Carbon Nanodots-Based Fluorescent Turn-On Sensor Array for

Jun 12, 2017 - Principal component analysis (PCA) was applied to process the resulting response pattern data, and a clustering map formed for a cleare...
1 downloads 0 Views 4MB Size
Article pubs.acs.org/ac

Carbon Nanodots-Based Fluorescent Turn-On Sensor Array for Biothiols Yapei Wu, Xue Liu,* Qiuhua Wu, Jie Yi, and Guolin Zhang* Liaoning Province Key Laboratory for Green Synthesis and Preparative Chemistry of Advanced Materials, College of Chemistry, Liaoning University, Shenyang, Liaoning 110036, P. R. China

Anal. Chem. 2017.89:7084-7089. Downloaded from pubs.acs.org by IOWA STATE UNIV on 01/25/19. For personal use only.

S Supporting Information *

ABSTRACT: Biothiols play important roles in biological processes. In this study, a novel sensor array-based method was proposed to detect and differentiate biothiols. The sensor array was constructed using three kinds of Ag+−sensitive carbon nanodots (CDs). The CDs were synthesized with amino acids and urea as carbon sources via a simple microwave method. Results revealed that Ag+ can bind with CDs and depress the fluorescence of CDs, while the subsequently joined biothiols can take Ag+ away from CDs and recover the fluorescence of CDs. Due to the different binding ability between Ag+ and various CDs, as well as Ag+ and various biothiols, the CD−Ag+ array exhibits a unique pattern of fluorescence variations when interacting with six biothiol samples (cysteamine, dithiothreitol, mercaptosuccinic acid, glutathione, mercaptoacetic acid, and mercaptoethanol). Principal component analysis (PCA) was applied to analyze the pattern and generate a clustering map for a clearer identification of these biothiols. PCA can also be employed to simplify the established three-sensor array into a two-sensor array. Both the three- and two-sensor arrays can identify these biothiols in a wide biothiol concentration range (>10 μM).

T

beneficial characteristics, CDs are promising candidates for sensor arrays.28,29 Biothiols widely exist in nature and play an important role in biological processes in animals and plants. For example, glutathione is involved in many cellular processes, and abnormal glutathione levels can lead to cancer, abnormal aging, heart problems, and other illnesses.30 Thioglycollic acid can cause environmental pollution, skin poisoning, genotoxicity, and mutagenicity.31 Mercaptamine can act as a radiation protective agent to treat acetaminophen poisoning and prevent serious liver injury.32 Mercaptoethanol can enhance the immune function of lymphocytes.33 As for mercaptosuccinic acid, it effectively assists nitro-glycerine to enable the full potential of the hypotensive effect.34 Dithiothreitol can affect some oxygen catalytic reactions in living organisms.35 Biothiols have been detected by using some CDs with Ag+ as a bridge through an interesting on−off−on three-state fluorescence emission.36,37 The first step of adding Ag+ can result in fluorescence quenching of CDs because of electron or energy transfer during the CD−Ag+ complex formation process. Given the strong thiophilicity of Ag+, CD−Ag+ complexes can be disassembled with further addition of biothiols, and thus, the

he development of rapid, sensitive, portable, and inexpensive sensing systems for various analytes has become an urgent societal need. Traditional sensors with high selectivity are designed through tailoring the sensing materials according to a given analyte, which always requires timeconsuming tedious synthetic procedures.1 Inspired by human olfactory and gustatory sensing systems,2,3 cross-reactive sensor arrays have been extensively investigated.4−6 Sensor arrays are constructed by utilizing a series of nonselective sensors, and analytes can be recognized through cumulative nonspecific responses from all the sensors.7−9 Highly specific sensors in sensor arrays are unnecessary, and thus, designing and constructing sensing systems based on sensor arrays is more time- and labor-saving. The construction of sensor arrays needs to incorporate appropriate sensing materials. Sensing materials with simple synthetic methods and excellent sensing performances can propel the development of sensor arrays. Carbon nanodots (CDs) are sensing materials exhibiting these advantages.10−12 CDs can be prepared from extensive and low-cost carbon sources by using simple and convenient methods.13 The high photostability of CDs guarantees stable fluorescence signal output and accurate detection results. Up to now, extensive analytes have been detected on the basis of CD sensors, including metal ions,14−17 small organic molecules,18−20 biomolecules,21,22 pH,23,24 and temperature.25−27 With these © 2017 American Chemical Society

Received: March 15, 2017 Accepted: June 12, 2017 Published: June 12, 2017 7084

DOI: 10.1021/acs.analchem.7b00956 Anal. Chem. 2017, 89, 7084−7089

Article

Analytical Chemistry

μM. Twenty μL of biothiol solution was added into the mixed solution to recover the fluorescence of CDs.

fluorescence of CDs recovers. However, these detecting systems can only recognize given biothiol or sulfhydryl compounds but cannot efficiently differentiate among various biothiols. In this study, a sensor array was constructed by using three kinds of Ag+−sensitive CDs to detect and differentiate various biothiols. Six kinds of biothiols with sufficiently different structures and functionalities, including cysteamine (C-SH), dithiothreitol (D-SH), glutathione (G-SH), mercaptosuccinic acid (MS-SH), mercaptoacetic acid (MA-SH), and mercaptoethanol (ME-SH), were chosen as analytes to evaluate the sensor array. Principal component analysis (PCA) was applied to process the resulting response pattern data, and a clustering map formed for a clearer identification of these biothiols. Meanwhile, the three-sensor array could also be simplified to a two-sensor array in the PCA process. Both the three- and twosensor array performed a highly differentiable and sensitive detection of biothiols. Scheme 1 is the working mechanism of the as-prepared CD−Ag+ sensor array for biothiols.



RESULTS AND DISCUSSION Characterization of CDs. Three CDs were prepared using a microwave method. Three kinds of amino acids combined with urea were respectively chosen as the carbon source to prepare CDs. The CDs prepared from glycine, histidine, and leucine were labeled as G-CDs, H-CDs, and L-CDs. All of the CDs show a relatively high fluorescence quantum yield (10.32− 17.19%) (Table S-1). The structures of three CDs were further characterized using the UV−vis test. Characteristic absorption bands of H-CDs at 220 and 280 nm (Figure 1A), of G-CDs at

Scheme 1. Working Mechanism of the Sensor Array for Biothiols Based on the CD−Ag+ System

Figure 1. UV−vis spectra of H-CDs (A), G-CDs (B), and L-CDs (C).

210 and 300 nm (Figure 1B), and of L-CDs at 210 and 300 nm (Figure 1C) were obvious. This is because there were π−π* and n−π* conjugates. According to the TEM, the sizes were as follows: H-CDs around 12 nm, G-CDs around 8 nm, and LCDs around 10 nm (Figure 2). The Response of CD with Ag+ and Biothiols. From the correlation curves between the fluorescence quenching value of CDs and the Ag+ concentrations, we can observe that the Ag+binding ability is different for various CDs. Different kinds of amino acids contribute to the subtle structural difference between various CDs. Glycine, histidine, and leucine have been reported to present various Ag+-binding ability,38−42 and their resulted CDs inherit these properties. The CDs are unresponsive on biothiols (Figure 3). However, the fluorescence intensity of CDs decreases when quantitative Ag+ is added to a CD solution (Figure 4). The subsequent addition of biothiols can take Ag+ away from the CDs and recover the fluorescence of each CD to a certain extent (Figure 5). The discriminative Ag+-binding effect of biothiols depends on the specific functional groups of biothiols. First, apart from thiol groups, other functional groups can also generate weak interaction with Ag+, such as the electrostatic interaction between carboxyl groups and Ag+. Besides, the donor−acceptor electronic radical can also influence the interaction between thiol groups and Ag+. Therefore, the Ag+-binding effect of biothiols is a result of synergistic action.43−47 The linear correlation information on H-CD−Ag+−biothiols was also given (Figure S-1 and Table S-2). H-CDs display a wider linear response range for Ag + compared with the other two CDs (Figure 4B,D,F). Therefore, the Ag+ concentration (400 μM) at the inflection points of its titrations to H-CDs was chosen to construct the sensor array for biothiols to obtain a maximum detection range and sensitivity. Biothiols with the same concentration (400 μM) were added to the CD−Ag+ solution, and the fluorescence



EXPERIMENTAL SECTION Synthesis of CDs. All of the CDs were prepared through the microwave method. 0.3 g of amino acid (glycine, histidine, and leucine) and 1 g of urea were dissolved in ultrapure water, and then, the solution was heated in the microwave oven for 4 min. The resulted solid powder was dissolved with 10 mL of ultrapure water. The supernatant was collected by centrifugation at 12 000 rpm for 5 min and then dialyzed against ultrapure water through a dialysis membrane for 48 h to remove the excess precursors and small molecules. The glycine CDs (G-CDs), histidine CDs (H-CDs), and leucine CDs (LCDs) were obtained, and the resultant CDs were maintained at 4 °C for further use. Fluorescence Quenching of CDs Using Ag+. In order to minimize the concentration effect of CDs, the absorbance in CD solution was calibrated to 0.1 at their optimum excitation wavelength. The optimum excitation wavelength was the excitation wavelength where the fluorescence intensity of CDs reaches the maximum (λG‑CD = 330 nm; λH‑CD = 350 nm; λL‑CD = 350 nm). On this basis, fluorescence quenching and recovery experiments were carried out. 0.5 mL of Ag+ solution with a calculated concentration was added into 3.5 mL of CD solution, and then, the PL spectra were recorded. Fluorescence Recovery of CD−Ag+ Using Biothiols. 3.5 mL of CD solution was mixed with 0.5 mL of Ag+ solution, and the concentration of Ag+ in the mixed solution was set as 400 7085

DOI: 10.1021/acs.analchem.7b00956 Anal. Chem. 2017, 89, 7084−7089

Article

Analytical Chemistry

Figure 2. TEM images of H-CDs (A), G-CDs (B), and L-CDs (C).

Figure 3. Response patterns of CDs in the presence of various biothiols. (A) G-CDs; (B) H-CDs; (C) L-CDs.

Figure 4. PL spectra of the CDs with different concentrations of Ag+, as well as the correlation curves between the fluorescence quenching value and Ag+ concentration. (A, B) G-CDs; (C, D) H-CDs; (E, F) L-CDs.

The first two PCs contribute ca. 85.6% of the total variability. Therefore, they can be applied as horizontal and vertical coordinates to plot a PCA scattergraph (Figure 7A). In the scattergraph, all of the data points from five repeated trials for each biothiol are close to each other and can be marked with a circle, representing an exclusive zone for a specific biothiol. No evident intersection among the data groups for various biothiols is observed, and this finding indicates that six biothiols can be differentiated through the scattergraph. The high level of data dispersion in the PCA scattergraph can be attributed to the high cross-reactivity given by the CD−Ag+−biothiol system. Sensor array research mainly aims to use a few sensor units to obtain good sensing performance. One of the most important applications of PCA is decreasing data dimensionality, and thus, the as-constructed sensor arrays can be simplified using PCA.50 Considering our description, we chose PC 1 and PC 2 as the PCs of the simplified sensor

recovery value was collected and normalized to construct the fingerprint for the biothiols (Figure 6). The fingerprints contain relevant information about the CD−Ag+ sensor array responding to these biothiols. For example, for MA-SH, GCD−Ag+ is the most sensitive response system, whereas LCD−Ag+ is the most insensitive one. Principal Component Analysis (PCA). The multidimensional response pattern (3 sensors × 6 biothiols × 5 trails) generated by the sensor array in the presence of six biothiols was statistically analyzed through PCA. PCA can convert the fluorescence recovery data in the fingerprints into a new set of linearly uncorrelated principal components (PCs).48 According to Kaiser rule, PCs with eigenvalues greater than 1 are statistically significant.49 Therefore, only the first PC (PC 1, eigenvalue = 1.422) and the second PC (PC 2, eigenvalue = 1.145) are listed in Table 1. PC 1 accounts for 47.4% of the total variability in the data, whereas PC 2 contributes 38.2%. 7086

DOI: 10.1021/acs.analchem.7b00956 Anal. Chem. 2017, 89, 7084−7089

Article

Analytical Chemistry

Figure 5. PL spectra of the H-CD−Ag+ system in the presence of various concentrations of biothiols (Control: CD solution without Ag+ and biothiol; 0−1000 μM: CD solution with 400 μM Ag+ and various concentrations of biothiols; A: C-SH, B: D-SH, C: G-SH, D: MS-SH, E: ME-SH, F: MA-SH).

sensor H-CDs contribute the most (79.0%) to PC 2. G-CDs and H-CDs are identified as the most important contributors to the sensor array. Hence, the sensor array was reconstructed using these two sensors to decrease the size of the array, and the PCA scattergraph was replotted in Figure 7B. The new PCA scattergraph displays separate clusters without a clear overlap between two kinds of biothiols. The reduction of the number of the sensor units has not significantly affected the working performance of the sensor array, and just two sensors are capable of differentiating between 6 analytes. Practical Application of Sensor Array. Double blind experiments and interference experiments were performed to evaluate the practicability of the sensor array. In the double blind experiments, two kinds of unknown biothiols (UN1−SH and UN2−SH) were randomly selected from the previous six biothiols. Their fluorescence parameters generated by the sensor array were taken into the formula and plotted in the PCA scattergraph (Figure 8A). When combined with Mahalanobis distance analysis (MDA), the data points of UN1−SH are close to those of MS-SH, which indicates that UN1−SH is MS-SH. In a similar way, UN2−SH can be evaluated as G-SH. The interference experiments were carried out in local tap water. The sensor array also performed well to discriminate six kinds of biothiols (Figure 8B), which confirmed the practical utility and stability of our sensing system. Optimal Concentration Range. The concentration of biothiols may affect the working performance of the sensor array. Hence, the simplified two-sensor array was applied to investigate and discuss the biothiol concentration at which the sensor array is capable of differentiating between different biothiols. We chose seven typical biothiol concentrations in the concentration range of 0−2000 μM. A series of fingerprints related to these 6 biothiols at a given concentration exist (Figure S-2). Applying PCA to process the data at each concentration, seven sets of PC score data were generated and plotted in one PCA scattergraph (Figure 9). From the scattergraph, it is difficult to differentiate between different biothiols at lower concentration (≤10 μM), implying the

Figure 6. Fingerprints (response patterns) of various biothiols generated by the CD−Ag+ sensor array.

Table 1. PCA Results of the CD−Ag+ Sensor Array Including 3 Sensors

array. Their correlation formulas are shown in the Supporting Information, and factor scores are listed in Table 1. Factor scores can serve as important parameters to evaluate the contribution of each individual sensor to the construction of PCs.51 A perfect sensor unit can provide the highest contribution to each PC with statistical significance. Sensor G-CDs exhibit the highest contribution (68.4%) to PC 1, while 7087

DOI: 10.1021/acs.analchem.7b00956 Anal. Chem. 2017, 89, 7084−7089

Article

Analytical Chemistry

Figure 7. (A) The original sensor array contains 3 sensors (G-CDs, H-CDs, and L-CDs). (B) The simplified sensor array contains only 2 sensors (G-CDs and H-CDs).

Figure 8. (A) Identification of unknown biothiols using sensor array. (B) Working performance of sensor array in local tap water.



CONCLUSIONS



ASSOCIATED CONTENT

In this study, a novel sensor array based on a CD−Ag+ system was designed and constructed to detect and differentiate biothiols. The sensor array utilizes CD−Ag+−biothiol on−off− on fluorescence signaling variation to establish the fingerprints of various biothiols. These fingerprints can be analyzed through PCA, and a clustering map was generated and applied for a clearer visible differentiation of biothiols. Meanwhile, the threesensor array can also be simplified into a two-sensor array without influencing the working performance of the sensor array. The sensor array can clearly differentiate six kinds of biothiols in a wide biothiol concentration range (>10 μM). Considering the advantages of CDs with simple synthetic methods and abundant carbon sources, we think that the CDbased sensor array can provide satisfactory resolution for extensive analytes through subtle design and construction of diverse sensing systems.

Figure 9. PCA scattergraph for the response of the sensor array with six biothiols at different concentrations. Six data points with the same color represent 6 biothiols at the same concentration.

sensors are close to their detection limits in this concentration range. At the concentration range of 100−500 μM, the data points at the same concentration present a clearer dispersive distribution as the biothiol concentration increases, indicating a good discriminative performance of the sensor array at these concentrations. When the biothiol concentration reaches a higher level (≥700 μM), the data points at various concentrations present a similar distribution, which means that the working performance of the sensor array is no longer influenced by biothiol concentration. From the working mechanism of the sensor array, biothiol is excessive in this concentration range and Ag+ can be completely removed from the CD sensors. These results confirm the as-constructed CD− Ag+ sensor array for biothiols can perform well in a wide biothiol concentration range (>10 μM).

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.7b00956. Chemicals and measurements; calculation of fluorescence quantum yields; additional data analysis from PCA; biothiol assay based on the H-CD−Ag+ system; fingerprints of biothiols at various concentrations (PDF) 7088

DOI: 10.1021/acs.analchem.7b00956 Anal. Chem. 2017, 89, 7084−7089

Article

Analytical Chemistry



(30) Yin, J.; Kwon, Y.; Kim, D.; Lee, D.; Kim, G.; Hu, Y.; Ryu, J. H.; Yoon, J. J. Am. Chem. Soc. 2014, 136, 5351−5358. (31) Jewell, S. A.; Bellomo, S. A.; Thor, H.; Orrenius, S.; Smith, M. Science 1982, 217, 1257−1259. (32) Kusmierek, K.; Bald, E. Biomed. Chromatogr. 2008, 22, 441−445. (33) Heidrick, M. L.; Hendricks, L. C.; Cook, D. E. Mech. Ageing Dev. 1984, 27, 341−358. (34) Saeho, C.; Ho-leung, T. Biochem. Pharmacol. 1991, 42, 1433− 1439. (35) Kim, K.; Rhee, S. U.; Stadtman, E. R. J. Boil. Chem. 1985, 260, 15394−15397. (36) Ran, X.; Sun, H. J.; Pu, F.; Ren, J. S.; Qu, X. G. Chem. Commun. 2013, 49, 1079−1081. (37) Jiang, K.; Sun, S.; Zhang, L.; Wang, Y.; Cai, C. Z.; Lin, H. W. ACS Appl. Mater. Interfaces 2015, 7, 23231−23238. (38) Stewart, S.; Fredericks, P. Spectrochim. Acta, Part A 1999, 55, 1641−1660. (39) Sanader, Z.; Mitric, R.; Bonacic-Koutecky, V.; Bellina, B.; Antoine, R.; Dugourd, P. Phys. Chem. Chem. Phys. 2014, 16, 1257− 1261. (40) Cayuelas, A.; Serrano, L.; Nájera, C.; Sansano, J. M. Tetrahedron: Asymmetry 2014, 25, 1647−1653. (41) Stewart, S.; Fredericks, P. Spectrochim. Acta, Part A 1999, 55, 1615−1640. (42) Hannig, F.; Kehr, G.; FrÖ hlich, R.; Erker, G. J. Organomet. Chem. 2005, 690, 5959−5972. (43) Cathcart, N.; Kitaev, V. J. Phys. Chem. C 2010, 114, 16010− 16017. (44) Pendyala, N. B.; Rao, K. S. R. K. Colloids Surf., A 2009, 339, 43− 47. ́ I.; Ras, R. H. A. Nanoscale 2011, 3, 1963−1970. (45) Dlez, (46) Adhikari, B.; Banerjee, A. Chem. Mater. 2010, 22, 4364−4371. (47) KrÓ likowska, A.; Bukowska, J. J. Raman Spectrosc. 2007, 38, 936−942. (48) Shaw, P. J. A. Multivariate statistics for the Environmental Sciences; Wiley: New York, 2003. (49) Norman, C. Psychol. Bull. 1988, 103, 276−279. (50) Palacios, M. A. P.; Wang, Z.; Montes, V. A.; Zyryanov, G. V.; Anzenbacher, P. J. Am. Chem. Soc. 2008, 130, 10307−10314. (51) Carey, W. P.; Beebe, K. R.; Kowalski, B. R.; Illman, D. L.; Hirschfeld, T. Anal. Chem. 1986, 58, 149−153.

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Xue Liu: 0000-0001-5187-0354 Notes

The authors declare no competing financial interest.



ACKNOWLEDGMENTS We are grateful to College of Chemistry, Liaoning University, Shenyang. This work was supported by the National Natural Science Foundation of China (51403093 and 51373073).



REFERENCES

(1) Potyrailo, R. A.; Mirsky, V. M. Chem. Rev. 2008, 108, 770−813. (2) Buck, L.; Axel, R. Cell 1991, 65, 175−187. (3) Malnic, B.; Hirono; Sato, J. T.; Buck, L. B. Cell 1999, 96, 713− 723. (4) Albert, K. J.; Lewis, N. S.; Schauer, C. L.; Sotzing, G. A.; Stitzel, S. E.; Vaid, T. P.; Walt, D. R. Chem. Rev. 2000, 100, 2595−2626. (5) Diehl, K. L.; Anslyn, E. V. Chem. Soc. Rev. 2013, 42, 8596−8611. (6) Askim, J. R.; Mahmoudi, M.; Suslick, K. S. Chem. Soc. Rev. 2013, 42, 8649−8682. (7) Na, N.; Zhang, S.; Wang, S.; Zhang, X. J. Am. Chem. Soc. 2006, 128, 14420−14421. (8) Wu, Y.; Na, N.; Zhang, S.; Wang, X.; Liu, D.; Zhang, X. Anal. Chem. 2009, 81, 961−966. (9) Kong, H.; Liu, D.; Zhang, S.; Zhang, X. Anal. Chem. 2011, 83, 1867−1870. (10) Baker, S. N.; Baker, G. A. Angew. Chem., Int. Ed. 2010, 49, 6726−6744. (11) Li, H. T.; Kang, Z. H.; Liu, Y.; Lee, S. T. J. Mater. Chem. 2012, 22, 24230−24253. (12) Lim, S. Y.; Shen, W.; Gao, Z. Q. Chem. Soc. Rev. 2015, 44, 362− 381. (13) Zhang, J.; Yu, S. H. Mater. Today 2016, 19, 382−393. (14) Zhang, R.; Chen, W. Biosens. Bioelectron. 2014, 55, 83−90. (15) Ju, J.; Chen, W. Biosens. Bioelectron. 2014, 58, 219−225. (16) Gao, X.; Lu, Y.; Zhang, R.; He, S.; Ju, J.; Liu, M.; Li, L.; Chen, W. J. Mater. Chem. C 2015, 3, 2302−2309. (17) Liu, Y.; Liu, Y.; Lee, J.; Lee, J. H.; Park, M.; Kim, H. Y. Analyst 2017, 142, 1149−1156. (18) Zhang, L.; Jiang, C.; Zhang, Z. Nanoscale 2013, 5, 3773−3779. (19) Zhang, K.; Zhou, H.; Mei, Q.; Wang, S.; Guan, G.; Liu, R.; Zhang, J.; Zhang, Z J. Am. Chem. Soc. 2011, 133, 8424−8427. (20) Esteves da Silva, J. C. G.; Gonçalves, H. M. R. TrAC, Trends Anal. Chem. 2011, 30, 1327−1336. (21) Zhang, R.; Zhao, J.; Liu, B.; Liu, R.; Zhao, T.; Han, Y.; Zhang, Z.; et al. J. Am. Chem. Soc. 2016, 138, 3769−3778. (22) Jiang, C.; Liu, R.; Han, G.; Zhang, Z. Chem. Commun. 2013, 49, 6647−6649. (23) Liu, X.; Li, T.; Wu, Q.; Yan, X.; Wu, C.; Chen, X.; Zhang, G. Talanta 2017, 165, 216−222. (24) Lu, S.; Cong, R.; Zhu, S.; Zhao, X.; Liu, J.; Tse, J. S.; Meng, S.; Yang, B. ACS Appl. Mater. Interfaces 2016, 8, 4062−4068. (25) Yu, P.; Wen, X.; Toh, Y.; Tang, J. J. Phys. Chem. C 2012, 116, 25552−25557. (26) Cayuela, A.; Soriano, M. L.; Carrillo-Carrión, C.; Valcárcel, M. Chem. Commun. 2016, 52, 1311−1326. (27) Long, Z.; Fang, D. C.; Ren, H.; Ouyang, J.; He, L. X.; Na, N. Anal. Chem. 2016, 88, 7660−7666. (28) Wang, Z.; Xu, X.; Lu, Y.; Chen, X.; Yuan, H.; Wei, G.; Ye, G.; Chen, J. Sens. Actuators, B 2017, 241, 1324−1330. (29) Wu, Y.; Liu, X.; Wu, Q.; Yi, J.; Zhang, G. Sens. Actuators, B 2017, 246, 680−685. 7089

DOI: 10.1021/acs.analchem.7b00956 Anal. Chem. 2017, 89, 7084−7089