Luminometric Label Array for Quantification and Identification of

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Luminometric Label Array for Quantification and Identification of Metal Ions Sari Pihlasalo, Ileana Montoya, Niklas Hollo, Elina Hokkanen, Tapio Pahikkala, and Harri J. Härmä Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b00453 • Publication Date (Web): 18 Apr 2016 Downloaded from http://pubs.acs.org on April 28, 2016

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Analytical Chemistry

Luminometric Label Array for Quantification and Identification of Metal Ions Sari Pihlasalo1,2,*, Ileana Montoya Perez3, Niklas Hollo2, Elina Hokkanen2, Tapio Pahikkala3, and Harri Härmä1,2 Email: [email protected], Phone: +358 2 333 6720 1

Laboratory of Materials Chemistry and Chemical Analysis, Department of Chemistry, University of

Turku, Vatselankatu 2, 20500 Turku, Finland 2

Department of Cell Biology and Anatomy, Institute of Biomedicine, University of Turku,

Kiinamyllynkatu 10, 20520 Turku, Finland 3

Department of Information Technology, University of Turku, Vesilinnantie 5, 20500 Turku, Finland

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ACS Paragon Plus Environment


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ABSTRACT Quantification and identification of metal ions has gained interest in drinking water and environmental analyses. We have developed a novel label array method for the quantification and identification of metal ions in drinking water. This simple ready-to-go method is based on the nonspecific interactions of multiple unstable lanthanide chelates and non-antenna ligands with sample leading to a luminescence signal profile, unique to the sample components. The limit of detection at ppb concentration level and average coefficient of variation of 10% were achieved with the developed label array. The identification of fifteen different metal ions including different oxidation states Cr3+/Cr6+, Cu+/Cu2+, Fe2+/Fe3+, and Pb2+/Pb4+ was demonstrated. Moreover, binary mixture of Cu2+ and Fe3+ and ternary mixture of Cd2+, Ni2+, and Pb2+ were measured and individual ions were distinguished.

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INTRODUCTION The determination of metal ion concentrations is important in varying environmental, chemical, and food industrial applications. Metal ion contaminations in drinking water, such as Cd2+, Cr6+, Hg2+, and Pb2+, are potentially a substantial health risk due to their acute toxicity and carcinogenicity, especially in developing countries. In industrialized countries, the quality of the household water is generally good and hazards are normally avoided. However, the quality problems still exist occasionally due to unpleasant taste and odor in drinking water. For instance Al3+, Cu2+, Fe2+, and Zn2+ can enter to the household water e.g. from the soil to the water network as a result of corrosion of seepages, pipings and fittings, source water, household plumbing materials, or coagulants used during water treatment and impart unpleasant metallic taste. Cd2+, Ni2+, and Pb2+ are present in batteries and have ended up to the environment and, thus, potentially also to the household water. Although metals, such as chromium, copper, manganese, and zinc are essential to our bodies, intake of excess can be harmful. Metal ions are typically quantified and identified with atomic absorption1 (AAS) and inductively coupled plasma atomic emission spectrometric1 (ICP) methods. AAS is a single-element method, as a different lamp is generally used for each element to be determined. The limit of detection depends on the element and varies from 0.15 to 3000 µg/L for flame AAS.1 Higher sensitivity with limits of detection between 0.002 and 3 µg/L is reached with a graphite furnace compared to flame AAS.1 The limits of detection are 0.04-5 µg/L for inductively coupled plasma optical emission spectrometer (ICPOES) and 0.01-1 ng/L for inductively coupled plasma mass spectrometry (ICP-MS).1 However, the methods require investment to high cost instruments, special expertise, and mainly high sample volumes of several milliliters. Moreover, AAS methods suffer from low throughput and from the inability to differentiate between oxidation states of metal ions. Ion selective electrodes2 with detection limits at the ppb level provide more economical method for the quantification of metal ions compared to AAS and ICP. These selective electrodes have simple instrumentation and are commercially available for several metal ions. However, each metal ion requires a specific electrode and the throughput is low. Polarography3,4 utilizing dropping or hanging mercury drop electrode has also relatively high sensitivity at the ppb level and is an economical and

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selective method for the quantification of heavy metal ions. The disadvantages are low throughput, investment to the instrument, the use of toxic mercury, and environmental impacts.

Colorimetric, spectrophotometric, and fluorometric methods have been developed for the detection of metal ions. The limits of detection for colorimetric methods vary between 1-10000 µg/L depending on the method and the metal ion. Methods generally suffer from the interfering metal ions and need for different assay protocols, modified synthetized ligands, and absorption wavelengths. Thus, each metal ion requires its own assay protocol, incubation times, and reagents.5-24 Fluorometric methods utilize specific fluoroionophores containing a receptor with metal binding site and fluorescent reporter. The fluoroionophores25-45 based on the receptors, such as cryptands37,38, (aza-)crown ethers39, EDTAderivatives, BAPTA40,41, nitrilotriacetic acid42, or dipicolylamine43-45, have been developed as selective for each analyte metal ion. The limits of detection vary between 0.6-40000 µg/L depending on the metal ion and the fluoroionophore. However, fluoroionophores require long synthesis and different metals their own specific fluoroionophore.

Absorbance- and fluorescence-based arrays utilizing fingerprinting for identification of metal ions have been reported in literature.46-55 They relied on different sensing elements, such as poly(ethylene glycol)-polystyrene resin beads, environmentally sensitive fluorescent dyes, 8-hydroxyquinoline conjugated with different chromophores, BODIPY or coumarin-enamine probes, anionic conjugated polyelectrolytes, or fluorescent self-assembled monolayers, which reacted differently to varying metal ions. The response times are usually fast. However, the sensing elements, such as metal coordinative group conjugated with different chromophores, fluorescent self-assembled monolayers, and surfacemodified beads, might require organic syntheses with high cost or they might be difficult or laborious to prepare. The methods might have complex detection or experimental procedures, limited applicability for instance to divalent metal ions, or the applicability has been shown only for few metal ions. Moreover, the limits of detection vary from few micrograms to few grams per liter, which are clearly higher than for AAS and ICP methods.

Previously, we reported on the label array method utilizing unstable lanthanide chelates for the identification of honey and cacao brands and for the detection of their adulteration.56 The interaction of the liquid sample and the chelates led to the sample-dependent modulation of the luminescence signal. 4


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In this article, we present a novel label array for the quantification and identification of metal ions in liquid samples. Here, competitive binding of analyte metal and the Tb3+ ion to a non-antenna and an antenna ligand or luminescence quenching of the lanthanide chelate by the analyte metal ion provided the basis for signal modulation within the array (Figure 1). The presence of the analyte metal ion is detected as the increase or decrease in the luminescence signal. The versatility and the performance of the array were demonstrated in the quantification and identification of fifteen different metal ions and in the analysis of binary and ternary mixtures in household water.

EXPERIMENTAL SECTION Materials Black polystyrene 96-well microtiter plates were from Perkin-Elmer Life and Analytical Sciences (Turku, Finland). Metal salts, aluminium(III) chloride AlCl3, chromium(III) chloride hexahydrate CrCl3 · 6 H2O, chromium(VI) oxide CrO3, copper(I) chloride CuCl, iron(II) chloride tetrahydrate FeCl2 · 4 H2O, iron(III) chloride hexahydrate FeCl3 · 6 H2O, lead(II) chloride PbCl2, manganese(II) chloride tetrahydrate MnCl2 · 4 H2O, and zinc(II) chloride ZnCl2 were purchased from Acros Organics (Geel, Belgium), cadmium(II) chloride monohydrate CdCl2 · H2O from Merck KGaA (Darmstadt, Germany), cobalt(II) chloride hexahydrate CoCl2 · 6 H2O from Alfa Aesar GmbH & Co KG (Karlsruhe, Germany), calcium(II) chloride dihydrate CaCl2 · 2 H2O from Avantor Performance Materials J.T.Baker (Center Valley, PA), and copper(II) chloride CuCl2, lead(IV) oxide PbO2, magnesium(II) chloride hexahydrate MgCl2 · 6 H2O, mercury(II) chloride HgCl2, nickel(II) chloride hexahydrate NiCl2 · 6 H2O, and sodium(I) chloride NaCl from Sigma-Aldrich (St. Louis, MO). Terbium(III) chloride hexahydrate TbCl3 · 6 H2O, chelidamic acid (CDA) hydrate, diethylenetriaminepentaacetic acid (DTPA), and diethylenetriaminepentakis(methylphosphonic acid) (Dequest 2060) were bought from Sigma-Aldrich (St. Louis, MO) and 4,5-dihydroxy-1,3-benzenedisulfonic acid (Tiron) disodium salt monohydrate from Acros Organics (Geel, Belgium). The household water used to the most of the experiments was taken from an apartment house in the Turku city center. Additionally, different water samples from households in different cities, Turku (row house), Pori (row house), Tampere (row house), and Seinäjoki (ranch house) were tested. All water samples were from the water pipe network of the cities. High purity MilliQ water was used to prepare all aqueous solutions. 5



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Methods Preparation of Modulator Plates

The 96-well microtiter plates containing solid modulators were prepared for the arrays. The modulator solutions were pipetted as two separate 3.0 µL droplets to the wells. The first solution was 5.0 µM TbCl3 and the second contained different antenna and non-antenna ligands as described in Table 1. The water of the droplets was evaporated at approx. 70 °C for 30 min.

Label Array

The metal ion stock solutions were prepared at high concentration to MilliQ water (except PbO2 in 67% acetic acid). For the quantification and identification of metal ions, metal salts (AlCl3, CdCl2, CrCl3, CrO3, CoCl2, CuCl, CuCl2, FeCl2, FeCl3, HgCl2, MnCl2, NiCl2, PbCl2, PbO2, and ZnCl2) were spiked to household water by dilution from the stock solutions. For the analysis of binary and ternary mixtures, CuCl2 and FeCl3 or and NiCl2, PbCl2, and CdCl2 were spiked to household water at 10-1000 or 20-500 µg/L total concentrations of metal ions and 0-100% metal ion mass percentages at twenty percent intervals. For the assays, 100 µL of these samples were added to the prepared modulator plates and the plates were shaken. The plates were incubated at least 30 min. After the incubation, terbium luminescence emission intensities were measured in a 400-µs window after a 400-µs delay time with 320 nm excitation and 545 nm emission wavelengths using a Victor2 (Wallac, Perkin-Elmer Life and Analytical Sciences, Turku, Finland) or a Labrox plate reader (Labrox, Turku, Finland). All array experiments were performed in triplicates, and an average of the signals is presented in curves with standard deviation as the error bars and all replicates in principal component analysis. Moreover, the emission spectra of all ten modulators in household water at the final concentrations utilized in the array were measured in a quartz cuvette with Cary Eclipse fluorescence spectrofluorometer (Varian, Palo Alto, CA).

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Data Analysis

To analyze the linear separability of analyte metal ions, a binary logistic regression was applied to the measured luminescence signals. Each metal ion was separated from the rest of the metal ions by inferring a separating hyperplane. Formally, the hyperplane is represented by a d-dimensional vector w and an intercept term b, where d is the number of modulators. The values of w and b are obtained via minimizing the following sum with respect to w and b: log(1 + (〈, 〉

Here, xi denote the d-dimensional vectors consisting of the d modulator values for each data, the value of yi is 1 if the data corresponds to the metal ion type under consideration and −1, if it is one of the other ion types, and denotes the inner product. The metal ions are projected on the lines perpendicular to the separating hyperplanes with circles corresponding to the metal ions under consideration and crosses the rest of the metal ions. Namely, the projections pi are obtained by applying the linear model for each data: = 〈, 〉 + that is, the values indicate the locations of the data on the lines. The projections are further transformed logarithmically and scaled according to the variations of the metal ions under consideration.

For the analysis of mixture samples from the measured luminescence signal profiles, a k-nearest neighbor regression (KNNR) model was constructed. The nearest neighbors for each water sample were determined by the standard Euclidean distance between the signal vectors of the water sample with unknown percentages and the samples with known percentages. The determined percentage for the unknown sample is the average of those of its k-nearest neighbors. A combination of an inner and outer cross-validation known as the nested cross-validation57 was applied in the analysis. The outer leave-one-out cross-validation (LOOCV) was implemented to estimate the prediction performance of the method, in which a separate KNNR model was constructed during each round of LOOCV and its predictive power was evaluated on the left out sample. As a part of the KNNR model construction, the number of neighbors was selected from 1, 2, or 3 using an inner 10-fold cross-validation (10-FCV). This 10-FCV was run separately during each round of the outer

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LOOCV so that the model selection was completely separated from the evaluation of the prediction performance. As the measures of the prediction performance, we used the coefficient of determination and Concordance index on the known percentages and the outer LOOCV predictions.

RESULTS AND DISCUSSION Luminometric label array was developed for the quantification and identification of metal ions in drinking water. The detection is based on nonspecific interactions and competition of reactants in an array format. The array was created in the microtiter plate wells, in which samples were added, and the wells were measured with a luminescence plate reader using time-gated measurements. Analyte metal ion and the Tb3+ ion compete with non-antenna and antenna ligands to form various chemical entities with different sample-specific luminescence signal. In aqueous solutions, the Tb3+ ion is in practice nonluminescent due to its low absorption of light, coordination with water, and efficient deactivation of luminescence. Thus, the method utilizes the excitation of antenna ligands and subsequent energy transfer to the Tb3+ ion enabling high luminescence signal. Additionally, luminescence quenching of the lanthanide chelates by the analyte metal ion provides an additional dimension to the detection (Figure 1). The detection relies on the complex formation of Tb3+ ion and an antenna (and a nonantenna) ligand. Two alternatives without or with a non-antenna ligand are applied in this array. Without a non-antenna ligand, Tb3+ ion and an antenna ligand form a complex in the absence of analyte metal ions and a high luminescence signal is monitored. In the presence of the analyte metal ion, the luminescence may decrease due to the luminescence quenching or due to the competition in binding between analyte metal and the Tb3+ ion to the antenna ligand. For the system with a non-antenna ligand, the Tb3+ ion competes in binding with the non-antenna and antenna ligand in the absence of the analyte metal ion. As the Tb3+ complex with the non-antenna ligand is essentially non-luminescent, the luminescence signal is low. The signal is increased in the presence of the analyte metal ion, as the analyte metal ion forms a complex with the non-antenna and the Tb3+ ion complexes with the antenna ligand. The dependence of the luminescence signal on the concentration of analyte metal ion enables the quantification of the analyte metal ions. Moreover, the differentiation of the measured analyte metal ions is enabled by the difference in quenching efficiency and in metal binding properties to different non-antenna and antenna ligands.

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The term modulator is used in this article for the combination of the Tb3+ ion and the mixture of nonantenna and antenna ligands. The emission spectra of all ten modulators utilized in this article were measured (see Figure S1 in the Supporting Information) and the results support the principle of the luminometric label array. Figure 1 is simplified and only the principal complexes and interactions are shown. However, quenching may occur also in the system with a non-antenna ligand and several nonantenna and/or antenna ligands can be bound to Tb3+ and analyte metal ions. Moreover, the Tb3+ or analyte metal ion may bind several ligands. The stoichiometry of Tb3+:Tiron and Tb3+:CDA complexes have been studied and reported in literature. For the Tb3+:Tiron a 1:2 complex and for Tb3+:CDA a 1:3 complex has been suggested. However, the stoichiometry depends on the buffer conditions.58-62

An attempt was made to solve the mechanism of the interactions without a non-antenna ligand (modulators 1 and 7) by examining the luminescence lifetime (τ) in the presence or absence of metal ions. The lifetime was estimated from the measurements made with different delay times (200-1500 µs) using the constant window time of 200 µs in the microtiter plate. The lifetime was calculated from the slope (−1/τ) of lines (logarithm of signal vs. delay time) with linear regression (see Table S1 in the Supporting Information). Although the luminescence signal was clearly decreased in the presence of metal ions (except Cd2+ and Hg2+), as the metal ion concentration was increased, the lifetime remained nearly unchanged for most of the metal ions. The lifetime decreased significantly only for Mn2+ and slightly for Co2+, Cr6+, Cu+, Fe2+, Fe3+, Ni2+, Pb4+, and Zn2+. In the static quenching, the lifetime remains the same in the presence and absence of quencher while in the dynamic quenching, the lifetime and signal decrease at the same ratio with the added quencher. The static quenching would require a formation of a stable complex between the Tb3+ chelate and the metal ion, which does not seem likely. Thus, these results suggest that the signal decreases mainly due to the formation of a complex between a metal ion and an antenna ligand, and the dynamic quenching. Moreover, we tested the effect of antenna ligand concentration (50, 150, 500, and 1500 µM) at constant Tb3+ ion concentration of 5.0 µM for the sensitivity (data not shown). The sensitivity decreased, as the antenna ligand concentration increased. As an exception, the sensitivity was not altered for Mn2+ in the presence of the CDA ligand. This result suggests that the formation of a complex between a metal ion and an antenna ligand is the main mechanism for the decrease of the signal, as the quenching efficiency is not expected to change significantly upon the increase of the antenna ligand concentration.

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Signal modulators, consisting of the Tb3+ ion and varying non-antenna and antenna ligands, were dried on the bottom of a microtiter plate to create an array and only sample was added. Among lanthanides, Tb3+ chelates were chosen to enable the preparation of these ready-to-go plates. As the Tb3+ ligands possess high water solubility, they redissolve readily to the aqueous sample. Moreover, Tb3+ chelates have a low tendency to hydrophobic interactions with other reagents and sample components providing good solubility and improved selectivity for metal ions. The chelating structures used in this work were all unstable to enable the competition between Tb3+ and analyte metal ions and optimal modulation of the signal. The equilibrium depends on the number of coordination bonds formed. The utilized nonantenna ligands, DTPA and Dequest 2060 as octa- and 18-dentate ligands, have higher affinity compared to antenna ligands, CDA and Tiron as tri- and hexadentate ligands. Thus, the non-antenna ligand concentrations are equal or lower than the antenna ligand concentrations (Table 1).

Quantification of Metal Ions

The quantification of 15 different metal ions with varying properties and consumer impact was measured with the developed array. Metal ions, such as Cd2+, Cr6+, Hg2+, Pb2+, and Pb4+, form serious health risks especially in drinking water while metals, such as Fe2+, Co2+, Cr3+, Cu2+, Mn2+, and Zn2+ are essential trace elements. Moreover, e.g. Al3+, Cu2+, Fe2+, and Zn2+ give also unpleasant taste for drinking water and most of the population can taste as low ion concentrations as 50 µg/L Fe2+ and 600 µg/L Cu2+.63 Also staining of laundry and plumbing due to these metal salts may affect to the consumer satisfaction.63 Ni2+, Cd2+, and Pb2+ have been and are still exploited in batteries. Thus, these metals are often found in the environment entering potentially to natural and household water. To demonstrate the functionality of the method, metal salts were spiked to household water and a single modulator was separately optimized to each metal ion to reach high sensitivity. The optimization was performed by testing several modulators containing Tb3+ and several different non-antenna and antenna ligands at varying concentrations. The optimized calibration curves are shown in Figures 2a and b measured using modulators without or with non-antenna ligands and observed as the decrease or increase of signal. Based on the calculation of the standard deviation (3×SD) of the luminescence signal at a zero metal ion concentration, the limits of detection are all at ppb concentration level between 0.7 and 200 µg/L (Table 2) generally being well below the World Health Organization health guideline levels for the drinking water. However, these guideline levels have been previously exceeded, as over 260 µg/L 10


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of Ni2+,64 1300 µg/L of Al3+,65-67 and 70000 µg/L of Cu2+,68 have been reported in drinking water. The limit of detection below 50 µg/L was reached for the majority of the metal ions and the average CV using three replicates for all measured calibration curves was 10%. The oxidation state of the analyte metal ion affects to the limit of detection due to the different affinities to the ligands. Therefore, we could not demonstrate the assessment of the total concentration of same metal ion at different oxidation states with a single modulator. However, the application of several modulators might enable the identification and quantification of all species. The incubation of 30 min was found sufficient for reaching high sensitivity (data not shown). Part of the modulators (1, 4, 7, and 10) was also tested for their sensitivity to Mg2+, Na+, and Ca2+ (data not shown). These metal ions did not affect to the signal up to 50 or 500 mg/L of Mg2+, Na+, or Ca2+ suggesting that variations in the total salt content does not interfere with the detection of heavy metal ions. The achieved limits of detection of the developed array were compared to the limits of the existing methods. The limits of detection were found to be in the same range with the flame atomic absorption spectrometer (FAAS). The limits are approximately tenfold lower for the inductively coupled plasma optical emission spectrometer (ICP-OES) and ten- to hundredfold lower for graphite furnace atomic absorption spectrometer (GFAAS) compared to the label array.1 Although the limits of detection were not improved compared to the existing methods, the benefits of the developed label array are obvious: low cost and high throughput. AAS and ICP methods also require experts to perform the analyses and high investments need to be made to the instruments.

Other chemicals in the unknown sample matrices may additionally affect the signal. However, the array can be taught to distinguish or mask different contaminants and matrices. This can be regarded also as an advantage, as the array can be expanded to detect a large range of contaminants. We studied the effect of the sample matrix to the developed array by measuring the calibration curves for Ni2+ spiked in five different household water samples. Household water samples were taken from four different geographical sites (Pori, Tampere, Seinäjoki, and two households from Turku) in Finland and the measurements were performed with three modulators. All water samples were from the water pipeline network of the corresponding site. Ni2+ was detected in all water samples and the calibration curves overlapped sufficiently suggesting that the presence of the metal ions can be detected at equal sensitivity below 10 µg/L independent on the sample water matrix.

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Identification of Metal Ions

Nine different modulators were chosen to demonstrate the array for the identification of single metal ions in household water. The metal ions were spiked to household water for the demonstration. The protocol for the identification with the array is shown in Figure 4. This protocol enables the identification (and subsequent quantification) of the metal ion without the knowledge of the metal ion concentration in the unknown sample. As most of the metal ions quenched the luminescence with modulator 1 (except for Cd2+ and Hg2+), the metal ion concentration of samples was standardized based on this modulator. Therefore, different dilutions of the sample were prepared and measured with modulator 1 to obtain the diluted sample solution providing a 50% signal level compared to the zero metal ion concentration. The corresponding metal ion mass concentration cMz+ depends on the metal ion, as the metal ions have different quenching efficiency. Similarly, the dilution corresponding to the 500% signal level of the signal at the zero metal ion concentration for modulator 2 was determined for Cd2+ and Hg2+. After the standardization, further dilutions of the sample 0.3 × cMz + , 1× cMz + , and 4 × cMz + ( 0.9 × cMz + , 3 × cMz+ , and 12 × cMz + for Cd2+ and Hg2+) were prepared and applied to all nine

modulators to identify the metal ion present in the sample. A unique signal profile was obtained for each metal ion. The linear separation of the metal ions from each other was analyzed, individually for each metal ion, by inferring a separating hyperplane between the metal ion under consideration and the rest of the metal ions (see Methods for the details). The inference was performed with binary logistic regression. The projections of the metal ions on the lines perpendicular to the separating hyperplanes are presented in Figure 5. All three replicates measured for a metal ion could be distinguished from the replicates of all other metal ions. Only the replicates of Hg2+ were somewhat overlapping with the replicates of Fe2+ and Fe3+, as the linear model was applied. However, the separation of Fe2+ and Fe3+ by this linear model shows that Fe2+ and Fe3+ can be separated from all other metal ions including Hg2+. Thus, the result suggests that all 15 metal ions could be separated by applying the linear models consecutively. Even the metal ions, Cr3+/Cr6+, Cu+/Cu2+, Fe2+/Fe3+, and Pb2+/Pb4+, with different oxidation states were identified. The elements can be identified with AAS and ICP methods, as each element has specific absorption lines or different masses. However, the identification of the oxidation states is not achieved with AAS and ICP alone and ICP coupled to liquid chromatograph is required. Additional separation/identification/reduction processes must be applied for the samples containing different oxidation states before the analysis with AAS or ICP, whereas only total metal ion 12


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concentration or specific oxidation state can be determined with these methods.69-71 Thus, the methods are time-consuming and laborious.

Mixture Analysis

The analysis of samples with mixtures of ions using the developed label array was demonstrated with binary mixtures of Cu2+ and Fe3+ and ternary mixtures of Cd2+, Ni2+, and Pb2+. Different mixtures were prepared at twenty percent intervals of each metal ion at varying known total concentrations (10-1000 µg/L for Cu2+/Fe3+ and 20-500 µg/L for Cd2+/Ni2+/Pb2+) and the array measurements were performed with three or four modulators (see Figure S2 and S3 in the Supporting Information). The array performance for the determination of mass percentages was evaluated at varying total concentrations or as their combinations. The data analysis was performed using k-nearest neighbor regression as the algorithm to determine mass percentages (see Methods for the details). The determined mass percentages correspond relatively well to the spiked percentages at all tested total metal ion concentrations for binary mixtures of Cu2+ and Fe3+ (see Figure S4 in the Supporting Information). The difference between the determined and spiked values and also the standard deviations are the lowest at the total concentration of 100 µg/L for Cu2+/Fe3+ (Figure 6a). However, the assessment of mass percentages in ternary mixtures of Cd2+, Ni2+, and Pb2+ was not accurate with none of the measured total concentrations 20, 40, or 500 µg/L (see Figure S5 in the Supporting Information). Instead, the combination of 20 and 40 µg/L or 20, 40, and 500 µg/L for Cd2+/Ni2+/Pb2+ increased the accuracy significantly and the highest accuracy and precision were obtained for the combination of 20, 40, and 500 µg/L (Figure 6b). The Concordance index and coefficient of determination indicating the performance of the prediction are also optimal for the total concentration of 100 µg/L Cu2+/Fe3+ and for the combination of 20, 40, and 500 µg/L Cd2+/Ni2+/Pb2+ (see Table S2 and S3 in the Supporting Information) providing the highest accuracy for the assessment. The deviation of the determined percentages from the spiked ones was 0% for the Cu2+/Fe3+ mixtures at the total concentration of 100 µg/L and maximally 12% for the Cd2+/Ni2+/Pb2+ mixtures at the combination of 20, 40, and 500 µg/L. Thus, the mass percentage can be estimated for samples with total concentration of minimally 100 µg/L for Cu2+/Fe3+ or 500 µg/L for Cd2+/Ni2+/Pb2+, which increases the number of measurable samples, as high sample concentration or enrichment step is not required. The mixture analysis was performed with known total concentrations. However, the method is likely to be improved for the analysis of the

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mixtures with unknown total concentrations by utilizing different masking agents which is the objective of our future work.

CONCLUSIONS We have developed a novel label array in the microtiter plate format for the quantification and identification of metal ions. The method is simple ready-to-go approach where the array was created by drying chemical entities on microtiter plate wells and liquid sample was pipetted to the wells. The method is also rapid, as the incubation of only 30 min was sufficient after the addition of the sample. Dependent on the metal ion, the detection limits of 0.7-200 µg/L were measured and found to be similar to the ones obtained with FAAS. The label array allowed the differentiation of fifteen metal ions including different oxidation states, which are unavailable with the existing AAS and ICP methods. The determination of mass percentage of metal ion components in binary and ternary mixtures with known total metal ion concentration was achieved at wide (more than two orders of magnitude) metal ion concentration range. This suggests that the method is versatile in different applications. It requires no specific hands-on skills or expertise opposite to the AAS and ICP methods, as the sample is simply added to the array of microtiter wells containing dried modulator components and the luminescence is measured with microtiter plate readers allowing time-gated measurements. Such instruments are commonly found in biological laboratories. The developed method could be utilized e.g. for the detection of metals leaching from water pipes, for the comparison of pipe materials, or for the quality control of household/drinking water. As the array utilizes a ready-to-go system with microtiter plates containing the reagents, it could be exploited immediately at the test field and especially in developing countries. Furthermore, it could allow high-throughput system for process samples in different industry fields. The change in the fingerprint of the sample can be related to the change in the metal content of the sample enabling to find the samples that require more extensive examination.

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SUPPORTING INFORMATION The emission spectra for modulators, luminescence lifetimes for modulators 1 and 7 in the presence and absence of different analyte metal ions, the signals for the mixtures of Cu2+ and Fe3+ and Cd2+, Ni2+, and Pb2+ at varying total concentration of metal ions, determined mass percentages in the mixtures of Cu2+ and Fe3+ and Cd2+, Ni2+, and Pb2+ at varying total concentrations and at combinations of several total concentrations, and Concordance index and coefficient of determination obtained for the determination of mass percentages in Cu2+/Fe3+ and Cd2+/Ni2+/Pb2+ mixtures using k-nearest neighbor regression. This material is available free of charge via the Internet at http://pubs.acs.org.

ACKNOWLEDGMENTS This work was supported by the funding from the Academy of Finland (Grant 258617), the Maj and Tor Nessling Foundation, and the High Technology Foundation of Satakunta.

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LEGENDS Figure 1. (a) Structures of antenna and non-antenna ligands utilized in the luminometric label array. (b) Principle of the luminometric label array for the quantification and identification of metal ions. Two systems with or without non-antenna ligand were developed and demonstrated with chelidamic acid as an antenna ligand for Tb3+ and with diethylenetriaminepentaacetic acid as a non-antenna ligand. In the system without non-antenna ligand, the signal is decreased in the presence of the analyte metal ion Mz+ due to the quenching of the luminescence or the formation of a non-luminescent complex between the analyte metal ion Mz+ and the antenna ligand. In the system with a non-antenna ligand, the formation of the luminescent Tb3+ chelate is disturbed in the absence of Mz+ and Tb3+ ion is bound to a non-antenna ligand. In the presence of Mz+, the luminescence is increased, because Mz+ is bound to the non-antenna ligand instead of Tb3+ ion.

Table 1. Antenna and non-antenna ligand mixtures used in modulators 1-10 together with 5.0 µM TbCl3. Figure 2. Quantification of metal ions in household water with single modulators divided into (a) increasing and (b) decreasing curves. Modulators: 1 for Cr3+, Cr6+, Fe2+, Fe3+, and Pb4+, 2 for Cd2+, Co2+, Cu+, Cu2+, Hg2+, Ni2+, and Zn2+, 3 for Al3+ and Mn2+, and 4 for Pb2+. Table 2. Limit of detection1 for each metal ion measured with the developed label array and existing methods flame atomic absorption spectrometer (FAAS), graphite furnace atomic absorption spectrometer (GFAAS), and inductively coupled plasma optical emission spectrometer (ICP-OES) and the health guideline levels72 of the World Health Organization (WHO). Figure 3. Quantification of Ni2+ with modulators (a) 2, (b) 3, and (c) 4 spiked in household water taken from different cities.

Figure 4. Protocol for the identification of metal ions with the array. Standardization of the metal ion concentration in the unknown sample is performed with modulator 1 for quenching metal ions by determining the dilution (corresponding metal ion concentration cMz+ ) providing 50% of the signal S0 at 19



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zero metal ion concentration. Subsequent dilutions with metal ion concentrations 0.3 × cMz + , 1× cMz + , and 4 × c Mz + are prepared and measured with modulators 1-9 for the identification. Equally, modulator 2 was applied for non-quenching metal ions and 500% of S0 was the standardization signal level for further dilutions of 0.9 × cMz + , 3 × c Mz + , and 12 × cMz + to identify each metal ion.

Figure 5. Identification of metal ions in household water measured with nine modulators 1-9. Each metal ion was separated from the rest of the metal ions by inferring a separating hyperplane with binary logistic regression. The ions are projected on the lines perpendicular to the separating hyperplanes with filled circles corresponding to the ions under consideration and crosses the rest of the ions. The projections are transformed logarithmically and scaled according to the variations of the ions under consideration. Figure 6. Determination of mass percentages in the mixtures of (a) Cu2+ and Fe3+ with modulators 1, 4, 7, and 10 at 100 µg/L total concentration and (b) Cd2+, Ni2+, and Pb2+ with modulators 2, 3, and 4 at 20, 40, and 500 µg/L total concentrations as combined for the data analysis.

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FIGURES Figure 1. (a)

Antenna ligands

Chelidamic acid (CDA)

Non-antenna ligands

Diethylenetriaminepentaacetic acid (DTPA)

OH O

S

O

O HO

S OH O

OH

4,5-dihydroxy-1,3-benzenedisulfonic acid (Tiron)

Diethylenetriaminepentakis(methylphosphonic acid) (Dequest 2060)

21



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(b)

Table 1.

Modulator

Antenna ligand

1 2 3 4 5 6 7 8 9 10

50 µM Tiron 20 µM CDA 500 µM CDA 20 µM CDA 50 µM Tiron 50 µM CDA 50 µM CDA 50 µM Tiron 50 µM CDA 250 µM CDA

Non-antenna ligand 10 µM Dequest 2060 20 µM Dequest 2060 20 µM DTPA 20 µM Dequest 2060 20 µM Dequest 2060 50 µM DTPA 50 µM DTPA 50 µM DTPA

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Figure 2.

(a)

50

Normalized signal

3+

Al 2+ Cd 2+ Co + Cu 2+ Cu 2+ Hg 2+ Mn 2+ Ni 2+ Pb 2+ Zn

40

30

20

10

0

1

10

100

Concentration (µg/L)

(b)

1,0

Normalized signal

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0,8 0,6 3+

0,4

Cr 6+ Cr 2+ Fe 3+ Fe 4+ Pb

0,2 0,0 1

10

100

1000


23



Table 2. Metal ion

Label Array

Limit of Detection (µg/L) FAAS GFAAS

ICP-OES

WHO (µg/L)

0.1

1

900

0.8

0.002

0.1

3

9

0.15

0.2

3

0.004

0.2

50

1.5

0.014

0.4

2000

5

0.06

0.1

300

0.009

0.6

0.005

0.1

0.07

0.5

70

15

0.05

1

10

1.5

0.02

0.2

3+

1

45

Cd

2+

2

Co

2+

0.7

3+

3

6+

200

Al

Cr Cr

Cu+

2

2+

2

Cu

Fe2+

30

3+

60

Hg2+

20

Fe

Mn

2+

8

2+

1

Pb

4+

100

Pb

2+

20

Ni

Zn2+

1.5

1

6

Figure 3.

(a) 40

Normalized signal

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Turku1 Turku2 Pori Tampere Seinäjoki

30

20

10

0 1

10

Concentration (µg/L) 24


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(b) 70 Turku1 Turku2 Pori Tampere Seinäjoki

Normalized signal

60 50 40 30 20 10 0 1

10


(c) 225 Turku1 Turku2 Pori Tampere Seinäjoki

200

Normalized signal

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


175 150 125 100 75 50 25 0 1

10


25



Figure 4.

For quenching metal ions: Standardization of metal ion concentration with modulator 1:

0.3 × cMz+

For non-quenching metal ions: Standardization of metal ion concentration with modulator 2:

0.5 × S0

5 × S0

cMz+

cMz+

1× cMz+

4 × cMz +

0.9 × cMz+

3 × cMz +

12 × cMz+

Identification of single metal ion using modulators 1-9 for signal profiling

Linear model Identified metal ion 1 Identified metal ion 2

...

...

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26


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Figure 5.

Figure 6.

(a) Determined mass percentage of Cu2+ (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


100 80 60 40 20 0 0

20

40

60

80

100

Spiked mass percentage of Cu2+ (%)

27



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(b)

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FOR TOC ONLY

high signal

+ analyte metal ion Mz+ low signal

+ antenna ligand

Tb3+ + non-antenna and antenna ligands + analyte metal ion Mz+ low signal

high signal

29


Luminometric Label Array for Quantification and Identification of

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