Plasma-Assisted Cataluminescence Sensor Array for Gaseous

May 8, 2012 - Because of the difference in the component of hydrocarbons in breath, exhaled breath samples from donors with and without lung cancer we...
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Plasma-Assisted Cataluminescence Sensor Array for Gaseous Hydrocarbons Discrimination Na Na,† Haiyan Liu,† Jiaying Han,† Feifei Han,† Hualin Liu,‡ and Jin Ouyang*,† †

College of Chemistry, Beijing Normal University, Beijing 100875, People’s Republic of China Circulation Industry Promotion Center of the Ministry of Commerce, Beijing 100747, People’s Republic of China

‡

S Supporting Information *

ABSTRACT: Combining plasma activation and cross-reactivity of sensor array, we have developed a plasma-assisted cataluminescence (PA-CTL) sensor array for fast sensing and discrimination of gaseous hydrocarbons, which can be potentially used for fast diagnosis of lung cancer. Based on dielectric barrier discharge, a lowtemperature plasma is generated to activate gaseous hydrocarbons with low cataluminescence (CTL) activities. Extremely increased CTL responses have been obtained, which resulted in a plasma assistance factor of infinity (∞) for some hydrocarbons. On a 4 × 3 PA-CTL sensor array made from alkaline-earth nanomaterials, gaseous hydrocarbons showed robust and unique CTL responses to generate characteristic patterns for fast discrimination. Because of the difference in the component of hydrocarbons in breath, exhaled breath samples from donors with and without lung cancer were tested, and good discrimination has been achieved by this technique. In addition, the feasibility of multidimentional detection based on temperature was confirmed. It had good reproducibility and gave a linear range of 65−6500 ng/mL or 77−7700 ppmv (R > 0.98) for CH4 with a detection limit of 33 ng/mL (38 ppmv) on MgO. The PA-CTL sensor array is simple, low-cost, thermally stable, nontoxic, and has an abundance of alkaline-earth nanomaterials to act as sensing elements. This has expanded the applications of CTL-based senor arrays and will show great potential in clinical fast diagnosis.

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sensing elements can be recorded simultaneously.17,20 Based on the cataluminescence (CTL) generated during the catalytic oxidation of analytes on surfaces of nanomaterials, we have reported a nanomaterials-based CTL optical sensor array. Because of different catalytic activities of nanomaterials, the distinct patterns of CTL were achieved for the fast discrimination of analytes.21−23 It was a fast detection with simple and low-cost devices, and it had given stable responses for its nonconsumption of catalysts.21,24 However, it showed low performance for the analysis of hydrocarbons, because of their poor CTL reactivity. Thus, it is required to seek a pathway to expand the application field of this technique. Plasma is a chemically active media composed of high energetic electrons, radicals, ions, and metastable species, which enables various applications such as ionization, surface coating, waste destruction, gas treatments, chemical synthesis, etc.25−30 Plasma-assisted catalysis (PAC), constructed by integrating plasma and thermal catalysis, has been employed for increasing the reactivity of analytes or the catalytic activity of catalysts.31−34 A PAC−CTL system has been proposed for the enhancement of CTL, and it has been used for the

he detection of gaseous hydrocarbons is significant in clinical diagnoses, environment monitoring,1,2 and research on planetary and cometary formations.3 Especially, hydrocarbons in the exhaled breath could be candidate markers of lung cancer.4−7 Numerous techniques have been used for gaseous hydrocarbon monitoring, such as gas chromatography,8−10 electrochemical methods,2 mid-infrared (mid-IR) related techniques,11,12 surface-enhanced Raman scattering (SERS),13 and modified surface acoustic wave (SAW) devices.14 However, these methods still have some limitations such as using expensive instruments, time-consuming procedures, complicated pretreatments, or having low sensitivities.11 Therefore, the development of a new strategy for hydrocarbon sensing is still needed. Recently, the interest in molecular sensing has been slowly shifting from sensors toward sensor arrays, because the conventional lock-and-key sensors are not particularly useful for analyzing complex samples of relatively similar compounds. These sensor arrays, which mimic olfactory systems of animals by utilizing cross-reactive patterns, were therefore called “artificial olfactory systems” or “electronic noses”.15,16 Typically, the optical sensor array has been used for detecting chemical species based on changes of optical properties, such as luminescence emission intensity, wavelength, lifetime, and spectral shape.17−19 By imaging, multiple responses from © 2012 American Chemical Society

Received: February 11, 2012 Accepted: May 8, 2012 Published: May 8, 2012 4830

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For the discrimination, the matrix of data was processed using classical linear discriminant analysis (LDA) in SPSS (Version 16.0). The mode of “all groups equal” was employed to all variables during the analysis. The CTL pattern signals were transformed to canonical patterns and visualized in the canonical score plots. Then, we obtained Mahalanobis distances of each individual pattern to the centroid of each group in a multidimensional space. The assignment of the case was based on the shortest Mahalanobis distance. PA-CTL Sensor Array Fabrication. The plasma assistance probe was fabricated based on dielectric barrier discharge (DBD). As shown in Figure 1, a copper stick (1.5 mm in

detection of benzene, toluene, ethylbenzene, and xylenes (BTEX).35 Thus, it might be a pathway to enhance CTL responses of gaseous hydrocarbons. Furthermore, the fast discrimination of hydrocarbons could be achieved if we combine plasma-assisted CTL (PA-CTL) with “artificial olfactory systems” of the sensor array. In the present work, combining plasma activation and crossreactivity of sensor array, a PA-CTL-based sensor array has been constructed for the fast discrimination of gaseous hydrocarbons. Taking a series of alkaline-earth nanomaterials as sensing elements, the characteristic CTL patterns were obtained with the assistance of low-temperature plasma, while no obvious signal was recorded without the plasma assistance. Therefore, the fast discrimination of gaseous hydrocarbons was achieved according to the cross-reactive patterns. In addition, the discrimination of exhaled breath from donors with and without lung cancer showed potentials of this method in fast diagnosis of lung cancer. This method has the characteristics of low cost, simple configuration, fast response, and easy operation for gaseous hydrocarbons detection. It will show potential in clinical diagnoses, environment monitoring, industrial controls, or interstellar research.

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EXPERIMENTAL SECTION Chemicals and Catalysts Preparation. All reagents were of analytical-reagent grade. The reagents of MgSO4, CaCl2, SrCl2·6H2O, BaCl2, Ba(OH)2·8H2O, Na2CO3, Na2SO4, NaOH, and NH3·H2O were obtained from Beijing Chemical Co., Ltd. The helium gas (99.99%) and gaseous hydrocarbons including methane, ethane, propane, n-butane, ethylene, and propylene were supplied by Beijing Haipu-Gas Co., Ltd. Water was deionized and further purified using a Milli-Q water purification system (Millipore, Bedford, MA). The sensing elements of alkaline-earth nanomaterials were synthesized by sol−gel methods. CL properties were examined by the procedures described previously.21,24 The prepared alkaline-earth nanomaterials then were spotted orderly onto the surface of a ceramic chip to form a 4 × 3 array (∼100 μm in thickness and 1 mm in diameter for each one). Apparatus and Softwares. CTL signals were detected using a BPCL ultraweak chemiluminescence analyzer (Biophysics Institute of the Chinese Academy of Science in China) equipped with a CR-105 photomultiplier tube (PMT) (Hamamatsu, Tokyo, Japan). The CTL responses were recorded in the wavelength range of 230−680 nm without inserting any interference filters, which was determined by the performance of the PMT. The CTL spectra were obtained by recording signals through a series of interference filters from 230 nm to 680 nm, which was inserted between the sensor and the PMT. Data acquisition was employed by BPCL software and exported to Origin 6.0 (Microcal Software, Inc., USA). For imaging, the CTL images were recorded by a VILBER FUSION-SL5-3500 Imaging System (Vilber Lourmat, France) or a camera. The helium gas flow was controlled by a flow meter (Beijing Keyi Laboratory Instrument Co., Ltd., Beijing, PRC). A CTP2000K alternating discharge power with a peak-to-peak voltage of 0−30 kV and a frequency of 5−20 kHz was purchased from Nanjing-Suman Electronic Co. Ltd. (Nanjing, PRC). A continuous air was provided by a XWK-III oil-free air pump (Huasheng Analysis Instrument Co., Ltd., Tianjin, PRC). The temperature of the sensor array was controlled by a digital temperature controller.

Figure 1. Schematic diagram of the plasma-assisted cataluminescence (PA-CTL) sensor array.

diameter) was inserted in a T-glass tube (4.0 mm i.d. × 6.0 mm o.d.) as an electrode, and a piece of copper sheet wrapping the T-tube as another electrode. The helium gas flowed through the T-tube at a flow rate of 100−200 mL/min. The glass not only served as the gas tube, but also acted as a discharge barrier for sustaining the nonequilibrium low-temperature plasma at atmospheric pressure. The plasma was generated when an alternating voltage of 3.5−4.5 kV at 18.0−25.0 kHz and 5−30 W was applied to the two electrodes. As shown in Figure 1, the plasma probe was inserted into a conical flask with a continuous air flowing through. Samples were then activated by the plasma probe, which was introduced into the gas flow at the position of P-1 or P-2 (see Figure 1). The activated samples were subjected to a 4 × 3 sensor array, which adopted alkaline-earth nanomaterials as sensing elements. By imaging, we finally obtained characteristic CTL patterns based on the different CTL signals from each sensing element on the array. Human Subjects and Collection of Exhaled Breath. Six patients suffering from lung cancer at different stages (median age 61.8 years and an age range of 46−70 years), as well as four healthy controls, were recruited. The cancer patients were exsmokers, or they had been dissuaded from smoking. (In Chinese hospitals, smoking is prohibited when the patients are hospitalized.) In addition, the healthy controls were nonsmokers. All individuals gave informed consent to participate in the study. All subjects consumed food (without drinking alcohol) no later than 1 h before breath sampling. Samples of exhaled breath were collected in an Aluminum Foil Gas Sampling Bag (Shanghai, PRC). Before the collection of breath, all bags were cleaned by flushing with nitrogen gas (99.99%), and then filled with nitrogen to be heated at 85 °C for 5 h with the complete evacuation. 4831

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RESULTS AND DISCUSSION CTL Signals Comparison with and without Plasma Assistance. The plasma assistance effect was examined by comparison of CTL signals obtained with and without plasma assistance. As shown in Figure 2, no CTL signal was recorded

the CTL enhancement by plasma assistance, we select C3H6 as an example for the detection of the exhaust gas composition by gas chromatography−mass spectrometry (GC-MS). We collected exhaust gases after C3H6 was passed through the sensor in the presence and absence of the plasma assistance, as well as gas that did not pass through the sensor and was without sample injection (blank). The data were confirmed by comparison with the standard GC-MS spectrum from the NIST Standard Reference Database (National Institute of Standards and Technology (NIST),Washington, DC, USA). As demonstrated (see Figure S1 in the Supporting Information), only N2 and O2 were detected in the blank. With the injection of C3H6 to the sensor without plasma assistance, we only found the signals of C3H6, N2 and O2, which demonstrated that the sample possessed no or very low reactivity in the absence of the plasma. However, with the assistance of plasma, the exhaust gas composition was quite complicated, including not only N2, O2, and C3H6, but also H2O, CO2, and even some other hydrocarbons. This might be generated from the plasma activation for C3H6 catalytic oxidation. In addition, some hydrocarbons might be produced through radical reactions with the plasma activation. Simultaneously, the stronger CTL signals were obtained during these CTL reactions, which were in accordance with CTL signal comparison in Figure 2. Therefore, the plasma affords higher reactivity to hydrocarbons or higher catalytic activity to catalysts, thus generating high CTL signals. In addition, post-sample processing (P-1) and pre-sample processing (P2), in which the plasma probe located downstream or upstream from the samples (Figure 1), gave different assistance effects. The post-sample processing has much better enhancement effect for the PA-CTL reaction, which provided over 1700 au of PA-CTL intensities for CH4 and C3H6 (Figure

Figure 2. CTL signals obtained by post-sample processing (P-1 in Figure 1), pre-sample processing (P-2 in Figure 1) and without plasma assistance: (A) CH4 and (B) C3H6. The catalyst was BaCO3 nanomaterials. The working temperature was 195 °C. The concentration of each analyte was 0.185% (v/v).

without the assistance of plasma for both CH4 and C3H6 on BaCO3 surfaces. However, the significant CTL signals were obtained with the plasma assistance. In order to better illustrate

Figure 3. CTL intensity (left) and plasma assistance factor (right) with and without plasma assistance: (A, B) different gaseous hydrocarbons catalyzed by MgO and (C, D) propane catalyzed by different nanomaterials. The working temperature was 195 °C. The concentration of each analyte was 0.185% (v/v). 4832

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ination using nonspecific response patterns.16,21 To examine the cross-reactivity of a PA-CTL sensor array, ethane, propane, and propylene were selected as models to be catalyzed by different nanomaterials. As expected, the PA-CTL signals on different catalysts were distinct for a given compound. For example, ethane gave the strongest signal on MgCO3, but a weak signal on BaO (see Figure 4A). Similarly, the same

2, P-1). However, very low signals were recorded by the presample processing (Figure 2, P-2). For example, 75 au of PACTL was obtained for CH4 (Figure 2A, P-2), which was ∼23 times lower than the signals obtained by post-sample processing (Figure 2A, P-1). The higher assistance effect of post-sample processing might be generated from its high activation efficiency when samples passing through the plasma, which could provide preconverting of reactants into easier converted products to accelerate the catalysis, or afford chemically reactive species for catalysis. However, samples run faster than the plasma in the pre-sample processing, so what we obtained was the inadequate activation. Thus, we selected the plasma zone location of P-1 (post-sample processing) in the next studies. Plasma Assistance Effect for Samples on AlkalineEarth Nanomaterials. The plasma assistance effect was further examined by analyzing different gaseous hydrocarbons catalyzed by MgO, which is a type of alkaline-earth nanomaterial. Without plasma assistance, there were no CTL response for methane, ethane, propane, and ethylene, as well as quite weak signals of n-butane and propylene (green columns in Figure 3A). However, CTL intensities increased dramatically when the plasma was turned on (red columns in Figure 3A). In order to demonstrate the enhanced efficiency of the plasma assistance technique, a plasma assistance factor for hydrocarbons was defined as plasma assistance factor =

Ion Ioff

Figure 4. Cross-reactive PA-CTL responses with an average of three parallel measurements: (A) ethane, (B) propane, and (C) propylene. The working temperature was 195 °C. The concentration of each analyte was 0.185% (v/v).

where Ion is the PA-CTL intensity with the plasma assistance (plasma on) and Ioff is the PA-CTL intensity without the plasma assistance (plasma off). According to CTL signals, the different plasma assistance factor was demonstrated in Figure 3B. The infinity of plasma assistance factor for methane, ethane, and propane was obtained for their zero CTL responses without plasma assistance. Furthermore, distinguishable CTL signals for different samples were recorded using the PA-CTL technique. For instance, we got the high CTL signal of nbutane (>98 000 au), while obtaining signals ∼20 times lower for ethane (∼5000 au). Similarly, the plasma assistance effect was also examined by detecting propane catalyzed by different alkaline-earth nanomaterials. As shown in Figure 3C, there was no obvious CTL response of propane without plasma assistance (green columns in Figure 3C). However, when the plasma was turned on, different CTL signals were recorded, catalyzed by different nanomaterials (red columns in Figure 3C). Plasma assistance factors are shown in Figure 3D. These different signals were generated due to the difference in activation energy for the oxidation on different catalysts.21 In addition, we obtained much higher CTL signals and better assistance effects for hydrocarbons catalyzed by alkaline-earth nanomaterials than BTEX catalyzed by ZrO2 nanomaterials. For example, a CTL intensity of 38 032 au with an Ion/Ioff value of ∞ was obtained for propane catalyzed by MgCO3, while a CTL signal of only 3300 au with Ion/Ioff = 65 was observed for m-xylene on ZrO2.35 Thus, the PA-CTL technique does have a high assistance effect and gives distinct CTL signals for different samples catalyzed by different nanomaterials. This might be useful for fast discriminations. Cross-Reactivity and the Discrimination by Sensor Array. The cross-reactive responses are crucial for discrim-

catalyst exhibited different PA-CTL properties upon exposure to different hydrocarbons. On the surface of BaSO4, we obtained the highest signal of 19 488 au for propane (Figure 4B), a medium signal of 7138 au for ethane (Figure 4A), and the lowest response of 5320 au for propylene (Figure 4C). Therefore, the cross-reactivity of PA-CTL method was confirmed, which could be used for fabricating a cross-reactive sensor array to discriminate gaseous hydrocarbons. In order to clarify any discrimination among hydrocarbons by this plasma-assisted approach, six types of hydrocarbons, including methane, ethane, propane, n-butane, ethylene, and propylene, were selected as models for the test. The PA-CTL intensity patterns of the training matrix (9 nanomaterials × 6 hydrocarbons × 3 replicates) were subjected to linear discriminant analysis (LDA). It was resulted that the first two canonical factors contain 88.4% and 6.1% of the variation, occupying 94.5% of total variation. As shown in Figure 5, the canonical patterns were clustered into six different groups to achieve good discrimination. Therefore, the present method can be used to discriminate between the hydrocarbons. Discrimination of Gaseous Hydrocarbons by Imaging. A 4 × 3 PA-CTL sensor array was used for the discrimination of gaseous hydrocarbons by imaging. As a result, in Figure 6A, no signal was recorded without plasma assistance. However, when the plasma was on, distinct patterns were obtained for propylene, ethane, and propane. Only three remarkable spots were observed on the pattern of propylene (Figure 6B), while a pattern with the shape of “< 0.98). The detection limit was 33 ng/mL (38 ppmv) on the surface of MgO, which was much lower than the sensitivity of another optical sensor based on NIR light-emitting diodes (LEDs) (500 ppmv).11

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CONCLUSIONS In conclusion, based on the dramatically increased cataluminescence (CTL) signals obtained by the plasma assistance, a simple and low-cost plasma-assisted cataluminescence (PACTL) sensor array achieved fast discrimination of the gaseous hydrocarbons. Alkaline-earth compounds were selected as the sensing elements, which gave the sensor array more characteristics of having good thermal stability, being nontoxic, and being resource rich in catalysts. Multidimensional detection, based on temperature, was also achieved to enhance the discrimination ability of the sensor array. Considering that the exhaled breath samples from donors with and without lung cancer were well-discriminated, the PA-CTL sensor array has potential in the field of fast clinical diagnosis.

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ASSOCIATED CONTENT

S Supporting Information *

Information regarding the GC-MS detection of exhaust gases is provided as Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Fax: +86-10-62799838. E-mail: [email protected]. 4835

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Notes

(28) Na, N.; Zhang, C.; Zhao, M. X.; Zhang, S. C.; Yang, C. D.; Fang, X.; Zhang, X. R. J. Mass Spectrom. 2007, 42, 1079−1085. (29) Hill, S. L.; Kim, H. H.; Futamura, S.; Whitehead, J. C. J. Phys. Chem. A 2008, 112, 3953−3958. (30) He, Y. H.; Lv, Y.; Li, Y. M.; Tang, H. R.; Tang, L.; Wu, X.; Hou, X. D. Anal. Chem. 2007, 79, 4674−4680. (31) Chen, H. L.; Lee, H. M.; Chen, S. H.; Chao, Y.; Chang, M. B. Appl. Catal., B 2008, 85, 1−9. (32) Wallis, A. E.; Whitehead, J. C.; Zhang, K. Appl. Catal., B 2007, 74, 111−116. (33) Harling, A. M.; Demidyuk, V.; Fischer, S. J.; Whitehead, J. C. Appl. Catal., B 2008, 82, 180−189. (34) Harling, A. M.; Glover, D. J.; Whitehead, J. C.; Zhang, K. Environ. Sci. Technol. 2008, 42, 4546−4550. (35) Almasian, M. R.; Na, N.; Wen, F.; Zhang, S. C.; Zhang, X. R. Anal. Chem. 2010, 82, 3457−3459. (36) Machado, R. F.; Laskowski, D.; Deffenderfer, O.; Burch, T.; Zheng, S.; Mazzone, P. J.; Mekhail, T.; Jennings, C.; Stoller, J. K.; Pyle, J.; Duncan, J.; Dweik, R. A.; Erzurum, S. C. Am. J. Resp. Crit. Care 2005, 171, 1286−1291.

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors gratefully acknowledge the support from the National Nature Science Foundation of China (Nos. 21005007, 20975016, 91027034), SRFDP (20100003120014), and the Fundamental Research Funds for the Central Universities.

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REFERENCES

(1) Davis, J. B.; Squires, R. M. Science 1954, 119, 381−382. (2) Fujio, Y.; Plashnitsa, V. V.; Elumalai, P.; Miura, N. Talanta 2011, 85, 575−581. (3) Mumma, M. J.; DiSanti, M. A.; Dello Russo, N.; Fomenkova, M.; Magee Sauer, K.; Kaminski, C. D.; Xie, D. X. Science 1996, 272, 1310− 1314. (4) Filipiak, W.; Sponring, A.; Filipiak, A.; Ager, C.; Schubert, J.; Miekisch, W.; Amann, A.; Troppmair, J. Cancer Epidem. Biomar. 2010, 19, 182−195. (5) Bajtarevic, A.; Ager, C.; Pienz, M.; Klieber, M.; Schwarz, K.; Ligor, M.; Ligor, T.; Filipiak, W.; Denz, H.; Fiegl, M.; Hilbe, W.; Weiss, W.; Lukas, P.; Jamnig, H.; Hackl, M.; Haidenberger, A.; Buszewski, B.; Miekisch, W.; Schubert, J.; Amann, A. BMC Cancer 2009, 9. (6) Ligor, M.; Ligor, T.; Bajtarevic, A.; Ager, C.; Pienz, M.; Klieber, M.; Denz, H.; Fiegl, M.; Hilbe, W.; Weiss, W.; Lukas, P.; Jamnig, H.; Hackl, M.; Buszewski, B.; Miekisch, W.; Schubert, J.; Amann, A. Clin. Chem. Lab. Med. 2009, 47, 550−560. (7) Phillips, M.; Gleeson, K.; Hughes, J. M. B.; Greenberg, J.; Cataneo, R. N.; Baker, L.; McVay, W. P. Lancet 1999, 353, 1930− 1933. (8) Sirichai, S.; Khanatharana, P. Talanta 2008, 76, 1194−1198. (9) Tanner, D.; Helmig, D.; Hueber, J.; Goldan, P. J. Chromatogr. A 2006, 1111, 76−88. (10) Li, W.; Zheng, C.; Fan, G.; Tang, L.; Xu, K.; Lv, Y.; Hou, X. Anal. Chem. 2011, 83, 5050−5055. (11) Stewart, G.; Massie, C.; McGregor, G.; Gilchrist, J. R. Sens. Actuators B 2006, 113. (12) Pyun, S. H.; Cho, J.; Davidson, D. F.; Hanson, R. K. Meas. Sci. Technol. 2011, 22. (13) Lopez-Tocon, I.; Otero, J. C.; Arenas, J. F.; Garcia-Ramos, J. V.; Sanchez-Cortes, S. Anal. Chem. 2011, 83, 2518−2525. (14) Rapp, M.; Boss, B.; Voigt, A.; Gemmeke, H.; Ache, H. J. Fresenius J. Anal. Chem. 1995, 352, 699−704. (15) Tisch, U.; Haick, H. MRS Bull. 2010, 35, 797−803. (16) 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. (17) Palacios, M. A.; Wang, Z.; Montes, V. A.; Zyryanov, G. V.; Anzenbacher, P. J. Am. Chem. Soc. 2008, 130, 10307−10314. (18) Lin, H. W.; Jang, M.; Suslick, K. S. J. Am. Chem. Soc. 2011, 133, 16786−16789. (19) Lim, S. H.; Feng, L.; Kemling, J. W.; Musto, C. J.; Suslick, K. S. Nat. Chem. 2009, 1, 562−567. (20) Rakow, N. A.; Suslick, K. S. Nature 2000, 406, 710−713. (21) Na, N.; Zhang, S. C.; Wang, S. A.; Zhang, X. R. J. Am. Chem. Soc. 2006, 128, 14420−14421. (22) Wu, Y. Y.; Na, N.; Zhang, S.; Wang, X.; Liu, D.; Zhang, X. R. Anal. Chem. 2009, 81, 961−966. (23) Na, N.; Zhang, S. C.; Wang, X.; Zhang, X. R. Anal. Chem. 2009, 81, 2092−2097. (24) Zhu, Y. F.; Shi, J. J.; Zhang, Z. Y.; Zhang, C.; Zhang, X. R. Anal. Chem. 2002, 74, 120−124. (25) Tendero, C.; Tixier, C.; Tristant, P.; Desmaison, J.; Leprince, P. Spectrochim. Acta B 2006, 61, 2−30. (26) Na, N.; Zhao, M. X.; Zhang, S. C.; Yang, C. D.; Zhang, X. R. J. Am. Soc, Mass Spectrom 2007, 18, 1859−1862. (27) Na, N.; Xia, Y.; Zhu, Z. L.; Zhang, X. R.; Cooks, R. G. Angew. Chem., Int. Ed. 2009, 48, 2017−2019. 4836

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