Importance of Tilt Angles of Adsorbed Aromatic Molecules on

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Importance of Tilt Angles of Adsorbed Aromatic Molecules on Nanoparticle Rattle SERS Substrates Grace Lu, Binaya Kumar Shrestha, and Amanda J. Haes J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.6b02023 • Publication Date (Web): 28 Mar 2016 Downloaded from http://pubs.acs.org on March 30, 2016

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The Journal of Physical Chemistry

Importance of Tilt Angles of Adsorbed Aromatic Molecules on Nanoparticle Rattle SERS Substrates Grace Lu, Binaya Shrestha, and Amanda J. Haes* Department of Chemistry, University of Iowa, Iowa City, Iowa 52242

1 ACS Paragon Plus Environment


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Abstract Solution-phase nanoparticles are extensively used as surface enhanced Raman scattering (SERS) substrates, but signal intensities depend on dynamic nanoparticle optical properties and stabilities as well as molecular identity and orientation. To evaluate how these contributions influence the detection of aromatic thiols, internally etched silica encapsulated gold coated silver (IE Ag@Au@SiO2) nanoparticles are used. First, localized surface plasmon resonance (LSPR) spectroscopy is implemented to estimate molecular tilt angle. Different tilt angles are then related to functional group induced surface density differences. Next, evaluation of SERS intensities and vibrational modes suggest that molecular tilt angle and surface selection rules govern the behavior observed in SERS intensities. Finally, concentration-dependent SERS signals are modeled using the Langmuir adsorption model. Equilibrium constants and free energies associated with adsorption are consistent with differences from London dispersion force stabilization between the molecules and the metal surface. These studies suggest that the SERS intensities observed for these thiolated ligands are highly sensitive to adsorbate tilt angle relative to the nanoparticle surface, which are easily estimated because of the optical stability and controlled adsorbate interactions with IE Ag@Au@SiO2 nanoparticles and could be extended to other molecules in the future to better understand and evaluate reproducible applications using SERS.


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Introduction Solution-phase nanoparticles are extensively used as surface enhanced Raman scattering (SERS) substrates1-6 but often fail at providing reproducible signals for a given molecular concentration because of their dynamic optical properties3 and sometimes poor solution-phase stabilities.7 Furthermore, molecule orientation and surface selection rules8-9 can lead to often unexpected SERS intensities. For instance, molecules exhibit different SERS enhancements based on the polarizability and symmetry of vibrational modes. Vibrational modes oriented perpendicular to the surface exhibit the largest enhancements while those parallel to the surface can be absent. Molecular orientation of aromatic molecules with C2V symmetry, for example, was used to understand SERS behavior using colloidal nanomaterials.9-11 Selective enhancement of in-plane vs. out-of-plane modes was attributed to molecular orientation differences.12 Because solution-phase nanoparticles exhibit inherently high surface energies, these trends are often difficult to assess because of nanoparticle aggregation and fluctuating electromagnetic properties of the metal nanoparticles.13 As a result, quantitative detection is often limited using these materials.14 Understanding molecular orientation is important in SERS as the adsorption process can influence the measured signals. Molecular adsorption and orientation, however, are influenced by adsorbate concentration and composition. For instance, the adsorption dynamics of DNA onto citrate stabilized Au nanoparticles,15 Hg(II) adsorption on polyrhodanine-coated Fe2O3 nanoparticles,16 and oxalic acid on TiO2 nanoparticles17 were previously reported. These studies successfully yielded information regarding surface chemistry and nanoparticle stability yet provided limited to no information regarding molecular orientation. In addition, contributing factors from nanoparticle aggregation kinetics and molecular adsorption dynamics are largely



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influenced by surface energy.17 Thus, nanoparticle stability and adsorption dynamics must be considered if molecular orientation or quantitative detection using SERS is desired. To promote nanoparticle stability and adsorption dynamics, surface modification of SERSactive substrates can be achieved using silica and/or polymers.1, 18-22 One substrate that provides both optical stability of the SERS-active metal surface and molecular accessibility to the metal is internally etched silica coated metal nanoparticles or nanoparticle rattles.1,

4, 23

Quantitative

SERS detection was achieved with these materials by the formation of internal voids in the silica matrix near the metal nanoparticle core, which allowed for both molecular diffusion to the metal surface and maintained optical stability of the metal nanostructures. Only molecules that diffused through the silica membrane were detected. Additionally, averaged signals from molecules on the entire metal surface24 contributed to the overall SERS intensity. This is fortunate given that these SERS signals were modest at best because the silica shells prevented electromagnetic coupling between the metal cores. Herein, internally etched silica stabilized, gold coated silver (IE Ag@Au@SiO2) nanoparticles are synthesized for their use as SERS substrates to ensure electromagnetic stability of the metal cores and surface accessibility for molecular adsorption as well as systematic SERS studies. The implications of molecular identity and concentration on molecular adsorption and SERS intensity are evaluated using localized surface plasmon resonance (LSPR) spectroscopy, SERS, and Langmuir adsorption isotherm modeling. Three molecules including benzenethiol, paminothiophenol, and 4-mercaptobenzoic acid are selected because they possess similar structures but differ in para-group functionality. Synergistic results suggest molecule-dependent tilt angles on the Ag@Au nanoparticle surface, which are estimated using LSPR spectroscopy, systematically influence SERS activity. While multiple vibrational modes are observed for each


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molecule, molecule to molecule SERS intensity variations for a given vibrational mode are consistent with slight differences in molecular tilt angle relative to the metal surface. These findings are confirmed through Langmuir adsorption isotherm modeling in which equilibrium constants and free energies of adsorption suggest slightly more favorable binding for 4mercaptobenzoic acid and p-aminothiophenol vs. benzenethiol. These differences are attributed to London dispersion force stabilization between the ligands and the metal surface and are easily observed because of the optical stability and controlled adsorbate interactions with IE Ag@Au@SiO2 nanoparticles. As such, this approach could be extended to other molecules in the future to better understand and evaluate the reproducible and quantitative capabilities of SERS.

Materials and Methods Chemical Reagents. Gold(III) chloride trihydrate, sodium citrate dihydrate, Amberlite MB150 mixed bed exchange resin, (3-aminopropyl) trimethoxysilane (APTMS), sodium chloride (NaCl), sodium trisilicate (27%), tetraethyl orthosilicate (TEOS), silver perchlorate, sodium borohydride, benzenthiol, 4-mercaptobenzoic acid, p-aminothiophenol, and hydroxylamine hydrochloride were purchased from Sigma. Ethanol, ammonium hydroxide (NH4OH), hydrochloric acid (HCl), and nitric acid (HNO3) were purchased from Fisher Scientific (Pittsburgh, PA). Ultrapure water (18.2 MΩ·cm-1) was obtained from a Barnstead Nanopure System and used for all experiments. All glassware items were cleaned with aqua regia (3:1 HCl/HNO3) and rinsed thoroughly with water before oven (glass) or air (plastic) drying. Nanoparticle Synthesis. Ag@Au nanoparticles were synthesized using a seeded growth method previously described in the literature.25-26 Briefly, 100 mL of a 0.3 mM sodium citrate solution prepared in nitrogen-purged water was stirred on an ice bath in the dark. Freshly prepared sodium borohydride (final concentration = 1 mM) was added to the citrate solution. 5 ACS Paragon Plus Environment


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Next, 1 mL of 10 mM silver perchlorate was added to the solution within 2 minutes, and the resulting silver nanoparticle solution was stirred for 3 minutes. Stirring was stopped, and silver nanoparticles with average diameters of 11.5 ± 3.2 nm formed within 3 hours. Next, 20 mL of water was added to 25 mL of as-synthesized Ag seeds and stirred for ~2 minutes (4 °C). Fifteen mL of both 6.25 mM hydroxylamine hydrochloride and 0.465 mM gold salt were added slowly (3 mL/min) using a syringe pump. This Ag@Au nanoparticle solution was stirred for 1 hour to ensure nanoparticle formation and stored at 2 – 4 °C until use. The concentration of these materials was estimated using a standard estimation model for the silver seeds,27 and an average diameter of 18.5 ± 2.3 nm was determined using transmission electron microscopy (TEM). Scheme I

Silica shells on Ag@Au (Ag@Au@SiO2) nanoparticles were synthesized via a modified Stöber method.18, 28-30 This process is summarized in Scheme I. Briefly, the pH and conductivity of 25 mL of the as synthesized Ag@Au nanoparticles were adjusted to 5 and ~110 µS/cm using NH4OH and Amberlite resin, respectively. After resin removal via filtration, 129.2 µL of 1 mM APTMS was added drop-wise to the nanoparticle solution (with stirring). After 30 minutes, 201 µL of 2.7% sodium silicate was added slowly to the solution and stirred for 24 hours. The silica shell thickness was further increased by adding ethanol (final ratio of 1 part water to 4.4 parts ethanol). After 6 hours, 20 µL of 1 mM APTMS and 20 µL TEOS were added. The pH of the solution was increased to ~11 using concentrated ammonium hydroxide. After 16 hours, the Ag@Au@SiO2 nanoparticles were centrifuged (45 minutes, 9383xg) three times with ethanol then three times with water. The Ag@Au@SiO2 nanoparticles were then passed through


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Sephadex-50 column to remove Ag@Au nanoparticles that did not contain complete silica shells2 and stored in ethanol until use. Silica shells were converted into silica membranes via an internal silica etching process induced at basic pH values using concentrated NH4OH.1,

18

Because the Ag@Au@SiO2 nanoparticles were stored in ethanol, the samples were triply centrifuged and redispersed in water to a concentration of 3 nM. This concentration was estimated using known concentrations of the silver seeds, which was determined via the molar extinction coefficient for 11.5 nm diameter particles (8.9x108 M-1cm-1).27 The reaction was quenched by adding 100 mM HNO3 until the solution pH was ~6. Finally, the nanoparticles were washed 3 times in water and passed through a Sephadex-G50 column to remove defect particles. These samples exhibited average diameters of 81.4 ± 15.7 nm with an average silica shell thickness of 31.4 ± 7.9 nm. TEM. TEM was performed using a JEOL JEM-1230 microscope equipped with a Gatan CCD camera. Samples were prepared on 400 mesh copper grids that were coated with a thin film of Formvar and carbon (Ted Pella). The nanoparticle solution was diluted in a 50% water−ethanol mixture, and ~10 µL of the solutions were pipetted onto grids and dried. At least 200 nanoparticles were analyzed (Image Pro Analyzer) to estimate average nanoparticle diameters, and average silica shell thicknesses were determined by calculating the differences between Ag@Au and Ag@Au@SiO2 nanoparticle diameters (error is from propagated error in these measurements). Extinction and SERS Spectroscopies. LSPR spectra were collected using disposable methacrylate cuvette (pathlength = 1 cm) and an ultraviolet-visible (UV-vis) spectrometer (Ocean Optics USB4000). Deuterium and halogen lamps were used for UV and visible excitation, respectively. LSPR spectra were collected in transmission geometry every minute for



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2 hours (integration time = 60 msec, average = 25 scans, and boxcar = 10), and extinction maximum wavelengths (λmax) were determined from the zero-point crossing of the first derivative and processed using MATLAB. SERS measurements were performed using 6 nM IE Ag@Au@SiO2 nanoparticles prepared in 10 mM phosphate buffer (pH 7.4) and mixed with various concentrations of benzenethiol, p-aminothiophenol, or 4-mercaptobenzoic acid. Prior to SERS measurements, samples were mixed and incubated for 1 hour. SERS data were collected using an ExamineR 785 spectrometer (DeltaNu) with an excitation wavelength (λex) of 785 nm, power = 64 mW, and integration time (tint) = 30 sec for benzenethiol and 60 sec for 4mercaptobenzoic acid and p-aminothiophenol. SERS spectra were processed and intensities quantified using MATLAB and Origin Pro 9.1. Equilibrium geometries of the molecules were calculated in water using density functional theory (DFT), B3LYP method using the 6-31G* basis set using Spartan ’10 V1.1.0 and imported into ChemBioOffice Ultra 2010.

Results and Discussion Layer Thickness and Molecular Orientation. Thiols readily adsorb to gold surfaces and were used to evaluate how aromatic thiolated molecules with different para-group substitution and charge (benzenethiol, 4-mercaptobenzoic acid, and p-aminothiophenol) impact monolayer formation and SERS signals. These molecules are shown in Figure 1A in their protonated/deprotonated states at pH 7.4. Assembly and packing densities of each molecule on metal surfaces through sulfur are dependent on variations in molecular tilt angle (θ) and molecule-molecule interactions as well as surface roughness and composition. Because molecular packing density depends on the composition and roughness of the substrate,10,

12, 31

changes in LSPR spectra of IE Ag@Au@SiO2 nanoparticles are used to evaluate how molecular tilt angle impacts SERS measurements. First, 6 nM nanoparticle concentrations are suspended in 8 ACS Paragon Plus Environment

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10 mM phosphate buffer (pH 7.4). A representative TEM image of this sample is shown in Figure 1B. These materials contain relatively homogeneous Ag@Au nanoparticles, which serve as reproducible SERS substrates when encapsulated in internally etched silica membranes.23 The metal core is on average 18.5 ± 2.3 nm and the silica membrane is 31.4 ± 7.9 nm thick. Large variations in silica shell thickness are observed and attributed to residual metal reagents in solution prior to silica coating. Because of the porous silica membrane, the LSPR properties of the metal cores are electromagnetically stable. Here, we demonstrate that the pores in the silica membrane are large enough and facilitate the diffusion of the previously described thiolated molecules. To monitor molecular binding to the metal surfaces, changes in the LSPR spectra of the IE Ag@Au nanoparticles are used for tilt angle estimations. Previously, shifts in the LSPR wavelength maximum (∆λmax) of metal nanoparticles were shown to provide an accurate measure of alkanethiol layer thickness.32-34 Specifically, changes in ∆λmax is related to local refractive index changes by the equation 2t

where λmax,SAM

(1) ∆λmax = λmax, SAM - λbulk =m∆n(1-e ld ) is the extinction maximum wavelength after self-assembled monolayer (SAM)

formation, λbulk is the extinction maximum wavelength before functionalization, m is the linear refractive index sensitivity of the nanoparticles (170 nm/RIU),23 ∆n is the change in refractive index resulting from surface functionalization and is a difference of the refractive index of the SAM (nSAM = 1.61)35 and the nanoparticles prior to functionalization (neff = 1.38), t is the effective monolayer thickness (where t = maximum ligand layer thickness*cos(θ) and θ is the tilt angle of the SAM relative to the surface normal),7 and ld is the local electromagnetic field decay length of the metal nanoparticles (11.0 ± 0.2 nm).23 Using DFT calculations, the maximum ligand thicknesses were determined using energy minimized structures and were 8.08, 9.19, and 9.45 Å for benzenethiol, 4-mercaptobenzoic acid, and p-aminothiophenol, respectively. 9 ACS Paragon Plus Environment


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Figure 1. (A) Chemical structures of (1) benzenethiol, (2) 4-mercaptobenzoic acid, and (3) p-aminothiophenol. (B) TEM image of IE Ag@Au@SiO2 nanoparticles with nanoparticle diameter of 81.4 ± 15.7 nm and an effective local refractive index of 1.38. (C) Representative LSPR spectra of Ag@Au@SiO2 nanoparticle rattles (1) before (black, solid) and (2) after (red, dotted) incubation with the thiolated molecules. (D) Shifts in the λmax of 6 nM IE Ag@Au@SiO2 nanoparticles incubated for 1 hour in (1) benzenethiol (red, solid), (2) 4-mercaptobenzoic acid (blue, dotted), and (3) p-aminothiophenol (green, dashed). Error bars represent the standard deviation of the ∆λmax changes from at least three replicate measurements. Lines represent Langmuir adsorption model analysis. Because these metal nanoparticles are electromagnetically isolated, LSPR data can be used to estimate effective monolayer thickness and molecular tilt angles on the Ag@Au nanoparticle surfaces. To do this, 6 nM concentrations of the IE Ag@Au@SiO2 nanoparticles are incubated in varying concentrations of the thiolated molecules for 1 hour, and LSPR spectra are measured. For instance, example LSPR spectra before and after incubation with 30 µM p-aminothiophenol are shown in Figure 1C. The LSPR λmax red shifts from 556.1 to 561.0 nm as molecules bind to the nanoparticle surfaces. The red shift in λmax arises from an increase in local refractive index from molecular binding on the nanoparticles.33 Notably, the only significant change in LSPR properties are these small variations in extinction maximum wavelength. As such, this 10 ACS Paragon Plus Environment

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demonstrates that Ag@Au@SiO2 nanoparticle rattles are electromagnetically stable, an important parameter for reproducible SERS measurements. In addition, these wavelength shifts depend on the identity and concentration of the thiolated molecules. These data are summarized in Figure 1D. Initially, increasing the molecular concentration causes the ∆λmax to increase in magnitude for all molecules. Additional increases in concentration yield no additional changes in ∆λmax, which indicates that the metal surface is saturated by the molecules, and no additional change in effective refractive index is observed. Importantly, the magnitude of the ∆λmax at saturation, however, depends on molecule composition. These vary from 5.4 ± 0.22, 5.2 ± 0.25, and 4.9 ± 0.29 nm for benzenethiol, 4mercaptobenzoic acid, and p-aminothiophenol, respectively. Using equation 1, the effective self-assembled monolayer thicknesses for the three molecules are ~8.02, 7.65, and 7.28 Å, respectively. In addition, tilt angles relative to the surface normal are estimated at ~7, 33, and 40°, respectively. While these values likely depend on the metal composition of the nanostructures, this suggests that benzenethiol forms a tight monolayer with a nearly perpendicular orientation relative to the surface. The charged molecules, however, exhibit significant tilt angles likely from the amine and carboxylic acid groups exhibiting some affinity for the metal surface and repulsion from neighboring molecules. These results are consistent with previous literature studies,13,

24, 36-37

which can be used to understand the relative SERS

enhancements of various vibrational modes associated with the molecules. Molecular Adsorbate, Concentration, and SERS Vibrational Mode Dependence. Solutionphase nanoparticles are extensively used as SERS substrates but often fail at providing reproducible S/N for a given molecular concentration because of their dynamic optical properties3 and often poor solution-phase stability.7 Furthermore, molecule orientation and



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Figure 2. SERS spectra as a function of molecular concentration using 6 µM IE Ag@Au@SiO2 nanoparticles with a constant void volume. (A) SERS spectra for (1) 2, (2) 5, (3) 10, (4) 20, and (5) 32 µM benzenethiol. (B) SERS spectra for (1) 4, (2) 8, (3) 12, (4) 20, and (5) 32 µM 4-mercaptobenzoic acid. (C) SERS spectra for (1) 1, (2) 2, (3) 5, (4) 20, and (5) 30 µM p-aminothiophenol. All vibrational mode assignments are found in Table 1. SERS parameters: λex = 785 nm, tint = 30 for benzenethiol or 60 s (others), and P = 64 mW. surface selection rules can lead to unexpected SERS intensities.8-9, 13, 24, 36-37 As shown in Figure 1, IE Ag@Au@SiO2 nanoparticles do not exhibit electromagnetic coupling and are ideal for studying molecule concentration- and identity-dependent SERS intensity changes. As a 12 ACS Paragon Plus Environment

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compromise, these substrates do not exhibit large hot-spots,38-39 which give rise to large SERS enhancement factors and are difficult to control with solution-phase SERS substrates. As such, all SERS intensities reported represent averaged SERS signals from all molecules on the metal.40 Although all three molecules are structurally similar, SERS allows molecular identification using unique vibrational modes. As in the LSPR studies, 6 nM IE Ag@Au@SiO2 nanoparticles are incubated with benzenethiol, 4-mercaptobenzoic acid, and p-aminothiophenol for 1 hour to ensure equilibrium binding conditions. Additionally, the internal void volume and effective refractive index surrounding the nanomaterials (composed of silica and buffer) is maintained at 1.38 to ensure that the effective 3D SERS volume is constant in all experiments.41-42 As shown in Figure 2, unique vibrational modes are observed for each molecule, and these agree well with previously reported vibrational frequencies (Table 1). A similar number of vibrational frequencies are observed for each molecule but differ slightly given the functional groups attached to the benzene ring. Many similarities and differences are noted. First, symmetric CS stretches (a1) for benzenethiol, 4-mercaptobenzoic acid, and paminothiophenol are located at 1075.0±0.2, 1076.6±0.2, and 1084.1±0.4 cm-1, respectively. This vibrational mode is the most intense feature in all the spectra indicating that these molecules are bound through the formation of a gold-sulfur bond. As such, this mode experiences the strongest electromagnetic fields that are located near the metal surface vs. the other functional groups and enhanced to the greatest degree.43-45 Next, another intense vibrational mode for all three molecules exhibit a1 symmetry and include CH in-plane bending. Benzenethiol exhibits the strongest vibrational mode intensity for this feature followed by 4-mercaptobenzoic acid then paminothiophenol. We attribute the differences in intensity for these three structurally similar molecules to tilt angle variations of the adsorbed molecules relative to the surface and/or



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molecular packing density. Third, the out-of-plane bending mode is observed for 4mercaptobenzoic acid (715.4±0.7 and 519.8±1.0 cm-1) and of p-aminothiophenol (723.7±0.8 cm1

) but not in the benzenethiol spectra. Finally, the CS bending mode exhibits molecular specific

trends. An observable vibrational frequency for this mode is observed at 364.6±0.9 and 395.4±0.6 cm-1 for 4-mercaptobenzoic acid and p-aminothiophenol, respectively. In contrast, this feature is not observed with benzenethiol (~328 cm-1). Table 1. Vibrational frequency assignments for possible species present in these samples. Molecule

Benzenethiol

4-Mercaptobenzoic Acid

p-Aminothiophenol

Vibration Mode (symmetry)

Raman-Shifted Frequency (cm-1) Literature This Work 1573 1574.1±0.6 stretch 1073 1075.0±0.2

Ring stretch (a1) CS + CC combination (a1) CH in-plane bend (a1) CCC in-plane bend (a1) CH out-of-plane deformation (b1) CCC in-plane bend + CS stretch combination (a1) CS stretch + CCC in-plane bend combination (a1) CS bend (b2) Ring stretch (a1)

COO- stretch CH in-plane bend (a1) CS + CC stretch combination (a1) Ring breathing (a1) CCC out-of-plane bend (b1) Ring out-of-plane bend (b1) CCC out-of-plane deformation CS bend (b2) Ring stretch + CH bend/CC stretch + CN stretch combination (a1) CH in-plane bend (a1)

Ref 10, 46 10, 46

1020 1000, 999 741, 734

1021±0.6 997.8±0.7 ND

10, 46

698, 691

691.4±0.7

10, 46

420

420.1±0.3

10, 46

328 1584

ND 1583.1±0.4

46

1375 1186 1077

1374±1.4 1182.1±1.7 1076.6±0.2

11, 48

1012 718, 710 525 454

1012.2±0.5 715.4±0.7 519.8±1.0 448.3±2.8

49

357, 367 1587

364.6±0.9 1587±0.8

49-50

1181

1174.5±1.1

51

10, 46 46-47

11, 48

11, 48 11, 48

11, 48 49 48

51


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CH bend (b2) CS + CC stretch combination (a1) CCC in-plane bend (a1) CC wagging + CS wagging + CH wagging combination (b1) CS bend (b2)

1142 1078

1141.0±2.1 1084.1±0.4

51

1004, 1006 719

ND 723.7±0.8

51-52

396

395.4±0.6

53

51

52

Surface selection rules can be used to explain these results. Given the small (< 10°) tilt angle estimated for benzenethiol, the polarizability tensor for the CS bending vibrational modes are not occurring parallel to the metal surface normal and not significantly enhanced. In contrast, the vibrational modes with x and y polarizability tensor components for the other two molecules are moderately enhanced because of the relatively larger tilt angles.9-10 Finally, 4-mercaptobenzoic acid, in particular, reveals a broad spectral feature centered at 1374 cm-1. This vibrational mode is attributed to a COO- stretch and suggests that the carboxylic acid groups for these molecules are deprotonated11, 48 and likely close to the metal surface because the molecules exhibit a large tilt angle as suggested in the previous LSPR studies. Now that the vibrational modes are understood with respect to their orientation relative to their surfaces, concentration dependent trends for each molecule are evaluated. Figure 2 shows representative SERS spectra while Figure 3 reveals spectral trends for distinct vibrational modes collected for various concentrations of benzenethiol, p-aminothiophenol, and 4-mercaptobenzoic acid incubated for one hour with the IE Ag@Au@SiO2 nanoparticle samples. In general, similar concentration dependent trends are observed for each vibrational mode for a given molecule.



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Figure 3. SERS signals as a function of molecular concentration and vibrational mode. (A) SERS intensity for (1) 1075 (CS/CC symmetric stretch), (2) 1021 (CH in-plane bend), and (3) 741 cm-1 (CH out-of-plane bend) as a function of benzenethiol concentration. (B) SERS intensity for (1) 1077 (CS/CC symmetric stretch), (2) 1182 (CH in-plane bend), (3) 715 (CCC out-of-plane bend), and (4) 365 cm-1 (CS bend) as a function of 4mercaptobenzoic acid concentration. (C) SERS intensity for (1) 1084 (CS/CC symmetric stretch), (2) 1175 (CH in-plane bend), (3) 724 (CCC out-of-plane bend), and (4) 395 cm-1 (CS bend) as a function of p-aminothiophenol concentration. Averages and standard deviations represent those of 3 measurements. Same experimental conditions as in Figure 2. While the orientation (i.e., tilt angle) of each molecule likely varies throughout these studies, all vibrational modes follow the Langmuir adsorption model. By fitting the data to this isotherm


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model, SERS intensity as a function of molecule concentration can be estimated by the following equation ISERS =Imax SERS ൤1+K

Keq *C

൨

eq C

(2)

௠௔௫ where ISERS is a SERS intensity for a measurement, ‫ܫ‬ௌாோௌ is the maximum SERS intensity, Keq is

the equilibrium constant (in µM-1), and C is the concentration of analyte added to the nanoparticle solution. This suggests that SERS signal response is related to the concentration of the analyte in a surface area (or SERS 3D volume) dependent manner. At the highest concentrations, these signals saturate suggesting surface saturation. This is consistent with previous studies that suggested that SERS signals are directly proportional to the number of molecules in the SERS volume until molecular saturation on metal surface occurs.54-56 In addition, the magnitude of the vibrational mode intensities should depend on both average molecular tilt angle and number of molecules near the metal surface.10 Figure 3 compares the magnitude for several vibrational modes in the SERS spectra for each molecule. SERS intensities increase as molecules bind to the metal surface then saturate as equilibrium is reached. Similar trends are observed for all vibrational modes, but this discussion will focus on the vibrational mode with the largest SERS intensity (CS symmetric stretch centered at 1075.0±0.2, 1076.6±0.2, and 1084.1±0.4 cm-1 for benzenethiol, 4-mercaptobenzoic acid, and p-aminothiophenol, respectively) as this is representative of all vibrational mode trends for a given molecule. Using the results from the Langmuir adsorption model, the maximum theoretical SERS signals for these vibrational modes and same molecules are ~0.57, 0.43, and 0.37 cts·mW-1·s-1. The trend in maximum SERS intensities for benzenethiol, 4-mercaptobenzoic acid, and p-aminothiophenol follow tilt angle trends estimated from LSPR data (7, 34, and 40º, respectively). This suggests



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Table 2. Selected vibrational frequency ratios of benzenethiol, mercaptobenzoic acid, and paminothiophenol. (ND = not detected) Molecules (C2v)

Benzenethiol 4-Mercaptobenzoic Acid p-Aminothiophenol

βCH Bend/CS δCCC + δCH Stretch Bend/CS Stretch 0.44 ± 0.05 ND 0.11± 0.01 0.17 ± 0.01 0.13 ± 0.01 0.21 ± 0.01

Estimated Angle (°)

Tilt

7 33 40

that tilt angle and the symmetry of the vibrational modes play a major role in the magnitude of the SERS signals. To obtain packing density-independent trends and to further evaluate the role of molecular orientation on SERS intensities and the adsorption process, vibrational band intensities for other modes are divided by the intensity of the CS stretching mode. SERS spectra of benzenethiol at various concentrations indicate the absence of vibrational modes with polarizability tensors that are not parallel to the surface normal. These data are summarized in Table 2. First, the strongly enhanced CH in-plane bending mode at 1021 cm-1 (ratio of the CH in-plane bending to CS symmetric stretching vibration is ~0.44) for benzenethiol and the absence of the other symmetry modes (b1, b2, and a2) suggest that benzenethiol is oriented nearly vertical to the surface as previously suggested in the LSPR studies. As a consequence, the CS bending mode would not be enhanced as this vibrational mode would not be oriented parallel to the metal surface. In contrast, SERS spectra of both 4-mercaptobenzoic acid and p-aminothiophenol show relatively weak enhancements for the CH in-plane bending mode compared to CS symmetric stretching (peak ratios ~0.11 and 0.13, respectively). Weaker SERS intensity ratios for these molecular vibrational modes indicate their associated polarizability tensors exhibit smaller contributions along the surface normal compared to benzenethiol and are only weakly enhanced.


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On the other hand, comparison of the CCC and CH out-of-plan bending with respect to the CS symmetric stretching mode follow orientation dependent trends for all three molecules. As shown in the Table 2, the highest relative ratio of CCC and CH out-of-plane bend (0.21 ± 0.01) is observed in p-aminothiophenol and followed by 4-mercaptobenzoic acid (0.17 ± 0.01) with estimated tilt angles of 40° and 33° on the surface, respectively. This ratio is zero for benzenethiol as CCC and CH out-of-plane bending is not observed as a result of the small tilt angle associated with these molecules on the metal nanoparticle surfaces. Adsorption Isotherm Analysis. The Langmuir adsorption isotherm model is one of the simplest and most widely used models to describe adsorption processes and assumes monolayer adsorption, homogeneous binding sites, no adsorbate – adsorbate interactions, and dynamic equilibrium between adsorbed and free molecules in the solution. Deviations from Langmuir conditions are likely minimal (reversible adsorption, heterogeneous binding sites with nonuniform adsorption affinities, and multilayer formation57-58). Using equation 2 and the data in Figure 3, equilibrium adsorption constants can be calculated for benzenethiol, 4mercaptobenzoic acid, and p-aminothiophenol and are estimated at 0.27 ± 0.07, 0.52 ± 0.09, and 0.56 ± 0.10 µM-1, respectively (Table 3). Furthermore, adsorption differences can be quantified by calculating the free energy of adsorption (∆Gads = -RT ln Keq). The free energies of adsorption Table 3. Langmuir adsorption isotherm results from the SERS data. Molecule

max (cts·mW-1·s-1) ISERS

Keq (µM-1)

∆Gads (kcal/mol)

Benzenethiol

0.57 ± 0.4

0.27 ± 0.07

-7.40 ± 0.15

4-Mercaptobenzoic Acid

0.43 ± 0.01

0.52 ± 0.09

-7.79 ± 0.10

p-Aminothiophenol

0.37 ± 0.01

0.56 ± 0.10

-7.84 ± 0.10



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for these molecules range from -7.40 – -7.84 kcal/mol,

which

are

similar

to

those

previously observed for 1-octadecanethiol (5.6

kcal/mol)

and

1-octanethiol

(-4.4

kcal/mol) on a flat gold surface.59 A closer evaluation of these free energies

Figure 4. Surface adsorbed orientations and

reveals interesting trends. By comparing

tilt angles for (A) benzenethiol, (B) 4-

∆Gads for the three molecules, the adsorption

mercaptobenzoic

of

4-mercaptobenzoic

acid

or

p-

acid,

and

(C)

p-

aminothiophenol. (Yellow = sulfur, black = carbon, blue = hydrogen, red = oxygen, and

aminothiophenol is slightly more favorable

purple = nitrogen. Both the surface normal

vs. benzenethiol. This small, ~0.44 kcal/mol

and estimated tilt angle are included.

difference likely arises from weak London dispersion interactions between the benzene rings in these molecules and the gold surface.60 Furthermore, this slight favorability of 4-mercaptobenzoic acid and p-aminothiophenol to adsorb to the metal surface is likely because their larger molecular footprint on the metal surface resulting from the tilt angles these molecules possess relative to the surface as shown in Figure 4. Finally, slight intermolecular repulsive forces between the charged groups in 4-mercaptobenzoic acid (pKa = 5.8) and p-aminothiophenol (pKa = 6.8) are found to influence molecular assembly, and as a result, molecular tilt angle on the metal surfaces and resulting SERS intensities.

Conclusions In summary, the implications of molecular identity and concentration on molecular adsorption and SERS intensity were evaluated using LSPR spectroscopy, SERS, and Langmuir adsorption isotherm modeling. Three molecules including benzenethiol, p-aminothiophenol, and 420 ACS Paragon Plus Environment

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mercaptobenzoic acid were selected because they are structurally similar yet differ in para-group functionality. Internally etched silica coated Ag@Au nanoparticles were used to ensure electromagnetic stability of the metal cores and molecular availability for SERS detection. First, shifts in the LSPR maximum wavelength were used to estimate molecular tilt angles for these ligands. 4-Mercaptobenzoic acid and p-aminothiophenol exhibited the largest tilt angles relative to benzenethiol. These differences were attributed to functional group differences in molecular surface density. Next, SERS was evaluated as a function of molecule concentration. Multiple vibrational modes were observed for each molecule and exhibited similar adsorption behavior for each vibrational mode for that molecule. By comparing the magnitude of the totally symmetric CS stretch for each molecule, the largest signals were observed for benzenethiol. The maximum SERS intensities were only slightly smaller for the two charged molecules. These differences were attributed to the relatively larger tilt angle of these molecules relative to the surface normal. This was confirmed through Langmuir adsorption isotherm modeling. Equilibrium constants and the free energy associated with adsorption were calculated for the molecules. Binding was estimated to be more favorable for the two charged molecules vs. the others from likely London dispersion force stabilization between the ligands and the metal surface. All in all, these studies suggest that the SERS intensities observed for these thiolated ligands are highly sensitive to vibrational mode symmetry and their tilt angles relative to the nanoparticle surface. These differences were easily observed because of the optical stability and controlled adsorbate interactions with IE Ag@Au@SiO2 nanoparticles. These optical properties and orientation dependencies could be used for future studies where quantitative SERS detection of other molecules and/or in applications of trace detection using solution-phase nanoparticles.



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AUTHOR INFORMATION Corresponding Author *University of Iowa, Department of Chemistry, 204 IATL, Iowa City, Iowa 52242; Phone: (319) 384-3695; Fax (319) 335-1270; Email: [email protected]. Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Acknowledgement This work was funded by the National Science Foundation, (CHE-1150135).

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Table of Contents Image


Importance of Tilt Angles of Adsorbed Aromatic Molecules on

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