J. Phys. Chem. C 2007, 111, 6813-6820
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Studies on Reduction Dynamics of Nickel Atoms Incorporated in MCM-41 by XANES with Two-Dimensional Correlation Spectroscopy Approaches Melke A. do Nascimento,† Carlos A. Paskocimas,‡ Arilson J. N. Silva,† and Renato C. Ambrosio*,† UniVersidade Federal do Rio Grande do Norte, Departamento de Quı´mica, Departamento de Engenharia de Materiais, CP 1662, 59078-970, Natal/RN-Brasil ReceiVed: January 23, 2007; In Final Form: March 5, 2007
The reduction kinetics of Ni cations incorporated in MCM-41 was investigated by in situ XANES measurements performed at a dispersive beamline. The detailed analysis of XANES spectra was conducted by perturbationcorrelation moving-window 2D correlation spectroscopy (PCMW2D) and generalized 2D correlation spectroscopy (2DCORA) formalisms. PCMW2D results showed important changes on white-line intensities associated with water desorption and dehydroxilation during the heating process. PCMW2D successfully located the onset of nickel reduction during H2-TPR and characterized the pattern of changes on white-line and preedge intensities from rapid to gradual. The contributive character of progressive 2D correlation analysis calculations induced neglecting of variations on rate of spectral feature changes. To circumvent this limitation, we employed an alternative approach, calculating contributions to the 2D correlation function using a subset of spectra to take into account the temporal complexity structure of in situ collected data. The results show that the Ni reduction is a dynamic process in which the rate of spectral variations changes as the reaction progresses.
Introduction Low-dimensional systems of metals and metal oxides are of great interest both from the theoretical point of view and because of their potential applications in several fields of science, for example, physics, chemistry, and material science.1 In particular, metallic guests anchored into the channel network of mesoporous materials like MCM-41 may present interesting catalytic properties with respect to their homogeneous-phase counterparts.2-4 This family of nanomaterials is widely used in the field of heterogeneous catalysis and materials science like, as an example, catalytically grown single-walled carbon nanotubes.2-4 Reducibility of metal cations incorporated into the MCM-41 framework has been related to its catalytic activity and selectivity.3 Understanding the reduction behavior of nickel in NiMCM-41 is, therefore, essential for the development of catalysts. Temperature-programmed reduction (TPR) and isothermal reduction are very convenient techniques for studying the reduction behavior of metallic cations incorporated on oxide supports. The finite size of metallic particles resulting from NiMCM-41 reduction prevents its full characterization with traditional techniques like X-ray diffraction (XRD) and transmission electron microscopy (TEM).4-6 On the other hand, X-ray absorption spectroscopy (XAS) does not require crystalline arrangement of atoms or vacuum for its measurements.5 XAS is a particularly powerful tool for the investigation of a chemical-specific local structure centered on the atomic sites of a selected absorbing element. The oscillatory structure in the absorption spectrum immediately around the edge (X-ray absorption near-edge structure, XANES), generally within 50 * To whom correspondence should be addressed. E-mail: rcanha@ ccet.ufrn.br. † Departamento de Quı´mica. ‡ Departamento de Engenharia de Materiais.
eV, is particularly sensitive to the geometric distribution of the near-neighbor atoms, that is, cell symmetry and the electronic (valence) state of the absorbing atom.5,6 The energy-dispersive variant of the technique (DXAS) is suited for in situ study of the system under reaction conditions through the use of polychromatic X-ray focusing optics and rapid-response spatially resolving detectors.7 On the other hand, the presence of overlapped bands and a large amount of spectra make the detailed analysis and assignment of spectral regions difficult. To circumvent this limitation, the generalized 2D correlation spectroscopy (2DCORA) can be a powerful technique when applied to kinetic studies.8-12 The 2DCORA is a powerful technique, applicable to the indepth analysis of in situ-collected spectral data. The mathematical formalism of 2DCORA compares changes at every variable (e.g., energy, wavenumber) with changes at all other variables and leads to two 2D correlation maps with the variable axes, one synchronous and the other asynchronous.8-12 The peaks in these plots provide evidence for changes in the spectral intensities in the data set. The changes in the spectral intensity can be caused by variations in temperature, pressure, or concentration of reactants, which result in perturbations that affect the system. The synchronous plot indicates whether intensity changes are occurring in the same direction or in the opposite direction, while the asynchronous plot defines the order of change and indicates which spectral intensity changes occur prior to the others.8-12 The main advantages of 2D correlation spectroscopy lie in the simplification of complex spectra consisting of peaks over the second dimension, establishment of unambiguous assignments through correlation of bands, and probing the specific sequential order of spectral intensity changes taking place during the measurement.8 The synchronous and asynchronous correlation with variable (E) indicates to what extent the changes in
10.1021/jp070574r CCC: $37.00 © 2007 American Chemical Society Published on Web 04/14/2007
6814 J. Phys. Chem. C, Vol. 111, No. 18, 2007 the dynamic signals at two spectral bands are temporally inphase or out-of-phase with each other. The 2D correlation analysis is only in its infancy, and further developments have emerged, for example, the 2D samplesample correlation analysis, introduced by Sasic et al.13,14 In sample-sample (ss) correlation, generalized 2D correlation analysis is applied to a set of transposed data to yield a new type of 2D spectrum having two sample axes, which indicates the relationship between different samples observed under different states of the perturbation by examining the similarity or difference of their spectral trace patterns along the spectral variable. After band correlation has been established by means of variable-variable (vv) 2D plots, additional knowledge about species concentration dynamics can be extracted by means of sample-sample 2D correlation.13,14 More recently, a new method of analysis, perturbationcorrelation moving-window two-dimensional correlation spectroscopy (PCMW2D), was proposed by Morita et al.15,16 For a spectral data set collected under an external perturbation, this method provides a pair of synchronous and asynchronous twodimensional correlation spectra plotted on a plane between a spectral variable (e.g., energy) axis and a perturbation variable (e.g., temperature) axis. One of the advantages of this new correlation analysis method is that it can clearly point out both a specific perturbation (e.g., melting temperature) and a characteristic spectral variable (e.g., corresponding band position) from spectral variations induced by a perturbation, even when it is applied to complicated spectra such as spectra where the band positions cannot be identified in the raw data. With the interpretation of PCMW2D correlation spectra, the pattern of structural changes in an object in all perturbation ranges can be elucidated. Even though the concept of generalized 2D correlation analysis was established at the beginning of the 1990s,8 to the best of our knowledge, no work was published using this methodology in interpretation of XANES spectra collected at dispersive beamlines. Recently Haider et al.11 published pioneering work about the application of 2DCORA in the interpretation of XANES spectra collected during the reduction process of the Co-MCM-41 system. Although this work presented advances in XANES data analysis by spreading spectral peaks over the second dimension, the measurements, conducted at a conventional X-ray absorption beamline where the photon energy is scanned step by step, can take a couple of minutes and, in a relatively fast process, can result in the introduction of artifacts in the data analysis. In this work, we employed the perturbation-correlation moving-window 2D correlation spectroscopy and generalized 2D correlation approaches to the interpretation of in situ XANES spectra collected at a dispersive beamline during the Ni-MCM-41 temperature-programmed and isothermal reduction. Experimental Sections In Situ XANES Measurements. Nickel-substituted MCM41 was prepared following a procedure described elsewhere.17 The Ni-MCM-41 catalyst used in these studies has 4 wt % nickel loading, an average pore diameter of 32.1 Å, and a surface area of 615 m2 g-1 as determined from BJH and BET methods, respectively, in excellent agreement with the work of Yang et al.4 XRD results showed the presence of diffraction peaks characteristic of the mesoporous MCM-41 structure with the absence of any reflection peak related to Ni phases, suggesting its atomic dispersion on pore walls. The evolution of Ni atoms
do Nascimento et al. during catalyst reduction was investigated using X-ray absorption near-edge spectroscopy (XANES) at the dispersive beamline of the Brazilian National Synchrotron Radiation Laboratory (LNLS). The energy dispersive X-ray absorption spectrometer of LNLS (DXAS Beamline) uses a bending magnet as the source, and the optics combine a vertical focusing mirror and a Bragg dispersing geometry that focuses the polychromatic beam to a spot size of 300 × 300 microns. The full spectrum of the transmitted beam is collected by a CCD-based position-sensitive detector in a few tens of seconds. Approximately 100 mg of the catalyst was pressed by 5 tons using a hydraulic pellet press, resulting in a round, selfsupporting wafer. The thickness and mass of the pellets produce an absorption jump around 1. Wafers were placed into a quartz reaction chamber. The reaction chamber, which allows controlled temperature and atmosphere, was placed at the focal point. In the first experiment performed, the system was heated from room temperature to 700 °C at ∼10 °C min-1 in flowing helium (100 mL min-1). After the temperature reached 700 °C, the flux of pure H2 (100 mL min-1) was initiated. XANES spectra were continuously recorded from ∼120 eV below to ∼340 eV above the Ni K-edge during heating of the reaction chamber and isothermal catalyst reduction by hydrogen at 700 °C. A second experiment performed consisted of temperatureprogrammed reduction (TPR) with pure hydrogen, where the system temperature was increased to 700 °C at ∼10 °C min-1. Using a time exposure of 100 ms and 30 accumulations, each spectrum was recorded in approximately 25 s. XANES Data Analysis. Data analysis was performed using homemade software.18 The experimental data measured as a function of position on the CCD array position-sensitive detector was converted to an energy scale. The energy scale of the calibration system was previously established by measurements made on a Ni foil using a standard energy-scanning XAS spectrometer. XANES spectra were corrected for background absorption by fitting the preedge data (from ∼100 to 20 eV below the edge) to a linear formula, followed by extrapolation and subtraction from the data over the energy range of interest. Finally, the spectra were carefully normalized, taking the jump edge as reference. Each normalized spectrum was arranged in the columns of the data matrix, every line corresponding to the energy points. Prior to the calculation of 2D correlation spectra, the experimental data set was reconstructed by abstract factor analysis for noise reduction, resulting in the reconstructed data matrix [M].19 Then, the average spectrum was subtracted to obtain mean-centered dynamic spectra y˜ , arranged on the columns of matrix [A].8 The detailed methods for calculating the synchronous and asynchronous correlation are described elsewhere.8 In this work, the calculation of synchronous correlations was performed straightforward by
[Φ] )
1 [A][A]t c-1
where [Φ] is the 2D matrix, c is the number of columns in [A], and t stands for the transpose operation. Each element of this matrix corresponds to the synchronous correlation (here, equivalent to statistical covariance) of the intensity variations measured at two different energies
Φ(E1,E2) )
1
c
∑y˜ (E1)‚ y˜ (E2) c - 1 J)1
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J. Phys. Chem. C, Vol. 111, No. 18, 2007 6815
where J is the spectrum number, that is, a column of matrix A, and y˜ (Ei) corresponds to the intensity at the ith row of [A]. The computation of asynchronous 2D correlation intensity is somewhat more complicated, and the simplest and most computationally efficient method for estimating this correlation matrix is given by8,9
1 [A][N][A]t c-1
[Ψ] )
where each element of this matrix corresponds to the asynchronous correlation of the intensity variations measured at two different energies
Ψ(E1,E2) )
c
1
c
∑y˜ J(E1)‚ k)1 ∑ NJk y˜ k(E2) c - 1 J)1
The term NJk corresponds to the Jth row and kth column element of the discrete Hilbert-Noda transformation matrix given by NJk ) 0 if J ) k and NJk ) 1/π(k - J) otherwise. Mathematical procedures for the calculation of PCMW2D correlation have been described in detail elsewhere.15 PCMW2D correlation spectroscopy uses a small window of the data matrix with a window size of 2m + 1. A submatrix of the data matrix M is constructed by choosing only 2m + 1 columns around the Jth column of the data. This procedure is carried out by picking up a series of Jth spectra by incrementing J from j - m to j + m, where j corresponds to the index of a window. In this case, the reference spectrum was taken as the mean spectrum of the window, and the dynamic spectrum was obtained by mean centering.15 The average perturbation and the dynamic perturbations in the jth column window are calculated as follows
pjj )
j+m
1
∑
2m + 1 J)j-m
pj
p˜ J ) pJ - pjj The synchronous (ΠΦ) and asynchronous (ΠΨ) PCMW2D correlation spectra are calculated as follows
ΠΦ(E,pj) )
ΠΨ(E,pj) )
1
1
j+m
∑
2m J)j-m
y˜ (E,pJ)‚ p˜ J
j+m
∑
2m J)j-m
j+m
y˜ (E,pJ)‚
∑
NJk‚ p˜ k
J)j-m
The synchronous and asynchronous PCMW2D correlation spectra are proportional to the spectral gradient (first perturbation derivative) and the negative rate of the spectral gradient change (perturbation second derivative) along the perturbation direction. Thus, a spectral intensity variation at a typical point on the plane between a spectral variable axis and a perturbation variable axis, such as a transition temperature at an index band of crystallinity, should be visualized in the PCMW2D. The spectral variations can be interpreted by the combination of the synchronous and asynchronous signs of spectra using the rules of PCMW2D correlation analysis derived by Morita et al.15 for the case of linear perturbation. The rules are summarized in Table 1. Results and Discussion Representative normalized Ni K-edge XANES spectra collected during the catalyst treatment in the first experiment are
Figure 1. Dynamic XANES spectra collected during the first experiment.
TABLE 1: Rules of PCMW2D Correlation Spectroscopy in the Case of Linear Perturbation asynchronous
asynchronous
spectral change
+ + + 0 -
+ 0 0 + 0 -
convex increment linear increment concave increment constant convex decrement linear decrement concave decrement
shown in Figure 1. The first 135 spectra correspond to heating the system in flowing helium from room temperature to 700 °C, and the last 165 spectra were collected during the isothermal reduction in a hydrogen atmosphere. White-line intensities at the onset of hydrogen injection are consistent with oxidized Ni atoms. An important feature of the XANES profiles in Figure 1 is the small preedge peak (inset of Figure 1). This absorption peak is attributed to a 1s-3d transition and is normally dipoleforbidden but becomes allowed when nickel has a tetrahedral coordination.4 Therefore, the presence of such a preedge peak in the XANES profiles is an indication of tetrahedral coordination of Ni atoms isomorphously substituted for silicon ions on the pore walls of MCM-41. The experimental XANES spectra were divided in three principal regions (inset of Figure 1) to simplify peak assignments on the 2D correlation plots. The region “A” in the energy range of 8328-8342 eV corresponds to the preedge and main energy features, which are associated with the symmetry around the absorbing atom and its valence. The region “B” at 8343-8355 eV corresponds to white-line intensity, related with the density of empty states at the Fermi level, while the region “C” at 83728386 eV is associated with multiple scattering (MS) resonance features whose intensities and shape strongly depended on the scattering of the outgoing photoelectron by the coordination shells beyond the first one. Since the changes in preedge and white-line intensities are highly associated with the chemical state of nickel atoms, our analysis will be centered on these two features. To obtain information about the temperature dependence of XANES spectral features, the spectra collected during the heating process under a He and H2 atmosphere (first and second experiment, respectively) were analyzed by means of PCMW2D with a window size of 2m + 1 ) 15. Figures 2 and 3 present
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Figure 2. PCMW2D correlation spectra calculated from the temperature-dependent XANES spectra during the heating of Ni-MCM-41 under helium a atmosphere; (a) synchronous and (b) asynchronous PCMW2D spectra. In (b), blue hues show negative peaks.
the PCMW2D synchronous and asynchronous correlation spectra plotted between the energy and temperature axes calculated with the spectra collected during the first and second experiment, respectively. Three important temperature intervals, where the white-line intensity changes during the heating under the He atmosphere, are seen by the analysis of the PCMW2D synchronous map (Figure 2a). The first peak observed in the PCMW2D synchronous spectrum shows variations in the white-line intensity with ΠΦ(B, t ≈ 100 °C) > 0, indicating an increase in the intensity of this feature. The same increase in white-line intensity was observed in the TPR experiment (Figure 3a). An important decrease in white-line intensity is observed when the catalyst is heated under He in the temperature range of 180-300 °C
do Nascimento et al.
Figure 3. PCMW2D correlation spectra calculated from the temperature-dependent XANES spectra during the temperature-programmed reduction of Ni-MCM-41; (a) synchronous and (b) asynchronous PCMW2D spectra. In (b), blue hues show negative peaks.
without any visible changes in the preedge region. At this temperature range, ΠΦ(B, 180 < t < 300 °C) is always negative, while ΠΨ(B) changes its signal from positive to negative at 250 °C, indicating that the decrease in the intensity of the white line becomes rapid at ∼250 °C, from a convex decrease to a concave decrease. The same observations can be made for temperature ranges of 550-640 °C, with a transition temperature of 600 °C. It is well-known that the MCM-41 wall surface is composed of silanol groups.20,21 The density of surface silanol groups may be expected to decrease with an increasing fraction of the Ni ions distributed on the subsurface and when the nickel loading increases beyond 4% results in a higher Ni2+ concentration close to the surface.4 Assuming that an important fraction of Ni atoms are located in the subsurface, forming Ni-OH bonds, the
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J. Phys. Chem. C, Vol. 111, No. 18, 2007 6817
Figure 4. Synchronous plots using different numbers of spectra recorded during isothermal reduction of Ni-MCM-41. Blue hues show negative peaks.
intensity of the white line could be influenced by the strong negative inductive effect of the O-H group. Since no pretreatment was performed to produce a Ni-MCM-41 clean surface prior to XANES experiments, on the as-prepared wafers, the adsorbed water forming hydrogen bonds with the hydroxyl groups should reduce the inductive effect of O-H. Thus, the positive ΠΦ at around 100 °C can be associated with a water desorption process, which results in an increase of the whiteline intensity. With further heating under a helium atmosphere, the decrease in white-line intensity is likely due to the surface dehydroxilation, that is, 2 OH- ligands have been replaced by an O2ligand (with a greater electron donation effect) in the coordination sphere of nickel and silicon atoms.11 This conclusion is in agreement with Lin et al.,20 who reported the removal of a significant amount of OH groups on the surface of Co-MCM41 with inert gas at a high temperature due to hydroxyl condensation. This process does not change the symmetry of the nickel atoms since the preedge feature does not show considerable correlations, suggesting that a great part of the hydroxyls removed from the wall surface are bound to silicon ions. The PCMW2D correlation plots calculated with the spectra collected during the TPR experiment (Figure 3) show a strong negative synchronous PCMW2D correlation for the temperature range of 180-450 °C, ΠΦ(B, 180 e t e 400 °C) < 0, and ΠΨ(B, 180 e t e 400 °C) ) 0, with no correlations with the preedge, indicating a linear decrease on the nickel density of empty states with an increase in temperature. At ∼450 °C, the decrease of white-line intensity becomes more important, and ΠΦ(B) < 0 and ΠΨ(B) > 0, indicating a convex decrement, and at ∼530 °C, ΠΨ(B) becomes zero, suggesting a transition to a slower linear decrease. Clear reduction of nickel can be observed at 460 °C since the synchronous PCMW2D correlation becomes positive (ΠΦ(A) > 0) with ΠΨ(A) < 0, indicating a fast concave increment of preedge intensity, which at ∼480 °C changes to a slow linear increment with ΠΦ(A) ) 0. The simultaneous decrease in the white-line and the increase in the preedge intensities is indicative of changes in the local symmetry and valency of the nickel species in the MCM-41 silica framework and nucleation of the metallic clusters. When a comparison is made between the PCMW2D synchronous correlation intensities in the temperature range of 200450 °C of the first and second experiments, it can be seen that, during TPR, the decrease in the white-line intensities (i.e.,
spectral gradient) is higher than that from heating in He. The large decrease in the white-line intensity, observed during TPR, suggests that, in a H2 atmosphere, the surface dehydroxilation can occur by a different mechanism other than just hydroxyl condensation. To gain insight into the isothermal Ni reduction dynamics, progressive correlation analysis (ProCora) was performed on the spectra collected in the hydrogen atmosphere at 700 °C (experiment 1). The ProCora consists of sequentially analyzing groups of spectra using the generalized 2D correlation method, starting from the first two spectra, and increasing the analyzed matrix by one at each stage.11 The synchronous and asynchronous intensities are plotted in Figures 4 and 5, respectively. When the 2D correlation analysis was performed on the first two spectra in the series (Figure 4a), variations in the intensities of white-line (peak “B”) and MS resonances (peaks at “C” region) occur, while no changes in the preedge intensity can be observed to occur when examining the synchronous plot, suggesting that the white-line intensity changes prior to the preedge. The corresponding asynchronous plot (not shown here) showed only noise since two spectra are insufficient to define its characteristics. The introduction of the third spectrum to the series analyzed by the 2D correlation method results in the appearance of an auto peak related to the preedge region (peak “A”) and a forth characteristics located at 8400 eV in the synchronous plot. There are also three cross peaks located off the diagonal in the synchronous contour plot in Figure 4b. Including four or more spectra in the analysis results in a constant shape of the synchronous plots with cross peaks related to correlations between white line-preedge (negative B-A correlation), MS resonances-preedge (positive C-A correlation), and MS resonances-white line (negative C-B correlation). The negative correlation of the B-A peak is to be expected since the main edge energy shifts toward lower values and is accompanied by an increase in the preedge intensity at 8335 eV, with a simultaneous decrease in the whiteline intensity at 8355 eV. The synchronous plots also suggest an increase in the MS feature, in agreement with the inset of Figure 1. The sequential order of the spectral changes is obtained by examining the corresponding asynchronous plots (Figure 5). The asynchronous plot has no auto peaks, and its cross peaks emerge only when changes in the spectral intensities occur out-of-phase. According to Noda’s interpretation rules,8 the sign of the cross
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do Nascimento et al.
Figure 5. Asynchronous plots using different numbers of spectra recorded during isothermal reduction of Ni-MCM-41. Blue hues show negative peaks.
peaks in the synchronous plots can be either positive or negative, depending on whether the simultaneous changes are in the same direction or in the opposite direction, while the sign of the auto peaks is always positive since these peaks are related to the variability of the spectral characteristic analyzed. The sign of the asynchronous cross peak is positive if the intensity change at energy E1 occurs prior to the change of intensity at E2 (E1 > E2) in the sequence of spectra, although the rule is reversed if the sign of the corresponding synchronous cross-peak intensity is negative.8-10 The asynchronous plot obtained with the first three spectra (Figure 5a) shows a negative band related to correlations of MS resonances (D and C features) and white-line (B feature) intensities with the preedge intensity (A feature). Positive correlation can be seen between the C and B features. The application of Noda’s rule suggests that white-line intensity decreases first, followed by increase in the preedge intensity prior to an increase in the C feature. The introduction of the fourth and fifth spectra does not change the trend of white-line intensity decrease prior to preedge intensity increase. On the other hand, the asynchronous plots resulting from the inclusion of the sixth to the whole series of spectra on the analysis results in a positive correlation between the B and A features, which suggests a reversion of order, that is, the preedge changes prior to the white line instead of the white line first. This result is intriguing, as any change in nickel symmetry should occur after changes in the density of empty states at the Fermi level (i.e., valence) of nickel atoms.
The intensity of the asynchronous cross peaks corresponds mathematically to the orthogonal correlation function of spectral intensity variations, calculated incrementally between the first and the last spectra included in the analysis.8 Unfortunately, this methodology loses all information along the dynamic coordinate. Since the asynchronous plots are highly sensitive to nonmonotonic behavior of the spectral data,11 the development of efficient exploratory analysis methods of spectroscopic data is therefore crucial. The methods used for analysis of this kind of data should be local and be able to take into account the complex temporal structure of in situ-collected spectra. In order to understand the Ni-MCM-41 isothermal reduction process, which involved the measurements of several spectra, we employed a variant of the original moving-window 2D correlation analysis (MW2D)15,22 method, where correlation analysis is performed on the subset of spectra included in a window of fixed size, which is moved one column downward along the series of spectra, sliding the window position from the first to the last analyzed spectrum. The results of MW2D analyses can be represented in a 3D plot in which each slice corresponds to the incremental contribution of the added spectrum to the correlation function. Since the quantitative analysis of the 3D plot is not straightforward, especially with a great number of spectra, we introduce an alternative approach to follow the individual contributions for correlations between two spectral features. In this approach, we selected a square on the 2D plot related to correlations
Reduction Dynamics of Ni Incorporated in MCM-41
J. Phys. Chem. C, Vol. 111, No. 18, 2007 6819 Considering the waveform of white-line and preedge intensity changes well described by exponential functions (see Figure 6b), the intensity of the corresponding asynchronous correlation is expressed in a closed analytical form in which the asynchronicity is uniquely determined by the rate constant difference.8 Thus, we concluded that the reason for white-line-preedge asynchronous correlation fluctuations is governed by changes in the rates constant, that is, the filling of empty states at the Fermi level occurs simultaneously with a change in symmetry of nickel atoms, but the rate of the processes are exchanged with time. This conclusion is reinforced with PCMW2D results of the TPR experiment, which show variations in the rate of the increase in the preedge and the decrease in the white-line intensities at different stages of the reduction process. Conclusions XANES measurements were performed during the reduction of Ni embedded in a MCM-41 molecular sieve. Perturbationcorrelation moving-window two-dimensional correlation analysis was successfully applied to the series of XANES spectra during thermal treatment and temperature-programmed reduction in hydrogen between room temperature and 700 °C. This data analysis method has been proven useful for the observation of small changes in white-line intensities associated with water desorption and dehydroxilation, respectively. The dehydroxilation process was found to possess distinct rates under a He and H2 atmosphere. The onset of nickel reduction was clearly located by means of PCMW2D. To circumvent the limitations of ProCora, we proposed a variation in the MW2D correlation analysis, in which the contributions to the synchronous and asynchronous correlation were calculated along the data matrix. This new approach proved to be useful in the interpretation of the Ni-MCM-41 isothermal reduction that showed to be a dynamic process in which the rate constants for white-line and preedge intensities changed as the reaction progressed.
Figure 6. (a) MW2D spectral contributions for synchronous (ΩΦ) and asynchronous (ΩΨ) correlation between the white line and preedge and (b) variation of the intensities of the preedge feature and of the white line during isothermal Ni-MCM-41 reduction.
between a pair of spectral bands. Then, a sum was performed on all values, followed by division by the number of summed points, resulting in an average contribution to the correlation function. This procedure was performed on each slice of the 3D correlation contribution plot, and the values were retained in a vector. The calculated contributions of synchronous (ΩΦ) and asynchronous (ΩΨ) correlation between white-line and preedge peak intensities are depicted in Figure 6a, while the variation in the intensities of the preedge feature and of the white line during isothermal Ni-MCM-41 reduction are shown in Figure 6b. As can be seen, the early stages of reduction contribute negatively to the asynchronous correlation, which favors the white-line intensity decrease prior to the preedge peak increase. After five spectra, the contribution becomes positive, and the order is reversed. The apparent dilemma resulting from ProCora is elucidated from further analysis of Figure 6a, which indicates the periodic exchange of the sign of the asynchronous correlation contribution. The conservation of the signal of asynchronous ProCora plots after six spectra can be related to smaller contributions to the correlation function after the first few spectra.
Acknowledgment. The authors wish to thank CNPq (Brazilian Research Council) and LNLS (National Synchrotron Light Laboratory), Brazil, for their technical and financial support. References and Notes (1) Vralstad, T.; Glomm, W. R.; Ronning, M.; Dathe, H.; Jentys, A.; Lercher, J. A.; Oye, G.; Stocker, M.; Sjoblom, J. J. Phys. Chem. B 2006, 110, 5386. (2) Ciuparu, D.; Chen, Y.; Lim, S.; Yang, Y.; Haller, G. L.; Pfefferle, L. J. Phys. Chem. B 2004, 108, 15565. (3) Lim, S.; Ciuparu, D.; Chen, Y.; Pfefferle, L.; Haller, G. L. J. Phys. Chem. B 2004, 108, 20095. (4) Yang, Y.; Lim, S.; Du, G.; Chen, Y.; Ciuparu, D.; Haller, G. L. J. Phys. Chem. B 2005, 109, 13237. (5) Dı´az-Moreno, S.; Koningsberger, D. C.; Mun˜oz-Pa´ez, A. Nucl. Instrum. Methods Phys. Res., Sect. B 2005, 133, 15. (6) Song, Y.; Modrow, H.; Henry, L. L.; Saw, C. K.; Doomes, E. E.; Palshin, V.; Hormes, J.; Kumar, C. S. S. R. Chem. Mater. 2006, 18, 2817. (7) Bowron, D. T.; Diaz-Moreno, S. Anal. Chem. 2005, 77, 6445. (8) Noda, I. In General Theory of Two-Dimensional (2D) Analysis. Handbook of Vibrational Spectroscopy; Chalmers, J. M. , Griffiths, P. R., Eds.; Wiley: New York, 2002. (9) Noda, I. Appl. Spectrosc. 2000, 54, 994. (10) Zhang, J.; Tsuji, H.; Noda, I.; Ozaki, Y. Macromolecules 2004, 37, 6433. (11) Haider, P.; Chen, Y.; Lim, S.; Haller, G. L.; Pfefferle, L.; Ciuparu, D. J. Am. Chem. Soc 2005, 127, 1906. (12) Noda, I.; Ozaki, Y. Two-Dimensional Correlation Spectroscopy. Applications in Vibrational and Optical Spectroscopy; Wiley: New York, 2004. (13) Sasic, S.; Muszynski, A.; Ozaki, Y. J. Phys. Chem. A 2000, 104, 6380. (14) Sasic, S.; Muszynski, A.; Ozaki, Y. J. Phys. Chem. A 2000, 104, 6388.
6820 J. Phys. Chem. C, Vol. 111, No. 18, 2007 (15) Morita, S.; Shinzawa, H.; Noda, I.; Ozaki, Y. Appl. Spectrosc. 2006, 60, 398. (16) Watanabe, A.; Morita, S.; Ozaki, Y. Biomacromolecules 2006, 7, 3164. (17) Walcarius, A.; Delacote, C. Chem. Mater. 2003, 15, 4181. (18) The relevant program is available upon request from the authors.
do Nascimento et al. (19) Hu, Y.; Li, B.; Sato, H.; Noda, I.; Ozaki, Y. J. Phys. Chem. A 2006, 110, 11279. (20) Lim, S.; Yang, Y.; Ciuparu, D.; Wang, C.; Chen, Y.; Pfefferle, L.; Haller, G. L. Top. Catal. 2005, 34, 31. (21) Guo, W.; Li, X.; Zhao, X. S. Microporous Mesoporous Mater. 2006, 93, 285. (22) Thomas, M.; Richardson, H. H. Vib. Spectrosc. 2000, 24, 137.
