Analytical Chemistry Chemical Cytometry Quantitates Superoxide

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eader has a basic understanding of them. Hereafter, we shall go directly to the presentation of the main results. Figure 1(a) shows the average signal over 400 spectra taken with integration time of 1 s, with an electrochemical modulation period of 20 s. The average has clear fingerprint peaks of both dyes; for example the 590 and 610 cm -1 modes of NB and RH6G, respectively. The intensity of these peaks is modulated differently in the potential window selected, that is, their redox potentials are different. This is a case nevertheless where both signals of the coadsorbed species are clearly visible, and show in some cases some degree of overlap (like the 1645 and 1650 cm -1 modes of NB and RH6G, respectively). We first show an analysis in Figure 1 based on a spectral region that contains fingerprint modes of both dyes, like the region around ∼600 cm -1 shown in a dashed box in Figure 1(a). A PCA analysis of this region 40 immediately identifies the presence of two main modulated components in the spectra. As explained in the SI though, being a linear decomposition technique, PCA always provides a linear combination of the right answer in the form of principal “eigenvector” spectra (in this case the first two of them, shown in Figure 1(b)). In order to go from this two PCA eigenvectors to the real spectra that represent the individual contributions of NB and RH6G, a linear transformation (which we shall call “rotation”) of these eigenvectors is required. An entirely equivalent situation occurs with the application of PCA to the analysis of fluctuations from single molecule spectra; as explained in full length in ref 40. The “rotated” eigenvectors obtained in this case are shown in Figure 1(c), and represent the isolated contributions of NB and RH6G to each individual spectrum. Once these two eigenvector spectra (v 1 and v2 in Figure 1(c)) are known, a standard linear decomposition can be performed on each spectra Ii in the time series (i ) 1, 2, ..., 400), to represent it as a linear combination (with two coefficients) of the rotated PCA eigenvectors; that is, Ii ) c1v1 + c2v2. The values of the coefficients, therefore, represent the particular contribution of NB and RH6G to an individual spectrum, and this is shown explicitly in Figure 1(d). Figure 1(e), in addition, shows a blown-out region of the time evolution of the coefficients where it can be appreciated that the two signals are being modulated differently at different times, accounting for the different intrinsic electrochemical properties of both dyes as a function of the sweeping potential. Note that time evolution can be converted to potential, since we know exactly the period of electrochemical modulation applied externally. An example of this is shown in the SI. The coefficients in Figure 1(d) and (e) are obviously in arbitrary units, since the Raman intensity itself is in arbitrary units too. What matters is not the absolute units, but rather the relative values of the coefficients representing the particular contributions to the total coming from the different electrochemical species. Therefore, Figure 1 demonstrates clearly that the combination of electrochemical modulation with PCA analysis can (i) resolve the problem of spectral congestion, and (ii) identify in addition the ranges of electrochemical potentials where the different species experience their modulation. This is emphasized in Figure (40) Etchegoin, P. G.; Meyer, M.; Blackie, E.; Le Ru, E. C. Anal. Chem. 2007, 79, 8411. (41) Jolliffe, I. T. Principal Component Analysis; Springer Verlag:Berlin, 2002. (42) Hyvrinen, A.; Karhunen, J.; Oja, E. Independent Component Analysis; John Wiley & Sons: New York, 2001. 6922 Analytical Chemistry, Vol. 82, No. 16, August 15, 2010 1(e), where the “dephasing” of the coefficients c 1 and c2 representing the relative contributions of NB and RH6G, respectively, can be easily seen. The fact that RH6G only starts to be reduced toward the lowest negative range of the potential (E < -400 mV) appears as a “dephasing” of the coefficients in time, while the successive electrochemical cycles evolve. Figure 1(d) also shows a slight reduction in the amplitude of the coefficients produced by photobleaching at the observation point. It may also have partial contribution from natural long-term desorption of the dyes into the buffer. Photobleaching, in general, tends to be one of the major limiting factors to obtain unlimited sampling of the signal. Moving on from the simplest case in Figure 1, if we want to carry out the analysis over the entire spectral range (rather than a small window with fingerprint modes, like in Figure 1), special care must be taken with background contributions. Backgrounds are in general problematic in SERS 43 and, unlike the Raman peaks themselves, they can have a variety of origins (including molecules that are not in contact with the electrode). Furthermore, there will be in general a correlation between the background and the Raman signal created by the dispersion of the plasmon resonances producing SERS. 43-45 Here, we want to concentrate in a first approximation only on the Raman signals themselves and, therefore, the background will be removed 46 to avoid its presence in the PCA analysis. This should be done with care and the results should be assessed on a case-by-case basis. Backgrounds in SERS can be particularly problematic in the single molecule limit, where the dispersion of an individual plasmon resonance at a hot-spot can be revealed. 43,47 Measuring an average over many molecules is in a way an advantage, for differing types of backgrounds tend to average out and we are left with a case of background contribution that is easier to subtract. The background of each individual spectrum is subtracted with a wavelet transform that is fully described in ref 46 (including the program which is freely available 46 ). The success of the background subtraction procedure to deconvolute the spectra is judged here by the ability of the PCA analysis to distinguish the two main contributions to the signal in the full spectral range (which are known in this case). Effectively, Figure 2 shows the equivalent analysis of Figure 1 in the full spectral range. Once the backgrounds are subtracted, and the two main PCA eigenvector spectra rotated, we recover the independent spectra of NB and RH6G from the mixture. Except for a few minor imperfections, the separation of the spectral components is almost perfect, and can be also distinguished in terms of the respective electrochemical modulation ranges for the potential. As in Figures 1(d) and (e), the dephasing of the coefficients from the contributions of NB and RH6G, according to the different values of the redox potentials over the cyclic voltammetry runs, can be clearly observed again in Figure 2(d). It is particularly worth emphasizing that the spectral deconvolution (43) Buchanan, S.; Le Ru, E. C.; Etchegoin, P. G. Phys. Chem. Chem. Phys. 2009, 11, 7406. (44) Le Ru, E. C.; Etchegoin, P. G.; Grand, J.; Felidj, N.; Aubard, J.; Levi, G.; Hohenau, A.; Krenn, J. R. Curr. Appl. Phys. 2008, 8, 467. (45) Le Ru, E. C.; Grand, J.; Felidj, N.; Aubard, J.; Levi, G.; Hohenau, A.; Krenn, J. R.; Blackie, E.; Etchegoin, P. G. J. Phys. Chem. C 2008, 112, 8117. (46) Galloway, C.; Le Ru, E. C.; Etchegoin, P. G. Appl. Spectrosc. 2009, 63, 1371. (47) Itoh, T.; Yoshida, K.; Biju, V.; Kikkawa, Y.; Ishikawa, M.; Ozaki, Y. Phys. Rev. B 2007, 76, 085405.
Figure 2. With a suitable removal of the background 46 for all events, the full spectra of NB and RH6G can be deconvoluted again from the modulated time series through PCA. The region analyzed now is the full spectral window shown in (a) (dashed line). The two main eigenvectors and the “rotated” ones 40 (representing the independent contributions of NB and RH6G) are shown in (b) and (c), respectively. The spectra of two compounds can be deconvoluted, and their time (or potential) dependence followed through the time evolution of the two coefficients in (d), that describe each event as a linear combination of v1 and v2 in (c). The dephasing of the electrochemical modulation of the two compounds can again be appreciated in (d). through the electrochemical modulation works very well even in regions where a substantial overlap of peaks occurs. An example of the latter is shown in Figure 3, for the region around ∼1650 cm -1 , which has contributions from breathing modes of both NB and RH6G (but at slightly different frequencies). As can be appreciated from Figure 3, the different modulation induced through the electrochemical cycle on both compounds is good enough to “deconvolute” closely laying peaks within their natural widths. Mixture of NB and CV. All in all, the case presented in the previous section represents a relative “easy” case of spectral deconvolution for PCA with electrochemical modulation; starting from the fact that both compounds have comparable contributions to the intensity of the spectra and can be easily identified in terms of their contributions to fluctuations (which is the whole basis of PCA 41,42 ). A substantially more challenging case is to try recover a small signal from a background of other contributions to the spectra (that might be unavoidable in many real cases). We treat this case here also with an example. Figure 3. The deconvolution of the spectra works even in regions where there is a substantial overlap of peak intensities for the different compounds. Here we show explicitly the region ∼1650 cm -1 , where the RH6G peak is deconvoluted from the equivalent ring-breathing mode in NB, which is at a slightly lower frequency. Consider the case of a mixture of 80 and 20 nM of CV and NB, respectively, as shown in Figure 4(a). The mixture is prepared purposely so that the signal of NB is barely visible in the average spectrum (Figure 1(a)). This is shown explicitly, for example, with the arrows pointing at the ∼590 and ∼1650 cm -1 fingerprint peaks of NB in Figure 4(a), which are almost buried in the immediate surrounding of much larger peaks from CV. Both NB and CV experience (different) electrochemical modulations in the potential range between -50 and -550 mV (the electrochemistry of CV is also summarized in the SI). We can basically follow in a first approximation the analysis performed in the previous section. With an unlimited amount of sampling, PCA has all what it needs to distinguish the different components in the spectra. This is because the eigenvalues and eigenvectors of the covariance matrix (which is the basis of PCA 41,42 ) will eventually distinguish what is correlated and what is random in the signal. For the same reason that unlimited integration time should in principle always lift a signal above the noise level, sufficient sampling will always provide PCA with all the information required to distinguish the different contributions to the spectra. Nevertheless, when big signals are mixed with very small ones from other independent contributions, it is the comparative (with respect to other larger signals in the spectrum) size of the small signals that fix the amount of sampling we need to obtain the principal components from the spectra. This amount of sampling can be, however, prohibitively large. In the case of SERS, for example, photobleaching and other long-term stability problems of the signal puts serious limitation to how much sampling we can obtain. We are left here basically with a conundrum: random fluctuations in large signals with limited sampling might be comparablesas far as contributions to the covariance matrix of PCA is concernedsto the small signals we are trying to modulate and detect on purpose. An example of this is explicitly shown in Figure 4; the region encircled in a box in Figure 4(a) contains a contribution from NB and a much larger peak of CV in the same range. This region is PCA-analyzed and the first four eigenvectors are shown in Figure 4(b). We can see that there is no clear signature of the NB peak in the PCA analysis. The first eigenvector is clearly dominated Analytical Chemistry, Vol. 82, No. 16, August 15, 2010 6923
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Anal. Chem. 2010, 82, 6745-6750 Let
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purchased from Invitrogen-Molecular
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Figure 3. Electropherograms of TPP-
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Anal. Chem. 2010, 82, 6751-6755 Res
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(pH 8.0) with cysteine and cystamin
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Figure 4. Ion mobility mass spectra
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GOx glucose + O298 gluconic acid +
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coater at 2000 rpm for 20 s, and th
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Figure 5. Comparison of cyclic volt
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in channels with either no grooves
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indicators of atmospheric processin
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Figure 1. GC/MS total ion chromatog
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Table 2. Concentrations and Stable
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on a substrate are preferred. 20-24
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Figure 4. SERS analysis of NAADP co
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Anal. Chem. 2010, 82, 6775-6781 Hig
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tion, 2 µL of proprionaldehyde wer
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Figure 4. Analysis of 2a by HPLC-MS
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eaction of 1a with PBH can be condu
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Numerous references had demonstrate
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Figure 1. TEM images of the prepare
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Figure 4. Schematic representation
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esult in a big SPR signal change wi
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also reduces chemical noise, which
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Table 1. Extraction Yields, Liquid
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scanning of AMPP amides of the anal
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Anal. Chem. 2010, 82, 6797-6806 δ
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Figure 1. Schematic view of the pre
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from the specimen and enclosed in a
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Figure 4. (A) IRMS mass-44 chromato
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Table 3. Ambient Measurement Result
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Anal. Chem. 2010, 82, 6807-6813 Dir
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Polymerase (1 U per sample). Reacti
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of which was constant for all ampli
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analytically useful signals at less
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carbon black and RP-C18 for the ext
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solution in an equal volume, and 1
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Table 2. Concentrations and Ratios
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Anal. Chem. 2010, 82, 6821-6829 Mac
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mg/mL protein, followed by separati
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Figure 2. Productivity of SEQUST an
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Figure 4. High-resolution MS/MS spe
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Figure 6. Characterization of PSMs
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containing T-T mismatches. 23 Based
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Figure 1. Extinction spectra of sol
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Figure 3. (A) The value of Ex650 nm
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Figure 5. Extinction spectra and co
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wished to explore the dehydration o
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constant medium for separation; we
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Figure 2. Standards of (Pi)n, n ) 1
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Figure 5. Quantitative calibration
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Anal. Chem. 2010, 82, 6847-6853 Met
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Figure 1. 226 Ra spectrum by liquid
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Table 1. Counting Properties and De
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Table 3. Analysis of 226 Ra in Sedi
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mercial microarray scanner and fabr
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Figure 2. Optical transmission meas
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Figure 5. Volcano plots detailing t
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data as well. The 41 genes in Table
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corresponding compound if its chemi
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Figure 1. Schematic illustration of
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Table 1. Absolute Quantification Re
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ment. Therefore, the long-time drea
Page 127 and 128: Figure 1. Chemically actuated micro
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Page 131 and 132: Figure 5. Device to eject and mix s
Page 133 and 134: Anal. Chem. 2010, 82, 6877-6886 Imm
Page 135 and 136: NaCl, phosphate buffer saline (PBS)
Page 137 and 138: Figure 2. Product ion mass spectra
Page 139 and 140: -70 °C resolved the problem, givin
Page 141 and 142: Table 1. Intraday Precision and Acc
Page 143 and 144: Anal. Chem. 2010, 82, 6887-6894 Fer
Page 145 and 146: Figure 1. Infrared spectra of (A) u
Page 147 and 148: Figure 3. Cyclic voltammograms obta
Page 149 and 150: Figure 6. Calculated charge from ch
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Page 153 and 154: Table 1. Chemical Structure, pKa Va
Page 155 and 156: pH with a tilted baseline (Figure 1
Page 157 and 158: Table 2. Linearity and Detection Li
Page 159 and 160: ascorbic acid (AA), uric acid (UA),
Page 161 and 162: the multielement capabilities, the
Page 163 and 164: RESULTS AND DISCUSSION Sulfur Detec
Page 165 and 166: Table 2. Molecular Properties and C
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Page 169 and 170: dilution and hybridization buffer.
Page 171 and 172: solution under appropriate incubati
Page 173 and 174: Figure 4. Standardization curve for
Page 175 and 176: Anal. Chem. 2010, 82, 6919-6925 Ele
Page 177: the ×10 objective, to have a large
Page 181 and 182: the NB signal in a much better foot
Page 183 and 184: Scheme 1. Reactions of Selenium Rea
Page 185 and 186: Figure 2. (a) ESI-MS spectrum showi
Page 187 and 188: Figure 4. (a) ESI-MS spectrum showi
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Page 191 and 192: trode 28 by a finite element using
Page 193 and 194: Figure 4. Comparison between simula
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Page 199 and 200: Figure 2. Normalized product ion ab
Page 201 and 202: Figure 4. EID (a) and IRMPD (b) of
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Page 205 and 206: Figure 1. Schematic flowchart showi
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Page 211 and 212: Figure 5. Extracted ion current ESI
Page 213 and 214: m/z 1172.935 was observed for Ser14
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Page 217 and 218: Scheme 2. Fragmentation Mechanism o
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Figure 6. The average measured resp
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for the fill time, and we find that
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nucleotide tails. 3-5 Thus, the amo
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allow the use of higher aptamer con
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quent ligation of the aptamers afte
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Anal. Chem. 2010, 82, 6983-6990 Imp
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Figure 2. Configuration editor wind
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Figure 4. Dependencies between volu
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Since the time for liquid expulsion
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Anal. Chem. 2010, 82, 6991-6999 Int
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Figure 1. Representation of the Car
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Table 2. Capillary Electrophoresis
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Figure 4. (A) Typical raw electroph
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field. DNA profiles were delivered
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Another approach to handle this lim
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(principal components, PCs) in whic
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Figure 5. Relevant score plots and
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CONCLUSIONS In this paper we have i
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electroactive-species loaded liposo
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Figure 2. Qdot-based FLFTS response
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Figure 6. Fluorescence imaging of Q
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Anal. Chem. 2010, 82, 7015-7020 Sel
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Table 1. Effect of 1 D-LC Condition
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Figure 3. Peak-production rate vers
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Anal. Chem. 2010, 82, 7021-7026 Cel
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luciferase (T7 control vector) was
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Figure 3. Inhibitory effects of luc
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Anal. Chem. 2010, 82, 7027-7034 Pat
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Electrochemistry. Electrochemical m
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Figure 2. SEM images of Au substrat
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Table 3. Film Thickness Measurement
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Anal. Chem. 2010, 82, 7035-7043 Qua
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Figure 1. (A) FL spectra of BSPOTPE
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Figure 4. (A) Variation in the FL i
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Figure 6. (A) FL spectra of BSPOTPE
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Scheme 1. Proposed Mechanism for Fl
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one or more drawbacks including poo
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Figure 2. Free fluorophores do not
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Anal. Chem. 2010, 82, 7049-7052 Dev
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Figure 2. Comparative analysis of d
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