Assessing Metal Nanofabricated Substrates for ... - OSA Publishing

Assessing Metal Nanofabricated Substrates for Surface-Enhanced Raman Scattering (SERS) Activity and Reproducibility JASON GUICHETEAU, STEVEN CHRISTESEN, DARREN EMGE, PHILLIP WILCOX, and AUGUSTUS W. FOUNTAIN III* U.S. Army, Edgewood Chemical Biological Center, Research and Technology Directorate, Aberdeen Proving Ground, Maryland 21010-5424

Surface-enhanced Raman spectroscopy (SERS) has been shown to be an effective technique for increasing the detection sensitivity in chemical and biological applications. SERS has a distinct advantage over normal Raman spectroscopy, with enhancements typically greater than 104 over the normal Raman signal; however, this advantage in sensitivity comes with a caveat: controlling the spectroscopic reproducibility and enhancement activity of metal nanostructured substrates can be difficult. We present a survey and subsequent data analysis performed on several nanostructured substrates designed for SERS, including silver and gold colloids, silver nanorods, gold nanoshells, and commercially manufactured gold nanostructures. Index Headings: Surface-enhanced Raman spectroscopy; SERS; Nanoparticle substrates; Raman spectroscopy; Reproducibility; Spectral discrimination; Standardized testing protocols; Substrate performance.

INTRODUCTION Surface-enhanced Raman spectroscopy (SERS) is attractive for analytical applications because it can provide fingerprint spectra of analytes in aqueous solution and under ambient conditions. First observed from roughened Ag electrodes,1,2 it has since been determined that virtually any noble metal substrate roughened on the nanometer scale can provide an enhancement of the Raman signal, although the magnitude of enhancement varies greatly with material.3 Nonetheless, SERS remains promising for chemical, biological, and explosive analyte detection because of its potential as a nearly universal sensor.4 The enhancement of the Raman scattering observed in SERS is generally agreed to be the result of two distinct mechanisms:5 chemical (CE) and electromagnetic enhancement (EM). CE is related to analyte-specific interactions between the metal and the molecule of interest through the formation of a molecule–metal charge-transfer complex that can resonantly enhance the Raman scattering signal from analyte molecules. Sites of atomic scale roughness, such as metal clusters or adatom defects, are thought to act as chemically active sites that facilitate the creation of charge-transfer complexes, but the nature and identity of these sites has not been conclusively determined. The chemical mechanism is site and analyte specific and is thought to contribute to the overall enhancement by a factor of at most 102. The electromagnetic enhancement results from an increase in the electromagnetic field of noble metal substrates due to surface plasmons produced by absorption of incident photons. It is generally thought that the EM enhancement accounts for Received 27 August 2010; accepted 2 November 2010. * Author to whom correspondence should be sent. E-mail: augustus.w. [email protected]. DOI: 10.1366/10-06080

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the majority (.104) of the total signal enhancement seen in SERS. Because the intensity of the Raman scattering from an adsorbed analyte molecule is proportional to the square of the local electromagnetic field (EMF), the increase in the magnitude of the EMF at the metal surface increases the number of photons that are inelastically Raman scattered. Due to the interaction of the electromagnetic fields that can be created, the largest calculated enhancements are found at the interstitial sites between metal particles.6,7 The surface plasmon resonance of SERS substrates can also be tailored to coincide with the excitation wavelength employed by manipulating the size, shape, and interparticle spacing of the substrate. Even though Raman spectra can be obtained with virtually any excitation wavelength, the largest SERS enhancements are produced through a coordination of the surface plasmon resonance of the substrate with the excitation wavelength being used.8 Although hundreds of SERS papers are published each year, SERS has not been successfully transitioned from the research laboratory to the analytical laboratory. The limited acceptance of SERS as a routine analysis technique is largely due to the lack of reproducible SERS-active substrate fabrication methodologies.9 Stringent control of the surface plasmon resonance of active substrates has proven to be one of the most significant hindrances to reproducible substrate fabrication. Because the surface plasmon resonance of a substrate is dependent on feature size and interparticle distance, even relaxed control over these characteristics has a dramatic impact on the wavelength of the localized surface plasmon resonance (LSPR). For example, relatively recent reports demonstrate that the LSPR between two interacting Au particles is red-shifted by a reduction in the particle-to-particle distance.10,11 Changing the nanoparticle aspect ratio and volume can additionally shift the LSPR by tens of nanometers. Understanding the impact of these effects on the system level is crucial because the largest SERS enhancements are expected when both the incident laser frequency and the Raman scattered frequency approach the LSPR.12 The substrates most widely investigated and capable of single-molecule SERS, colloidal particles, are inherently inhomogeneous and subject to inconsistency in LSPR resulting from the variability in particle morphology and the degree of particle clustering. Even though strides have been made in the reproducibility and controllability of colloidal synthesis13 it has proven nearly impossible to specifically engineer colloidal substrates that take the most advantage of the EM for reliable SERS detection upon analyte introduction. Additionally, other types of substrates reported to be capable of single-molecule SERS, i.e., Ag thin metal films (TMFs), gold films over nanospheres, and others,14–16 can be produced with specific

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APPLIED SPECTROSCOPY

LSPRs but are hindered by uniform signal enhancement across the entire surface. Though these substrates differ greatly in morphology and electromagnetic environments, their success at providing the largest magnitudes of SERS enhancement indicates that an in-depth study of their similarities and differences could reveal insights into the mechanisms of SERS. Substrates produced with e-beam lithography are particularly beneficial for SERS because of the stringent control over the LSPR that can be exercised by changing the spacing and aspect ratios of the particles produced. Electron beam lithography is a mask-less semiconductor fabrication technique routinely used for the patterning/growth of nanometer-scale features. Briefly, a beam of electrons focused across approximately 100 kV potential (beam current ¼ 0.5–1.0 nA) is directed onto an electron resist-coated support. Regions of the resist film impacted by this focused electron beam are structurally modified to have an increased solubility in alcohol solutions (e.g., methyl isobutyl ketone/isopropyl alcohol). Following vapor deposition with the noble metal (e.g., Au or Ag), the remaining metal-coated resist film is removed by a subsequent wash with N-methylpyrrolidone. This final wash yields an array of size-controlled, precisely placed nanostructures constituting a spatially uniform, highly reproducible SERSactive substrate with the potential of providing large calculated SERS enhancements. Substrates created by nanosphere lithography (NL) have also been calculated to provide large enhancements. Produced by vapor deposition of metal through a template created by the self-assembly of polystyrene nanospheres, particle arrays created by NL have been experimentally demonstrated to provide enhancements up to 109. While this technique does not provide the same measure of flexibility as electron beam lithography, NL provides a facile means of reproducible SERS substrate production.8 The lithography techniques discussed provide efficient methods of controlling the formation of specific nanostructures that are otherwise relatively expensive to produce. Another way to produce ordered structures is with templated selfassembly of colloidal nanoparticles, a process pioneered by Orlin Velev at North Carolina State University (NCSU).17,18 These substrates have enhancements comparable to the better silver nanoparticle substrates. However, because they are made of inert gold, they show better long-term stability, allowing for continuous sampling experiments. These substrates are made very easily, inexpensively, and without any complex equipment. One very important feature of the substrate fabrication method is that it allows tuning the porosity on two hierarchical length scales. Large interconnected cavities are formed after the templating microspheres are removed. In addition, smaller pores are formed between the gold nanoparticles in the assembled structure; the size of these pores can be controlled by varying the diameter of the gold nanoparticles used in the process. Collaborative studies between Edgewood Chemical Biological Center (ECBC) and NCSU have demonstrated that these substrates have excellent stability and high enhancement.18 More recent efforts have characterized the effects of the nano- and microporosity on the SERS enhancement.19 It is our belief that SERS holds great promise as an extremely flexible sensor platform because the plasmon resonance of a metal/substrate can be tuned over a wide range of wavelengths. Ultimately this allows for a vast trade space with respect to lasers, spectrographs, and detectors. However,

different substrate types may not possess the same SERS response for the same analyte. In response to the difficulties in reliability and reproducibility encountered when using various SERS substrates, we undertook a matrix study of commonly employed SERS substrates that incorporate different substrate refinements with the goal of providing an objective evaluation of the substrates with regard to spectral discrimination and repeatability. Statistical analysis was used to compare all of the SERS substrates investigated in order to determine which substrates are best suited for analytical SERS. A comparison of the spectra produced by multiple substrate manufacturing techniques allows for direct analysis of their relative strengths and weaknesses, ultimately leading to a better understanding of the properties that lead to high spectral discrimination and reproducibility. We present a comparison of six substrates exhibiting different molecular discrimination and spectral repeatability toward two analytes: phenylalanine and methyl phosphonic acid (MPA). While many SERS studies have been published using molecular probes that either form covalent bonds or charge-transfer complexes with the metal surface, these molecules were chosen as simple chemical surrogates for more militarily relevant analytes. Phenylalanine is commonly found in many biological systems and MPA is a known stimulant for nerve agents. Ag and Au colloidal particles were included in the investigation to gain insight into the factors that allow such disparate substrates to be the most successful for lowconcentration SERS. Specifically engineered reproducible substrates, including nanoparticle arrays, created by electron beam lithography, nanosphere lithography, and templated selfassembly of Au nanoparticles, were investigated to evaluate their utility as reliable SERS substrates.

EXPERIMENTAL Substrate Types. All substrates and surfaces investigated in this study were prepared outside of our laboratory except deposited silver nanoparticles. The descriptions given below are brief abridged versions and were provided by the designers themselves or summarized from received reports. For complete descriptions of listed substrates please see the cited references. All received substrates were stored properly in their original shipping packages until analyzed. Gold Nanoparticles. The Au hydrosols used in this study and subsequent slide functionalization were prepared as previously described20 and were approximately 25 nm in size. Briefly, quartz slides were cleaned in a Piranha (3 : 1 H2SO4 : H2O2) bath, functionalized with 3-aminopropyltrimethoxy silane (APTMS), and dried under nitrogen. To the prepared slides, 1 mL of colloid was added and dried under nitrogen in a low-humidity, low dust atmosphere, followed by two additional depositions for a total of three coatings. Silver Nanoparticles. Silver nanoparticle suspensions were prepared following a modified procedure of Lee and Meisel21 as described previously.22 The resulting nanoparticle suspensions appeared yellowish-brown with an electronic absorption kmax of 399 nm and an average full width at half-maximum (FWHM) of 65 nm with an average particle size of 36 nm. Silver colloids prepared for this study were deposited onto aluminum-coated slides and allowed to dry, after which the proper analyte was deposited as described below. Commercially Available Substrate. Commercially available gold substrates were acquired from Renishaw Diagnostics. The


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substrates are designed from silicon surfaces with a regular arrangement of inverse pyramid patterns that have been coated with gold. The plasmonic bands of the substrates allow a wide range of wavelengths to be used.23 Gold Film Over Nanospheres. Gold film over nanospheres (GFONS) consisted of a three layer build.24 Initially, 430 nm diameter silica spheres on a microscope slide were coated with a 100 nm thickness (as measured with a quartz crystal microbalance, QCM) of gold on top of the ordered spheres. ˚ thick created by This was followed by silver oxide islands 2 A depositing silver islands and allowing them to oxidize in atmospheric conditions. The particles were then over-coated with another 100 nm thick layer of gold and another layer of silver oxide islands and a final 100 nm layer of gold. Silver Nanorods. Aligned silver nanorod array substrates fabricated using the oblique angle deposition (OAD) method have been previously described in detail.25 Glass microscope slides (Gold Sealt Cat. No. 3010) were cut into 1 3 1 cm pieces, carefully cleaned with piranha solution, and rinsed with deionized water. The substrates were then dried with a stream of N2 before loading into a custom-designed, oil-free electron beam evaporation system. A 20 nm thin film of Ti (Alfa Aesar, Ward Hill, MA, 99.99%) followed by a 500 nm thin film of Ag (Kurt J. Lesker, Clairton, PA, 99.999%) were evaporated onto the substrates at an angle normal to the surface at a rate of 1.0 ˚ /s and 4.0 A ˚ /s, respectively. The Ti served as an adhesion A layer. The substrates were then rotated to 868 with respect to the surface normal. Ag nanorods were grown at this oblique ˚ /s for 100 angle in which Ag was deposited at a rate of 3.0 A min. Each deposition step was automated using a feedbackloop-integrated QCM to record the deposition rate and thickness and a computer-controlled power supply to adjust the e-beam current. As reported elsewhere,25,26 these deposition conditions result in optimal SERS substrates with overall nanorod lengths of ;900 nm, diameters of ;100 nm, and densities of ;13 nanorods/lm2. Gold Nanoshells. Gold metallic nanoshells were prepared as previously described27 and consisted of spherical (dielectric) silica nanoparticle cores surrounded by a uniform gold shell layer with an average particle size of approximately 100 nm. Spectroscopic Acquisition. A Bruker Optics Senturion Raman microscope operating at 785 nm with a spectral resolution of 10 cm1 was used for data collection. Spectra were collected at 603 magnification with ten-second integration times and the laser power set at approximately 5.3 mW. The choice of using only 785 nm excitation for this study as opposed to wavelengths that may be more advantageous for particular substrates (532 nm or 633 nm for silver) was guided by potential future instrument choices. As more and more portable Raman systems are now available commercially, the most common wavelength used to date is 785 nm. We specifically chose this wavelength to evaluate the efficacy of near-term current off-the-shelf options that could be applied for SERS applications. Due to a limited number of available substrates, only two substrates were used for each of the two analytes studied with the exception of the commercially manufactured gold substrates, in which only one was used for MPA. Fifty data points were collected on each of the two substrates following a raster pattern to incorporate as much of the substrate as possible. An initial water background was acquired for each substrate by pipetting up to 1 mL H2O (18.3 MX) onto a slide to achieve

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complete surface coverage then allowing it to dry. Spectral data for the water background were collected as described above for each of the two substrates. Two new substrates were then prepared by depositing a 0.1 mg/mL phenylalanine solution, which was allowed to dry in a chemical hood, after which spectra were acquired as described above. Finally two new substrates were prepared by depositing 0.1 mg/mL methyl phosphonic acid (MPA) on each substrate, which was allowed to dry and spectral data collected. Although we understand that the ‘‘drop and dry’’ method may lead to nonuniform coverage, this method was deemed the best compromise with the limited number of substrates provided. Signal Processing. All spectral data were processed using MATLAB 2009b, Mathworks, Natick, MA. A variable-width iterative median filter with a 300 cm1 window was used to remove underlying low-order trends and/or baseline.22 The data were then truncated to the region of interest from 400 to 1400 cm1 before processing and analysis. In order to remove extreme outliers that would bias the resulting analysis, the average spectrum for each substrate and analyte pair was determined and used as a reference to which all spectra of that pair were compared (100 data points per substrate/analyte, 50 from each substrate). The spectra were then normalized to unit area followed by principle component analysis (PCA) decomposition using the MATLAB function ‘princomp.m’.28 The PCA plot provides a visual measure of how well the SER spectra from a given substrate are able to differentiate phenylalanine, methyl phosphonic acid, and the water blank. Box-and-stem plots were created from the analysis of the relative peak heights of the phenylalanine ring breathing mode at 1000 cm1 and the methyl phosphonic acid PC vibration at 760 cm1 (Fig. 1). These plots show the relative sensitivities of each substrate to a 0.1 mg/mL solution of the analyte. Analyses were also conducted to evaluate both intrasubstrate (within a substrate) and inter-substrate (between different substrates from the same manufacturing process) signal repeatability using a cross-correlation measure, which is the covariance matrix normalized by the product of the individual standard deviations. This measure reflects the degree to which the spectra are linearly related, with a value of 1 meaning they are essentially identical.

RESULTS AND DISCUSSION Figure 2 shows the SEM and macro images of the six substrates investigated. The results discussed below are divided into two parts; analyte sensitivity and spectral repeatability. Both discussions compare the analytes investigated, phenylalanine and methylphosphonic acid. The former will be discussed in greater detail, with the latter presented as a possible warning on the difficulties inherent in the development of a universal SERS substrate. SERS-active substrates have proven time and again to be analyte specific and the ability to prepare a universal SERS substrate to be active across the breadth of chemical and biological materials could prove to be an extremely difficult endeavor.29 As mentioned above, a limited number of substrates were available for the study and this should be taken into account when considering these results. Spectral Discrimination. The substrates were first analyzed to determine whether the analyte spectra were different than the water blank, i.e., did the substrates provide sufficient spectral information to discriminate an analyte from the background

FIG. 1. SERS spectra of methylphosphonic acid (MPA) and phenylalanine exhibiting target bands at 760 cm1 and 1000 cm1, respectively.

and the other analyte. If a particular substrate exhibited poor discrimination toward one of the analytes then it was not included in the spectral repeatability analysis. This reasoning is due to the fact that if a substrate is not sufficiently responsive toward a particular analyte then repeatability measurements were inconsequential. Principal component analysis was used to visualize whether the substrate was responsive to the analyte and was spectrally discernable from the water blank. Initially, PC analysis between the 400 and 1400 cm1 spectral region, encompassing the strongest SERS bands from both phenylalanine (1000 cm1) and MPA (760 cm1), was used to determine discrimination. If spectra for the tested analytes and water background show distinct spectral differences then component separation should occur.

FIG. 2. SEM and macroscopic (inset) images of the six substrates investigated.

Figures 3a through 3f display the principle component plots for the first three scores comparing the water background to the analytes of interest each at a concentration of 0.1 mg/mL. The concentric ellipsoids around each center of mass of the data represent 1r and 2r confidence limits signifying 68% and 95% confidence intervals, respectively, for all data contained in the ellipsoids. A threshold of 0.15 was used to statistically identify and remove outliers from the set, which resulted in 1.6% of the data being classified as outliers. The PC scores observed outside the confidence rings fell beyond the 2r but above the 0.15 outlier threshold. At first glance, four out of the six substrates show good separation between the two analytes and the water blank. The deposited gold colloids (Fig. 3b) and the gold nanoshells (Fig. 3f) perform the most poorly with the gold nanoshells showing no sensitivity toward either phenylalanine (red) or MPA (green), essentially indicating spectra for the two analytes compared to water and background were indiscernible. The silver nanorods (Fig. 3e) and GFONs (Fig. 3c) exhibit a unique result as the phenylalanine is clearly defined in PC space, but the MPA does not separate completely from the water. For the remaining substrates, silver colloids (Fig. 3a) and commercially manufactured gold (Fig. 3d) all show good discrimination for both analytes. To further explore spectral discrimination of the investigated substrates, box-and-stem plots were prepared. These plots were constructed by calculating the histogram of the peak height across multiple data collections as described above. Collected spectral data of water were treated as the essential background of each substrate under comparison. Figure 4 shows the distribution of the phenylalanine peak (1000 cm1) versus water/background for the region between 977 cm1 and 1015 cm1, while Fig. 5 shows the results for MPA (region 726 cm1 to 806 cm1). Immediately noticeable in Fig. 4 (phenylalanine data) is how the error bars (2 standard deviations) show a slightly different picture than what is shown in the PC analysis. As before, the gold nanoshells and Au deposited colloids still show the least separation between background water and phenylalanine. However, the error bars for the deposited Au colloid do not overlap, indicating greater sensitivity to phenylalanine. For the Au commercial results, even though the means of the peak areas are significantly separated, the error bars slightly overlap. This is due to poor spectral repeatability and will be discussed further below. As before, silver nanorods, silver colloids, and GFONs all show good discrimination toward phenylalanine. Figure 5 shows the relationship between the water background and MPA in the


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FIG. 3. PC analysis showing analyte seperability across (a) silver deposited colloids, (b) gold deposited colloids, (c) gold film over nanospheres, (d) gold commercial, (e) silver nanorods, and (f) gold nanoshells. Red ¼ phenylalanine, green ¼ MPA, and blue ¼ water background.

target region of the PC stretch. All substrates except Au commercial do not exhibit clearly defined separation between the background and MPA as evidenced by the overlapping error bars. Although the mean MPA peak height values are different from the water blank for all substrates, the error bars overlap significantly due to the poor reproducibility of the SERS enhancement. The inclusion of the full spectrum (PCA plots) contains more information and indicates better separability at a significantly increased cost in computational complexity and time, while the box-and-stem plots focus solely on the target band areas of interest. In comparing the two analysis methods, PCA and boxand-stem plots, it is not fully apparent that discrimination toward either analyte is good for the majority of investigated substrates. While the PC data would paint the picture that Ag deposited colloids, Au commercial, and potentially silver nanorods and GFONs are all capable of discriminating the analytes from the water background, when viewing box-andstem plots based solely on a target peak height, none of the substrates show clear discrimination toward either analyte based on the error bars associated with the data. This reiterates our argument discussed earlier that any analysis of SERS sensitivity towards analytes should not be

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based on limited data sets. To truly discern whether or not a SERS substrate is responsive toward a particular analyte, a statistically large number of substrates must be compared. We will make this point further in the next subsection, where we discuss spectral repeatability for the different substrates. Spectral Repeatability. Following use of the PCA and boxand-stem plots to determine spectral discrimination for each substrate, we analyzed for spectral repeatability of the samples. While intra-substrate variability was readily assessed, we were unable to complete an exhaustive analysis of inter-substrate variability, having only two of each substrate type. However, a substrate’s analytical performance based on even this limited data set of 100 sample spectra between two substrates can still reveal general inter- and intra-substrate trends. An ideal substrate, with little to no spectral variation, would exhibit a cross-correlation plot dominated by a single shade on the color map (Fig. 6), shown in our case as green (green corresponds to high correlation and red corresponds to low correlation). This would signify that the spectral uniformity across the data region has a correlation value close to 1 and suggests an ideal spectral repeatability across the substrate (intra) and between different substrates (inter). Figure 6a shows the cross-correlation images for the phenylalanine data. A line

FIG. 4. Distribution of 1000 cm1 phenylalanine peak versus water for the six substrates investigated showing seperability between the background water region and target analyte vibration.

of symmetry (dark diagonal line) is drawn to focus attention to either the upper or lower triangle, as they are mirror images. Data from the Au nanoshells were removed because they were deemed indiscriminate toward either analyte, thus making repeatability measurements unwarranted. For the most part, the

five substrates do very well with regard to both intra- and interspectral repeatability, exhibiting correlation values greater than 0.8. Only the Au commercial substrate shows some signs of nonuniformity between the two substrates analyzed. A different picture emerges when the MPA data is processed

FIG. 5. Distribution of 760 cm1 MPA peak versus water for the six substrates investigated showing seperability between the background water region and target analyte vibration.


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FIG. 6. Cross-correlation plots of (a) phenylalanine results and (b) MPA results. Diagonal line represents line of symmetry.

in the same manner. Figure 6b shows the cross-correlation images for MPA (Au nanoshell, Au colloid, and GFONs data were not analyzed for reasons described above). It is interesting to note that the commercial Au substrate showed the best discrimination toward MPA compared to all other substrates but also had relatively poor correlation across not only the individual substrates (intra) but between substrates (inter) as well. The analysis of the Ag nanorods presented the most interesting result and again raised the greatest concerns over analyzing too few substrates for any given method. The Ag nanorod data shows a large variability between the two substrates, indicated by the poor correlation between the spectra measured on the different substrates. This is an ideal example of the dangers of analyzing two few SERS substrates for any given target analyte. If only a single substrate were used, the information obtained for the Ag nanorods would lead to the perception of very good spectral repeatability, which would be truly misleading.

CONCLUSIONS What is presented here is an initial effort to analyze multiple substrates, treated in a similar fashion, and apply statistical methodology to determine SERS activity, selectivity, and spectral repeatability. We show that more than one data analysis method is needed to determine spectral discrimination and repeatability. By no means do we think this is an

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exhaustive study, as we were limited in the number of substrates analyzed. However, we are still able to establish representative data trends. We encourage researchers to thoroughly present a statistical validation over multiple substrates when presenting new SERS results in order to present a clearer picture of the overall substrate performance. Our future efforts will be to expand this study and to incorporate two years worth of data analysis that the Edgewood Chemical Biological Center (ECBC) and the Army Research Lab (ARL) have been performing as part of our role in the DARPA SERS S&T Fundamental program. We believe that with greater scrutiny over substrate reproducibility and the establishment of standardized testing protocols, SERS can eventually be pushed forward into relevant analytical applications. Our ultimate goal is the development of sensitive and reproducible SERS substrates tailored to the detection of chemical, biological, and explosive threats. We feel that a new analytical methodology is needed to provide an unbiased assessment of substrate reproducibility and sensitivity based on standard sample sets and conditions, including the use of Receiver Operating Characteristics or ROC curves for the quantitative comparison of SERS substrates. ACKNOWLEDGMENTS This work was performed by the Research and Technology Directorate, Edgewood Chemical Biological Center, using internal research funding. The authors would like to thank Dr. Marc Ulrich, Physics Division, Army Research Office, and Dr. Paul Pellegrino, Army Research Laboratory, Sensors and

Electronic Devices Directorate, for providing SERS substrates developed or produced under contract for the Department of Defense. We would also like to thank Dr. Caterina Netti, Director of Technology, D3 Technologies Ltd, Southampton, UK, for providing the substrates from Mesophotonics. Portions of this work could not have been completed without the assistance of Dr. Ashish Tripathi (SAIC), Mrs. Leanne Chacon (ECBC), and Dr. Tracey Hamilton (MRICD). The opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

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