SurfaceâBulk Vibrational Correlation Spectroscopy - Analytical

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Surface-Bulk Vibrational Correlation Spectroscopy Sandra Roy, Paul A. Covert, Tasha A. Jarisz, Chantelle Chan, and Dennis K. Hore Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.5b04544 • Publication Date (Web): 08 Apr 2016 Downloaded from http://pubs.acs.org on April 9, 2016

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Analytical Chemistry

Surface–Bulk Vibrational Correlation Spectroscopy Sandra Roy, Paul A. Covert, Tasha A. Jarisz, Chantelle Chan, and Dennis K. Hore∗ Department of Chemistry, University of Victoria, Victoria, British Columbia, V8W 3V6, Canada E-mail: [email protected] Abstract Homo- and heterospectral correlation analysis are powerful methods for investigating the effects of external influences on the spectra acquired using distinct and complementary techniques. Nonlinear vibrational spectroscopy is a selective and sensitive probe of surface structure changes, as bulk molecules are excluded on the basis of symmetry. However, as a result of this exquisite specificity, it is blind to changes that may be occurring in the solution. We demonstrate that correlation analysis between surface-specific techniques and bulk probes such as infrared absorption or Raman scattering may be used to reveal additional details of the adsorption process. Using the adsorption of water and ethanol binary mixtures as an example, we illustrate that this provides support for a competitive binding model and adds new insight into a dimer-to-bilayer transition proposed from previous experiments and simulations.

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Introduction Elucidating the structure of molecules on surfaces is a fundamental aspect of optimizing and understanding the characteristics of biosensors, 1,2 catalytic systems, 3 chemical separations, 4–7 and environmental monitoring. 8 Vibrational spectroscopy is a particularly valuable family of techniques, owing to the fine structural detail and non-invasive nature of the methods, requiring only that the interfaces are accessible to light. Of the many techniques available, ones capable of addressing buried interfaces, that is between two condensed phases whether solid–liquid, liquid–liquid, or solid–solid, are particularly valuable. The choice of method ultimately depends on the length-scale of the interface. This too can have many definitions, even for the same system, as the profile as a function of distance into the bulk phase may be described in terms of composition, density, or molecular orientation. The structure of water on solid surfaces is a critical component for mediating adsorption of other molecules—ions, synthetic surfactants, proteins, cells. 9,10 Experimental and theoretical studies have demonstrated that water at surfaces may be substantially different from bulk water in terms of orientation, and the nature and degree of hydrogen bonding. 11–14 However, the same studies reveal that these differences extend over a distance of approximately 1 nm, after which the water structure rapidly resembles that of isotropic bulk water. If we consider a probe such as attenuated total internal reflection infrared (ATR-IR) spectroscopy, the penetration depth is on the order of 0.5–1.5 µm, depending on the details of the experiment. The contribution of the few ordered layers would therefore be lost in the large bulk response. For this reason, techniques based on even-order terms in the nonlinear susceptibility are of interest, as they exclude bulk contributions based on symmetry, regardless of the penetration depth of the probe light. Consider the relationship between the applied electromagnetic field E from the probe and the induced polarization P , 1 1 P = ε0 χ(1) E + ε0 χ(2) EE + ε0 χ(3) EEE + · · · 2 6 2


(1)

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where ε0 is the vacuum permittivity and χ(n) is the nth-order electric susceptibility. In centrosymmetric environments (such as bulk isotropic achiral solids or liquids), all elements of the even-ordered susceptibility tensors vanish. Such symmetry is often broken at interfaces, therefore providing surface-specific signals. The simplest of the methods that provides convenient access to vibrational information is visible-infrared sum-frequency generation (SFG) spectroscopy. 15–17 In the most common SFG experiment, a fixed frequency (ωvis ) beam far from any vibrational or electronic resonance is overlapped with a tuneable or broadband IR beam (ωIR ) at the surface of interest. Any molecules aligned in such a way that they lack inversion symmetry have χ(2) 6= 0, and give rise to a new beam at ωSFG = ωvis + ωIR that is detected, and contains information on the vibrational resonances encoded in the up-conversion. Such experiments have proven to be extremely valuable in isolating the response of interfacial molecules, especially when the same species are present in an adjacent bulk phase. This applies to water in the bulk aqueous phase, solutes in the aqueous phase, or substrate molecules (such as polymers) in the bulk substrate. In many cases, changes that occur in the bulk during surface adsorption are negligible. However, there are situations in which adsorption events occur as a result of significant changes in bulk solution conditions. In such cases, vibrational SFG spectroscopy continues to offer unparalleled selectivity and sensitivity to surface structure, but it cannot offer any insight into bulk effects. It is then useful to combine SFG with other methods, such as IR absorption or Raman scattering. Generalized two-dimensional correlation spectroscopy (2DCOS), first proposed by Noda, 18–22 has seen a widespread and growing application for studying changes due to a perturbation such as time, temperature, concentration, pH, or any other external variable of interest. The idea is to calculate correlation coefficients between two spectral variables, for example wavenumbers ω1 and ω2 . If the correlation is high, then the associated vibrational bands likely have the same origin. This is especially of interest in the infrared, as spectra may be highly congested with many overlapping modes, yet neighboring modes may respond dif-

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ferently to the same perturbation. While 2DCOS has been utilized by the IR and Raman communities, there has not been any application to SFG spectroscopy. Perhaps one reason is that the raw SFG intensity is not well suited for direct 2DCOS analysis. In a previous publication, 23 we have provided some suggestions for ways that 2DCOS might be applied to SFG data. We now provide the first experimental demonstration of this approach. We use binary ethanol-water mixtures to demonstrate that homo- and heterospectral 2DCOS between surface-specific vibrational SFG spectra, and bulk probes such as IR absorption or Raman scattering enables investigations of how surface effects are correlated to corresponding changes in the bulk solution phase.

Vibrational Spectra and Data Treatment IR absorption data. ATR-IR data was collected using a Bruker Vertex 70 FTIR spectrometer with a Pike HATR accessory fitted with a 80 mm × 10 mm × 4 mm ZnSe crystal cut at 45◦ to yield 10 reflections. To vary the alcohol–water mole fractions, anhydrous ethanol (Commercial Alcohols) was mixed with 18.2 MΩ· cm water. Raw data are shown in Figure 1a, plotted as AATR = − log(R/R0 ),

(2)

with the baseline reflectance R0 measured using a dry ZnSe crystal. Analysis of the resonant lineshape from the IR (and SFG) spectra requires corrections to the raw experimental data involving the complex refractive index for all materials at all frequencies. Dispersion data in the visible and mid-infrared for water were taken from Segelstein, 24 and ethanol from Rocha et al. 25 The refractive index of the ethanol–water mixtures was estimated using the Lorentz-Lorenz effective medium approximation 26 ε−1 = fEtOH ε+2

εEtOH − 1 εEtOH + 2

4

+ fwater

εwater − 1 εwater + 2


(3)

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effective probe depth is plotted in Figure 2c. We then obtain the spectra of interest according to Im[χ(1) ] = cATR AATR where cATR ∝

2n1 cos θ AATR NR dp ωIR

(5)

with NR = 10 accounting for the 10 reflections in our ZnSe crystal. This correction factor is plotted in Figure 2d, and the resulting Im[χ(1) ] spectra are shown in Figure 1b. Previously identified vibrational modes in this area include ethanol methyl symmetric stretching at 2880 cm−1 , methylene symmetric stretching at 2890 cm−1 , methylene antisymmetric stretching at 2917 cm−1 , methyl Fermi resonance at 2930 cm−1 , and methyl antisymmetric stretch at 2980 cm−1 . 29,30 In addition, the hydrogen-bonded O–H stretching region for water and ethanol observed in the region 3050–3600 cm−1 often appears with two broad peaks, one centered near 3200 cm−1 and one near 3450 cm−1 . For reference, vertical lines are drawn at these wavenumbers in all spectra. Larger high-resolution versions of the spectral data, including all mole fractions of ethanol used in this study appear in Figure S1. Raman scattering data. Raman data were collected using a lab-built semi-confocal instrument. A 50 mW 532 nm diode laser (B&W Tek. BWN-532-50E) was attenuated with an OD 1 neutral density filter, and then incident on a dichroic mirror oriented at 45◦ transmitting wavelengths greater than 550 nm (Thorlabs DMLP550). This directed the 5 mW beam to a 10× infinity-corrected microscope objective with a working distance of 9.6 mm. The solutions were prepared in a custom Teflon liquid cell with a window consisting of a 1 mm thick microscope slide. The reflected Raman signal was collected by the same objective, transmitted by the dichroic mirror. The Rayleigh line was further rejected with a 532 ± 17 nm notch filter (Thorlabs NF533-17) before the light was focused onto the 200 µm entrance slit of a 750 mm spectrometer (Acton SP-2500i) fitted with a 1200 groove/mm grating blazed at 750 nm. Spectra were collected using a CCD camera (Princeton Instruments Pixis 400B) operating at −80◦ C, averaging 5 spectra with an exposure time of 1 s. Raw intensity data IR are shown in Figure 1c. The detected signal is proportional to

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the laser intensity I0 through IR = I0 N zσ

(6)

where N is the number density of scatterers, z is the scattering path length, and σ is the Raman cross section. In the experiment, the detector measures the power, PR . For the Raman scattered light IR = PR hc(ω0 − ωi )

(7)

where h is Planck’s constant, c is the speed of light, ω0 is the laser frequency, and ωi is the frequency of the vibrational mode. The power of the laser P0 may be derived from its intensity via I0 = P0 hcω0 .

(8)

Considering the frequency dependence of the Raman cross section

σ′ =

σ , (ω0 − ωi )4

(9)

we can combine these expressions to arrive at the relation 31 PR = P0 σ ′ ω0 (ω0 − ωi )3 .

(10)

Finally, we note that the the frequency-dependent scattering cross section σ ′ is proportional to Im[χ(3) ], the imaginary component of the third-order susceptibility. 32 Even though the Im[χ(3) ] profiles (Figure 1d) are nearly identical to the uncorrected Raman intensity (Figure 1c), we are including them for completeness. Larger high-resolution versions of the spectral data, including all mole fractions of ethanol used in this study appear in Figure S2. SFG data. The visible 532-nm beam for the SFG experiment was created using the doubled output from a 10 Hz, 20 ps Nd:YAG laser (Ekspla PL2241). Mid infrared light was generated by difference frequency generation in AgGaS2 , mixing the 1064 nm YAG

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fundamental with the idler from parametric generation using some of the the 532 nm beam in BBO. This created tuneable IR in the range ωIR = 2800–3700 cm−1 for this experiment. S-polarized visible and p-polarized IR beams were made collinear and temporally adjusted so the pulses arrived at the alcohol/water–silica prism interface coincident in time at an angle of 70◦ . The reflected SFG was separated from the reflected visible beam by two 532 nm notch filters (Thorlabs NF533-17), and its s-polarized component was focused onto the slit of a 200 mm monochromator (MS2001) equipped with a 2400 groove/mm grating and a photomultiplier tube (Hamamatsu R7899). Raw SFG spectra (corrected for background light and variations in the pump beam intensities) are shown in Figure 1e. These spectra are similar to those in previous SFG studies that have investigated ethanol-water mixtures at silica surfaces. 30,33,34 The first step in our treatment is to extract the dispersion of Im[χ(2) ] from the SFG data. We consider the expression for the SFG intensity

ISFG ∝

2 ωIR |LSFG eˆSFG χ(2) · Lvis eˆvis · LIR eÎR |2 Ivis IIR nSFG nvis nIR cos2 θSFG

(11)

where ni are the refractive indices of fused silica, θSFG is the angle of the reflected SFG beam, Li are elements of the macroscopic local field correction, 16,35 and ei are elements of the unit polarization vectors. We isolate components that depend on ωIR or are complexvalued, that is components that modulate the intensity or phase of χ(2) (ωIR ). Collecting these components into f (ωIR ) we have ISFG ∝ |f · χ(2) |2 , where f = |f | exp(iφf ) s 2 ωIR = LSFG Lvis LIR . nSFG nIR cos2 θSFG

(12)

All other elements in Eq. 11 are real and independent of ωIR . We therefore obtain a signal proportional to |χ(2) |2 from ISFG /|f |2 . Using model data, we have previously demonstrated that 2D correlation analysis of SFG 9



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spectra works best when the imaginary components of χ(2) are available. 23 There are two reasons for this. The first is that the coherent sum of the responses of each vibration is (2)

squared to result in |χyyz |2 , in contrast to the IR lineshape in which the oscillator strengths (square of the transition dipole moment) are summed directly. Therefore, especially in the case of broad or overlapping resonances, working with |χ(2) |2 spectra potentially obscures some features and may introduce artifacts into the 2D correlation maps. A second point is that the Im[χ(2) ] spectra are linear in concentration, and so are naturally better suited for correlation analysis. Several methods have been proposed for arriving at the imaginary χ(2) spectrum. One option is to measure the phase explicitly using a heterodyne SFG experiment. 36–42 Although this has been well established for vapor-liquid and vapor-solid interfaces, it is considerably more challenging for solid-liquid interfaces. 43–45 If the phase of one of the spectral components is known (perhaps through the polarity of a certain functional group, or a nonresonant background), then fitting the spectrum to a model lineshape may be used to estimate the phase. 46–49 This is the most popular approach, but is also challenging for congested spectra as the frequency and width of the resonant modes are difficult to assign unambiguously. Another option is to use Kramers-Kronig 50–52 or maximum entropy (MEM) approaches. 53–55 Each of these methods has its challenges and limitations; the suitability of a given approach depends on the nature of the question being addressed. In this case, as we do not wish to limit ourselves by assigning a specific lineshape function to the spectrum, we will use MEM. One aspect of MEM is that it leaves an arbitrary error phase φ′ to be determined, and this requires an additional consideration to resolve. As we are working at angles above TIR, we must also consider the phase of f (ωIR ) in Eq. 12. Dividing the spectra by |f |2 , we have (2)

already obtained |χ(2) |2 . After performing the MEM analysis that returns χMEM and φMEM , we must then still consider φf in addition to the MEM error phase. The dispersion of φf may be seen in Figure 2f. We approach the total phase correction by making use of the fact that the nonresonant background of χ(2) is real for dielectric materials, and so we expect Im[χ(2) ]

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to approach zero in regions outside of vibrational resonances. We therefore seek values of φ′ that minimize |Im[χ(2) ]| at 2800 cm−1 and 3700 cm−1 where the sample is transparent, using the definition (2)

χ(2) = |χMEM | exp [i (φMEM + φ′ − φf )] .

(13)

The resulting Im[χ(2) ] spectra are shown in Figure 1f for a few select mole fractions in the range xEtOH = 0–1. The pure water spectrum is in good agreement with heterodyne SFG spectra reported for the quartz–water interface. 56 Our pure ethanol spectrum has its methyl symmetric stretch with Im[χ(2) ]> 0 on resonance, as expected for CH3 hydrogens directed away from the surface in this experimental geometry. 30,39,57 Since the SFG spectra undergo large changes at high ethanol mole fractions, and some peaks are strongest at intermediate ethanol mole fractions, the data has been divided into two concentration regions for subsequent analysis. Larger high-resolution versions of the spectral data in both regions, including all mole fractions of ethanol used in this study appear in Figs. S3 and S4.

Homospectral correlation The details and applications of generalized two-dimensional correlation spectroscopy (2DCOS) have been described extensively in the literature. 18–22 In its more conventional (homospectral) form, 2DCOS analysis allows for the investigation of how spectral features are correlated in response to time, temperature, concentration, or any external perturbation. In this case, the perturbation is the increasing mole fraction of ethanol in the bulk solution phase of the ethanol–water mixtures. If we consider the set of Im[χ(1) ] spectra (Figure 1b) derived from the ATR-IR data, one can compute the correlation function

Chomo = Im[χ(1) ](ω1 ), Im[χ(1) ](ω2 ) Z ∞ Y1 (ω) · Y2∗ (ω) dω = 0

= Φ(ω1 , ω2 ) + iΨ(ω1 , ω2 ) 11


(14)



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concentration. However, as the C–H and O–H intensities evolve in opposite directions, the (C–H)–(O–H) cross peaks are negative, as indicated by blue contours. Asynchronous correlations reveal the spectral changes that are out of phase with each other. In the case of the IR and Raman data, the asynchronous signals are small (a maximum of 10% of the synchronous values, see Figure S5). This is expected, since the spectral response is approximately linear with the ethanol mole fraction. Small deviations from linearity arise from effects such as complex formation between ethanol and water molecules. 58 Examining the Raman data (Figure 3b), O–H stretching autocorrelations are too weak to be observed on the same scale as the C–H auto peaks and (C–H)–(O–H) cross peaks. Looking at the original spectra, we observe that there is comparatively little variation in the O–H stretching intensity as the ethanol content increases. There is a much larger change in the appearance of the C–H modes, so we expect the C–H auto- and cross peaks to dominate the correlation maps. We also note that the largest asynchronous feature is only 9% of the maximum synchronous value (see Figure S6), an indication that most spectral changes upon increasing ethanol fraction are occurring concomitantly. The spectral changes in Im[χ(2) ] derived from the SFG data are not monotonic. For ethanol mole fractions in the range 0–0.81 (Region 1), there is a growing negative feature near 2975 cm−1 that then shrinks and disappears between mole fractions 0.82–1 (Region 2). This can be seen in Figure 1f, and more clearly in Figs. S3 and S4. As 2DCOS relies on monotonic changes in the signal, we have split the analysis of the SFG data into these two concentration regions. The Φ map of Region 1 is shown in Figure 4a. Strong auto peaks between 2975–2985 cm−1 arise from an increase in the magnitude of the ethanol CH3 antisymmetric stretch with increasing ethanol mole fraction. The strongest cross peak occurs between the negative 2975 cm−1 band and the positive 2985 cm−1 band, appearing as a negative feature in Figure 4a. As a result of the bisignate nature of Im[χ(2) ], care is required in the interpretation of the sign of such cross peaks, considering the sign of the originating peaks in the raw spectra. 23 In this case, the negative cross peak indicates that

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Figure 4: Synchronous Φ and asynchronous Ψ homospectral correlation maps for the SFG data transformed into Im[χ(2) ] for (a,b) Region 1, EtOH mole fractions 0–0.81, and (c,d) Region 2, EtOH mole fractions 0.82–1. Red contours indicate positive values; blue contours indicate negative values. The percentage in the inset of the asynchronous map indicates the ratio of the largest signal in Ψ compared to Φ.

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these modes are synchronously increasing in magnitude with increasing xEtOH . All C–H modes are strongly correlated with bands in the O–H stretching region, but predominantly with the red- (less than 3200 cm−1 ) and blue-shifted (above 3450 cm−1 ) regions of the broad O–H response. This is due to the central O–H region (3200–3450 cm−1 ) containing ethanol and water O–H contributions, while the band edges are mainly due to water. As a result, the band edges are decreasing in intensity as the ethanol mole fraction increases. Asynchronous correlations reveal the spectral changes that are out of phase with each other, and therefore have no diagonal peaks. Before interpreting these data, it is important to take note of the size of the Ψ signals. We have reported the ratio between the largest signal in Ψ compared to the largest signal in Φ, as indicated in the inset of the asynchronous maps. Looking at the asynchronous map in Figure 4b, the first thing we notice is that we have up to 26% of the intensity of the synchronous map. This indicates that there is a significant lag between the changes in different vibrational modes. Noda has developed rules for the interpretation of the asynchronous maps. 18 When a cross peak at (ω1 , ω2 ) is positive, the change in ω1 happens prior to the change in ω2 . When the Ψ peak is negative, the change in ω2 occurs first. This only holds true if the corresponding peak in the synchronous map is positive. The rules are reversed if the Φ peak is negative. In this case, since our perturbation is in the direction of increasing ethanol mole fraction, before refers to lower xEtOH . Examining the asynchronous response in Region 1 (Figure 4b), taking into account the signs of the cross peaks and the rules stated above, we can conclude that changes in all surface C–H modes occur at lower bulk ethanol concentrations than changes in surface O–H modes. In addition, a strong cross peak at (2980 cm−1 , 2985 cm−1 ) is observed, where the change at 2980 cm−1 occurs at lower bulk ethanol concentration. We attribute this to a splitting of the 2980 cm−1 CH3 antisymmetric stretch into a weak 2975 cm−1 and strong 2985 cm−1 mode as a result of methyl-methyl head-to-head interactions due to dimer formation. Studying the high ethanol mole fraction region (Region 2, xEtOH > 0.82) the synchronous correlation (Figure 4c) displays a strong auto peak at 2980 cm−1 . This change is concomitant

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with a large change in the 2930 cm−1 mode, evident from the cross peak at (2980 cm−1 , 2930 cm−1 ). There are no strong asynchronous correlations in Region 2 (no more than 6% of the largest synchronous peak). In general, changes in χ(2) may result from changes in surface concentration and/or orientation. The overall strong changes in SFG intensity for such a small bulk ethanol concentration change is likely indicative of significant orientation changes. Previous experimental and simulation studies of ethanol on silica have found evidence of dimer formation. 33,34,59 Unlike the case of adsorption on hydrophobic surfaces where an initial monolayer is formed before a bilayer develops, hydrophilic surfaces such as silica have shown evidence of dimers formed in solution, and then attaching to the surface. As the surface coverage of these dimers increases, a full bilayer develops, though the top layer (closest to the bulk) is much less oriented, resulting in a net significant methyl stretching SFG intensity. Based on our observations and this dimer-to-bilayer concept, we propose that up to xEtOH = 0.81 dimers are attaching to the silica surface. As the bilayer forms in Region 1, the 2980 cm−1 mode splits into two bands at 2975 and 2985 cm−1 due the different environments of the bottom (ordered, planar) and top (less ordered) layer of opposing methyl groups. In the high xEtOH concentration Region 2, as the bulk water population is reduced, the remnant surface water that could have stabilized the bilayer is removed, causing the top bilayer to become disordered and adopt a more bulk-like structure. This in turn significantly increases the Im[χ(2) ] intensity since there is less signal cancellation from the bilayer. Furthermore, as the bottom layer becomes the main contributor to the SFG signal, the CH3 anti-symmetric vibration mode returns to its original (unsplit) frequency of 2980 cm−1 .

Heterospectral correlation A particularly powerful variation of this correlation analysis is heterospectral 2DCOS, 60–63 where two different sets of experimental data are analyzed with respect to the same perturbation, in this case the mole fraction of ethanol. As an example, using the set of Im[χ(2) ]

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spectra (Figure 1f) derived from the SFG data, and the corresponding set of Im[χ(1) ] spectra (Figure 1b) derived from the IR data, one can compute the heterospectral correlation function

Chetero = Im[χ(1) ](ω1 ), Im[χ(2) ](ω2 ) Z ∞ Y1 (ω) · Z2∗ (ω) dω =

(15)

0

= Φ(ω1 , ω2 ) + iΨ(ω1 , ω2 ) for every combination of IR probe beam wavenumbers ω1 and SFG probe ω2 , where Y1 is the Fourier transform of Im[χ(1) ] and Z2∗ is the inverse Fourier transform of Im[χ(2) ]. Following this procedure, the interpretation of the heterospectral Φ and Ψ maps follows the same rules as outlined above. The choice of internal reflection element and angle of incidence (ZnSe and 45◦ here) for the ATR-IR deserves some discussion in the context of heterospectral 2DCOS. These two parameters determine the probe depth according to the evanescent wave penetration depth. Eq. 4 reveals that dp may be reduced by choosing materials with a higher refractive index (such as Ge) and/or operating at a higher angle of incidence. ATR-IR experiments take advantage of this when trying to achieve greater surface sensitivity, as a lower dp will mean a larger surface/bulk ratio. In our case, all conclusions from our heterospectral 2DCOS apply to the fused silica surface, as the SFG experiments were performed on fused silica. Evidence from experiments and molecular simulations suggests that most liquids, even at charged interfaces, do not orient past ≈ 1.2 nm from the surface. 64,65 This means that, for such systems, ATR-IR is a very effective probe of the bulk solution, as it is insensitive to such short length scales in its surface/bulk probe ratio with dp in excess of 1 µm at these wavelengths. This is convenient, as the SFG experiments may be performed on the solid surfaces of interest, and any material may be chosen as the internal reflecting element for the complementary ATR-IR studies. In fact, so long as the bulk phases are not too absorbing so as to degrade the signal-to-noise, it may be advantageous to choose low index materials such as ZnSe for the ATR-IR measurements in order to maximize dp . SFG and ATR-IR 17



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spectroscopy are therefore ideal candidates for surface-bulk correlation, comparing Im[χ(2) ] with Im[χ(1) ].

Figure 5: Synchronous Φ and asynchronous Ψ heterospectral correlation maps for (a,b) the IR–SFG data, and (c,d) the Raman–SFG data for Region 1, 0 < xEtOH < 0.81. Red contours indicate positive values; blue contours indicate negative values. The percentage in the inset of the asynchronous map indicates the ratio of the largest signal in Ψ compared to Φ. The corresponding synchronous map obtained from SFG and IR data in Region 1 (0 < xEtOH < 0.81) is shown in Figure 5a. In general, as the bulk concentration is increasing, the surface concentration is increasing, and the expected synchronous correlations are observed. For example, as the bulk O–H signal (from Im[χ(1) ]) decreases, all surface C–H modes (from 18


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Im[χ(2) ]) are increasing over this concentration region. More interesting and useful information can be obtained from the asynchronous correlation, keeping in mind the importance of the sign of the corresponding Φ peak in order to make the causality argument from Ψ. The Ψ map in Figure 5b indicates strong asynchronous signals, up to 26% of the corresponding synchronous peaks. Analysis of the features reveals a strong positive region between the surface and bulk O–H stretches, indicating that the bulk O–H begins to change at lower xEtOH . Strong negative features are observed between the surface antisymmetric CH3 at 2985 cm−1 and all the bulk C–H stretching modes, indicating that surface C–H signal changes at lower xEtOH . This can be explained by the surface concentration of ethanol being higher than the bulk concentration, strong evidence of competitive adsorption where ethanol preferentially covers silica. Previous studies have arrived at this conclusion from analysis of Langmuir isotherms constructed from SFG data. 30,34 Although many experiments will lend themselves to offline surface and bulk measurements as was the case here, there are situations in which both types of spectra are best collected on the same sample at the same time. For example, in cell adhesion studies it is important to apply one set of perturbations and measure both sets of responses simultaneously. This is encountered in the case of cell adhesion studies, for example. ATR-IR experiments are not ideal to be performed in the same experimental setup as SFG, as the prism must then be transparent to visible and IR beams. This makes materials such as ZnSe or Ge unsuitable for SFG, and creates challenges as fused silica does not have a high enough refractive index to ensure TIR throughout the entire spectral range. When surface and bulk spectra must be acquired in a single experimental configuration, Raman scattering is then a better choice. When using a prism for TIR SFG configurations, this requires only a laser powerful enough to excite Raman modes with a collimated beam, or an objective with a working distance long enough to reach the liquid through the prism. It is also possible to use the SFG visible laser pulse directly, and gate the Raman signal. As a result of these options, we have considered SFG–Raman heterospectral correlation as an alternate way of arriving at the surface–bulk

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information. One would expect that the Φ and Ψ heterospectral Im[χ(2) ]–Im[χ(1) ] correlation maps would contain the same information as found in the Im[χ(2) ]–Im[χ(3) ] maps. This is in fact observed (Figs. 5c and d), with the exception of some peaks being more or less prominent between the IR and Raman datasets, for the same reason as discussed for the Im[χ(1) ] vs Im[χ(3) ] homospectral correlation.

Figure 6: Synchronous Φ and asynchronous Ψ heterospectral correlation maps for (a,b) the IR–SFG data, and (c,d) the Raman–SFG data for Region 2, 0.82 < xEtOH < 1. Red contours indicate positive values; blue contours indicate negative values. The percentage in the inset of the asynchronous map indicates the ratio of the largest signal in Ψ compared to Φ. Studying the high ethanol bulk concentrations in Region 2, the asynchronous Im[χ(2) ]– 20


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Im[χ(1) ] correlation (Figure 6b) and asynchronous Im[χ(2) ]–Im[χ(3) ] correlation (Figure 6d), reveal that changes in surface CH3 intensity (2980 cm−1 ) occur at a higher ethanol concentration than any change in the bulk spectral response. This indicates that, even after the bulk concentration is no longer changing significantly, the surface response is still changing considerably. In general, the SFG signal is a combination of the surface density, oscillator strength (through the vibrational hyperpolarizability), and the degree of orientation at the surface. Even the most SFG-active vibrational modes will have little to no intensity in the absence of polar orientation. In this case, the presence of the asynchronous peak suggests that changes in surface ethanol orientation are most likely, consistent with our proposal that the upper regions of the bilayer are becoming less ordered in this concentration region.

Conclusions For molecular systems in which surface adsorption and desorption events are triggered by changes in bulk composition, understanding the relationship between surface and bulk effects requires a combination of bulk probes, and those that have sensitivity to the interfacial region. Nonlinear vibrational spectroscopy has extreme surface specificity on account of its symmetry requirement, but is therefore incapable of monitoring bulk changes. When combined with a bulk probe such as IR absorption or Raman scattering, these effects may be examined together. For the data shown in this study, the IR and Raman signals probe isotropic distributions on account of their penetration depth. But these techniques are also applicable to linear optical probes of aligned systems, such as monolayers deposited at solid– vapor interfaces. If the same techniques were used to study ordered systems, they would be probing orthogonal components of the orientation distribution. We have illustrated that two-dimensional correlation analysis is a powerful method for treating such combined data sets. In the case of binary mixtures of ethanol–water adsorbing on silica surfaces, we present strong evidence of competitive adsorption favoring ethanol, and a more detailed picture of

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