The ability to enhance the Raman signal from low concentrations of molecules using nanoparticles has made surface-enhanced Raman scattering (SERS) an attractive approach for trace detection in diverse applications in medicine, energy and the environment (1). Understanding SERS has improved significantly since the initial discovery in the 1970s (2,3). The detection and imaging, via tip-enhanced Raman scattering (TERS), of individual molecules suggests the ultimate in detection sensitivity (4–7). Particularly, recognizing how the energy from the excitation laser is concentrated by the nanoparticles through localized surface plasmon resonances has helped foster understanding of the appropriate experimental conditions to use for SERS measurements. Hot spots are important for generating the largest enhancements (8). In addition to enhancing the local electric field, other effects can occur on the surface of nanoparticles and impact the observed signals. Considering how the excitation laser intensity is concentrated and confined into small regions, it is important to consider the incident laser power. When performing SERS experiments, the excitation laser power needs to be kept at a level that enables detection but avoids damaging the sample.
Generally, surface-enhanced Raman spectroscopy (SERS) practitioners often recommend the laser power be kept below 1 mW in a diffraction limited laser spot. The spot size in a microscope objective can be approximated by the diffraction limit below
where changes with the numerical aperture (NA) of the objective and laser wavelength (λ) will alter the spot size. 1 mW of power in a 1 μm spot diameter corresponds to an energy density of approximately 1 mW/μm2 (equivalent to 105 W/cm2), which is a common experimental condition for many SERS experiments. High NA objectives and shorter wavelength lasers require lower powers. Similarly, other factors that lower the energy density can also prevent sample damage in SERS experiments.
Figure 1 gives an illustrative example of how too much laser excitation can significantly damage a sample. In this example, individual nanoparticles and nanoparticle dimers were confined in an array of lithographically fabricated wells on the surface. Single nanoparticles are evident as green spots in the dark field image on the left. Coupling between nanoparticles to form a dimer causes the scattering to appear red from some of the wells in the darkfield image. After exciting with a few milliwatts (1–2 mW) of 633 nm laser excitation, the energy released destroyed the sample as shown on the right. Figure 1 is relatively dramatic, and the exact mechanism of the damage is unclear; however, it illustrates that small amounts of laser power can detrimentally impact your experiment.
There are several ways increased laser power is detrimental to SERS. For example, sustained heating at 250 °C converts nanorods into nanospheres in less than an hour (9). The strong dependence of the SERS signal on the nanostructure shape and spacing means melting can easily diminish the observed SERS intensity. In my own laboratory, we have seen that the temperature increase associated with increased laser power has a detrimental effect on the SERS signal (10). The energy absorbed and heat produced by the nanoparticles can easily exceed 250 °C for aggregated nanoparticles in air. Keeping the nanoparticles in a medium with higher heat dissipation (water as opposed to air) during the SERS measurement can minimize the heat-related damage. Generally, photo-induced heating is minimized by providing a way for heat to dissipate. Laser power has also been reported to damage the molecules present on the nanoparticle surfaces. This is also not too surprising, because the same plasmonic nanoparticles that are used for signal enhancement are also used for chemical catalysis, where the excited electrons accelerate chemical reactions at the nanoparticle surface (11). When molecules capture these energetic electrons, bonds can break or the vibrations of the molecule change, which alters, or even destroys, the SERS signal (12,13). This effect may explain changes between the spontaneous Raman spectrum and the observed SERS spectrum for any given molecules. In many cases, using low laser power for excitation can minimize these effects.
Figure 2 shows the spectra obtained from a protein deposited and dried on a commercial SERS substrate (Silmeco, Au SERStrate) at different laser excitation powers. The spectra recorded with increased laser power do show more signal intensity; however, much of the fine detail that is used for chemical analysis is lost at the higher powers. The distinct vibrational bands near 900 and 1500 cm-1 at low power are lost at higher laser power excitation. It is worth noting that the specifications provided by Silmeco recommend using <10 W/cm2, which corresponds to a power of ~0.1 μW in a diffraction limited laser spot. It can be difficult to observe signals at such low powers, but damage will be avoided. The spectra shown in Figure 2 were acquired with a 785 nm laser and a 0.5 NA microscope objective, which corresponds to an energy density ranging from 2.0 x 104 to 1.6 x 105 W/cm2. In the normalized spectra, the lowest power acquisition clearly shows bands that disappear with increased laser power. These lost bands may result from changes in conformation, thermal damage, or even chemical changes to the protein on the surface. Additionally, the lost features are particularly valuable when using SERS spectra from chemometric, or machine learning, models to decipher important properties of mixtures and complex samples.
To conclude, the examples provided illustrate how using the incorrect laser power can degrade, or in some cases destroy, your sample. Table I lists recommendations to help you make successful SERS measurements. Some factors that can help are acquiring spectra in an environment with higher heat dissipation, such as in water compared to in air. Other approaches that illuminate a larger number of spots, such as line focus or rapidly moving the beam around the surface, can help avoid sample damage. Overall, less power is often better when making SERS measurements.
References
(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. Perez-Juste, 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, ACS Nano 14(1), 28–117 (2019).
(2) D.L. Jeanmaire and R.P. Van Duyne, J. Electroanal. Chem. Interf. Electrochem. 84(1), 1–20 (1977). DOI: https://doi.org/10.1016/S0022-0728(77)80224-6.
(3) M. Fleischmann, P.J. Hendra, and A.J. McQuillan, Chem Phys. Lett. 26(2), 163–166 (1974).
(4) 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, Nature 498(7452), 82–86 (2013).
(5) S.M. Nie and S.R. Emery, Science 275(5303), 1102–1106 (1997).
(6) K. Kneipp, Y. Wang, H. Kneipp, L.T. Perel- man, I. Itzkan, R.R. Dasari, and M.S. Feld, Phys. Rev. Lett. 78(9), 1667–1670 (1997). DOI: 10.1103/PhysRevLett.78.1667.
(7) J. Lee, K.T. Crampton, N. Tallarida, and V.A. Apkarian, Nature 568(7750), 78–82 (2019). DOI: 10.1038/s41586-019-1059-9.
(8) Y. Fang, N-H. Seong, and D.D. Dlott, Science 321(5887), 388–392 (2008).
(9) H. Petrova, J. Perez Juste, I. Pastoriza-Santos, G.V. Hartland, L.M. Liz-Marzán, and P. Mulvaney, Phys. Chem. Chem. Phys. 8(7), 814–821 (2006). DOI: 10.1039/B514644E.
(10) Z.C. Zeng, H. Wang, P. Johns, G.V. Hartland, and Z.D. Schultz, J. Phys. Chem. C. Nanomater Interf. 121(21), 11623–11631 (2017). DOI: 10.1021/acs.jpcc.7b01220.
(11) E. Cortés, L.V. Besteiro, A. Alabastri, A. Baldi, G. Tagliabue, A. Demetriadou, and P. Narang, ACS Nano 14(12), 16202–16219 (2020). DOI: 10.1021/acsnano.0c08773.
(12) J. Szczerbiński, L. Gyr, J. Kaeslin, and R. Zenobi, Nano Lett. 18(11), 6740–6749 (2018). DOI: 10.1021/acs.nanolett.8b02426.
(13) S. Sloan-Dennison, C.M. Zoltowski, P.Z. El-Khoury, and Z.D. Schultz, J. Phys. Chem. C. 124(17), 9548–9558 (2020). DOI: 10.1021/ acs.jpcc.0c01436.
Zachary D. Schultz is with the Department of Chemistry and Biochemistry at The Ohio State University. Direct correspondence to: schultz.133@osu.edu.●
Year in Review: The Latest in Raman Spectroscopy
December 26th 2024This year-in-review showcases the standout technical articles, compelling interviews, and key news stories that defined the pages of Spectroscopy. In this year in review, the editors of Spectroscopy highlight some of the top published technical articles, interviews, and news content published.
Remembering Engineering Pioneer Sir David McMurtry
December 16th 2024The world of engineering and innovation mourns the loss of a towering figure with the passing of Sir David McMurtry, CBE, RDI, FREng, FRS, CEng, FIMechE, co-founder and Non-Executive Director of Renishaw. Known for his brilliance, humility, and groundbreaking contributions to metrology and manufacturing, McMurtry leaves a legacy that has profoundly shaped modern engineering.
Nanometer-Scale Studies Using Tip Enhanced Raman Spectroscopy
February 8th 2013Volker Deckert, the winner of the 2013 Charles Mann Award, is advancing the use of tip enhanced Raman spectroscopy (TERS) to push the lateral resolution of vibrational spectroscopy well below the Abbe limit, to achieve single-molecule sensitivity. Because the tip can be moved with sub-nanometer precision, structural information with unmatched spatial resolution can be achieved without the need of specific labels.
Raman Spectroscopy and Deep Learning Enhances Blended Vegetable Oil Authentication
December 10th 2024Researchers at Yanshan University have developed a groundbreaking method combining Raman spectroscopy and deep learning models to accurately identify and quantify components in blended vegetable oils.