+ and Cr3+ in biofliuds. The results were well consisted with that of traditional ICP-AES method, confirming that our proposed method has great promise in practical application with great accuracy. Table 1. Detection of Cu2+ in biofluids using PDA/PEIn sensor array. Entry* Tears Saliva Sweat

Figure 4. Discrimination of various concentrations of metal ions using PDA/PEIn sensor array. Canonical score plots for fluorescence response patterns obtained with PDA/PEIn sensors against varying concentrations of (a) Cu2+, (c) Fe3+, and (e) Cr3+. Plots of PC1 vs the concentrations of (b) Cu2+, (d) Fe3+, and (f) Cr3+.

Urine

Actual 5 15 5 15 5 15 5 15

ICP-AES 5.02 14.98 5.12 14.92 5.03 15.12 5.05 15.09

PDA/PEIn 5.12 14.28 5.38 14.15 5.18 16.16 5.30 15.66

Recovery (%) 102.4 95.2 107.6 94.3 103.6 107.8 106.0 104.4

RSD(%) 0.62 0.67 1.80 1.44 1.72 1.65 0.43 0.41

*

Biofluids are variable both between individuals and in the same individual over time. In this respect, as a proof-of-test, stable artificial biofluids were prepared according to the reported literatures.

Besides Cu2+ and Fe3+, Co2+ is also one of the most important transition metal ions, playing essential roles in many biological processes. The excessive of Co2+ in human body


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Analytical Chemistry could lead to pulmonary disorders, vomiting, diarrhea, blood pressure, slowed respiration, hyperglycemia and so on.35 Based on the above facts, Cu2+, Co2+ and Fe3+ were chosen to elucidate the discrimination ability of PDA/PEIn sensors toward biofluid-relevant mixtures. For the mixture of Cu2+ and Co2+, we prepared a variety of complex samples. Cu2+ and Co2+ at varying molar ratios (Cu2+/Co2+ = 1:0, 0.75:0.25, 0.5:0.5, 0.25:0.75, 0:1, all in µM) was spiked to biofluids mentioned above. As a comparison, three test samples were created in artificial tears, D) 0.8 µM Cu2+ + 0.2 µM Co2+, E) 0.6 µM Cu2+ + 0.4 µM Co2+, and F) 0.3 µM Cu2+ + 0.7 µM Co2+. Figure 5a-b shows the PCA results from the above assays in artificial tears. From Figure 5b, the test samples (D, E and F) can be clearly observed in the right position matching their molar ratios. For instance, sample D was located among the two standard mixtures with Cu2+/Co2+ molar ratios at 1:0, and 0.75:0.25. This spot elucidates that sample D contains 0.75-1 µM Cu2+ and 0-0.25 µM Co2+, which coincides with the results detected by ICP-AES, indicating our sensors PDA/PEIn could effectively monitor metal ions in biofluid-relevant mixtures. On top of that, to judge the discrimination power of PDA/PEIn sensors to different valence states, we also prepared the other group of samples by spiking Fe3+ and Fe2+ at many molar ratios (Fe3+/Fe2+ = 5:0, 3:2, 1:4, 0.5:4. 5, 0:5, all in µM) to biofluids. And three test samples were created in artificial tears for comparison: A) 4.0 µM Fe3+ + 1.0 µM Fe2+, B) 2.0 µM Fe3+ + 3.0 µM Fe2+), C) 0.8 µM Fe3+ + 4.2 µM Fe2+). From the resultant PCA plot, those test samples also agree with the results above. Each PCA plot for Fe3+/Fe2+ with various molar ratios were followed certain patterns, not random, showing the potential applications of PDA/PEIn in discriminating metal ions with different valence states compared with ICP-AES. Figure S10-S12, and Table S4-S6 shows the detection of biofluid-relevant mixtures in the rest of biofliuds using PDA/PEIn sensors, those results emphasize the capacity of PDA/PEIn sensor array for semi-quantitatively sensing metal ion mixtures in biofluids. Hence, the simple label-free fluorescent nose/tongue-mimic chemosensor is a cost-efficient, mix-and-detect method comparable to ICP-AES approach.

We have provided the first example of PDA/PEIn sensor array as a label-free nose/tongue-mimic chemosensor to prove the largescale identification of metal ions in biofluids. Such PDA/PEIn-based pattern sensing system is devised by using the bioinspired PDA fluorescent copolymers. Just only three PDA/PEIn label-free sensors (PDA/PEI6, PDA/PEI18 and PDA/PEI48) were used to construct the sensor array. The PDA/PEIn sensors display different fluorescence responses to the test metal ions. Moreover, PCA analyses indicate that the sensor system could discriminate 18 metal ions, and verify metal ion mixtures with different valence states. The fluorescence lifetimes was also applied to characterize the interaction between PDA/PEIn sensors and metal ions. Significantly, this approach allows the discrimination of metal ions in biofluids, which has great promise in practical application. Unlike other pattern-based sensor arrays, the label-free PDA/PEIn system effectively avoids high-cost complicated synthesis. Because of its appealing features, the novel PDA/PEIn-based sensor array is instructive for the development of PDA-based materials for widespread applications, and we expect that our sensors will be used to profile metal ions in biofluid samples for the non-invasive diagnosis of diseases.

Supporting Information Supplementary data associated with this article can be available free of charge via the Internet at http://pubs.acs.org. Particle size distribution of materials; fluorescence response patterns of PDA/PEIn sensors against various concentrations of metal ions and their resulting heat maps; analysis of metal mixture in artificial biofluids; recognition of unknown metal ions using PDA/PEIn sensors; the analytical performance of PDA/PEIn sensor array compared with ICP-AES in artificial biofluids.

AUTHOR INFORMATION Corresponding Author *Min Zhang, Email: [email protected] Notes The authors declare no competing financial interest.

ACKNOWLEDGMENT The authors sincerely thank the support from National Natural Science Foundation of China (No. 21775044, 21675053 and 21635003) and China Scholarship Council (No. 201706145029).

REFERENCES Figure 5. Analysis of metal ion mixtures in artificial tears. Canonical score plots for fluorescence response patterns of PDA/PEIn sensors toward the standard mixtures and test samples of (a) Fe3+/Fe2+, and (b) Cu2+/Co2+. Table shows the analytical performances of PDA/PEIn sensors compared with that of ICP-AES.

Conclusion

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