Letter pubs.acs.org/ac
Dynamic Surface Enhanced Raman Spectroscopy (SERS): Extracting SERS from Normal Raman Scattering B. L. Scott† and K. T. Carron*,†,‡ †
University of Wyoming, Chemistry Department, 1000 E University Avenue, Laramie, Wyoming 82071, United States Snowy Range Instruments, 628 Plaza Lane, Laramie, Wyoming 82070, United States
‡
ABSTRACT: We have demonstrated two significant benefits of dynamic surface enhanced raman spectroscopy (DSERS) measurements: removal of instrumental and normal Raman interferences in surface enhanced raman spectroscopy (SERS) spectroscopy and site-selective spectroscopy of adsorbate populations on SERS-active particles. Our first example of shelled nanoparticles at very low concentrations confirmed the benefit of DSERS for removal of an overwhelmingly strong solvent spectral interference. The second benefit, site selection, was demonstrated with 4-mercaptopyridine on bare Au nanoparticles to observe a small population of molecules that were spectroscopically unique from the large population of molecules on the particles. The DSERS spectrum originated from excess variance between a small population of adsorbates on the ensemble of nanoparticles.
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EXPERIMENTAL SECTION Materials. All chemicals were purchased from the supplier indicated: HPLC grade water (Fischer), isopropyl alcohol (99.5%, Mallinckrodt), hydrogen tetrachloroaurate (HAuCl4) (reagent grade, Aldrich), 4-mercaptopyridine (4MP) (95%, Aldrich), sodium citrate dihydrate (99.0%, EMD), and 1,2bis(4-pyridyl)ethylene (BPE) (MP biomedical). All glassware was cleaned with aqua regia and rinsed with Milli-Q water. Au Nanoparticle Synthesis. Gold nanoparticles of 30−50 nm diameter were prepared according to the well-known Frens citrate reduction method.4 Twenty mg of HAuCl4 was added to 200 mL of hot HPLC grade water and brought to a boil with stirring. Sodium citrate dihydrate (1.2 mL of 1% (w/v)) was added at once, and the reaction mixture was covered and left to boil with stirring for 20 min. After 20 min, the heat was turned off and the Au colloid solution was cooled to room temperature and transferred to a foil-covered plastic container. A spectrum of this solution with BPE was acquired to verify SERS activity. 4MP Coated Au Nanoparticle Synthesis. A SERS-active stock solution was made by adding 250 μL of 0.1 mM 4MP in isopropanol to 0.5 mL bare gold nanoparticles, followed by 0.75 mL of water. A basic sample solution (pH 9) was made by adding 0.1 mL of stock solution to 1.9 mL of 1 M sodium bicarbonate in water. An acidic sample solution (pH 5) was made by adding 0.1 mL of stock solution to 1.9 mL of 1% (w/ v) sodium citrate in water. The pH was measured in the final colloidal solutions. Shelling Au Nanoparticles. Colloids were sized using SEM and were an average of 50 nm in diameter. Nanoparticle
onventional Raman spectrometers improve signal-tonoise by integration of signal in the wells of CCD chips. With proper cooling and readout circuitry, this approach leads to optical detection that follows Poisson statistics for shot noise-limited spectra. Therefore, within a spectrum, the variance in the signal is equal to the intensity. When individual spectra are compared, the dominant source of variation is rms laser noise which follows a normal distribution and is reduced through spectral averaging. However, this approach of time indiscriminate signal collection places photons from every possible source into the spectrum. Conventional Raman spectra contain signal contributions from the desired source in the sample as well as fluorescent, whether intrinsic or an impurity, stray light from the optical system and Raman interference from sample containers. Time correlation has been demonstrated as a way to discriminate between the instantaneous scattering events and delayed fluorescence signals.1 Colloidal nanoparticles are free floating particles that remain suspended through Brownian motion. Surface enhanced raman spectroscopy (SERS) from colloidal nanoparticles was described very soon after the initial discovery of SERS at electrode surfaces.2 The ease of making colloidal gold and silver particles has made it a popular method for performing SERS studies and analytical assays.3 Additionally, the large velocity imparted on nanoparticles through Brownian motion leads to an opportunity to discriminate between their spectroscopic signals and the relatively rapid fluctuations of free molecular species and continuum produced by solid state interferences. We describe a statistical method for specific extraction of SERS signals from colloidal SERS active nanoparticles. The difference in these particles’ sizes relative to the molecular matrix creates an opportunity to statistically differentiate between their signal and the relatively time indiscriminate fluorescence and matrix Raman signals. © XXXX American Chemical Society
Received: July 9, 2012 Accepted: September 20, 2012
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toluene solution with approximately 8 × 105 particles/cm3 of SiO2 coated nanoparticles with a BPE coating. At this concentration, the presence of the nanoparticles is undetectable in the average SERS spectrum which is derived from 100 spectra acquired for 100 ms. The middle spectrum (σTotal) represents the standard deviation of the 100 spectra at each data point. This spectrum is still dominated by the variation produced by the laser’s rms power fluctuations; the variation between the individual spectra is dominated by the laser fluctuations This signal independent noise contribution will produce a noise spectrum which has feature intensities that have values from all sources. The important exception of the instrumental noise sources is the nanoparticle’s SERS signal. Subtraction of the averaged spectrum (STotal) from the total noise spectrum (σTotal) spectrum divided by the number of averages, 100 in this case, produces the excess noise spectrum Sexcess. This is shown in the bottom spectrum of Figure 1 and closely represents a SERS spectrum of BPE. Examination of the original data set shows that we observed only one major particle event during the 10 s of data acquisitions. This is observed in Figure 2 (top) where an
concentration was determined as described by Haiss et al.5 After UV−vis spectroscopy and the calculations from that work, we determined the concentration of our nanoparticles to be 6.02 × 1010 nanoparticles per mL. Fresh colloids (4 mL) were labeled with 50 nM BPE and added to 20 mL of isopropanol (99%) at room temperature while stirring. Colloids were shelled with silica as detailed in Lu et al.6 These particles were diluted in toluene to produce a solution with a concentration of ∼8 × 105 nanoparticles per mL. Instrumentation. Raman spectra were acquired with a Sierra IM-52 Raman microscope (Snowy Range Instruments) using its liquid sampling feature. We used 40 mW of 785 nm laser excitation at the sample with a spectral resolution of 8 cm−1. The IM-52 permits multiple spectra to be acquired with a delay between acquisitions. The data were analyzed using custom software written in LabView (National Instruments).
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RESULTS AND DISCUSSION SERS Signal Extraction. Figure 1 illustrates the concept of dynamic SERS (DSERS) spectroscopy. The box on the left
Figure 1. DSERS concept. (Left) This schematic illustrates a colloidal nanoparticle moving through a focused laser beam. The standard deviation of the continuum, σcontinuum, will scale as the square root of the intensity while the σSERS from the nanoparticle will be larger. (Right) An illustration of the signals and standard deviations for a solution of toluene with two nanoparticle events in 10 s.
illustrates the dynamic processes that lead to the theory of DSERS. Raman spectrometers typically have a tightly focused laser beam to generate the Raman scattering. The small focal volume of the laser is illustrated as the pink cylinder in Figure 1. This volume of solvent generates a Raman signal that is shot noise limited and has a standard deviation equivalent to the square root of the signal. SERS signals are generated by particles moving rapidly into and out of the laser beam. These fluctuations produce a noise level (σSERS) greater than the square root of the average signal. The signal (SExcess) due to the excess noise contributed by the dynamic noise from the SERS active nanoparticles can be found from the difference between the total noise in the signal (σTotal) and total signal (STotal). The subtraction requires a factor (a) to account for the difference between the magnitude of the standard deviation and average signals. The spectra in Figure 1 (right) illustrate the results of a DSERS measurement. The top spectrum (STotal) is from a
Figure 2. Individual Raman spectra from Figure 1 and a plot of intensity at 1640 cm−1 vs the acquisition number.
overlay of the 100 spectra in the 1600 cm−1 region indicates that a large event occurred (red) and a smaller event occurred (violet) during the data collection. Plotting the 1640 cm−1 data point in time space, Figure 2 (bottom), shows the two events in spectrum 67 (major) and 21 (minor), respectively. Most significant about this aspect of the DSERS method is that it removes the interfering spectral features. Figure 1 illustrated this with the observation of a single nanoparticle’s SERS spectrum in a neat toluene matrix. In this case, we were able to extract a SERS spectrum with a one part per thousand relative intensity. The value of this method is its objective (autonomous) derivation of the pure SERS spectrum in the B
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presence of the overwhelming solvent spectrum. Even selection of the individual spectra with the nanoparticle present requires subtraction of toluene of a pure matrix spectrum with an unknown relative intensity to the SERS intensity. Sites Selective Spectroscopy. Hotspots between nanoparticle aggregates or gaps between nanoparticle features have been discussed as a possible mechanism for very large enhancements beyond those observed from single particles.7 Examples of experiments to prove this theory have included SEM combined with LSPR spectroscopy8 and tilted pillar experiments which show larger signals when pillars are collapsed to produce contact.9 The difficulty of proof is the differentiation between the SERS signal from the majority of the surface’s coverage and that of the small number of molecules in the gap region. Even with large gap enhancements, the small area associated with this enhancement will lead to relatively small signals that are difficult to detect in the total SERS signal. The challenge of site-selective nanoparticle spectroscopy is that the observed signals are derived from an ensemble of particles in the laser beam during the integration period. Schmit et al. recently showed the paradox between signal and fluctuation-induced noise in solution phase nanoparticle spectroscopy.10 As the number of particles decreases, the signal decreases, and the fluctuation-produced noise, as described by Brownian-induced fluctuations, increases. Increased acquisition times only exacerbate the problem by allowing more particles to traverse the laser beam and to enter into the observed signal. The DSERS method described herein exploits the negative effect of Brownian motion-induced fluctuations and enhances the individual particle or site selective signals. Shelled nanoparticles have particular application as bright reporters to sandwich paramagnetic11 or lab-on-a-bubble assays.10,12 Direct SERS assays are commonly reported for chemical analysis and are also affected by the degree of aggregation in the sample. Knowledge of the site of adsorption and the signal from strongly enhancing sites is valuable for assay development. For example, if specific sites enhanced more than others and a site-specific spectroscopy existed, then the possibility of more sensitive assays could be realized. The sensitivity of an assay can be described by the magnitude of the signal produced by an analyte molecule relative to the noise. If a site selective chemistry can be developed specifically at the “hotspots” of SERS active nanoparticles, the number of active sites will be dramatically decreased. In this case, as the number of analyte molecules approaches zero, the signal from adsorption at hotspots will be higher than it would be at adsorption to poorly enhancing spots, even at a higher concentration of these poorly enhancing locations. We performed a second study with unshelled nanoparticles coated with 4MP. Mullen et al.13 demonstrated that the ratio of peaks in the 1000 to 1100 cm−1 region of 4MP SERS spectra is pH dependent; the ratio of the 1091 cm−1 peak to the ring breathing mode at ∼1000 cm−1 is smaller under basic (unprotonated) conditions.14 These results are reproduced here and are illustrated in Figure 3 (left). We found that the average (SERS) spectra of 4MP-coated Au nanoparticles exhibiting a ratio of 1091 cm−1/1000 cm−1 ratio of 0.87 at high pH (9) and 2.12 at low pH (5). This is illustrated in Figure 3A,B. It is important to report that we observed small, but significant, frequency shifts in the ring breathing mode upon protonation.
Figure 3. Experimental results for 4MP on Au nanoparticles at basic and acidic pH. (A) The average spectrum of 1000−100 ms acquisitions at pH 9; (B) The average spectrum of 1000−100 ms acquisitions at pH 5; (C) DSERS spectrum from the data set used to produce (A); (D) DSERS spectrum from the data set used to produce (B). (E) Two individual acquisitions spectra at pH 9; (F) Intensity vs time subset of the 1000 acquisition at 1091 cm−1 at pH 9.
The DSERS spectra (Figure 3C,D) exhibit very different results. Absent in the DSERS spectra are the broad interfering contributions from the glass sample vial. This confirms the ability of DSERS to remove normal Raman interferences discussed above. More significantly, the DSERS spectra of 4MP are nearly identical in base and acid. In this case, a drastic deviation from the SERS and DSERS spectra is observed. Figure 3E illustrates the variation between two spectra in the 1000 spectra data set for pH 9. The spectra come from acquisitions 19 (green circle) and 86 (red circle) illustrated in Figure 3F. The relative intensities of the 1000 and 1091 cm−1 peaks to other features are not distorted; the anomalous equality of the spectra in Figure 3C,D does not appear to be due to anomalous particles, rather irregular variations in the intensities of these peaks over an ensemble of particles. While the sampling of spectra in Figure 3E demonstrates large variations in the 1091 cm−1 /1010 cm−1 ratio, they indicate extremes in the variations and clearly do not correlate to the spectrum in Figure 3C. None of the single acquisitions making up Figure 3A correspond to Figure 3C. The equality of the acid and base DSERS spectral features between 1000 and 1100 cm−1 peak must be due to a small population of identical molecules present on particles in both the acid and base solutions. Not only are the solvent spectral features and the glass vial’s features removed but also the SERS features that are common to all particles. Note: this experiment was performed with a higher concentration of nanoparticles than the shelled BPE-coated nanoparticle study. This will lead to a reduction in the nanoparticle peaks and will enhance the signals from variations between the particles. Figure 4 shows an expanded view of the data in Figure 3. We observed the ring breathing peak of 4MP at 1003.9, 1005.7, 1007.7, and 1010.2 at pH 5 (SERS), pH 5 (DSERS), pH 9 (DSERS), and pH 9 (SERS), respectively. The Raman shifts indicate that the species observed in the DSERS are inaccessible to protonation and are not located at either the acid or the base spectral shifts. The most likely conclusion is that we are observing excess noise due to a small population of adsorbate and, given the inaccessibility of the pyridyl nitrogen to protonation, these species are not exposed to the solvent. C
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spectroscopically unique from the large population of molecules on the particles. This study showed the same feature extraction benefit as described for the shelled nanoparticles but differed in that the DSERS spectra did not match any of the individual acquisitions or their average. The DSERS spectrum originated from excess variance between a small population of adsorbates on the ensemble of nanoparticles.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors would like to thank Snowy Range Instruments for the instrumentation and facility usage. B.S. would like to acknowledge the NSF GK-12 grant #0948027 for their kind support.
Figure 4. Magnified spectra from Figure 3A−D. The ring breathing mode shifts from 1003.9 cm−1 when protonated to 1010.2 cm−1 when deprotonated.
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This would be consistent with a model of SERS involving super enhancements of species in the gap between aggregates or in roughness features on particle surfaces.8 In conventional spectroscopy, these molecules would not be observable due to the large population of species not in the highly enhancing gap relative to the number in the gap. This experiment at relatively high nanoparticle concentration is enhancing those spectral features which are not present on all particles at the same intensity; it represents SERS signals that are buried in the spectrum of the ensemble of particles or the ensemble of molecules on a single particle. An alternative explanation might be that we are observing 4MP bound to the Au nanoparticles through its pyridyl nitrogen. This would account for the invariance to solution pH. However, it is unlikely that statistically significant variations in the population of 4MP bound through the thiol or through the pyridyl nitrogen would exist between particles. The DSERS spectrum is statistically significant and more indicative of a small population of aggregates with a small population of strongly enhanced 4MP molecules in the interparticle gap. Figure 2 demonstrated that two particles moving into the beam were sufficient to produce a DSERS spectrum from the overwhelming signal of neat toluene. The data in Figure 4 were acquired from a large number of SERS-active particles in the beam during any individual acquisition and the DSERS results from variations within this population. If the DSERS were a small population of sites on every particle, we would expect it to average and not produce an excess noise signal (Sexcess).
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CONCLUSION We have demonstrated two significant benefits of DSERS: removal of instrumental and normal Raman interferences in SERS spectroscopy and site-selective spectroscopy of adsorbate populations on SERS-active particles. Our first example of shelled nanoparticles at very low concentrations confirmed the benefit of DSERS for removal of an overwhelmingly strong solvent spectral interference. This benefit would be applicable to colloidal SERS studies in solvents or mixtures that produce strong interferences that might mask observation of the desired SERS features. The second benefit, site selection, provides a powerful method to study small populations of molecules adsorbed on SERS-active particles. In our example with 4MP, we were able to observe a small population of molecules that were D
dx.doi.org/10.1021/ac301914a | Anal. Chem. XXXX, XXX, XXX−XXX
