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Improved Quantitative SERS Enabled by Surface Plasmon Enhanced Elastic Light Scattering Haoran Wei, Weinan Leng, Junyeob Song, Marjorie R. Willner, Linsey C. Marr, Wei Zhou, and Peter J. Vikesland Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04667 • Publication Date (Web): 22 Jan 2018 Downloaded from http://pubs.acs.org on January 24, 2018

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Analytical Chemistry

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Improved Quantitative SERS Enabled by Surface Plasmon

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Enhanced Elastic Light Scattering

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Haoran Wei,†‡§ Weinan Leng,†‡§ Junyeob Song,ǁ Marjorie R. Willner,†‡§

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Linsey C. Marr,†‡§ Wei Zhou,ǁ Peter J. Vikesland†‡§*

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†

‡

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§

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ǁ

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Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia

Center for the Environmental Implications of Nanotechnology (CEINT), Duke University, Durham, North Carolina Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia *Corresponding author. Phone: (540) 231-3568, Email: [email protected]

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Manuscript prepared for Analytical Chemistry (full article)

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ACS Paragon Plus Environment

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Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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ABSTRACT: The application of surface-enhanced Raman spectroscopy (SERS) for everyday

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quantitative analysis is hindered by the point-to-point variability of SERS substrates that arises

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due to the heterogeneous distribution of localized electromagnetic fields across a suite of

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plasmonic nanostructures. Herein, we adopt surface-enhanced elastic scattering as a SERS

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internal standard. Both elastic and inelastic (i.e., Raman) scattering are simultaneously enhanced

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by a given “hot spot” and thus the surface-enhanced elastic scattering signal provides a localized

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intrinsic internal standard that scales across all of the plasmon-enhanced electromagnetic fields

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within a substrate. Elastically scattered light originates from the amplified spontaneous emission

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(ASE) of the commercial laser leading to the formation of a low wavenumber pseudo band that

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arises from the interaction of the ASE and the edge filter. A theoretical model was developed to

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illustrate the underlying mechanism supporting this normalization approach. The normalized

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Raman signals are independent of the incident laser intensity and the density of “hot spots” for

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numerous SERS substrates. Following “hot-spot” (HS) normalization, the coefficient of variation

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for the tested SERS substrates decreases from 10-60% to 2%-7%. This approach significantly

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improves SERS quantitation of four chloroanilines and enables collection of highly reproducible

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analyte adsorption results under both static and dynamic imaging conditions. Overall, this

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approach provides a simple means to improve SERS reproducibility without the need to use

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additional chemicals as internal standards.

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Surface-enhanced Raman spectroscopy (SERS) has long been proposed as an ultrasensitive

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analysis method with single molecule sensitivity, minimal need for sample pretreatment, rapid

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detection time, and potential for on-site deployment.1-5 However, in spite of the volume of

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research conducted to develop SERS substrates and optimize the technique, the poor

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reproducibility of the SERS signal makes it a challenge to achieve reliable quantitative analysis.

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SERS largely remains a laboratory curiosity, but with great potential for multiple real-world

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applications.6-9 One means to improve SERS reproducibility is to develop uniform SERS

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substrates through “top-down” nanofabrication.10-15 However, it is challenging to create such

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substrates at scale and at reasonable cost.

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An alternative approach to improve SERS reproducibility is to incorporate internal standards

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(IS) into the substrate.16-19 The IS undergoes the same enhancement as target analytes, thus

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reducing point-to-point variability in the signal caused by substrate heterogeneity, laser intensity

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fluctuations, or temperature variation. Although SERS quantitation can be achieved using an IS,

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it adds to the cost and complexity of substrate preparation, lacks universal applicability,

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generates interferent Raman bands, and the reference probe molecules may occupy SERS “hot

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spots”. In addition, it is nontrivial to find an appropriate IS that reflects the chemical and

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physical properties of the target analyte. Isotope-edited internal standards (IEIS) are ideal

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candidates for IS because the isotope analogues have the same Raman cross-section and the same

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affinity to the plasmonic surface.19,20 While analyte concentrations can be determined based on

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the ratios of the Raman band intensities of the two isotope analogues, their adsorption kinetics

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cannot be acquired. In addition, IEIS for a large number of chemicals of analytical interest are

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not readily available. Importantly, none of the aforementioned approaches is applicable for SERS


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sensing under dynamic conditions wherein “hot spot” densities change over time. Such

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conditions are prevalent in SERS assays for biomedical and environmental detection.21-25

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The SERS effect is inherently surface enabled with the highest signal enhancements observed

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at locations where the electromagnetic field is highest (i.e., either at edges or between two

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nanoparticles).9,26 Regions with the highest enhancement factors are typically referred to as

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SERS “hot spots”. Variation of SERS signals induced by the heterogeneous distribution of “hot

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spots” oftentimes surpasses that induced by the change of analyte concentrations on NP surfaces.

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Therefore, normalizing “hot spot” distribution across a single substrate or among different

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substrates is vitally important for improving the performance of SERS quantitation. It was

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recently reported that the elastic scattering undergoes the same electromagnetic enhancement as

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the inelastic scattering coming from the same SERS “hot spot”.27 Based on this study and

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considering the intrinsic drawbacks of the reported IS, we sought to explore the relationship

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between the measured elastic scattering signal and the SERS signal intensity.

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In this effort, we demonstrate a simple approach for improving SERS reproducibility that

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exploits surface plasmon enhanced elastic scattering signals as internal standards for SERS

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signal normalization. Both theory and experiments show that the intensity of the surface plasmon

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enhanced elastic scattering signal of a low-wavenumber pseudo-band (νe) scales linearly with the

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integrated “hot-spot” signal strength. This pseudo-band can be used to calibrate “hot spot”

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variations and minimize the signal heterogeneity of a SERS substrate, account for batch-to-batch

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substrate variability, and different types of SERS substrates. We first discuss the fundamental

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theory supporting our approach, provide supporting experimental results, and conclude by

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demonstrating “hot spot” normalization for 1) quantitative pH-triggered SERS detection of


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chloroanilines; and 2) differentiation of analyte adsorption and “hot spot” formation in a

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dynamic colloidal system.

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EXPERIMENTAL SECTION

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Materials. Gold (III) chloride trihydrate (HAuCl4·3H2O), sodium citrate tribasic dehydrate

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(Na3Cit·2H2O), silver nitrate (AgNO3), 4-chloroaniline (4-CA, 98%), 3-chloroaniline (3-CA,

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99%), 2-chloroaniline (2-CA, 98%), 2,4-dichloroaniline (99%), 3-bromoaniline (3-BA), 3-

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nitroaniline (3-NA), melamine, and 4-mercaptobenzoic acid (4-MBA) were purchased from

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Sigma-Aldrich. Sodium borohydride (NaBH4), hydrochloride acid (HCl), and ethanol were

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purchased from Fisher Scientific. Malachite green isothiocyanate (MGITC) was acquired from

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Invitrogen Corp. (Grand Island, NY). Thiolated poly(ethylene glycol) (HS-PEG; 5 kD) was

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purchased from Nanocs.

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Transmission Measurement. SERS spectra of DI water were collected using a transmission

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mode Raman system equipped with an inverted microscope (WITec alpha 300 RSA+). Two

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identical objectives were used (20×, NA=0.4) to focus into the sample with a spot size of ≈2.4

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um, and collect the signal through the sample. The laser excitation wavelength was 785 nm. We

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employed a longpass filter (cut-off wavelength: 68 cm-1) before the detector to generate pseudo

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peak νe, and a bandpass filter (center wavelength: 785 nm, Full Width-Half Max: 3 nm) was

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placed to block the elastic scattering process of ASE fields from the laser. Before the laser and

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detector, there were two small confocal pinhole apertures that increase optical resolution and

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suppress out-of-focus light.

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Backscattering Measurement. All SERS spectra except the laser emission profile shown in

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Fig. 1c were acquired using a confocal Raman spectroscopy system in backscattering mode

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(WITec alpha 500R). SERS maps were generally collected with a 10× objective. Each SERS


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map consists of 20×20 spectra across a 100×100 µm2 area. The laser wavelength was 785 nm

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and integration time was 0.5 s. The Raman signal was dispersed by a 300 gr/mm grating and

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detected using a Peltier charge-coupled device. To make HS normalized maps, spectra from a

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SERS map were imported into Matlab 2015 (The Mathworks, USA) and baseline corrected using

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in-house scripts. For the maps using PL as normalizing factor, only the dark background (Fig. 1e)

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was subtracted. As shown in Fig. S1, the Au-Cl Raman band at 267 cm-1 does not influence νe

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even when an edge filter cutting at 126 cm-1 was employed. Integrated intensities from 106-146

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cm-1 for νe at 126 cm-1 or 64-104 cm-1 for νe at 84 cm-1 were employed as the normalizing factor.

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The ratio of the analyte band to νe was projected as a normalized SERS map using Matlab.

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Preparation of SERS Substrates. Preparation and characterization of the AuNP/BC platform

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was described previously.7 Briefly, sixteen pieces of BC (0.5 cm × 0.5 cm) were immersed in 30

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mM HAuCl4 solution for 30 s and then transferred to boiling Na3Cit solution (1.2 mM unless

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otherwise denoted) for 1.5 h. Scanning electron microscopy images, energy dispersive X-ray

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spectroscopy, and UV-VIS extinction spectra are shown in Fig. S2&3 and in our previous

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publication.7 AgNP/BC was prepared by adding 0.7 mL of NaBH4 (25 mM unless otherwise

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denoted) into a tube containing 0.7 mL AgNO3 (25 mM) and 20 pieces of BC followed by vortex

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mixing. The preparation method for 14 nm AuNP seeds and 50 nm AuNPs is described in

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Supporting Information Note S1. Briefly, AuNP seeds were synthesized by adding Na3Cit

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solution (final conc.: 3.88 mM) to boiling HAuCl4 solution (final conc.: 1 mM) for 15 min. 50

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nm AuNPs were synthesized by seed-mediated growth. 100 mL HAuCl4 solution (final conc.:

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0.254 mM) was brought to boil, to which 0.818 mL seed solution and 0.44 mL citrate (38.8 mM)

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were subsequently added. 4-MBA coated AuNP aggregate suspension was prepared by ethanol-

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induced aggregation followed by thiolated poly(ethylene) glycol (HS-PEG) functionalization.28


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Manipulation of Hot Spot Density in AuNP/BC Substrate. One piece of AuNP/BC

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(0.5×0.5 cm2) hydrogel was immersed in 5 mL 4-MBA ethanol solution (1 mM) for 3 h. The 4-

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MBA concentration used here was high enough to ensure that a complete monolayer formed on

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the nanoparticle surface. SERS maps were collected from the wet AuNP/BC hydrogel in air

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every 20 min until 60 min. After the AuNP/BC was completely dry, SERS maps were collected

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using both 10× and 100× objectives.

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pH-Triggered Detection of Four Chloroanilines. Sample aliquots of 1 mL of 4-CA, 3-CA,

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2-CA, or 2,4-DCA in ethanol solutions with a concentration of 1 mM were added to 3 mL

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aqueous solution with pH pre-adjusted to values below their pKa. The final ethanol concentration

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(25%) had minimal influence on SERS detection (data not shown). One piece of AuNP/BC

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(0.5×0.5 cm2) hydrogel was immersed in chloroaniline solution. After vortex mixing for 30 s, the

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AuNP/BC hydrogel was taken out for SERS measurement.

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Quantitation of Chloroanilines. To determine the dynamic range of four chloroanilines, 1

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mL 4-CA, 3-CA, 2-CA, and 2,4-DCA in ethanol with concentrations from 1-1000 µM was added

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to 3 mL aqueous solution with pH preadjusted to 1.7-2.3. One piece of AuNP/BC (0.5×0.5 cm2)

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hydrogel was immersed in the chloroaniline solution. After vortex mixing for 30 s, the AuNP/BC

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hydrogel was taken out for Raman measurement.

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SERS Spectra Collection with a Variety of Analytes and Substrates. SERS spectra of a

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variety of analytes were acquired using an AuNP/BC platform via a pH-triggered approach.7 In

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this process, 1 mL of analyte in ethanol (1 mM) was added to 3 mL aqueous solution of pre-

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adjusted pH to ensure that solution pH values were below the pKa. The final pH values were:

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MGITC: 5.4; 4-CA: 2.3; 3-CA: 1.9; 2-CA: 1.9; 2,4-DCA: 1.7; Melamine: 2.3; 3-BA: 2.3; and 3-

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NA: 1.8. One piece of AuNP/BC (0.5×0.5 cm2) hydrogel was immersed in each solution. After


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vortex mixing for 30 s, the AuNP/BC hydrogel was taken out for Raman measurement. SERS

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spectra of blank AuNP/BC were collected at pH values of 5.4 and 2.3 as negative controls.

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Monitoring 4-MBA Adsorption to Stable and Dynamic SERS substrates. A piece of

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AuNP/BC was attached to the bottom of a small petri dish. After collecting the first SERS map

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(blank), 6 mL 4-MBA ethanol/water solution (50 µM) was added. SERS spectra were collected

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every 1 min for the first five spectra, every 5 min for the next five spectra, every 10 min for the

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next five spectra, and every 20 min for the remaining four spectra. To monitor 4-MBA

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adsorption to aggregating AuNP surface, 0.5 mL 4-MBA ethanol solution (100 µM) was added

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into 0.5 mL AuNP suspension. After vortex mixing, the mixture was transferred to a quartz cell

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and scanned. SERS maps (20 × 20 points, 1000×1000 µm2, 0.5 s int. time) were collected every

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5 min until 180 min. Before calculating the ratio between the Raman and elastic bands, the blank

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spectrum was subtracted from each SERS spectrum to exclude the influence of ASE photons

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scattered by water.

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RESULTS AND DISCUSSION

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Theoretical Basis for “Hot Spot” Normalization. Using a scalar phenomenological

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theory,29,30 we developed an analytical expression that predicts that the intensity of the surface

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enhanced elastic scattering signal is proportional to the SERS signal from the ensemble of

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analyte and background molecules in the vicinity of plasmonic nanostructures. We propose that

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the surface enhanced elastic scattering signal can serve as an intrinsic internal standard for

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quantitative SERS under carefully controlled conditions.

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As depicted in Figure 1a, a molecule is located at position r0 close to a plasmonic metal

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nanostructure (at r’) that supports localized surface plasmon resonances (LSPRs) and converts

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the incident fields E0(r0, ω0) into local scattered fields ER(r0, ω0) ~ f(r0, ω0)E0(r0, ω0), where f(r0,


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ω0) is the field enhancement factor. Laser illumination gives rise not only to the stimulated

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emission fields E0(r0, ω0) at the lasing frequency ω0, but also amplified spontaneous emission

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(ASE) fields E0(r0, ω1) over broad frequencies ω1 that have weaker amplitudes than the lasing

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emission. An analyte molecule at r0 experiences the total local fields E0 + Es ~ (1+ f(r0, ω))*E0(r0,

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ω) at lasing frequency ω0 and ASE frequencies ω1, respectively. The interaction of the molecule

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with the enhanced total local fields at ω0 gives rise to the dipole moment associated with

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inelastic

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r , ] r , , where ωvib is a vibrationally shifted frequency and α(ω0±ωvib, ω0) is the

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polarizability for the frequency mixing Stokes (ω0 - ωvib) or anti-Stokes (ω0 + ωvib) Raman

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scattering process. Similarly, the dipole moment associated with elastic scattering at frequency

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ω1 can be induced according to = [1 +fr , ] r , , where α(ω1) is the

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polarizability of the elastic scattering process. In the presence of plasmonic nanostructures, the

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Green function G(r∞, r0, ω) of the combined molecule-nanostructure system is represented as

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(1+f(r0, ω)) G0(r∞, r0, ω), where G0 is the free-space Green function in the absence of plasmonic

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nanostructures and f is the field enhancement factor at the radiation frequency. The electric field

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intensity IRaman of the radiation from the induced Raman scattering dipole ± ,

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depends on the incident field intensity I0 and can be expressed as:

199

r , ± = | , ± |

Raman

= |

200

201 202

≈

scattering

±!"# $ %& $

±!"# ) %$ & )

according

to

± , = ± , [1 +

['r , r , ± ] ± , |

|[1 + r , ± ]' r , r , ± ± , [1 +

r , ]| r , .


(Eq. 1)

10

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203


Similarly, the radiation intensity IElastic from the induced elastic scattering dipole is *+,-& , = | , | = |

204

≈

2 )

%$ & )

[' 00001, 00001, + ' 00001 00001 , , ] | ./

|[1 + r , ]' r , r , [1 + r , ]| r , .

(Eq. 2)

205

For most quantitative SERS applications, we want to quantify analyte molecule concentrations

206

by measuring Raman signals from an ensemble of analyte molecules in a dielectric environment

207

(e.g., various liquids). Under laser illumination both the inelastic Raman scattering and the

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elastic scattering signals come from an ensemble of analyte and background molecules.

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According to Eq. 1 and Eq. 2, the ratio of signal intensities between Raman scattering at ω0 ±

210

ωvib and elastic scattering at ω1 can be expressed as

211

345657 89 , ±!"#

3:;5 H89 ,8 ,2 I

≈

B D

" C 3" ∑"E2 45657 @89 ,8 , ±!"# A B D G

$

B D

=

212

G

J 3 ∑GE2 H8 ,8 ,2 I :;5 89 ,2

233

without being affected by the many experimental factors that give rise to uncontrollable spatial

234

and temporal perturbations (e.g., local refractive index environments, local field enhancement

235

factors, and local laser illumination fluxes).

provides quantitation of the molar concentration of analyte molecules NA

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We emphasize that the elastic scattering polarizability Y is many orders larger than the

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Raman scattering polarizability Z ± , and that NB is generally orders of magnitude

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higher than NA. Consequently, to maintain a large signal-to-noise for quantitative SERS

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applications, we must ensure that , (=excitation field intensity for Raman scattering) >>

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, (=excitation field intensity for elastic scattering) in equation 4. This condition can be

241

satisfied by using intense lasing light and weak ASE light in the laser emission.


12

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242 243 244 245 246 247 248 249 250 251 252 253 254


Figure 1 a) Schematic illustration of the relation between the incident field (E0) and scattered (ER) elastic and Raman fields passing through a “hot spot”. b) The system employed for backscattering confocal Raman micro-spectroscopy/imaging. c) Laser emission spectra in transmission mode with or without an edge or bandpass filter. d) Raman spectra of two SERS substrates and Si wafer collected using backscattering Raman mode compared to transmission mode laser emission spectra. e) Raman spectra of AuNP/BC exposed to 4-MBA excited by 633 nm and 785 nm lasers. The spectra are normalized to the intensity at 813 nm (= normalization point (NP)). Blue and red shaded areas represent contribution of elastic scattering and photoluminescence (PL), respectively, to νe. f) Raman spectra of 4-mercaptobenzoic acid (4-MBA) under various laser powers (band assignments in Table S1). g) Variation of band intensities of the laser peak, νe at 126 cm-1, and the principal 4-MBA bands at 1076 and 1587 cm-1 as a function of laser power. h) Variation of the intensity ratio between the 4-MBA bands at 1076 cm-1 and 1587 cm-1 and the νe pseudo-peak (at 126 cm-1) as a function of laser power.


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Origin of νe. As a proof of concept, we used a confocal Raman micro-spectroscopy/imaging

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setup (Figure 1b) to simultaneously collect and compare inelastic Raman and elastic scattering

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signals from a tightly focused spot on a nanostructured plasmonic substrate. As shown in Figure

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1c, the measured diode laser (785 nm) emission spectrum (red curve) consists of a narrow laser

259

line at 0 cm-1 along with a broad ASE background that extends beyond 200 cm-1. After blocking

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the laser emission with a longpass (edge) filter, we observe an asymmetric peak (νe) at 76 cm-1

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that is an artificial/pseudo spectral feature due to the convolution of the spectral profile of the

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ASE and the transmittance of the filter (cut-on wavenumber: 68 cm-1). By choosing longpass

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filters with different cut-on wavenumbers and cut-off slopes, we can control the spectral position,

264

shape, and amplitude of νe (Fig. S4). For instance, when we insert a bandpass filter in addition to

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the longpass filter, the pseudo peak (νe) at 76 cm-1 (green curve) is significantly attenuated

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showing that νe originates from the laser ASE.

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Because νe is weak, it has been typically overlooked based on the assumption that elastic

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scattering will be fully blocked by the longpass filter.31,32 This assumption deviates from the

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reality of many SERS studies. We speculated that the weak νe interacts with SERS “hot spots”, is

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elastically scattered by the molecules within them, and experiences the same electromagnetic

271

enhancement as Raman scattering.27 To test this hypothesis, backscattered Raman spectra of a Si

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wafer and a SERS substrate – gold nanoparticle/bacterial cellulose (AuNP/BC) – were collected.

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As shown in Fig. 1d, an asymmetric peak at 76 cm-1 appears in the Raman spectra for both Si

274

and the SERS substrate that reflects the line shape of the laser ASE (red curve). Notably, the νe

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of AuNP/BC was 2600× larger than that of the Si wafer, thus supporting the hypothesis that νe is

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significantly enhanced by SERS “hot spots” (Fig. 1d).


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As shown in Fig. S5 & Note S2, additional experiments using a second Raman instrument

278

equipped with an edge filter (cut-on wavenumber: 126 cm-1) to probe AuNP/BC, a commercial

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SERS substrate, and aggregated AuNP colloids corroborate this result. AuNP monomer colloid

280

exhibited an extremely weak νe, which was almost identical to that of DI water. νe was

281

significantly enhanced following the addition of phosphate buffer to the colloid, concomitant to

282

hot spot formation (Fig. S5). The intensities of νe *+,-& ,

of AuNP/BC (high hot spot

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density), AuNP cluster colloid (low hot spot density), AuNP monomer colloid (no hot spots), and

284

DI water (no hot spots) followed the trend: DI ≈ AuNP monomer < AuNP cluster < AuNP/BC at

285

any given incident laser power (Fig. S6). Across all experiments, νe is characteristically weak in

286

the absence of large numbers of SERS hotspots. Therefore, we neglected the contribution of

287

molecules and nanoparticles located outside hot spots to νe in the following discussion.

288

We emphasize that νe occurs in addition to the SERS background continuum that originates

289

from the combination of the photoluminescence (PL) of the plasmonic nanostructures and the

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fluorescence emitted by fluorophores near the surface under laser excitation.33-36 Fluorophores in

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resonance with the excitation laser are subject to surface-enhanced fluorescence (SEF) that

292

contributes to the background continuum.35,37,38 To minimize this possibility, we primarily used a

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non-resonant molecule, 4-MBA, and thoroughly washed our substrates such that residual

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fluorophores were removed. PL originates from the radiative recombination of sp band electrons

295

and excited d band holes in noble metals and can be significantly enhanced by the surface

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plasmon resonance of nanostructures.32,36,39 PL is an inherent characteristic of the SERS

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spectrum and is typically the primary contributor to the SERS continuum. Many SERS studies

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report spectra in a range far away from the excitation wavelength (> 200 cm-1)37,40,41 where the

299

PL signal can dominate the SERS continuum. It should be noted, however, that for any given


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SERS substrate the contribution of PL to the SERS continuum will be a function of the laser

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excitation wavelength.32,40 To illustrate, the AuNP/BC substrate was probed using both 633 and

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785 nm excitation. As shown in Fig. 1e, the PL background of the SERS spectrum obtained with

303

the 633 nm laser perfectly predicts the PL background of the SERS spectrum collected under 785

304

nm excitation. The contribution of PL to the pseudo-peak at 84 cm-1 (the red-shaded areas) is a

305

function of the laser excitation, with a larger contribution under 633 nm excitation (~50.1%) than

306

at 785 nm (~6.5%). Fig. 1e illustrates the additive contribution of surface enhanced elastic

307

scattering to νe relative to the broad, smooth PL background.36 The influence of PL on the

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intensity of νe is readily accounted for by baseline correction using published Matlab scripts (Fig.

309

S7).42

310

The strong correlation between PL and the dark-field scattering spectra of individual

311

plasmonic nanoparticles/clusters demonstrates that the PL spectrum is dependent on the localized

312

surface plasmon resonance (LSPR) of the nanostructures.32,34,36,43,44 This LSPR dependence was

313

experimentally demonstrated by the dissimilar SERS backgrounds of two SERS substrates,

314

AgNP/BC and AuNP/BC, for wavenumbers >188 cm-1 where the laser ASE is quite weak (Fig.

315

1d, Fig. S8). The νe line shapes for these substrates overlap and resemble the line shape of the

316

laser ASE 0.95) and substantially reduced error (Fig. 4f). Similar results were


23


a

b

Laser

Elastic 1078 1588 522

Raman scattering

66 min

Intensity (a.u.)

Elastic scattering

26 min 5 min 2 min 0 min

AuNP/BC hydrogel

0

500

c

1.0

150

0.6

0.4

30

60

90

120

0

30

60

f

-1

Raman band at 393 cm -1 Raman band at 652 cm -1 Raman band at 718 cm -1 Raman band at 1045 cm -1 Raman band at 1272 cm

60

0

473 474 475 476 477 478 479 480

0.5 0.4

90

-1.0

90

120

150

Time (min)

30

472

3000

60

150

IRaman/IElastic

120

2500

90

Time (min)

150

-1

0

0

180

2000

30

0.2

0.0

e

1500

Batch 1 Batch 2 Batch 3

120

IRaman

IRaman/IElastic

d

Batch 1 Batch 2 Batch 3

0.8

1000

Raman shift (cm )

4-MBA

IRaman

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 33

Raman band Raman band Raman band Raman band Raman band

-1

at 393 cm -1 at 652 cm -1 at 718 cm -1 at 1045 cm -1 at 1272 cm

0.3 0.2 0.1 0.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

-1.0

-0.5

Log C (µM)

0.0

0.5

1.0

1.5

2.0

2.5

Log C (µM)

Figure 4. a) Schematic of the experimental setup for monitoring 4-MBA adsorption kinetics onto AuNP/BC. b) Selected SERS spectra of 4-MBA collected on AuNP/BC at different time after adding 4MBA. c) Variation of the ratio between the Raman band at 1076 cm-1 and νe at 84 cm-1 as a function of time. d) Variation of the Raman band at 1076 cm-1 as a function of time. e) Variation of non-normalized SERS intensities of 2,4-DCA as a function of their logarithmic concentrations. f) Variation of normalized SERS intensities of 2,4-DCA as a function of their logarithmic concentrations. Error bars reflect the standard deviation of SERS intensities from three collected average spectra. Each average spectrum is the average of 400 spectra in a 100 µm × 100 µm SERS map.


24

Page 25 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


481

achieved with three other chloroanilines (Supporting Information Fig. S18) and a series of

482

organic molecules (e.g., anilines with different functional groups, melamine, MGITC; Fig. S19

483

& Note S3). These results show that HS normalization significantly improved the quantitation

484

performance of SERS.

485

HS Normalization Enables Analyte Quantitation Under Highly Variable Conditions. We

486

have illustrated the capacity for HS normalization to quantify analyte adsorption to stable SERS

487

substrates. To illustrate how HS normalization can be applied under dynamic conditions, we

488

used it to quantify the kinetics of 4-MBA adsorption onto suspended AuNPs. The kinetics of

489

AuNP aggregation in a water/ethanol co-solvent containing 4-MBA are easily controlled and

490

were recently optimized for the production of SERS pH probes.28 The lower dielectric constant

491

of water/ethanol co-solvent, relative to water alone, facilitates 4-MBA mediated AuNP

492

aggregation.54 Following 4-MBA addition, the 4-MBA SERS signal increases only slightly

493

during the first 40 minutes and then increases up to 162 min before plateauing. (Fig. 5a). It is

494

well recognized that the increase in the number of 4-MBA molecules on the AuNP surface and

495

the formation of “hot spots” due to AuNP aggregation simultaneously contribute to the

496

enhancement in the SERS signal with time, but these two effects cannot be differentiated using

497

existing approaches.

498

As shown by the green curve in Fig. 5a, the HS normalized ratio (=I1076/I126) rapidly increases

499

immediately after mixing of the AuNP suspension and 4-MBA solution (Phase 1). This increase

500

continued until 114 min, albeit with a slower rate (Phase 2), prior to plateauing (Phase 3). To

501

explain this process a schematic is shown in Fig. 5c. At the very beginning, AuNPs were present

502

as monomers with no 4-MBA on their surfaces (State 1). In State 1, the SERS spectrum only

503

exhibits the Raman bands from ethanol (Fig. 5b). State 2 is reached following 4-MBA adsorption


25


Page 26 of 33

504

in the first 40 min. In State 2, increasing numbers of 4-MBA molecules adsorb onto the AuNP

505

surface, but most AuNPs remain as monomers. In this state, the Raman bands of 4-MBA appear,

506

but are weak due to lack of “hot spots”. In Phase 2, the surfaces become saturated with 4-MBA

507

and large numbers of SERS “hot spots” form due to AuNP aggregation. This results in a very

508

strong 4-MBA SERS signal for State 3. In Phase 3, fewer 4-MBA molecules remain available to

509

associate with the AuNPs, yet “hot spots” continue to form. The increase in the 4-MBA SERS

510

signal during Phase 3 is attributed primarily to “hot spot” formation and not continued 4-MBA

511

adsorption.

512 513 514

Figure 5. a) Variation of SERS intensity of the Raman band at 1076 cm-1 of 4-MBA and the ratio of the Raman band at 1076 cm-1 to νe at 126 cm-1 (I1076/I126) as a function of time; b) SERS spectra collected


26

Page 27 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

515 516


from the 4-MBA, AuNPs, and co-solvent system at the different stages marked in Fig. 5a; c) Schematic of dynamic process of 4-MBA sorption to AuNP surface and AuNP aggregation.


27


Page 28 of 33

517

CONCLUSIONS

518

Theoretical and experimental analysis suggests that ASE light elastically scattered by a SERS

519

“hot spot” quantitatively reflects the integrated strength of the localized electromagnetic field.

520

The ratio between the elastic and inelastic scattering signals is dependent on the number of the

521

target analytes (NA) within a “hot spot” no matter how the size, shape, pattern, and density of

522

plasmonic nanostructures varies. Surface enhanced elastic scattering can be applied as a truly

523

intrinsic internal standard for SERS. Following HS normalization, the uniformities of colloidal,

524

hydrogel, and solid SERS substrates are improved without additional cost. Internal standards

525

based on surface plasmon enhanced elastic scattering signals are truly intrinsic to the plasmonic

526

nanostructures and provide new features that improve quantitative SERS analysis: (1) ultimate

527

photo-stability (i.e., not photo-bleachable); (2) minimal spectral interference by analyte Raman

528

signals; (3) no spatial competition with analyte molecules for SERS “hot spots”; (4) reduced

529

SERS substrate preparation costs by avoiding the incorporation of extrinsic reference probe

530

molecules molecules; (5) universal applicability for a suite of SERS substrates; and (6) the

531

capacity to differentiate analyte adsorption and “hot spot” formation under dynamic conditions.

532

We close by noting that while HS normalization can provide improved quantitation that it

533

nonetheless remains highly important that the affinity of any given analyte to the plasmonic

534

surface as well as the impacts of potential interferents are fully considered when determining

535

concentration.

536

SUPPLEMENTARY INFORMATION

537

SEM, EDS, and UV-VIS spectra of the SERS substrates; Additional SERS spectra of different

538

substrate, different edge filters, baseline correction, and different analytes; SERS spectra of


28

Page 29 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


539

anilines with different concentrations and fifty randomly selected spectra from their SERS maps;

540

Additional maps before and after “hot spot” normalization for suspension-based substrates,

541

hydrogel substrates in different areas, and different analytes; Tables show the Raman band

542

assignment and CV values reported in literature; Notes describe the experimental details. The

543

materials are available in the online version of the paper.

544

ACKNOWLEDGEMENTS

545

This research was supported by the National Institutes of Health (NIH) through the NIH

546

Director’s New Innovator Award Program (1-DP2-A1112243) to L.M. and through US National

547

Science Foundation grants CBET-1133746 and OISE-1545756 to P.V. Additional support for

548

H.W. and M.W. was provided by the Virginia Tech Graduate School through the Sustainable

549

Nanotechnology Interdisciplinary Graduate Education Program (VT-SuN IGEP). W. Z. and J. S.

550

are supported by startup funds from Virginia Tech. We thank K. Rodriguez and S. Renneckar for

551

their help with production of BC.

552

COMPETING FINANCIAL INTERESTS

553

The authors declare no completing financial interest.

554

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