Zachary D. Schultz

Surface enhanced Raman scattering (SERS), and the high-resolution imaging analog tip-enhanced Raman scattering (TERS), have emerged as ultrasensitive methods of chemical analysis (1). SERS derives from Raman scattering, and is a nondestructive, label-free, chemicalspecific method. In Raman spectroscopy, laser radiation incident on a molecule is inelastically scattered at frequencies that can be correlated to the vibrational modes of the molecule (2). These vibrational modes are similar to the energy levels probed in infrared spectroscopy and arise from how elements are connected within a molecule. The energy of each vibrational mode is sensitive to interactions with other molecules, temperature, and other conditions, providing insight into physical properties. Additionally, the unique organization and combination of elements that compose a molecule give rise to unique combinations of bands in the Raman spectrum, which is often considered a molecular fingerprint for identification. The challenge with Raman spectroscopy is that the probability of a photon inelastically scattering is very low (about a billion times less than that of a photon fluorescing). Fortunately, in the presence of silver and gold nanostructures, SERS can enhance Raman scattering signals by as much as 1011, such that single molecules can be investigated (3).

The general phenomenon responsible for SERS is shown in Figure 1 and arises from an intense localized electromagnetic field on the nanostructures when the laser excites a localized surface plasmon resonance (LSPR) (4). The LSPR is the collective oscillation of electrons in the conduction band of the metal nanoparticle. Silver and gold nanoparticles are most commonly used because their LSPR is found in the visible region of the electromagnetic spectrum, where most Raman spectroscopy experiments are performed. Other materials can also generate SERS; for example, aluminum nanoparticles exhibit a LSPR at ultraviolet wavelengths and can generate SERS (5). When excited, the oscillation of the electrons concentrates the laser radiation into intense local electromagenetic fields at the nanostructure surface. Molecules in close proximity (generally <5nm) to the nanostructure surface experience this field and generate increased Raman scattering. The Raman scattering occurs at a wavelength similar to the LSPR, causing the nanoparticle to further re-radiate the increased Raman scattering from nearby molecules. In essence, the nanoparticle acts as an antenna to amplify the Raman signal from molecules (6–8).

The electric field in the junction between two plasmonic nanoparticles is significantly larger than on the surface of a single particle and gives rise to very large Raman enhancements. Similarly, asymmetric nanoparticles, such as nanorods and nanostars (9,10), have higher enhancements at points and vertices. Highly enhancing locations are often called “hotspots” and have been shown to dominate the SERS signal (11). Plasmon-enhanced scattering has enabled Raman detection of single molecules and, in some applications, is more sensitive than fluorescence (12,13). At this point, it is well understood the physical interactions that generate a large SERS enhancement; however, controlling the interactions for quantitative analysis has been more challenging. The observed Raman intensity arises from the organization of the nanoparticles and the location of the analyte molecule interacting with the nanoparticles. Although nanoparticles are often modeled as spheres, small features on the surface can modulate the enhanced fields. Additionally, the spacing between particles affects the resulting fields, and forming reproducible aggregates can be challenging and affect the observed signals. Because only molecules near the surface can be detected, the signal saturates at higher concentrations.

The plasmonic effects that give rise to SERS can also be localized at the apex of a scanning probe microscope tip to perform TERS (14,15). This approach has been demonstrated using both atomic force microscopy (AFM) and scanning tunneling microscopy (STM) to control the position of the tip. The advantage of AFM-based TERS is it can be done on almost any sample, while STM–TERS requires a conductive sample. In either approach, a plasmonic metal nanoparticle (or sharp metal point) is located at the very apex of the tip to generate the Raman signal enhancement from molecules beneath the tip. By controlling the location and configuration of the enhancing nanostructure, high sensitivity spectroscopy can be obtained from precise locations on a sample (16). Figure 2 illustrates how, by scanning the sample under the TERS tip in the laser focus, high spatial-resolution images of samples can be obtained. In ultrahigh-vacuum (UHV) experiments, where molecular motion can be minimized, TERS imaging within a molecule has been demonstrated. Under ambient conditions, TERS experiments are capable of imaging with a spatial resolution of 1 to 2 nm (17–19).

A number of applications of SERS and TERS have been reported in the literature. Many SERS applications use the enhanced signal for trace detection. A large number of papers have been published describing many ways to generate a SERS substrate, and commercial solutions are available.

One common use is to create “SERS tags,” where a reporter molecule with a strong Raman response is attached to the surface of the nanoparticle along with some kind of recognition agent (such as an antibody or nucleic acid sequence) (20,21). The unique SERS signal from the reporter molecule is used to quantify, and image, the detected analyte. Although this is similar to fluorescence, SERS tags do not photobleach, and the narrow Raman bands allow multiple different tags to be detected and unmixed simultaneously (22). 

Other recent work has investigated novel ways to utilize the molecular enhancements for chemical sensing, such as SERS pipettes for optophysiology (23) and online detection with chromatographic separations (24). In addition to analytical experiments, new physical understanding is being uncovered at length scales suggesting picoscience (25). As SERS and TERS continue to mature, one can expect to see these techniques enabling new physical insights and methods of chemical analysis.

1. J. Langer, D. Jimenez de Aberasturi, J. Aizpurua, R.A. Alvarez-Puebla, B. Auguié, J.J. Baumberg, G.C. Bazan, S.E.J. Bell, A. Boisen, A.G. Brolo, J. Choo, D. Cialla-May, V. Deckert, L. Fabris, K. Faulds, F.J. García de Abajo, R. Goodacre, D. Graham, A.J. Haes, C.L. Haynes, C. Huck, T. Itoh, M. Käll, J. Kneipp, N.A. Kotov, H. Kuang, E.C. Le Ru, H.K. Lee, J.-F. Li, X.Y. Ling, S. Maier, T. Mayerhoefer, M. Moskovits, K. Murakoshi, J.- M. Nam, S. Nie, Y. Ozaki, I. Pastoriza-Santos, J. PerezJuste, J. Popp, A. Pucci, S. Reich, B. Ren, G.C. Schatz, T. Shegai, S. Schlücker, T. Li-Lin, K.G. Thomas, Z.-Q. Tian, R.P. Van Duyne, T. Vo-Dinh, Y. Wang, K.A. Willets, C. Xu, H. Xu, Y. Xu, Y.S. Yamamoto, B. Zhao, and L.M. Liz-Marzán, 

Modern Raman Spectroscopy: A Practical Approach

 (John Wiley & Sons, Hoboken, New Jersey, 2005).

4. P.L. Stiles, J.A. Dieringer, N.C. Shah, and R.R. Van Duyne, 

5. S. Tian, O. Neumann, M.J. McClain, X. Yang, L. Zhou, C. Zhang, P. Nordlander, and N.J. Halas, 

6. K. Yoshida, T. Itoh, H. Tamaru, V. Biju, M. Ishikawa and Y. Ozaki, 

7. P. Bharadwaj, R. Beams, and L. Novotny, 

9. B. Nikoobakht, Z.L. Wang, and M.A. El-Sayed, 

11. Y. Fang, N.H. Seong, and D.D. Dlott, 

12. D. Graham, R. Stevenson, D.G. Thompson, L. Barrett, C. Dalton, and K. Faulds, 

13. I. A. Larmour, K. Faulds, and D. Graham, 

14. R.M. Stockle, Y.D. Suh, V. Deckert, and R. Zenobi, 

15. N. Hayazawa, Y. Inouye, Z. Sekkat, and S. Kawata, 

16. R. Zhang, Y. Zhang, Z.C. Dong, S. Jiang, C. Zhang, L.G. Chen, L. Zhang, Y. Liao, J. Aizpurua, Y. Luo, J.L. Yang, and J.G. Hou, 

18. Z. He, Z. Han, M. Kizer, R.J. Linhardt, X. Wang, AM. Sinyukov, J. Wang, V. Deckert, A.V. Sokolov, J. Hu, and M.O. Scully, 

19. T. Deckert-Gaudig, E. Kaemmer, and V. Deckert, 

20. D. Graham, B.J. Mallinder and W.E. Smith, 

21. T. Vo Dinh, K. Houck, and D. L. Stokes, 

23. F. Lussier, D. Missirlis, J. P. Spatz, and J. F. Masson, 

24. L. Xiao, C. Wang, C. Dai, L.E. Littlepage, J. Li, and Z.D. Schultz, 

25. J. Lee, K.T. Crampton, N. Tallarida, and V.A. Apkarian, 

Articles

SERS and TERS

It is fairly easy to generate a surface enhanced Raman scattering (SERS) signal. However, there are severalissues to be aware of when trying to use these signals in analytical applications. The SERS signal arises from the combination of the number of molecules (

), the polarizability or cross section of the molecule (σ), and the electric field (

) experienced by the molecules. Understanding how these different variables interact to generate the enhanced SERS response is the key to applying SERS accurately. Here, we try to share experiences and some helpful references to assist people interested in applications using SERS.

It has been more than40 years since the realization that Raman signals are enhanced on nanostructured metal surfaces (1). Figure 1 illustrates the advantages of surface enhanced Raman scattering (SERS), which provides a larger signal from fewer molecules. Over those past 40 years, significant progress has been made toward understanding the origin of the signal enhancement, enabling SERS to become a promising technique for chemical-specific analytical analysis. It is now understood how the electric fields originating from localized surface plasmon resonances on nanoparticles can concentrate the incident laser and generate significant enhancements in Raman scattering from molecules at the surface (2). In particular, nanoparticle dimers and larger aggregates with nanometer-scale gaps and crevices produce some of the largest electromagnetic field enhancements (3). In fact, it is fairly easy to generate a SERS signal; however, as many SERS practitioners will testify, there are a few things to be aware of when trying to use these signals in analytical applications (Table I). The SERS signal arises from the combination of the number of molecules (

) experienced by the molecules. Understanding how these different variables combine to generate the SERS response is the key to applying SERS. Here, I share my experiences and try to provide some helpful references to help people interested in applications using SERS. The following sections highlight some key concepts that people should be aware of when using SERS.

Much of the pioneering work in SERS has been performed using a molecule such as rhodamine or other molecules that have electronic resonance in the visible region (2). These experiments are often called 

surface enhanced resonance Raman scattering

 (SERRS). Just as resonance Raman increases the cross-section (σ) of spontaneous Raman scattering, it also enables the lowest limits of detection in SERS experiments. Most people studying single molecules use this additional enhancement (4). It turns out that many of these molecules are fluorescent dyes, and even with excitation away from the peak absorption, some level of pre-resonance is accessed boosting the SERS response.

The other class of molecules that is commonly detected in SERS is aromatic thiols and pyridines. Although these molecules do not have an intrinsic resonance, they are reported to form charge transfer complexes on the plasmonic surfaces that also increase the relevant cross-section of the analyte and boost the enhancement. These charge-transfer mechanisms are commonly associated with “chemical enhancement” (which has become a default category for enhancement that is not from the enhanced electric field).

Some molecules are just difficult. Glucose is one example. Although glucose has been detected by SERS, this often involves some form of surface functionalization or capture, such as reacting with boronic acid on the surface (5,6). It is often worth measuring the spontaneous Raman limit of detection for a molecule; with SERS one should be able to detect the molecule at a lower concentration. This is one way to distinguish SERS from weak spontaneous Raman scattering. However, if molar concentrations are needed to see the spontaneous Raman signal, the SERS signal may be difficult to measure.

SERS Enhances the Signal from the Molecules at the Surface

The SERS effect is a very short-range enhancement. Studies done on the distance dependence of SERS show the signal enhancement goes away within a few nanometers (7,8). Other studies indicate the molecule is physisorbed to the surface (9). There was even discussion of a “first layer effect,” where the molecules adsorbed to the surface showed a larger enhancement (10).The number (

) of molecules at the surface can often be modeled with a Langmuir isotherm, which correlates the surface coverage to the affinity of the molecule to the surface. At concentrations where your molecule is unlikely to have significant coverage, it is unlikely that one will detect SERS. Both electromagnetic and chemical enhancement mechanisms increase the signal of molecules at the surface. For nanoparticles dropped onto a substrate and dried, the molecules that stick to the nanoparticles are easier to detect.

The idea of a SERS tag is to put a molecule on the surface that gives a strong signal (see above) and then incorporate some form of chemical recognition agent (such as DNA, an antibody, or an aptamer) that binds the target molecule (11,12). Although these approaches have been very successful, it is worth remembering that your limit of detection and sensitivity will now be controlled by the chemical recognition agent on your tag.

Intensities Depend on the Nanostructures, Too

It has been shown that the large majority of the SERS signal originates from “hotspots,” (13) generally regarded as the high electric-field (

) regions associated with gaps and crevices. This means that one molecule in an electric field enhanced region of 106 will be as bright as either 106 molecules in solution, or 105 molecules in a region with an electric field enhancement of 10. This tells us that small changes in the number of molecules in “hotspots” can create large intensity variations. This is particularly problematic when using colloidal nanoparticles, where it can be very challenging to aggregate nanoparticles in a reproducible manner.

A lot of effort has gone into making patterned nanostructures as SERS substrates (14). Many of these substrate approaches still have some variation in the electric field enhancements, and it is common to observe intensity variation on the scale of 10% in these systems. One approach to averaging out this heterogeneity is to measure multiple spots. One study suggested that more than 100 spots are needed to properly capture this variance (15).

This is not to say that SERS cannot be quantitative (16). The use of internal standards, assumed to have the same intensity distribution as an analyte, can correct for this variance. This correction can be a co-adsorbed molecule (17), or, perhaps preferably, a stable isotope variant of the target molecule (18).

The metric used to compare the enhancement observed from a SERS active material is the enhancement factor (

) (19). In theory, this number represents the increase in the Raman signal expected from a molecule. In practice, this is really difficult to determine. It requires that you measure a SERS signal (

SERS), and you have the spontaneous Raman signal (

Raman) from a known number of molecules (

Raman) under similar experimental conditions. With these four numbers, the EF can be calculated as follows:

In perhaps the simplest case, a monolayer of thiol on a patterned nanostructured surface, approximations are still required. Because of the influence of hotspots, the number of molecules detected by SERS (

SERS) is difficult to know. Further, the free molecule may have a different cross-section than the adsorbed molecule, making 

Raman also uncertain. Given these uncertainties, the 

 is at best a relative number for the conditions under which it was determined. When reporting 

s, it is important to provide as much information as possible about how the 

 was determined. As noted above, just because one molecule enhances well, another type of molecule may have a very different 

The Molecule You Put in Your Sample May Not Be What You Detect

One frustrating aspect of SERS is the vibrational frequencies observed may vary from those of the molecules in solution or powder forms, such as is observed in Figure 1. In SERS, there is a strong polarization dependence that selectively enhances those modes aligned with the enhanced electric field. In the past 10 years, experiments show that the electrons in the metals, which generate the plasmon resonance and enhance the Raman signal, can also do chemistry on the analytes. One classic example is para-aminothiophenol, where theories on changes in selection rules to activate Raman-inactive B2 modes were developed, only to find out the new frequencies arose from the formation of dimercaptoazobenzene on the surface (20). Understanding these effects is an active area of SERS research today. To avoid these effects, it is important to use low laser powers, generally less than 1 mW of power, in a diffraction-limited focus of a microscope objective focus. These low powers can minimize these photoreactions as well as heating that can cause problems in your measurement. For those seeking to use SERS, the best practice is to generate a calibration curve with known concentrations of your analyte at low laser powers, and use a strong band (or multivariate model) that provides the most accurate concentration determination.

P.L. Stiles, J.A. Dieringer, N.C. Shah, and R.R. Van Duyne, 

K.L. Wustholz, A.-I. Henry, J.M. McMahon, R.G. Freeman, N. Valley, M.E. Piotti, M.J. Natan, G.C. Schatz,and R.P. Van Duyne, 

B. Sharma, P. Bugga, L.R. Madison, A.I. Henry, M.G. Blaber, N.G. Greeneltch, N.H. Chiang, M. Mrksich, G.C. Schatz, and R.P. Van Duyne,

X. Gu, H. Wang, Z.D. Schultz, and J.P. Camden, 

J.A. Dieringer, A.D.McFarland, N.C. Shah, D.A. Stuart, A.V. Whitney, C.R. Yonzon, M.A. Young, X. Zhang, and R.P. Van Duyne, 

S. Lal, N.K. Grady, G.P. Goodrich, and N.J. Halas, 

D. Graham, B.J. Mallinder, and W.E. Smith, 

N.R. Isola, D.L. Stokes, and T. Vo-Dinh, 

B. Sharma, M.F. Cardinal, S.L. Kleinman, N.G. Greeneltch, R.R. Frontiera, M.G. Blaber, G.C. Schatz, and R.P. Van Duyne, 

A. Subaihi, Y. Xu, H. Muhamadali, S.T. Mutter, E.W. Blanch, D.I. Ellis, and R. Goodacre, 

P.L. Stiles, J.A. Dieringer, N.C. Shah, and R.P. Van Duyne, 

Y.-F. Huang, H.-P. Zhu, G.-K.Liu, D.-Y. Wu, B. Ren, and Z.-Q. Tian, 

is with the Department of Chemistry and Biochemistry at The Ohio State University, in Columbus, Ohio. Direct correspondence to:


SERS offers many advantages. However, there are several issues to be aware of when trying to use SERS signals in analytical applications.

Demystifying SERS: A Newcomer’s Guide to Using Surface Enhanced Raman Scattering

	Advances in instrumentation and data analysis suggest that surface enhanced Raman scattering (SERS) is a powerful tool to characterize complex samples. Since the initial experiments and subsequent realization that Raman signals are significantly enhanced on the surface of plasmonic, structured, metal surfaces, surface enhanced Raman scattering (SERS) has evolved into an ultrasensitive method for chemical analysis (1–3). Indeed, understanding of how light is concentrated and how the local fields are enhanced and re-radiated by plasmonic structures has driven SERS research forward (4). Advances in SERS substrates, now commercially available from a number of sources, and improved sampling strategies, suggest that SERS is poised to be potent analytical technique. Indeed, it is now relatively straightforward to produce a SERS signal, and with this greater understanding of signal enhancements, increased attention is being given to applications using SERS.

	However, since the early days of SERS, one of the more befuddling aspects of SERS analysis has been that the SERS spectrum observed from a molecule is not always the same as the spontaneous Raman spectrum. The origin of these spectral differences has been attributed to surface selection rules, charge-transfer effects, photo-reactions, and other phenomena (5–8). Indeed, these signal variations have raised suggestions that libraries of SERS spectra are needed to properly identify analytes in mixtures. This need for libraries is complicated by observations that the SERS signal from a sample can vary depending on common variables like the substrate or laser excitation wavelength.

	Although the identity of an unknown molecule, or molecules, in a complex system (such as human biofluids) that give rise to a SERS signal can be elusive, new advances in computing are enabling SERS analysis of complex systems through machine learning and related algorithms (9). The detected bands in a SERS spectrum provide an information-rich signal that can be analyzed with computer algorithms, even if an analyst does not know the molecular origin of the signal. Algorithms include neural networks, random forest, and artificial intelligence algorithms, as well as multivariate regression and other chemometric routines. With a rise in companies producing commercial software and the availability of web-based shareware routines, the activation barrier to these analyses methods continues to decrease.

	The underlying principle of machine learning is that given enough data, a computer can use small fluctuations and patterns to classify the outcome. The most common approach is to use a training set‚ samples of known classes to generate a neural network or other machine learning model that will properly capture the variance in the sample data and predict to what class an unknown sample belongs. As the cost of computing power decreases, more-sophisticated methods are accessible to analyze data. The clear advantage of machine learning is that the chemically rich SERS signal, where each vibrational mode detected acts as a variable, can classify the complex sample signals as arising from different specific classes, such as healthy or diseased cells (Figure 1). As the instrumentation and methods using SERS increase the ability to generate large amounts of SERS data quickly, these analyses provide an incredibly powerful way to utilize the chemical information in the SERS spectra for diagnostic assays and to monitor chemical changes. With increased training sets, there is potential to refine such classes further, such as to determine which treatment will likely be most effective for a disease.

	The downside to machine learning algorithms is that the models rely on appropriate training data and, for the most part, can only classify a sample into classes that have been predetermined. Both supervised and unsupervised methods exist, but all models require validation against data sets with known classifications. A further downside to these methods is the loss of information. Although the variance in the SERS data arises from molecular differences, the identity of these molecules can be difficult to recover. Returning to the biofluid example, the machine learning algorithm may classify a sample correctly; however, information, such as a specific upregulated biomolecule to be targeted for therapy, is lost. Complementary methods are often necessary to further assess the disease.

	In a recent study we were able to perform SERS detection following a high-performance liquid chromatography (HPLC) separation of a mouse tumor cell lysate and show that the molecular composition significantly changed between healthy and various types of cancerous cells (10). The molecules with consistent increases in concentration generated patterns that appear to be characteristic of the sampled cells. These patterns can be readily differentiated by machine learning algorithms. Again, although the identity of the molecules detected by HPLC are unknown, computer models can use the patterns in the observed spectra to track and diagnose disease. Additionally, analysis of the underlying data may indicate key biomolecules important in the disease, without knowing the identity of the actual molecules. In such cases where knowing the identity of a specific molecule is not necessary, the ability to rapidly generate SERS spectra and analyze them with machine learning makes it possible to capture the chemical information in SERS data to enable informative characterization.

M. Fleischmann, P.J. Hendra, A.J. McQuillan, Chem. 

J. Electroanal. Chem. Interfacial Electrochem. 

J. Langer, D. Jimenez de AberÂ­asturi, J. Aizpurua, R.A. Alvarez- Puebla, B. Auguié, J.J. BaumÂ­berg, G.C. Bazan, S.E.J. Bell, A. Boisen, A.G. Brolo, J. Choo, D. Cialla-May, V. Deckert, L. Fabris, K. Faulds, F.J. García de Abajo, R. Goodacre, D Graham, A.J. Haes, C.L. Haynes, C Huck, T. Itoh, M. Käll, J. Kneipp, N.A. Kotov, H. Kuang, E.C. Le Ru, H.K. Lee, J.-F. Li, X.Y. Ling, S.A. Maier, T. Mayerhöfer, M. Moskovits, K. Murakoshi, J.-M. Nam, S. Nie, Y. Ozaki, I. Pastoriza-Santos, J. Perez-Juste, J. Popp, A Pucci, S Reich, B Ren, G.C. Schatz, T. Shegai, S. Schlücker, L.-L. Tay, K.G. Thomas, Z.-Q. Tian, R.P. Van Duyne, T. Vo-Dinh, Y. Wang, K.A. Willets, C Xu, H. Xu, Y. Xu, Y.S YaÂ­mamoto, B. Zhao, and L.M. Liz- Marzán, 

J. SzczerbiÅski, L. Gyr, J. Kaeslin, and R. Zenobi, 

Y. Zhang, S. He, W. Guo, Y. Hu, J. Huang, J.R. Mulcahy, and W.D Wei, 

Y.-F. Huang, H.-P. Zhu, G.K. Liu, D.Y. Wu, B. Ren, and Z.Q. Tian, 

F. Lussier, V. Thibault, B. Charron, G.Q. Wallace, and J.-F. Masson, 

L. Xiao, C. Wang, C. Dai, L.E. Littlepage, J. Li, and Z.D. Schultz, 

is an associate professor at The Ohio State University. Direct correspondence to 

In celebration of Spectroscopy’s 35th Anniversary, leading spectroscopists discuss important issues and challenges in analytical spectroscopy.

Rise of the Machines: SERS Instrumentation and Machine Learning Enabling Complex Bioanalysis

It is fairly easy to generate a surface enhanced Raman scattering (SERS) signal. However, there are several  issues to be aware of when trying to use these signals in analytical applications. The SERS signal arises from the combination of the number of molecules (

		
		It has been more than 40 years since the realization that Raman signals are enhanced on nanostructured metal surfaces (1). Figure 1 illustrates the advantages of surface enhanced Raman scattering (SERS), which provides a larger signal from fewer molecules. Over those past 40 years, significant progress has been made toward understanding the origin of the signal enhancement, enabling SERS to become a promising technique for chemical-specific analytical analysis. It is now understood how the electric fields originating from localized surface plasmon resonances on nanoparticles can concentrate the incident laser and generate significant enhancements in Raman scattering from molecules at the surface (2). In particular, nanoparticle dimers and larger aggregates with nanometer-scale gaps and crevices produce some of the largest electromagnetic field enhancements (3). In fact, it is fairly easy to generate a SERS signal; however, as many SERS practitioners will testify, there are a few things to be aware of when trying to use these signals in analytical applications (Table I). The SERS signal arises from the combination of the number of molecules (

) experienced by the molecules. Understanding how these different variables combine to generate the SERS response is the key to applying SERS. Here, I share my experiences and try to provide some helpful references to help people interested in applications using SERS. The following sections highlight some key concepts that people should be aware of when using SERS. 

	Much of the pioneering work in SERS has been performed using a molecule such as rhodamine or other molecules that have electronic resonance in the visible region (2). These experiments are often called 

 (SERRS). Just as resonance Raman increases the cross-section (σ) of spontaneous Raman scattering, it also enables the lowest limits of detection in SERS experiments. Most people studying single molecules use this additional enhancement (4). It turns out that many of these molecules are fluorescent dyes, and even with excitation away from the peak absorption, some level of pre-resonance is accessed boosting the SERS response.       

	The other class of molecules that is commonly detected in SERS is aromatic thiols and pyridines. Although these molecules do not have an intrinsic resonance, they are reported to form charge transfer complexes on the plasmonic surfaces that also increase the relevant cross-section of the analyte and boost the enhancement. These charge-transfer mechanisms are commonly associated with “chemical enhancement” (which has become a default category for enhancement that is not from the enhanced electric field).

	Some molecules are just difficult. Glucose is one example. Although glucose has been detected by SERS, this often involves some form of surface functionalization or capture, such as reacting with boronic acid on the surface (5,6). It is often worth measuring the spontaneous Raman limit of detection for a molecule; with SERS one should be able to detect the molecule at a lower concentration. This is one way to distinguish SERS from weak spontaneous Raman scattering. However, if molar concentrations are needed to see the spontaneous Raman signal, the SERS signal may be difficult to measure.

	The SERS effect is a very short-range enhancement. Studies done on the distance dependence of SERS show the signal enhancement goes away within a few nanometers (7,8). Other studies indicate the molecule is physisorbed to the surface (9). There was even discussion of a “first layer effect,” where the molecules adsorbed to the surface showed a larger enhancement (10).The number (

	The idea of a SERS tag is to put a molecule on the surface that gives a strong signal (see above) and then incorporate some form of chemical recognition agent (such as DNA, an antibody, or an aptamer) that binds the target molecule (11,12). Although these approaches have been very successful, it is worth remembering that your limit of detection and sensitivity will now be controlled by the chemical recognition agent on your tag.

	It has been shown that the large majority of the SERS signal originates from “hotspots,” (13) generally regarded as the high electric-field (

) regions associated with gaps and crevices. This means that one molecule in an electric field enhanced region of 10

 molecules in a region with an electric field enhancement of 10. This tells us that small changes in the number of molecules in “hotspots” can create large intensity variations. This is particularly problematic when using colloidal nanoparticles, where it can be very challenging to aggregate nanoparticles in a reproducible manner.

	A lot of effort has gone into making patterned nanostructures as SERS substrates (14). Many of these substrate approaches still have some variation in the electric field enhancements, and it is common to observe intensity variation on the scale of 10% in these systems. One approach to averaging out this heterogeneity is to measure multiple spots. One study suggested that more than 100 spots are needed to properly capture this variance (15).

	This is not to say that SERS cannot be quantitative (16). The use of internal standards, assumed to have the same intensity distribution as an analyte, can correct for this variance. This correction can be a co-adsorbed molecule (17), or, perhaps preferably, a stable isotope variant of the target molecule (18).

	The metric used to compare the enhancement observed from a SERS active material is the enhancement factor (

), and you have the spontaneous Raman signal (

) under similar experimental conditions. With these four numbers, the EF can be calculated as follows:

	In perhaps the simplest case, a monolayer of thiol on a patterned nanostructured surface, approximations are still required. Because of the influence of hotspots, the number of molecules detected by SERS (

) is difficult to know. Further, the free molecule may have a different cross-section than the adsorbed molecule, making 

 also uncertain. Given these uncertainties, the 

	One frustrating aspect of SERS is the vibrational frequencies observed may vary from those of the molecules in solution or powder forms, such as is observed in Figure 1. In SERS, there is a strong polarization dependence that selectively enhances those modes aligned with the enhanced electric field. In the past 10 years, experiments show that the electrons in the metals, which generate the plasmon resonance and enhance the Raman signal, can also do chemistry on the analytes. One classic example is para-aminothiophenol, where theories on changes in selection rules to activate Raman-inactive B

 modes were developed, only to find out the new frequencies arose from the formation of dimercaptoazobenzene on the surface (20). Understanding these effects is an active area of SERS research today. To avoid these effects, it is important to use low laser powers, generally less than 1 mW of power, in a diffraction-limited focus of a microscope objective focus. These low powers can minimize these photoreactions as well as heating that can cause problems in your measurement. For those seeking to use SERS, the best practice is to generate a calibration curve with known concentrations of your analyte at low laser powers, and use a strong band (or multivariate model) that provides the most accurate concentration determination.

		P.L. Stiles, J.A. Dieringer, N.C. Shah, and R.R. Van Duyne, 

		K.L. Wustholz, A.-I. Henry, J.M. McMahon, R.G. Freeman, N. Valley, M.E. Piotti, M.J. Natan, G.C. Schatz,and R.P. Van Duyne, 

		B. Sharma, P. Bugga, L.R. Madison, A.I. Henry, M.G. Blaber, N.G. Greeneltch, N.H. Chiang, M. Mrksich, G.C. Schatz, and R.P. Van Duyne,  

		X. Gu, H. Wang, Z.D. Schultz, and J.P. Camden, 

		J.A. Dieringer, A.D.McFarland, N.C. Shah, D.A. Stuart, A.V. Whitney, C.R. Yonzon, M.A. Young, X. Zhang, and R.P. Van Duyne, 

		S. Lal, N.K. Grady, G.P. Goodrich, and N.J. Halas, 

		D. Graham, B.J. Mallinder, and W.E. Smith, 

		N.R. Isola, D.L. Stokes, and T. Vo-Dinh, 

		B. Sharma, M.F. Cardinal, S.L. Kleinman, N.G. Greeneltch, R.R. Frontiera, M.G. Blaber, G.C. Schatz, and R.P. Van Duyne, 

		A. Subaihi, Y. Xu, H. Muhamadali, S.T. Mutter, E.W. Blanch, D.I. Ellis, and R. Goodacre, 

		P.L. Stiles, J.A. Dieringer, N.C. Shah, and R.P. Van Duyne, 

		Y.-F. Huang, H.-P. Zhu, G.-K.Liu, D.-Y. Wu, B. Ren, and Z.-Q. Tian, 

is with the Department of Chemistry and Biochemistry at The Ohio State University, in Columbus, Ohio. Direct correspondence to: 

The SERS signal arises from the combination of the number of molecules, the polarizability or cross-section of the molecule, and the electric field experienced by the molecules. Understanding how these variables interact to generate the SERS response is the key to applying SERS accurately.