Multivariate Analysis of ATR−IR Spectroscopic Data: Applications to

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Multivariate Analysis of ATR-IR Spectroscopic Data: Applications to the Solid-Liquid Catalytic Interface Ivelisse Ortiz-Hernandez, D. Jason Owens, Michael R. Strunk, and Christopher T. Williams* Department of Chemical Engineering, Swearingen Engineering Center, UniVersity of South Carolina, Columbia, South Carolina 29208 ReceiVed October 19, 2005. In Final Form: December 21, 2005 It is demonstrated that attenuated total reflection infrared (ATR-IR) spectroscopy coupled with multivariate data analysis can be effectively used for in situ investigation of supported catalyst-liquid interfaces. Both formaldehyde adsorption/dissociation in water and acetonitrile adsorption in hexane on thin (ca 10 µm) films of 5 wt % Pt/γ-Al2O3 deposited on a germanium waveguide have been investigated. The multivariate analysis applies classical least squares (CLS) and partial least squares (PLS) methods to the ATR-IR data in order to correlate spectral changes with known sources of experimental variation (i.e., time, concentration of solution species, etc.). The formaldehyde adsorption experiments revealed no spectroscopic evidence for adsorbed molecular formaldehyde under the conditions examined. However, the dissociation product carbon monoxide was observed to form in atop configuration on Pt, likely on edges and terrace sites. Isotope labeling experiments suggest that a pair of peaks observed at 1990 and 2060 cm-1 during treatments of Pt in H2-saturated water arise at least in part from νPt-H stretching of adsorbed atomic hydrogen. Acetonitrile was found to adsorb on the Pt catalyst by σ-bonding of the CN group with the platinum, yielding apparent surface peaks that are almost identical to that observed in the liquid phase. A peak at 1641 cm-1 was observed which was assigned to the adsorption of the CN group in a tilted configuration involving a combination of end-on and π interaction with the surface. This species was found to be reactive toward hydrogen, suggesting that it might play a role in nitrile hydrogenation. The prospects of using this approach to examine solid-catalyzed liquid-phase reactions are discussed in light of these findings.

Introduction In recent years, there has been an increased desire to implement heterogeneous catalysis in fine chemicals and pharmaceuticals industries.1-3 These efforts are being driven, in part, by the goal of developing environmentally friendly processes for such specialty applications. Heterogeneous catalysis provides ease of separation and can reduce the amount of solvent required resulting in less waste produced. One of the typical features of such processes is that they occur in the liquid-phase, often in the presence of dissolved gas (e.g., H2). Unfortunately, examining surfaces of solid catalysts in the liquid phase with spectroscopy presents significant difficulty. First, it can be difficult to introduce the probe light to the surface, especially in solutions that are opaque or highly scattering. Second, there is often large spectral interference arising from the bulk liquid that can greatly obscure the desired surface signal. As a result, there are relatively few in situ vibrational spectroscopic investigations of solid catalyst or catalyst support surfaces in the liquid phase.4-8 Nevertheless, * To whom correspondence should be addressed. Fax: (803) 777-8265. E-mail: [email protected]. (1) Carpenter, K. J. Chem. Eng. Sci. 2001, 56, 305. (2) Mills, P. L.; Chaudhari, R. V. Catal. Today 1997, 37, 367. (3) Sheldon, R. A.; Downing, R. S. Appl. Catal. A: General 1999, 189, 163. (4) Ferri, D., Bu¨rgi, T.; Baiker, A. J. Phys. Chem. B 2001, 105, 3187. (5) (a) Ferri, D.; Bu¨rgi, T. J. Am. Chem. Soc. 2001, 123, 12074. (b) Ferri, D.; Bu¨rgi, T.; Baiker, A. Chem. Commun. 2001, 1172. (c) Ferri, D.; Bu¨rgi, T.; Baiker, A. J. Catal. 2002, 210, 160. (d) Ferri, D., Bu¨rgi, T. J. Phys. Chem. 2002, 106, 10649. (e) Ferri, D.; Bu¨rgi, T.; Frauchiger, S.; Baiker, A. J. Catal. 2003, 219 (2), 425. (f) Burgener, M.; Wirz, R.; Mallat, T.; Baiker, A. J. Catal. 2004, 228 (1), 152. (g) Ferri, D.; Bu¨rgi, T.; Baiker, A. Phys. Chem. Chem. Phys. May 2002, Published by The Royal Society of Chemistry. (6) (a) Ma, Z.; Kubota, J.; Zaera, F. J. Catal. 2003, 219 (2), 404. (b) Ma, Z.; Lee, I.; Kubota, J.; Zaera, F. J. Mol. Catal. A. Chem. 2004, 216 (2), 199. (7) (a) Chu, W.; LeBlanc, R. J.; Williams, C. T.; Kubota, J.; Zaera, F. J. Phys. Chem. B 2003, 107, 14365. (b) LeBlanc, R. J.; Williams, C. T. J. Mol. Catal. A 2004, 212, 277. (c) LeBlanc, R. J.; Williams, C. T. J. Mol. Catal. A 2004, 220, 207. (d) Chu, W.; LeBlanc, R. J.; Williams, C. T. Catal. Comm. 2002, 3, 547.

there are several vibrational spectroscopic approaches that can be employed for such studies. For example, surface-enhanced Raman spectroscopy (SERS)7 and infrared reflection-absorption spectroscopy (IRAS)6 have been shown to be very useful for probing polycrystalline metal surfaces. More recently, sum frequency generation (SFG) spectroscopy is being developed for this purpose.9 One approach that is receiving some recent attention in the field of catalysis is attenuated total reflection infrared spectroscopy (ATR-IR). In contrast to the extensive investigations of gasphase catalysis with infrared spectroscopy, the numbers of ATRIR studies of solid catalyst-liquid interfaces are relatively few. Baiker, Bu¨rgi, and Ferri have studied the adsorption of CO on platinum4 and the modification of platinum5a-5b and palladium5c catalyst with the chiral modifier cinchonidine for the study of enantioselective catalysis. In these studies, a model catalyst film was used that consisted of the metals sputtered onto a thin alumina film. This work was expanded to study a Pd/TiO2 powder catalyst during hydrogenation of pyrone in the liquid phase. Baiker et al. have also studied the effect of Lewis and Brønsted acid sites on SiO2 (Degussa) and TiO2 modified with sulfuric acid for the reaction of vinyl ether.5e More recently, citral hydrogenation on Pd/Al2O3 using various solvents has been investigated.5f We have also demonstrated that Pt/Al2O3 powder catalyst can be examined in a range of solvents (e.g., water, ethanol, and hexane) under flow conditions using ATR-IR.8 These studies can be considered as analogous to previous ATR-IR studies of McQuillan10 and Harris,11 who examined liquid-phase adsorption on thin oxide films prepared by sol-gel techniques. (8) Ortiz-Herna´ndez, I.; Williams, C. T. Langmuir 2003, 19 (7), 2956. (9) (a) Tadjeddine, A.; Peremans, A. In AdVances in Spectroscopy; Clark, R. J. H., Hester, R. E., Eds.; Wiley: Chichester, U.K., 1998; Vol. 26, p 159. (b) Miranda, P. B.; Shen, Y. R. J. Phys. Chem. B 1999, 103, 3292.

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One of the remaining challenges with this approach is the deconvolution of the catalyst surface and bulk liquid spectroscopic signals. Many of the catalytic ATR-IR studies reported to date have largely dealt with detection of surface species that exhibit vibrational peaks significantly different than those in the bulk phase or where the liquid-phase concentration is very small. However, at elevated concentrations in solution, there are likely to be weakly adsorbed species for which vibrational frequencies remain largely unchanged (or shift only slightly) from their liquidphase values. Furthermore, vibrational bands arising from strongly adsorbed surface species may coincide with those from the solvent. To overcome these difficulties, we have been utilizing the wellestablished method of multivariate analysis.12,13 This approach has been used previously by Harris et al.11 during ATR-IR studies of sol-gel films in solution and is an integral tool in process analytical chemistry for pharmaceutical and fine chemical manufacturing. In this paper, we provide an overview of the approach as we have implemented it for ATR-IR catalytic studies. The specific examples considered are H2 treatment of Pt/Al2O3 in water, formaldehyde adsorption on Pt/Al2O3 in water, and nitrile adsorption on Pt/Al2O3 in hexane. The results suggest that this approach is very effective for analysis of this type of in situ spectroscopic data.

Multivariate Analysis for Spectral Interpretation In the analysis of in situ ATR-IR data, it is necessary to detect components (i.e., surface species) which are present in the system at very low (trace) levels. The main challenge is to enhance the detection limits of ATR-IR in order to differentiate surface species from the bulk phase signal. This is accomplished by performing multivariate analysis on the data. This method allows the separation of the system components based entirely on their differential time response. Catalyst surface peaks are identified by studying the same system in an inert environment (e.g., running the experiment under the same conditions using an inert support material). Probe molecules, such as carbon monoxide, have been used to determine the corresponding limits of signal-to-noise.8 The results obtained are in agreement with the literature, and the magnitude of absorption was comparable with those obtained in the liquid environment. Pretreatment of the experimental data in the form of baseline correction and shape removal allows for the detection of small peaks that can be hidden due to interference (i.e., drift, noise, water, and bulk signal). Multivariate quantitative analysis using a combination of classical least squares (CLS) and partial least squares (PLS) has been demonstrated to be a powerful tool for time-resolved IR spectral analysis.12 A Matlab-based computer program combining the principles of CLS and PLS has been written in house. It allows baseline corrections and the determination of the main sources of variation in the spectroscopic data. CLS is used in (10) (a) Dobson, K. D.; McQuillan, A. J. Spectrochim. Acta Part A 1999, 55, 1395. (b) Dobson, K. D.; McQuillan, A. J. Spectrochim. Acta Part A 2000, 56, 557. (c) Dobson, K. D.; Roddick-Lanzilotta, A. D.; McQuillan, A. J. Vib. Spectrosc. 2000, 24, 287. (11) (a) Poston, P. E.; Rivera, D.; Uibel, R.; Harris, J. M. Appl. Spectrosc. 1998, 52, 1391. (b) Rivera, D. A. PhD. Thesis, Department of Chemistry, University of Utah, 2000. (12) (a) Otto, M. Chemometrics; Wiley-VCH: New York, 1999. (b) Beebe, K. R.; Pell, R. J.; Seasholtz, M. B Chemometrics: A Practical Guide; John Wiley and Sons: New York, 1998. (c) Haaland, D. M.; Melgaard, D. K. Appl. Spectrosc. 2001, 55, 1. (d) Wold, S.; Sjo¨stro¨m, M.; Erikson, L. Chemom. Intel. Lab. Sys. 2001, 58, 109. (13) (a) Halland, D. M.; Han, L.; Niemczyc, T. M. Appl. Spectrosc. 1999, 53 390. (b) Wehlburg, C. M.; Halland, D. M.; Melgaard, D. K. Appl. Spectrosc. 2002, 56 (5), 605. (c) Halland, D. M.; Melgaard, D. K. Vibrat. Spectrosc. 2002, 29, 171. (d) Melgaard, D. K.; Halland, D. M.; Wehlburg, C. M. Appl. Spectrosc. 2002, 56, 615. (e) Halland, D. M.; Melgaard, D. K. Appl. Spectrosc. 2000, 54, 130.

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order to determine the pure component spectra of the known sources of variation. PLS enables the determination of the weights (scores) of unknown variables (e.g., time dependence, concentration of reactant, etc.) and their corresponding spectral shape (loadings). The loading vectors and scores are combined to give a reconstructed data set. Prediction error is determined as the sum of squares in spectral space (residuals) in order to test the validity of the model. The main concepts covered in the program are discussed below. Augmented CLS and PLS promise to provide additional resolution to the current system in the future.12d,e In both methods (PLS and CLS), the output of the analysis generates scores and loading vectors. For CLS, these reduce to concentrations and pure component spectra. Interpretation is easier in the case of CLS due to the purity of the loading vectors. However, CLS requires knowledge of all contributing concentrations or all pure component spectra. The CLS model can be expressed in matrix notation as follows:

A ) CK + EA where A is a n × p matrix of measured p absorbance intensities and n the number of spectra collected, K is a m × p matrix with m being the pure-component spectra of all of the active species in the samples, C is the n × m matrix of concentrations, and EA is an n × p matrix that accounts for spectral residuals in the model. An estimate of K can be performed using calibration samples with known component concentration using least squares, given as

K ) (CTC)-1CTA After estimating the pure-component spectra of known components, the concentration of the experimental data can be determined using the pseudo inverse approximation as follows:

C ) AKT(KKT)-1 This allows for the sources of variation (i.e., slope, offset, H2O vapor, CO2, purging flow variation, ice formation in the detector, equipment drift, etc.) to be removed from the data set. To correct for baseline, a row of ones is added to the K matrix, which is then used to correct for variations other than the experimental sources of variations. This is performed by creating a baseline shape which is then subtracted from the dataset. The limitation of this technique is the requirement of a priori knowledge of all of the sources of variations. Unfortunately, this is not usually the case in complex systems; it does, however, allow the removal of large sources of variation. In contrast, PLS allows the determination of scores and loadings through a latent variable (matrix A). This offers the flexibility to analyze data by adjusting the spectral loadings to conform to experimentally (chemically) relevant weights. However, the approach may become limited by the difficulty in the interpretation due to mixing of pure component spectra. The limitation is overcome by restricting independent experimental (chemical) variables to be uncorrelated. First of all, CLS is used for the estimation and removal of known sources of variation in the spectral data. The first step in the PLS algorithm is determination of weight loading vectors (w). These vectors are the CLS estimates of the pure component spectra

w)A ˆ TC(CTC)-1 which are p × f matrixes, where f is the number of factors set in the data pretreatment window as shown in Figure 5. These

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factors determine the number of iterations that the PLS program will perform and, subsequently, the number of loading vectors and scores determined by the system. These weight loading vectors are then normalized to obtain the matrix W. To solve using PLS, the concentration can be related to the response as

A ˆ ) TB + E ˆA T)A ˆ W(WTW)-1 B)A ˆ TT(TTT)-1 C ) VT + EC V ) TTC(TTT)-1 where T is the matrix of scores with a size of n × f; B is the matrix of loading vectors with a size f × p; V relates the score to the concentration with a size f × 1. The goal is to determine B such that the concentrations can be estimated in an accurate fashion. In this case, the matrix A ˆ corresponds to the absorbance data corrected for baseline. In this relationship, the matrices E ˆA and EC represent the spectral and concentration residuals after fitting the data. Using the scores (T) and abstract spectra (B), the data matrix is reconstructed to analyze the residuals remaining. This is achieved by defining the response in terms of the weights. In these expressions, the regression parameters are estimated starting with an estimate of the concentration, C. The residuals are determined by the difference between the predicted and the measured absorbance. These residuals are then substituted for matrix A ˆ , which is used to determine the next loading vectors with the corresponding scores. Then the predicted error is calculated as the prediction error sum of squares (PRESS) from12 n

PRESSC )

∑ i)1

n

(Ci - C ˆ i)2, and PRESSA )

(Ai - A ˆ i)2 ∑ i)1

where the coefficients C and A stand for concentration and absorbance, respectively. This predicted error is used to determine the ideal number of factors required for the analysis of the data. For the following systems, the data is analyzed with respect to the components, which are time and concentration of reactants (H2, O2 and desired liquid phase reactants, i.e., formaldehyde and acetonitrile). Introduction of premixed, very stable, experimentally designed chemical perturbations to the flowing systems satisfies the condition of uncorrelated independent variables and allows separation of the spectral signatures of unknown chemical species. Experimental Procedures Reagents. Formaldehyde (37 wt % in water, containing ca. 15% methanol as a stabilizer) was obtained from Aldrich and used without further purification. The gases used for the experiments were ultrahigh purity (UHP) hydrogen, oxygen, and nitrogen from National Welders or Air Star, except for D2, which was 99.7% pure. H2PtCl6 (99.5%, Premion) was obtained from Alfa Aesar. Water was deionized (18 MΩ) and purified of organic contaminants using a Barnant B-pure dual filter with a Millipore 4-filter system. D2O (99.9% purity) was purchased from Aldrich. Supergradient HPLC acetonitrile (99.9+% purity) was obtained from Alfa Aesar. Hexanes (mixture of hexanes) HPLC grade were obtained from Fisher Scientific. Catalyst Preparation. The catalyst samples consisted of 5 wt % Pt/γ-Al2O3 and were prepared using standard wet (aqueous) impregnation with H2PtCl6 as the precursor. The support is γ-Al2O3 powder from Alfa Aesar with a mean particle size of 37 nm and a

Figure 1. Diagram for the automated flow system for ATR-IR studies of solid catalysts in the liquid phase. surface area of 45 m2/g as given by the manufacturer (and measured in our laboratory using the BET method). The dried impregnated support was calcined in O2 at 500 °C for 3 h and reduced in H2 for 2 h at 300 °C. The resulting catalyst had around 50% dispersion (obtained by H2 chemisorption) with a mean platinum particle size of 3 ( 2 nm, which was verified by high-resolution transmission electron microscopy. Infrared Spectroscopy. All spectra were acquired using a Nicolet 670 FTIR spectrometer with a liquid-nitrogen-cooled MCT detector. A horizontal ATR accessory (Spectra Tech) was used in conjunction with a home-built aluminum flow cell. The design of the flow cell has been previously reported.8 Two to four separate reservoirs equipped with glass frit-capped gas inlets allow saturation or purging of liquids with gases. The flow system was automated and controlled using a Labview (National Instruments) interface, which allows the running of experiments with identical concentration-time profiles. The automated system can handle up to four reservoirs without mixing between solutions. The desired liquid was pumped through the flow cell at a flow rate of 36 cm3/min using a PFTE-lined gear pump (Cole Parmer). Teflon tubing was used in this study based on its resistance to the solvents and reactants employed. The equipment schematic is shown in Figure 1. The flow rate used for each of the gases (O2, H2, and N2) through the desired reservoir was 100 cm3/min. For each experiment, the ATR accessory optics were aligned and optimized, and the sample was left under a flow of solvent until the system reached equilibrium (usually at least 2 h, as determined by achieving a consistent, unchanging absorbance spectrum). A catalyst pretreatment was then performed by flowing O2-saturated solvent for 30 min, purging the flowing solvent with N2 for 15 min, and finally flowing H2-saturated solvent for 30 min. This pretreatment was repeated three times to ensure proper cleaning of the sample and in order to obtain statistically meaningful data. The adsorption of formaldehyde was performed by the flow of diluted aqueous solution as a pulse change. This was made possible by using two independent reservoirs that allowed switching from one reservoir that contained the solvent to the second that contained the desired concentration of reactant. Prior to the switch between solutions, the flow cell and lines were purged of liquid in order to minimize mixing. The adsorption step was repeated at least three times. Data collection consisted of 128 scans per spectrum with a resolution of 4 cm-1 and a collection time of 69 s. All of the experiments were performed at room temperature (25 °C). In a similar fashion, we also performed experiments to test the adsorption of acetonitrile on 5% Pt/Al2O3 using hexane as a solvent. In this case, we started by adsorbing one concentration of acetonitrile in three pulses as performed in the formaldehyde dissociation studies. After the adsorption, we saturated the solvent with hydrogen to study the reaction of the adsorbed surface species.

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Results and Discussion

cm-1. To determine the relationship of these peaks with the presence of solution-phase hydrogen, multivariate analysis was carried out on data obtained during the pretreatment period. The results of this analysis in the region of 1900-2100 cm-1 are shown in Figure 3. The first loading vector obtained (Figure 3a, open circles) corresponds with the two spectral peaks at 1990 and 2060 cm-1 shown in Figure 2. The variation in the associated score (Figure 3b, open circles) tracks directly with the presence of H2 bubbling in solution (indicated by the dashed line). Furthermore, the score profile shows that, after the fast initial increase in intensity, there is a more gradual increase that looks very similar to adsorption behavior. According to Xu et al.,20 a band corresponding to terminal hydrogen on platinum should be detectable by infrared spectroscopy at ca. 2000 cm-1. Ogasawara

Pretreatment of 5% Pt/Al2O3 in H2O. Before performing adsorption studies, it is necessary to perform a standard pretreatment of our catalyst in solution. Baiker and co-workers have shown that successive O2-H2 treatments of Pt/Al2O3 in various solvents at room temperature can be effective in producing a relatively clean starting spectrum to proceed with experiments.4 As described in the Experimental Section, the treatment used here involves alternating pulsed steps of hydrogen and oxygen through the solution flowing over the catalyst sample. Here we consider the analysis of spectral changes observed during pretreatment in water. In constructing both Figures 3 and 4, we have included the score of the first loading vector obtained by multivariate analysis in the 1800-1900 cm-1 spectral region. Since there are no molecular vibrations that appear in this region, this analysis provides a good indication of what a “baseline” score would look like (i.e., a score exhibiting no variations). Figure 2 shows raw ATR-IR spectra obtained before (bottom) and after (middle) exposure of Pt/Al2O3 to H2 in H2O and after subsequent exposure to O2 (top). The spectra obtained during treatment with H2 show two peaks located at 1990 and 2060

Figure 2. Raw ATR-IR spectra obtained for Pt/Al2O3 in H2O before (bottom) and after (middle) exposure to H2 and after subsequent exposure to O2 (top).

The data set was analyzed by isolating the desired spectral region and studying the correlation of the data with the known variables (i.e., hydrogen, oxygen, and reactant). Schematic 1 describes the procedure for data treatment for a formaldehyde adsorption experiment. After loading the data (a), the shape of the baseline is subtracted from the data set (in this case ice in the detector) (b). After correcting for this baseline (c), the data is truncated in order to isolate areas that are changing with the presence of the reactant. The resulting data can then be mean centered if desired (d). This treated data set is then decomposed with partial least squares (PLS) into vectors and scores, which correspond to chemical response within the system (e). The scores describe how the vectors are changing and how they correlate with experimental variables such as time or concentration.

The Solid-Liquid Catalytic Interface

Figure 3. Multivariate analysis in the 1900-2100 cm-1 region of spectra collected during pretreatment of Pt/Al2O3 in various liquid environments: (a) First loading vectors obtained in H2O using O2-H2 (circles) and O2-D2 (squares) treatments, and second loading vector obtained in D2O using O2-D2 treatments (solid line). (b) Scores for the first loading vectors obtained in H2O using O2-H2 (circles) and for O2-D2 (squares) treatments. The score for a baseline obtained in the 1800-1900 cm-1 region for Al2O3 are shown as a solid line (see text for details). (c) Score for the second loading vector obtained in D2O using O2-D2 treatments. The score for a baseline obtained in the 1800-1900 cm-1 region for Al2O3 are shown with stars (see text for details). Dashed lines indicate the time profile of the introduction of H2 or D2 into solution.

and co-workers21 also reported that the presence of terminal hydrogen can be observed on platinum surfaces that are not perfectly oriented, with characteristic frequencies of 1990 and 2080 cm-1. Similar bands have been reported in electrochemical (14) Kitamura, F.; Takahashi, M.; Ito, M. Chem. Phys. Lett. 1986, 123 (4), 273. (15) Lapinski, M. P.; Silver, R. G.; Ekert, J. G.; McCabe, R. W. J. Catal. 1987, 105, 258. (16) Henderson, M. A.; Mitchell, G. E.; White, J. M. Surf. Sci. 1987, 188, 206. (17) Olivi, P.; Bulho˜es, L. O. S. J. Electroanal. Chem. 1992, 330, 583.

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systems in the case where hydrogen evolution takes place and the electrode surface is saturated with atomic hydrogen.22,23 For example, Tian and co-workers23 have demonstrated with SERS that adsorbed H is present on the surface of polycrystalline Pt at a frequency of around 2088 cm-1. We therefore tentatively assign these bands as arising (at least in part) from adsorbed atomic hydrogen. To further examine the origin of these peaks, we performed the treatment using deuterium. If the observed peaks (i.e., vector) correspond to νPt-H vibrations, substitution of deuterium should result in a frequency downshift of ca. 600 cm-1, as predicted from a simple harmonic oscillator approximation. Figure 3a (open squares) shows the vector obtained for the 1900-2100 cm-1 region during the D2 treatment in H2O, whereas Figure 3b (open squares) shows the associated score that again tracks directly with the presence of D2 in solution. The observed spectral response is thus found to be essentially identical to that observed for the case of H2 in H2O. To eliminate the chance of H-D exchange between any adsorbed deuterium and the hydrogen in water, a similar experiment was performed using D2O as the solvent. Figure 3a (solid line) shows the vector obtained in the 19002100 cm-1 region, revealing again a shape that corresponds to the two observed spectral peaks (although one of the peaks appears upshifted by around 10 cm-1). However, as shown in Figure 3c (solid line) the associated score for this vector is around a factor of 4 smaller than for the other two cases. In addition, the shape of the score profile during each D2 pulse (Figure 3c, solid line) is significantly different. The score increases almost immediately upon introduction of D2 but then begins to decay with time during the pulse. The analysis performed above clearly shows that there are spectral features in the 1900-2100 cm-1 region that are not associated with adsorbed atomic hydrogen. These features are likely formed from conversion of impurities on the catalyst surface into trace amounts of carbon monoxide or other carbonyl species. Formation of similar species during hydrogen/oxygen treatments of Pt/Al2O3 in CH2Cl2 has been observed previously by ATRIR spectroscopy.4 Furthermore, we cannot rule out that some CO is being formed by reaction of trace CO2 impurities with the H2, as has recently been demonstrated in an ATR-IR study of Pt/ Al2O3 in cyclohexane.5g Nevertheless, examination of the lower frequency region where the Pt-D stretches would be expected to appear suggests the presence of adsorbed hydrogen during treatment. Figure 4a shows the loading vectors obtained for all three sets of experiments in the region between 1300 and 1550 cm-1. All three vectors exhibit a peak-like shape located around 1450 cm-1. However, only in the two experiments involving D2 do the associated scores (Figure 4b,c) correlate strongly with the presence of gas in solution. In the case of H2 in H2O, the score increases with time but is not correlated with the multiple H2 introduction steps. This suggests that the variation in this spectral region is likely due to an experimental artifact such as incomplete background subtraction. Formaldehyde Dissociation on 5% Pt/Al2O3 in Water. As mentioned in our previous paper,8 we have been studying the dissociation of aldehydes on 5% Pt/Al2O3. Here we extend the (18) Pe´rez, J. M.; Mun˜oz, E.; Murallo´n, E.; Cases, F.; Va´zquez, J. L.; Aldaz, A. J. Electroanal. Chem. 1994, 368, 285. (19) Vidal, F.; Busson, B.; Six, C.; Tadjeddine, A.; Dreesen, L.; Humbert, C.; Peremans, A.; Thiry, P. J. Electroanal. Chem. 2004, 563, 9. (20) Xu, X.; Wu, D. Y.; Ren, B.; Xian, H.; Tian, Zhong-Qun Chem. Phys. Lett. 1999, 311, 193. (21) Ogasawara, H.; Ito, M. Chem. Phys. Lett. 1994, 221, 213. (22) (a) Nichols, R. J.; Bewick, A. J. Electroanal. Chem. 1988, 243, 445. (b) Peremans, A.; Tadjeddine, A. J. Chem. Phys. 1995, 103, 7197. (23) Tian, Z.-Q.; Ren, B. Ann. ReV. Phys. Chem. 2004, 55, 197.

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Figure 5. Typical baseline-corrected spectral data obtained for the adsorption/dissociation of formaldehyde on 5% Pt/Al2O3 from a 100 mM aqueous solution. The spectra are presented within a snapshot of the multivariate analysis program window.

Figure 6. Comparison of the liquid-phase spectra in the 900-1100 cm-1 region for formaldehyde in water (bottom) with raw experimental data obtained during ATR-IR experiments (top).

Figure 4. Multivariate analysis in the 1300-1550 cm-1 region of spectra collected during pretreatment of Pt/Al2O3 in various liquid environments: (a) First loading vectors obtained in H2O using O2-H2 (circles) and O2-D2 (squares) treatments, and in D2O using O2-D2 treatments (solid line). (b) Scores for the first loading vectors obtained in H2O using O2-H2 (circles) and O2-D2 (squares) treatments. The score for a baseline obtained in the 1800-1900 cm-1 region for Al2O3 are shown as a solid line (see text for details). (c) Score for the first loading vector obtained in D2O using O2-D2 treatments. The score for a baseline obtained in the 1800-1900 cm-1 region for Al2O3 are shown with stars (see text for details). Dashed lines indicate the time profile of the introduction of H2 or D2 into solution.

studies of formaldehyde dissociation using multivariate analysis. It is well established that formaldehyde readily dissociates on Pt to form adsorbed CO and hydrogen, which further can react to form CO2 and water14-18 in the presence of O2. Figure 5 shows the typical baseline corrected spectral data obtained for the adsorption of formaldehyde on 5% Pt/Al2O3 from a 100 mM aqueous formaldehyde solution (that contains 15 mM methanol as a stabilizer). In this case, the spectra are presented within a snapshot of the multivariate analysis program window. From

this window, the data can be truncated and the baseline can be corrected for spectral artifacts. After baseline correction has been performed, the data can then be analyzed with PLS by setting up the number of factors, which will determine the number of vectors used to describe the variations. As described in the Experimental Section, three pulses of formaldehyde solution were introduced in succession. The analysis is started by comparing the first vector obtained for formaldehyde adsorption on 5% Pt/Al2O3 to the spectrum of pure formaldehyde. Figure 6 shows a comparison of the liquidphase spectra for the formaldehyde solution with data obtained from the experiments. In this case, we observe a feature at ca. 1025 cm-1, which likely arises primarily from the νC-O vibration of methanol that is present as a stabilizer in solution and is a very strong liquid phase absorption band. The shape and width of this band in Figure 6 also suggests that there might be some contributions from hydrogen bonding interactions between formaldehyde and water. We were unable to detect peaks solely associated with liquid formaldehyde, likely do to their relative weak infrared absorption. Figure 7 shows the first loading vectors obtained in the region of 1000-1100 cm-1 for experiments on both Al2O3 and 5% Pt/Al2O3. These vectors are in agreement with the presence of the liquid phase formaldehyde/methanol mixture, as indicated by a feature at around 1026 cm-1 that is consistent with the data in Figure 6. Figure 7b, shows a comparison

The Solid-Liquid Catalytic Interface

Figure 7. Multivariate analysis in the 1000-1100 cm-1 region of spectra collected during exposure of Pt/Al2O3 and Al2O3 to multiple pulses of formaldehyde in water: (a) First loading vectors obtained for Pt/Al2O3 (squares) and Al2O3 (solid line). (b) Scores for the first loading vectors obtained for Pt/Al2O3 (squares) and Al2O3 (solid line). The dashed line indicates the time profile of the introduction of formaldehyde solution pulses.

of the time-dependent scores for Al2O3 and 5% Pt/Al2O3. In the case of the blank, we observed some baseline variations that were evident in various blank runs (only one is shown). Those variations can be due to small changes on the Al2O3 film, which has a peak close to the 1000 cm-1 region. Otherwise, the general profiles of the scores are in agreement with the formaldehyde concentration profile in both cases. Although we have been unable to discern any clear surface vibrational signatures from adsorbed formaldehyde (or methanol), there is clear evidence for formaldehyde dissociation. Figure 8a shows the first loading vectors obtained in the CO stretching region (1800-2100 cm-1) for bare alumina support (solid lines) and Pt/Al2O3 (squares). Although the vector for alumina does not have a shape that resembles a spectral feature, the peak at 2028 cm-1 in the Pt/Al2O3 vector can be clearly assigned to vibration arising from adsorbed linear carbon monoxide. The relatively low frequency suggests low local coverage of CO on the platinum surface, perhaps due to adsorption on smaller particles, edges, or kinks. Similar results have been observed in electrochemical studies and were attributed to the formation of CO on irregular sites.14,18,19 According to Vidal et al.,19 when adsorbed CO is formed during methanol oxidation, the hydrogen blocks the terminal sites which can in turn limit the CO formation to terraces and edges on the catalytic sites. A similar behavior was observed by Kitamura14 for both methanol and formaldehyde and was attributed to low CO coverage in the catalytic surface.

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Figure 8. Multivariate analysis in the 1800-2100 cm-1 region of spectra collected during exposure of Pt/Al2O3 and Al2O3 to multiple pulses of formaldehyde in water: (a) First loading vectors obtained for Pt/Al2O3 (squares) and Al2O3 (solid line) without intermediate O2-H2 treatments, and for Pt/Al2O3 with intermediate O2-H2 treatments (circles). (b) Scores for the first loading vectors obtained for Pt/Al2O3 (squares) and Al2O3 (solid line) without intermediate O2-H2 treatments, and for Pt/Al2O3 with intermediate O2-H2 treatments (circles). The dashed line indicates the time profile of the introduction of formaldehyde solution pulses.

The score for Pt/Al2O3 shown in Figure 8b (squares) reveals that the adsorbed CO correlates with the presence of solutionphase formaldehyde (indicated by the dashed line). When the formaldehyde is removed from solution, the CO desorbs from the surface relatively slowly, on the time scale of a few hours. Upon the second introduction step of formaldehyde (at ca. 700 min), the surface is replenished with CO but is unable to reach the same coverage as attained during the first step. This desorption-formation profile continues through the third step, with the CO reaching an even lower surface coverage. This gradual reduction in CO formation capacity is likely due to increasing amounts of other fragments that build up on the catalyst surface. To test this hypothesis, we performed several experiments involving three successive H2-O2 treatments of the catalyst between each formaldehyde step. The same carbon monoxide peak is observed in the first loading vector obtained for this experiment (Figure 8a, circles). However, its associated score is considerably different in that the CO is removed much faster (i.e., ca. 30 min versus ca. 3 h) between the formaldehyde steps by the gas treatment. Furthermore, the capacity of the platinum to adsorb the CO from formaldehyde dissociation is largely retained during each step. As is evident from Figure 8, we observe some small variations during the intermediate reduction treatments

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Figure 10. Raw ATR-IR spectral data obtained in the 2200-2325 cm-1 region both before (bottom) and after (top) switching the liquid flow from pure hexane to 40 mM acetonitrile in hexane for (a) bare Al2O3 (lines) and (b) 5% Pt/Al2O3 (squares).

Figure 9. Multivariate analysis in the 1900-2100 cm-1 region of spectra collected during exposure of Pt/Al2O3 to multiple pulses of methanol in water: (a) First loading vector. (b) Score of the first loading vector. The dashed line indicates the time profile of the introduction of methanol solution pulses.

after each formaldehyde adsorption step. This is consistent with the behavior observed in the hydrogen adsorption studies described earlier (cf. Figure 2). The presence of hydrogen reacts with surface carbonaceous species left in the catalytic surface along with adsorbing itself, thus contributing to the observed variations in the score. It is worth noting that the formaldehyde solution used in this study (and in many other infrared and electrochemical investigations14-21) contains a small but significant (maximum of 15%) amount of methanol as a stabilizer. To verify that the CO formation seen in the present study corresponds mainly to CH2O, a similar experiment was performed using methanol. In this case, the amount of methanol used was 15 mM, which is the approximate amount of methanol present in the formaldehyde solutions used above. Figure 9, panels a and b, shows the first loading vector and associated score, respectively, obtained in the 1900-2100 cm-1 region. The profile of the three methanol introduction steps is indicated by the dashed line in Figure 9b. It is readily seen that the amount of CO formed by methanol during the first step is around a factor of 5 smaller than in the case of formaldehyde. The downshifted peak at 2015 cm-1 is consistent with this apparently lower coverage. In addition, the CO disappears from the surface in around 1 h, well before the methanol has been removed from solution. Furthermore, no CO was observed to form during the other two steps. Therefore, the behavior observed in Figure 8 arises almost entirely from adsorption and dissociation of formaldehyde on platinum. Acetonitrile Adsorption and Hydrogenation on 5% Pt/Al2O3 in Hexane. Nitrile hydrogenation has been a well

studied class of reactions due to its importance for the production of amines. Nitrile hydrogenation is also intriguing on a fundamental level from the standpoint of selectivity toward different amines. De Bellefon and Fouilloux have provided an extensive discussion on reaction mechanisms for nitrile hydrogenation.24 The most common heterogeneous catalysts studied so far are Ni, Co, Ru, Pt, and Pd. Raney Ni is one of the most active and is readily used in industrial applications for the production of primary amines. However, the fragility of its skeletal structure coupled with the fact that it is pyrophoric and can be difficult to handle makes development of other more-robust catalysts desirable. Most recently, Sachtler and co-workers25,26 published a series of studies involving hydrogenation of nitriles on supported transition metals. One of their key findings was that tertiary and secondary amines could form equally well during both liquid- and gasphase hydrogenation conditions, suggesting that all of the reaction steps converting nitriles to primary, secondary, and tertiary amines and unsaturated compounds likely can take place on the catalyst surface.25 Unfortunately, most of the surface species that are proposed to exist on metals during these complex reactions have not been directly identified, even under ultrahigh vacuum (UHV) conditions. We are therefore interested in the adsorption and reaction of nitriles at heterogeneous catalytic interfaces. In our previous paper,8 we showed some preliminary results of the adsorption of butyronitrile on 5% Pt/Al2O3. In the present case, we consider the case of acetonitrile adsorption from hexane, again employing multivariate analysis of the ATR-IR data. Figure 10 shows the raw spectral data obtained in the CN stretching region (i.e., 2200-2325 cm-1) both before and after switching the liquid flow from pure hexane to 40 mM acetonitrile in hexane over bare Al2O3 (Figure 10a) and 5% Pt/Al2O3 (Figure 10b). The first loading vectors (Figure 11a) are essentially identical for the two surfaces, revealing the presence of two clear peaks at 2258 and 2295 cm-1. The scores for the vectors (not shown) confirm a very strong correlation with liquid-phase acetonitrile, and thus these two peaks are assigned to the CN stretches of acetonitrile in hexane. These two peaks are characteristics for free liquid-phase acetonitrile27,29,30 in solution with the peak at (24) De Bellefon, C.; Fouilloux, P. Catal. ReV. - Sci. Eng. 1994, 36, 459. (25) Huang, Y.; Sachtler, W. M. H. Appl. Catal. A 1999, 182, 365. (26) (a) Huang, Y.; Sachtler, W. M. H. Stud. Surf. Sci. Catal. 2000, 130, 527. (b) Huang, Y.; Sachtler, W. M. H. J. Catal. 2000, 190, 69. (c) Huang, Y.; Sachtler, W. M. H. J. Catal. 1999, 188, 215.

The Solid-Liquid Catalytic Interface

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2258 cm-1 corresponding to VC≡N and the band at 2290 cm-1 corresponding to a combination band (VC-C + VC-H) as indicated by the liquid-phase spectra referenced in Table 1. In the studies performed by Ou et al.,29 these two features were observed when Pt (111) was exposed to a multilayer of acetonitrile.

Although the first loading vector for either surface does not appear to represent a surface species, analysis of the second loading vector is more revealing. Figure 11b shows the second loading vector obtained for bare Al2O3 (solid line) and 5% Pt/ Al2O3 (solid line with squares) in the CN stretching region. As for the first loading vector, there are clearly contributions from two major peaks that are not shifted from their liquid-phase values of 2258 and 2295 cm-1. However, the 5% Pt/Al2O3 sample shows clear shoulders on each of these peaks that are blueshifted by around 15-20 cm-1 in each case. These blue-shifted shoulders are assigned to acetonitrile adsorbed onto platinum in an end-on configuration through the nitrogen. In the case of adsorption of acetonitrile on transition metals, a 20-70 cm-1 blue-shift is expected.28-30 This has been observed in both electrochemical and UHV environments. Sexton performed studies of acetonitrile adsorption on Pt(111) under UHV conditions using EELS and observed a peak at 2268 cm-1 during multilayer adsorption that was assigned to the CN stretch. Krtil et al.29 observed a shift to 2330 cm-1 under an electrochemical environment, which they assigned to a combination of adsorbed acetonitrile and CO2 formation due to the presence of trace amounts of water. Marinkovic et al.27 performed studies using subtractively normalized interfacial FTIR spectroscopy (SNIFTIRS) to study the adsorption of acetonitrile on a single reflection electrode. In their study, they observed evidence for free acetonitrile in solution and for adsorbed acetonitrile. These studies are also summarized in Table 1. The associated scores of the second loading vector are shown in Figure 11c, along with the experimental concentration-time profile of liquid-phase acetonitrile. Upon the first introduction of acetonitrile, the scores are observed to increase for both surfaces. However, upon removal of acetonitrile after the first step (i.e., at around 330 min), the scores remain at this higher value. Furthermore, they are largely unaffected by the two other acetonitrile concentration steps. In this figure, we have included the score of the second loading vector obtained by multivariate analysis in the 2400-2500 cm-1 spectral region (Figure 11c, star symbols). Since there are no molecular vibrations that appear in this region, this analysis provides a good baseline for the case of no spectral variation and highlights the significant changes observed in the 2200-2325 cm-1 region. Such behavior suggests that the second loading vector represents (at least partly) adsorbed species on both Al2O3 and Pt/Al2O3. However, the overlapping of such surface peaks with the bulk liquid signal results in a fair bit of uncertainty in the analysis. Nevertheless, adsorption of acetonitrile on both Al2O3 and Pt in an end-on configuration has been confirmed by sum-frequency spectroscopy (SFS),31 lending support to the present findings. Inspection of the spectral data in the 1500-1800 cm-1 region also revealed significant variations. Multivariate analysis of this region yields a first loading vector (Figure 12a) that shows a peak centered at 1641 cm-1. The associated score (Figure 12b) reveals that this vector correlates strongly with the presence of acetonitrile in solution. It is known that acetonitrile often has trace water impurity present, which might yield a similar peak in this region. However, analysis of data obtained for bare Al2O3 does not reveal a similar vector or score in this frequency region, which would be expected if the signal was coming from liquid water. According to previous UHV experiments on Pt(111) with EELS28,32 peaks at around 1640 cm-1 have resulted from the adsorption of acetonitrile via strong interactions with the CN

(27) Marinkovic, N. S.; Hecht, M.; Loring, J. S.; Fawcett, W. R. Electrochim. Acta 1996, 41 (5), 641. (28) Sexton, B. A.; Avery, N. R. Surf. Sci. 1983, 129, 21. (29) Ou, E. C.; Young, P. A.; Norton, P. R. Surf. Sci. 1992, 277, 123.

(30) Krtil, P.; Kavan, L.; Nova´k, P. J. Electrochem. Soc. 1993, 140, 3390. (31) Strunk, M., Williams, C. Langmuir 2003, 19 (22), 9210. (32) Hubbard, A. T.; Cao, E. Y.; Stern, D. A. Electrochim. Acta 1994, 39 (8/9), 1007.

Figure 11. Multivariate analysis in the 2200-2325 cm-1 region of spectra collected during exposure of Pt/Al2O3 and Al2O3 to multiple pulses of 40 mM acetonitrile in hexane: (a) First loading vectors obtained for Pt/Al2O3 (squares) and Al2O3 (line). (b) Second loading vectors obtained for Pt/Al2O3 (squares) and Al2O3 (line). (c) Scores for the second loading vectors obtained for Pt/Al2O3 (squares) and Al2O3 (solid line). The score for a baseline obtained in the 24002500 cm-1 region for Al2O3 are shown with stars (see text for details). The dashed line indicates the time profile of the introduction of acetonitrile solution pulses.

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Figure 12. Multivariate analysis in the 1500-1800 cm-1 region of spectra collected during exposure of Pt/Al2O3 to multiple pulses of 40 mM acetonitrile in hexane: (a) First loading vector. (b) Score of the first loading vector. The dashed lines indicate the time profiles for introduction of various components, as indicated within the figure. Table 1. Literature Peak Assignments for Bulk Acetonitrile and Its Adsorption on Platinum Surfaces monolayer adsorption

vibrational mode ν C-C r C-H δs C-H δd C-H ν CdN ν C≡N ν C-C + δ C-H

liquid this work ref 27 ATR-IR FTIR AN/hexane pure AN 918 1037 1375 1444 2253 2293

multilayer ref 28 EELS Pt(111) 920 1040 1430

2258 2295

2270

bond with the surface. For example, Sexton and Avery observed a feature at 1615 cm-1 during an EELS study of one monolayer of CH3CN on Pt (111). They assigned the peak to a CdN bond interacting with the Pt surface based in part on comparisons with the spectra obtained for CD3CN. Hubbard et al.32 also observed the presence of an HREELS feature at 1643 cm-1 for the adsorption of CH3CN onto Pt(111). Hubbard also observed a small feature at 1650 cm-1 for CH3CN adsorption in the presence of traces amount of water which they assigned to CdN stretching. Morin et al.33 studied the chemisorption of acetonitrile on Pt(111) and Pt(100) electrodes using potential difference infrared spectroscopy. They observed peaks at 1632 and 1614 cm-1 that were assigned to formation of a tilted acetonitrile on the surface, bonded through both the C and N. Table 1 reviews the vibrational spectroscopic data available for acetonitrile adsorbed on platinum using different spectroscopic approaches, including the present in-situ ATR-IR results. On the basis of comparisons with these literature assignments, we attribute the 1641 cm-1 peak to an adsorbed nitrile species on platinum that exhibits both end-on and π-bonded character (see Figure 12a). It is evident from the shape of the score that coverage of this species is much larger in the presence of liquid-phase acetonitrile. However, there is a definite accumulation of this species (as adjudged by the score) on the surface throughout the three acetonitrile pulses. The adsorbed species also appears to be stable on the surface even after the final acetonitrile pulse is completed. To determine whether this adsorbed species was (33) Morin, S.; Conway, B. E.; Edens, G. J.; Weaver, M. J. J. Electroanal. Chem. 1997, 421, 213.

ref 28 EELS Pt(111)

ref 32 HREELS Pt(111)

ref 27 SNIFTIRS Pt(111),(100)

950 1060 1375 1435 1615

966, 984

928, 938 1043

ref 33 PD-FTIR Pt(111), Pt(100)

1375 1456 1632, 1614

1427 1643 2273, 2285 2305, 2318

this work ATR-IR Pt/Al2O3

1641 ∼2275 ∼2310

reactive toward hydrogen, a final O2-H2 treatment was performed. Although the oxygen appeared to have no significant effect on the score, the treatment in hydrogen resulted in a return back to its baseline value.34 Therefore, it appears that this species is indeed reactive toward hydrogen, and thus may play a role in the mechanism for nitrile hydrogenation on platinum. This is the subject of ongoing investigation in our laboratory.

Concluding Remarks It has been demonstrated that attenuated total reflection infrared (ATR-IR) spectroscopy coupled with multivariate data analysis can be effectively used for in situ investigation of supported catalyst-liquid interfaces. From the standpoint of the liquid phase, the approach is very effective at correlating observed spectral peak evolution with controlled concentration profiles. This was the case with formaldehyde in the present case, for which the multivariate analysis confirmed its presence in solution during the course of the experiment. However, although the absolute infrared signal for formaldehyde in solution was very small, we were unable to detect any adsorbed molecular formaldyde. Nevertheless, adsorption is suggested by the presence of adsorbed carbon monoxide through dissociation. However, perhaps the (34) The spikes observed in Figure 12 and throughout are periodically seen in our multivariate analysis results. We believe that they are caused by spectral artifacts (often gas-phase water) that are not completely removed by the baseline corrections and subtractions. In the case of Figure 12, for example, other repetitions of this experiment also exhibit a few spikes in the scores, but in different locations. We do find that data obtained on days with less humidity typically result in score profiles that are cleaner with respect to spikes, especially in spectral regions that overlap with gas-phase water IR absorption.

The Solid-Liquid Catalytic Interface

most interesting observation from our formaldehyde adsorption experiments was the presence of variations in the spectra that appear at least partly associated with adsorbed atomic hydrogen. These peaks are very small in intensity, and multivariate analysis allowed the correlation of their presence with that of solutionphase H2. In the case of acetonitrile adsorption, some limitations of the approach are more apparent. In this case, the liquid-phase signal in the CN stretching region was very strong, with the first loading vector accounting for most of the observed variations. A second loading vector consisted of peaks that are similar to those in the liquid phase but with apparent shoulders. Although the scores indicated that this variation was consistent with adsorption, the data were very noisy. Furthermore, this second loading vector and its behavior might be affected by refractive index effects. This will be a general limitation of this method whenever a surface peak exists directly underneath a solvent or other liquidphase peak. However, the approach was successful in detecting a vector that revealed a peak at 1641 cm-1, which was assigned to the adsorption of the CN group in a tilted configuration involving a combination of end-on and π interaction with the surface. Analysis of the variation found this species to be reactive toward hydrogen, suggesting that it might play a role in nitrile

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hydrogenation. We are currently performing more direct reaction studies (i.e., hydrogen present in solution with the nitrile) to explore this possibility. On a more general note, this multivariate analysis method should be generally applicable not only for study of solid-liquid catalytic systems, but also infrared spectroscopy studies of catalysts in general. Automated flow and temperature control systems, coupled with the ability to write macros that control the infrared spectrometer function, allow for lengthy experiments to be performed where hundreds to thousands of spectra are recorded. Such data collection with multivariate analysis can capture behavior that might be missed during typical steady-state spectroscopic measurements that are often utilized in catalytic studies. Acknowledgment. This work was funded by the American Chemical Society Petroleum Research Fund (PRF) under ACSPRF#35610-G5 and also through a CAREER award to C.T.W. from the National Science Foundation (CTS-0093695). I.O. thanks the Sloan Foundation for a graduate fellowship. We thank Prof. Edward P. Gatzke of USC for numerous conversations and advice related to multivariate analysis methods. LA052821T