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Methanol adsorption on single crystal NiO(100) studied by HREELS deconvolution
Journal of Electron Spectroscopy and Related Elsevier Science Publishers B.V., Amsterdam
Phenomena,
59 (1992) 223-241
223
Methanol adsorption on single crystal Ni0(100) studied by HREELS deconvolution K.W. Wulser and M.A. Lange11 Department (Received
of Chemistry, 11 November
University
of Nebraska,
Lincoln, NE 68588-0304
(USA)
1991)
Abstract High resolution electron energy loss spectra (HREELS) of transition metal oxide substrates are dominated by very intense Fuchs-Kliewer surface phonons, limiting the use of HREELS in adsorbate vibrational analysis. A deconvolution algorithm, first proposed by Cox, Egdell and Williams, has been used to remove much of the phonon structure, allowing
a significantly broader range to be available for adsorption studies. The deconvolution process is found to be robust to the choice of instrument response function and surface quality, and is self-correcting to adsorbate and other induced changes in surface phonon structure. The procedure is applied to the NiO(lOOtOCH,,,d, adsorbate system, yielding deconvoluted HREEL adsorbate spectra that are comparable in numbers and positions of characteristic adsorbate vibrational modes.
INTRODUCTION
The adsorption and decomposition of methanol on transition metal substrates is of interest in model studies of Fischer-Tropsch [l] and partial oxidation catalysis [2]. Of particular significance is the role of co-adsorbed oxygen in the stability of molecularly adsorbed methanol and in the formation of an important synthetic intermediate, the surface methoxy M-OCH, [3-g]. In many systems, predosing the transition metal substrate with oxygen promotes the formation of surface methoxy and shifts thermally induced decomposition to substrate temperatures higher than that observed on the oxygen-free surface. Because oxide substrates are often used in partial oxidation synthesis, or can be expected to form on the transition metal under typical catalytic conditions, the adsorption of methanol on well characterized oxide substrates is relevant in establishing the adsorbate properties of methanol and the methoxy intermediate. Correspondence NE 685886304,
to: M.A. USA.
0368-2048/92/$05.00
0
Langell,
Department
1992 Elsevier
Science
of Chemistry,
Publishers
University
B.V.
of Nebraska,
All rights reserved.
Lincoln,
224
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High resolution electron energy loss spectroscopy (HREELS) has been able to determine unequivocally the form of adsorbed methanol on a variety of metal substrates [3-141. In HREELS, a monoenergetic beam of z 5 eV is directed at the substrate surface and the adsorbate vibrational spectrum is measured by energy analyzing the electron loss spectrum within 500meV (4000 cm-l) of the elastically scattered beam [15]. When HREELS is applied to oxides [16-201 and other compounds 121,221 with significant ionic character, the substrate lattice itself participates in the vibrational loss processes through the excitation of intense transverse optical vibrational modes known as Fuchs-Kliewer phonons [23]. The phonons, which are strongly coupled to the incoming electron beam, yield an intense single loss peak of up to 80% of the elastic peak intensity followed by a series of higher multiple losses with intensities decreasing in Poisson distribution [24]. Because adsorbate vibrational modes are only w 1% of the elastic peak intensity, the phonon structure totally obscures all but the highest frequency adsorbate vibrational losses. A potential solution to the phonon problem was proposed by Cox et al. [17]. Their solution attempts to deconvolute the higher multiple losses or “harmonics” by using information from the single loss phonon peak, thus freeing a substantial portion of the HREEL spectral range for adsorbate vibrational analysis. The logarithmic deconvolution algorithm, which is explained further in the text below, is applied here successfully to methanolexposed stoichiometric and HZ-reduced NiO(lO0) surfaces to establish the presence of methoxy adsorbate species by identification of its characteristic adsorbate vibrational losses. The algorithm is not without problems, however, and several caveats in its application are also discussed. EXPERIMENTAL
All analyses were performed in a 40 liter ultrahigh vacuum (UHV) bell jar ion pumped to a base pressure of 4 x lo-‘Pa. The system is equipped with a Physical Electronics 15-225G double pass cylindrical mirror analyzer (CMA) for AES and X-ray photoelectron spectroscopy (XPS), four grid retarding optics for low energy electron diffraction (LEED), a 2 keV inert ion bombardment gun of in-house design and a UT1 100 C quadrupole mass spectrometer for residual gas analysis. Auger electrons were generated with a 2 keV electron gun and were mode lock amplified by modulating the CMA at 12 kHz with a sine wave of 2 eV amplitude. XPS spectra, initiated with either an aluminum (Al Ka; 1486.6 eV) or magnesium (Mg Ka; 1253.6eV) anode, were obtained in a pulse count mode at constant CMA pass energy, specifics for which are given in the relevant figure captions. All XPS spectra are referenced to the NiO(100) 0 1s peak, previously cali-
K. W. Wulser, M.A. Langell]J. Electron Spectrosc. Relat. Phenom. 59 (1992) 22%241
-1000
I
0
I
loo0
I
2ooo
225
I
3ooo
4 00
Electron Energy Loss, in cmml Fig. 1. HREEL spectra of NiO(IO0) (a) showing the well developed phonon spectrum and (b) after deconvolution.
brated to be at 529.4 eV [25] and are reported with a precision of 0.1 eV unless otherwise noted. The HREEL spectrometer is also mounted on the bell jar and was built in-house of nonmagnetic materials. A set of Helmholtz coils surrounds the chamber and nulls out stray magnetic fields to -c 10mGauss in the region of HREELS analysis. A mu-metal shield surrounds the spectrometer and the sample region to further homogenize the field. The HREEL spectrometer is a single pass 127O sector design (designed by J. Gland, University of Michigan, with blueprints adapted by J-M. White, University of TexasAustin) [26] that provides a monoenergetic primary beam adjustable from l-10 eV at O-l-l.OnA beam current. Scattered electrons are detected specularly at 60° relative to the surface normal in a pulse count mode. When operating on metal substrates the spectrometer can achieve a resolution of 6-7 meV, measured as the full width half maximum (FWHM) of the elastically scattered peak. However, when measurements are performed on the semiconducting NiO(100) sample, a resolution of lO-15meV is more commonly obtained. A typical HREEL spectrum of a clean stoichiometric NiO(100) surface is shown in Fig. 1, which is discussed more fully in the text below. The NiO(lo@ sample, a single crystal of approximately 0.5 mm thickness and 0.5 cm diameter, was cut from a boule grown by the Czochralski method (kindly supplied by N. Peterson, Materials Science Division, Argonne National Laboratory). The sample was oriented to within lo of the (100)
226
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plane, as determined by Laue X-ray diffraction, and was polished with successively finer grades of alumina to a final 0.03 pm particle size. When first mounted in the UHV bell jar, the surface was covered with multilayers of a defect-riddled higher oxide contaminant, which were removed only after multiple cycles of argon ion bombardment @PA cmp2Ar+ at 2 keV) and oxygen annealing (798K at 7 x 10-5Pa). The final cycle was followed by a 10min UHV anneal at 798 K. The integrity of the stoichiometric NiO(100) surface was established by AES, XPS, LEED and HREELS. With present AES settings and CMA resolution, we obtain an AES O/Ni = 2.5 peak-to-peak ratio using the 0 KL,L,/Ni L,M,,,M,,, AES transitions and observe no AES peaks attributable to surface contamination. Although AES is sensitive to changes in oxygen concentration, we advise caution in its use as a primary measure of surface stoichiometry for metal oxides such as NiO(100). Although deviations from the appropriate O/Ni AES ratio indicate a definite change in average composition, obtaining the “appropriate” relative Auger signals for oxygen and nickel do not insure stoichiometry because of the three dimensional nature of the measurement. To insure that the surface is NiO(-iOO>,the XPS must have a single Is oxygen component (529.4eV) and a nickel 2p region appropriate to nickel oxide (2p,,, at 854.2eV, 2p,,, at 871.8eV), with correct shake-up structure [25,27]. The HREEL spectrum must also show a well developed and intense Fuchs-Kliewer phonon spectrum, with higher harmonics clearly visible and with the fundamental phonon energy as expected for NiO. Our value at 564 cm-l (70.5 f 1.0 meV) compares well with previously reported values [17]. It is only when these conditions are met that the surface characteristics, including adsorption behavior, become truly reproducible. RESULTS
Deconvolution algorithm applied to the NiU(lO0) adsorbate-free surface A representative NiO(100) Fuchs-Kliewer phonon spectrum is given in Fig. l(a), plotted as a function of loss energy from the elastically scattered peak. Surface scientists interested in gas adsorption phenomena generally report HREELS adsorbate loss models in wavenumbers (cm-l), continuing a precedent set in analytical IR spectroscopy for fingerprint identification of molecular species. To facilitate comparison with characteristic adsorbate spectra reported in the literature, we retain the wavenumber tradition. The data can, however, readily be converted to meV by dividing the loss “energy” by the conversion factor of 8.065 cm-lmeV1. The spectrum is taken with 5.0eV primary beam energy and shows a typical resolution of 100 cm-‘, measured as the FWHM of the elastic peak.
K. W. Wuker,
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The resolution is comparable to [17,22] or better than [US] that previously reported for HREEL phonon spectra of nickel oxide, although it is somewhat broadened over the achievable resolution of our spectrometer, owing to the limited conductivity of the NiO surface. The multiple loss structure is similar in form to previously reported spectra with a single loss energy of 564cm-' (70.5f 1.0meV) and a higher loss progression at integral multiples of the single loss value. Because 300 K thermal energies are sufficient to excite detectable amounts of Fuchs-Kliewer phonons, a small gain peak is also observed and is reported as a negative loss in Fig. l(a). The measured (single gain)/(single loss) intensity ratio of 0.068 is close to that of 0.0653calculated assuming a Boltzmann distribution _Igain I
=
e-‘h”/RT)
(1)
1OSS
In principle, all peak shape and energy information on the multiple loss phonons is contained in the single phonon loss. If the surface vibrations are assumed to be harmonic in nature, the relative intensities of the progression can be shown to follow a Poisson distribution [24]. Thus, in the harmonic approximation, the single loss feature determines all information on the higher loss intensities too. Cox and Williams [22]have proposed an algorithm for the removal of the higher loss phonons based on the harmonic approximation and have applied it to several oxide surfaces [17,18,22]. We follow their argument in the next paragraph. The HREEL spectrum s(w) can be written as a self-convolution of the fundamental phonon spectrum p(o) s(w)
=
i(0) @
[s(o) +
&.))
+
p(o) $ p(a) + Ia) @“p .
@ph) + . . . .
(2)
1
where the symbol @ represents convolution, i(w) is the instrument response function and 6(O) is the elastic contribution whose peak shape is determined entirely by i(w). Fourier transformation of eqn. (2) into the time domain gives S(z)
=
I(s)
[
P(rj2 p(+3 1 + p(z) + 7 + QI + . . . .
.
1
(3)
where the quantity in square brackets in eqn. (3) is simply a logarithmic expansion of P(T)
S(z) =
I((z)e-S~)
(4)
Rearranging eqn. (4)and taking the logarithm of each side yields P(z)
=
In
[
E
1
(5)
228
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Ekctron
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59 (1992) 223-241
which allows the vibrational single loss spectrum to be obtained deconvoluted from the higher loss functions p(w)
=
9-l
In 1
Ea>
(6)
Application of eqn. (6) to the NiO(100) HREELS data of Fig. l(a) results in the spectrum shown in Fig. l(b). The elastic peak has been used to approximate the instrument response function, as suggested by Cox and Williams [22], and in generating Fig. l(b) we have taken i(w) to be a Gaussian function with FWHM equal to that of the elastic peak, and with an exponential tail added similar in form to that applied in XPS analysis [28]. The form of the instrument response function is discussed more fully below. The elastic peak itself is removed by use of eqn. (6), and data to the left of the zero loss position is discarded prior to performing the forward Fourier transform. Thus, no elastic peak appears in the deconvoluted spectrum and no information is given below zero loss. Discarding information from the gain peak is necessary because Fourier transform methods typically expect cyclic functions, requiring that the data be wrapped around [29] the end of the spectrum to produce a symmetric function that begins and ends with the elastic peak. The Cooley-Tukey fast Fourier transform algorithm [30] was used in the present series of analyses. Removal of higher order phonon structure is not perfect and some intensity from the double excitation peak is clearly visible in Fig. l(b). Upon expansion of the data along the ordinate, it can be seen that the algorithm actually overcompensates slightly for the triple excitation loss and even allows some of the quadrupole loss intensity to remain. Nevertheless, the higher order loss peaks have been greatly suppressed, allowing adsorbate vibrational features to be competitive in intensity over a much wider spectral range than is possible without deconvolution. Unfortunately, the instrument response function is not easily obtainable and is generally not known exactly. Thus, its approximation was made above as a Gaussian with FWHM equal to that of the elastic peak. We examine below the effect of varying the instrument response function on the resulting deconvoluted spectrum. A Gaussian-Lorenztian peak shape of Sherwood’s [31]formulation was fit to the high energy side of the elastic peak to generate the instrument response function GL(r), where e-A(‘-M)lnZ G&e
and
=
1
+
AM
(7)
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In eqns. (7) and (8), M is the mixing ratio of Gaussian to Lorentzian contributions with M = 0 being full Gaussian, x0 is the center of the elastic peak and [FWHM] is the value of the FWHM of the peak (see figure captions). An optional tailing function to compensate for charge-induced tailing and other loss processes is fit to the low energy side of the peak. In this case the tail is generated by a simple exponential decay function, as shown in eqns. (9) and (10). Y(X) =
H[GL(x)
+ (1 -
GL(x))T(x)]
(9)
where Nis the spectral peak height and Z’(X), the tailing function, is given by
GL(x) is given by eqn. (8), H, is the maximum height of the tail, Eis the slope of the tail, E1 and E, control the shape of the tail and Y(X) is the total envelope of the Gaussian-Lorenztian plus tail to be used as the instrument response function, Deconvblution is first performed on spectra which have been fit with only Gaussian-Lorentzian peaks (no tail), as suggested by Cox et al. [173. GL(x) instrument response functions with several different halfwidths are defined as broad ([FWHM = 1.4 x EFWHM],Lstic), narrow ([FWHM] = 0.6 x P’WHM],L,tic 1, or matching (IFWHM] = [FWHM],l,,tii,), depending upon their relationship to the observed width of the elastic peak. The results of using narrow, wide, and matching width peaks are shown in Fig. 2. In all three cases, the frequency range below the phonon single loss is distorted somewhat by incorrect removal of the tailing. The higher frequency ranges of the matched (Fig. 2(a)) and broad (Fig- 2(b)) instrument response functions are quite similar in appearance and exhibit reasonably flat backgrounds with some residual multiple excitation intensity left in the spectrum. An interesting effect of the algorithm is the alternate under- and over-removal of the multiple excitation intensity as seen in Figs. 2(a) and 2(b). If too narrow an instrument response function is used (Fig. 2(c)), the spectrum is considerably distorted in the lower energy region immediately following the primary phonon loss, due to incomplete removal of the multiple losses. Too broad an instrument response function (Fig. 2(b)) oversmooths the spectrum and also emphasizes the intensities of higher energy features, thus giving a deconvoluted spectrum with greater residual multiple excitation intensity. Note the lower magnification of Fig. 2(b) relative to Figs. 2(a) and 2(c). Although oversmoothing and overemphasis of higher energy loss features are apt to obscure potentially useful adsorbate loss regions, they are not likely to result in features mistaken as adsorbate
230
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1
2000
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I
3000
Electron
Spectrosc.
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59 (1992) 22&241
I
Electron Energy Loss, iti cm-’ Fig. 2. Deconvoluted results using Gaussian-Lorenztian peaks with (a) matching FWHM, (b) broad FWHM, and (c) narrow FWHM. (In these spectra, E1 = 2, E, = 0.3 and M = 0.3. The other parameters vary with the fit.)
losses. Such is not the case when deconvolution is attempted with a narrower GL peak than that of the observed elastic peak. The results of fitting the spectra with tailed functions that more closely match the observed elastic peak shapes are shown in Fig. 3. These deconvoluted spectra illustrate the effect of varying the tailing function upon the deconvolution procedure. The traces from top to bottom show the deconvoluted results using tails with an under-compensated fit, an over-compensated fit and a “best” fit. The best fit was chosen with parameters which most closely approximate the shape of the observed elastic peak, as shown in Fig. 4. The over-compensated tail has a slope 1.1 times greater than the best fit slope, whereas the slope of the under-compensated is 0.9 times less, as shown in Fig. 4. The results show that the major effect of the poor fits is to distort the spectra region between the elastic and phonon peak. In the higher frequency range it is apparent that the fitted spectrum with over-tailing has removed too much intensity, causing an overall upward slope to be superimposed on the spectrum. This distortion tends to “smear” adsorbate
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30x I 1000
I
2000
I
3ooo
4
Electron Energy Loss, in cm’l Fig. 3. Deconvoluted results using instrument response functions with (a) best fit tailing, (b) excessive tailing, and (c) insufficient tailing. (In these spectra, E, = 2, E, = 0.3 and M = 0.3. The other parameters vary with the fit.)
vibrational loss peaks together, making positive identification of the number and positions of overlapping vibrational modes difficult. These results show that the algorithm is quite robust with regard to the choice of instrument response function as long as [FWHM] is at least as large as the observed [FWHM] of the elastic peak. It is thus not necessary to know i(m) exactly, provided its width is not severely underestimated. Distortions introduced by approximating the function as a symmetric mixed Gaussian-Lorenztian can be further ameliorated by more closely matching the observed peak shapes with a “tailed” loss function. Effect of stoichiometry on the phonon structure and deconvolution To investigate the effect that changes in the NiO(100) surface have on the success of the deconvolution process, we have slowly reduced the NiO(100) surface by heating it at 625 K under 1.3 x 10e4 Pa hydrogen for 1 h, causing the AES O/N ratio to decrease to 60% of the stoichiometric surface value. Surfaces reduced in this manner have been extensively analyzed in previous pubications [22,32]. Under these conditions, the surface remains
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1 W
a)
D
0
800
1600
2400
3200
4000
Electron Energy Loss, in cm-l Fig. 4. Spectra showing size of tails used: (a) matched tail; (b) overcompensated (c) undercompensated tail. (In these spectra, E1 = 2, E, = 0.3 and M = 0.3. The parameters vary with the fit.)
tail; other
sufl?ciently well ordered to maintain LEED of comparable quality to the stoichiometric NiO(100) surface with no additional diffraction features detectable for a considerable period of time into the reduction. During reductiori, lattice oxygen is removed from the near surface in a three dimensional fashion. The depth of the altered layer depends upon reduction time and substrate temperature, and under present conditions has been shown to extend into the crystal surface for several hundred Angstrijms [25]. The net effect of the reduction on the phonon spectrum is to decrease its intensity relative to the elastically scattered peak (Fig. 5(a)) with only minor changes in phonon peak shape and no detectable change in the peak position. Deconvolution (Fig. 5(b)) again yields reasonably satisfactory results. Fuchs-Kliewer phonons are long range phenomena and, under the present conditions of analysis, can be expected to penetrate w 100 A into the nickel oxide surface [15]. It is therefore expected that the phonon spectrum is not very sensitive to small changes in the near surface region anticipated for gas absorption studies. However, even significant changes
K. W. Wulser, M.A. LangelllJ. Electron Spectrosc. Relat. Phenom. 59 (1992) 22&241
233
00
Electron Energy Loss, in cm” Fig. 5. Hydrogen reduced NiO(100) HREEL spectra (a) prior and (b) post deconvolution.
in the phonon spectrum should not invalidate the use of eqn. (6) in the phonon deconvolution process. It should be emphasized that the algorithm given by eqn. (6) results in a true deconvolution (not a spectral subtraction) of the higher order phonon losses and that all information for the deconvolution resides in the elastic and single phonon loss peaks of the spectrum to be deconvoluted. No “standard spectrum” of the surface is required and any changes in the phonon spectrum that might be brought about by different ambient conditions, surface pretreatment or gas adsorption should be entirely self-correcting. HREELS
from CH,OH
adsorptiort
Nickel oxide adsorbs gases only very slowly under conditions typically attainable with UHV-based methods of analysis. To obtain detectable quantities of methanol under the ambient temperature of 298K, it is necessary to valve the chamber contining the NiO(100) sample from the pumping station and to backfill the chamber with near saturation pressures (z 17 kPa) of CH,OH. The HREEL spectrum obtained after 30 min or 0.2 teralangmuirs (TL) (1 L is a convenient unit of gas exposure equal to 1 x 10-“Torr s, equivalent to 1.33 x lo-* Pas), of methanol exposure is shown in Fig. 6(a). The overall intensity of the vibrational spectrum is diminished by about 50% over that obtained from the clean NiO(100) surface (Fig. l(a)) and upon expansion of the ordinate it is clear that
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TABLE
1
Adsorbate Vibrational
vibrations modes
‘CHB
6 CAB+ “c-o “C-O s C%
for methoxy
species (in cm-*) CH,OH
CD, OD
3056 2527 1461 1066”
2075 2236 1387 922
’By difference.
additional vibrational features have appeared as a result of the exposure. The exact number and positions of the new features, however, are difficult to ascertain because they are convoluted with the Fuchs-Kliewer phonons. Upon deconvolution of the multiple excitations (Fig. 6(b)), the adsorbate features are made more obvious. All adsorbate-related vibrations can be assigned to one of three species, the specific assignments for two of which, adsorbed methoxy (CH,O;,,) and adsorbed water, are listed in Tables 1 and 2, respectively. The third adsorbate is CO,,,, indicated by the C-O stretch at 2030cm-l and unavoidable as a contaminant in the extemely long exposure necessary to generate methoxy in sufficient quantities to be detectable by HREELS. The molecularly adsorbed water yields peaks at 3569,1677 and 844 cm-l, characteristic of stretching, scissoring and rocking H,O normal modes, respectively, [9,33,34]. At 3569 cm-‘, the value for the stretch is sufficiently high to indicate either isolated H,O species or only very weak hydrogen bonding among the H,O adsorbates [9]. At least some of the surface water might be attributed to adsorption of ambient water vapor, which builds up as a background contaminant under the long methanol exposures used here. However, condensation of surface hydroxyl groups generated in the dissociative adsorption of methanol cannot be ruled out as a contributing route to surface water production. The other adsorbate vibrational features are characteristic of the chemiTABLE
2
Adsorbate vibrations for adsorbed H,O (in cm-‘) Vibrational “Hz0 biZO PHaO
“Obscured.
mode
Hz0
D,O
3569 1677 644
2492 _a _a
K. W. Wulser,
MA.
LangelllJ.
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Spectrosc.
Relat. P&nom.
59 (1992) 223-24X
235
,846
b) J
1461
=1 -1000
0
1ooo2ooo3ooo4ooa
ElectronEnergyLoss, in cm” Fig. 6. HREELS of CH,OH exposed NiO(100) showing (a) phonon and adsorbate vibrational structure and (b) the spectrum after deconvolution of higher phonon modes.
sorbed methoxy species [3-g]. The stretching mode at 3056 cm-l is characteristic of the asymmetric stretch of C-H in methyl functional groups; a symmetric stretch is expected by comparison with gas phase methanol spectra at approximately 2860 cm-l, but is generally not resolved from the asymmetric stretch in HREELS methanol or methoxy adsorbate spectra. The carbon-oxygen stretch at 1461 cm-l is comparable to values observed for methoxy species coadsorbed with oxygen on transition metal substrates [9]. The data suggest that some molecularly adsorbed methanol may also be present, because the hydrocarbon stretching peak (3056 cm-l) appears somewhat distorted towards higher loss energy in the region expected for -OH stretching ( * 3250 cm-‘) from the undissociated alcohol functional group. The dominant form of the adsorbate is, however, clearly in the methoxy form. Unfortunately, the remaining fingerprint mode of the methoxy adsorbate, observed at z 1050cm-’ on transition metal substrates, occurs in the range still obscured by the unremoved portion of the second phonon harmonic, marked with an asterisk in Fig. 6(b). Fortuitously, the mode can be detected indirectly because it appears in a combination band with the methoxy C-O stretch. The BCH3+ vcc overtone has previously been reported in HREELS studies of methanol adsorption for several transition metal surfaces [10,35] and is found at 2527 cm-l in the present set of spectra. The combination band places the remaining methoxy vibrational mode at
K. W. Wulser, M.A.
236
I-~
542
Sk7
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A2
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A7
0 1 s Binding Energy, eV Fig. 7. Oxygen 1s XPS taken with Mg Kcc(1253.6 eV) radiation at 25 eV bandpass. Actual data is shown as discrete points with Gaussian curves fit to the individual components to yield the solid line.
1066 cm-‘, in good agreement with literature values for the methoxy GO stretch. XPS data taken after the HREEL spectrum was acquired confirm the adsorbate identity. Oxygen 1s photoemission (Fig. 7) shows evidence of three distinct surface oxygen species. The NiO(lO0) lattice oxygen peak at 529.4eV is the largest, because the kinetic energy of the photoelectron allows a near surface region of x 50 A [36] to be sampled. The most intense adsorbate 0 1s peak occurs at 531.4 + 0.3 eV, corresponding well to oxygen emission from methoxy adsorbate [37,39]. In addition, a small water peak is found at 533.4 eV. Carbon 1s (Fig. 8) spectra are noisier than that of the oxygen, due to the lower cross section for photoemission from carbon, but clearly yield two distinct peaks with approximate binding energies of 286 and 288 f 1 eV. Based on HREELS and 0 1s XPS data, the peaks are assigned to methoxy and molecularly adsorbed methanol, respectively. Considering the fairly large error in the C Is spectra, the binding energies are in fair agreement with previously reported XP spectra [37-391. As can be seen from a comparison of peak areas in Fig. 7, the methoxy represents about 70% of the alcohol-generated adsorbate. HREELS
from CD,OD adsorption
Support of vibrational assignments for hydrogen-containing molecules is often given by comparison with deuterated spectra. Fig. 9(a) shows the NiO(100) convoluted phonon and adsorbate HREEL spectrum after the substrate was exposed to saturation vapor pressures of deuterated methanol in a manner similar to that described for CH,OH above. Prior to
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275
Fig. 8. Carbon Is XPS taken with Mg Ka (1253.6 eV) radiation at 50 eV bandpass. Actual data is shown as discrete points with Gaussian curves fit to the individual components to yield the solid line.
deconvolution, the HREEL spectrum is of comparable quality to the CH,OH results (Fig. 6(a)). Some adsorbate vibrational structure can be discerned, especially after expanding the scale. However, a much richer adsorbate vibrational spectrum is made obvious by the deconvolution of the higher phonon modes (Fig. 9(b)). The spectrum contains -OCD, adsorbate features, assignments for which are given in Table 1. There are also contributions, however, from non-
-1obo
b
lb00
2obo
xi00
4 00
Electron Energy Loss, in crnsl Fig. 9. HREELS of CD,OD exposed NiO(100) showing (a} phonon and adsorbate structure and (b) the spectrum after deconvolution of higher phonon modes.
vibrational
238
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deuterated and partially scrambled H/D isotopes of the methoxy adsorbate. The scrambling results from the catalytic action of the NiO(100) surface combined with the natural H,O background of the stainless steel UHV chamber, and indiates that the C-H bond of the surface methoxy is labile on NiO(100). Despite the increased complexity of the spectrum, all peaks can still be accounted for by using vibrational modes of surface methoxy and adsorbed water. After removal of the -OCH, and H, 0 modes previously found in Fig. 6(b), the spectrum consists of modes from their deuterated and partially deuterated analogs. The deuterated methoxy modes characteristic of the adsorbate are found at 922 (de,,) and 1387 (v~_~), in agreement with previously reported values [40]. The peaks at 2075 and 2236 cm-l are due to the symmetric and asymmetric stretches respectively of aliphatic -CD,. The peak at 2236 cm-l presumably also has a contribution from the weaker 6 CD3 + vC-O combination band, although it is not resolved from the more intense aliphatic stretch. The O-D stretch from the deuterated water is found at 2492 cm-l, although the scissoring mode, expected at approximately 1100 cm-’ [41], is now unfortunately obscured by the unremoved portion of the double excitation phonon marked with an asterisk in Fig. 9(b). The remaining feature at 2836 cm-’ is a mixed isotope CDH, aliphatic stretching mode. The symmetric and asymmetric stretches are resolvable in the deuterated vibrational spectrum because of the larger energy spacing between the modes. DISCUSSION
In the present study a Fourier-log deconvolution algorithm has been used to extend the useful frequency range of NiO(100) HREEL adsorbate spectra. The algorithm has good stability towards the choice of instrument response function. When simple Gaussians of different halfwidth are used, it is found that functions which equal or exceed the FWHM of the elastic peak perform well as instrument response functions. However, narrower functions distort the spectrum in the low frequency range to a high degree and could potentially introduce artifacts into the HREEL spectrum. Thus, attempts to improve resolution by Fourier deconvolution techniques must be handled carefully. In an effort to further improve the deconvolution algorithm, tails were added to the instrument response function to compensate for the observed phonon peak shapes. A natural concern of such action is the result of a poor choice in tail fitting parameters. Instrument response functions with tailing that clearly overestimated or underestimated the true peak shape were used to study this effect. Whereas fits with a smaller tail than that observed worked well with the algorithm, primarily causing distortion in
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the frequency range below the fundamental phonon frequency, fits with too much tailing gave rise to distorted spectra with large sloping background. Thus, as with the choice of FWHM of elastic peak, it is safe to remove too little, but not too much, of the expected phonon multiple loss peaks. However, a generous amount of mismatch is needed to cause a significant amount of distortion and provided the peak shapes are fairly closely matched, the deconvolution works reliably. The effect of the stoichiometry of the surface is of concern if the algorithm is to be successfully applied to a wide variety of surface preparations. The phonon spectrum itself decreases noticeably in intensity upon gas adsorption or change in surface stoichiometry, but does not change significantly in peak shape or relative phonon intensities. The primary phonon loss energy (70.5 meV) also remains constant, regardless of surface pretreatment. Specifically relevant to the HREELS deconvolution application, the phonons retain their near-harmonic behavior. Thus information of the higher losses can be obtained from the primary loss regardless of their position, intensity and peak shape correlation with the initial adsorbate free surface. Phonon losses that are incompletely removed are now of comparable or lower intensity than potential adsorbate peaks. Because their positions are accurately known, they can be distinguished as phonon losses in the deconvoluted spectra and thus need not be confused with adsorbate losses. Studies of methanol-exposed NiO( 100) surface yield little distinct change in the phonon-dominated HREEL spectrum when compared to the unexposed NiO(100) phonon spectrum. Although some extra vibrational features can be detected at higher loss energies in the range of the aliphatic stretching, their exact peak positions are not clear. Post deconvolution, a considerably wider range of adsorbate vibrational losses can be sampled. The loss features obtained for this system (Tables 1 and 2) agree well with characteristic losses reported for the surface methoxy species and for coadsorbed water, species that are clearly identified as being present by XPS analysis of the adsorbate surface. Deuterated adsorbate losses shift accordingly. The deuterated spectra are somewhat complicated by the catalytic effects of the substrate, which causes H-D scrambling under adsorption conditions of the hydrogen-rich atmosphere of the stainless steel chamber. CONCLUSIONS
We have shown the power and utility of the Fourier-log deconvolution algorithm when applied to the metal oxide HREEL spectra. The Fourier-log deconvolution algorithm exhibits good stability under a variety of computational and surface conditions and choice of instrument response func-
240
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tional form. The algorithm was used to study the adsorption of methanol on NiO(100) which adsorbs dissociatively on the surface at room temperature to yield methoxy groups, surface hydroxyls, and adsorbed water. ACKNOWLEDGMENT
Acknowledgment is made to the Donors of The Petroleum Research Fund, administered by the American Chemical Society for support of this research. REFERENCES 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
29 30 31
M.A. Vannice, Catal. Rev. Sci. Eng., 14 (1976) 153. T.A. Garibyan and L.Y.A. Margolis, Cat. Rev_ Sci. Eng., 31 (1989/9O) 355. J.-P. Lu, M. Albert and S.L. Eernasek, Surf. Sci., 239 (1990) 49. G.D. Waddill and L.L. Kesmodel, Surf. Sci., 182 (1987) L248. S.W. Jorgensen and R.J. Madix, Surf. Sci., 183 (1987) 27. J.A. Gates and L.L. Kesmodel, J. Catal., 83 (1983) 437. B.A. Sexton, A.E. Hughs and N.R. Avery, Surf. Sci., 155 (1985) 366. J. Paul and F-M. Hoffmann, Surf. Sci.. 172 (1986) 151. P. Sen and C.N.R. Rao, Surf. Sci., 172 (1986) 269. J. Wang and R-I. Masel, Surf. Sci., 243 (1991) 199. K.D. Gibson and L.H. Dubois, Surf. Sci., 233 (1996) 59. J.E. Parmeter, X. Jiang and D.W. Goodman, Surf. Sci., 246 (1990) 85, J.E. Demuth and H. Ibach, Chem. Phys. Lett., 60 (1979) 395. K. Christman and J.E. Demuth, 3. Chem. Phys., 76 (1982) 6318. H. Ibach and D.L. Mills, ‘Electron Energy Loss Spectroscopy and Surface Vibrations,’ Academic Press, New York, 1982. G. Dalmai-Imelik, J.C. Bertolini and J. Rousseau, Surf. Sci., 63 (1977) 67. P. Cox, W.R. Flavell, A-A. Williams and R. Egdell, Surf. Sci., 152/153 (1985) 784. R. G. Egdell in M. Che and G.C. Bond (Eds.), Adsorption and Catalysis on Oxide Surfaces, Elsevier, New York, 1985, p. 173. B.G. Frederick, G. Apai and T.N. Rhodin, J. Electron Spectrosc. Relat. Phenom., 54/55 (1996) 415. J.M. Vohs and W.T. Petrie, Surf. Sci., 245 (1991) 315. Ph. Lambin, J.P. Vigneron and A.A. Lucas, Phys. Rev. B., 32 (1985) 8293. P.A. Cox and A.A. WilIiams, J. Electron Spectrosc. Relat. Phenom.. 39 (1986) 45. R. Fuchs and K.L. Kliewer, Phys. Rev- A, 140 (1965) 2076. A.A. Lucas and M. Sunjii, Progr. Surf. Sci., 2 (1972) 75. R.P. Furstenau, G. McDougal and M.A. Langell, Surf. Sci., 150 (1985) 55. M.E. Rudd and J.R. Craig, Rev. Sci. Instrum., 39 (1968) 137; 40 (1969) 1468. J. Haber, J. Stoch and L. Ungier, J. Electron Spectrosc. Relat. Phenom., 9 (1976) 469. A. Proctor and P.M.A. Sherwood, Anal. Chem., 54 (1982) 13. A.G. Marshal and F.R. Verdun, Fourier Transforms in NMR, Optical and Mass Spectrometry, Elsevier, New York, 1990. E. Brigham, The Fast Fourier Transform and Its Applications, Prentice Hall, NY, 1988. W. Press, B. Flannery, S. Teukolsky and W. Vetterling, Numerical Recipes: The Art of Scientific Computing, Cambridge University Press, Cambridge, 1986, p. 396. P.M.A. Sherwood, in D. Briggs and M.P. Seah (Eds.), Practical Surface Analysis by Auger and X-ray Photoelectron Spectroscopy, Wiley, New York, 1983. p. 445.
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Phenomena,
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Methanol adsorption on single crystal Ni0(100) studied by HREELS deconvolution K.W. Wulser and M.A. Lange11 Department (Received
of Chemistry, 11 November
University
of Nebraska,
Lincoln, NE 68588-0304
(USA)
1991)
Abstract High resolution electron energy loss spectra (HREELS) of transition metal oxide substrates are dominated by very intense Fuchs-Kliewer surface phonons, limiting the use of HREELS in adsorbate vibrational analysis. A deconvolution algorithm, first proposed by Cox, Egdell and Williams, has been used to remove much of the phonon structure, allowing
a significantly broader range to be available for adsorption studies. The deconvolution process is found to be robust to the choice of instrument response function and surface quality, and is self-correcting to adsorbate and other induced changes in surface phonon structure. The procedure is applied to the NiO(lOOtOCH,,,d, adsorbate system, yielding deconvoluted HREEL adsorbate spectra that are comparable in numbers and positions of characteristic adsorbate vibrational modes.
INTRODUCTION
The adsorption and decomposition of methanol on transition metal substrates is of interest in model studies of Fischer-Tropsch [l] and partial oxidation catalysis [2]. Of particular significance is the role of co-adsorbed oxygen in the stability of molecularly adsorbed methanol and in the formation of an important synthetic intermediate, the surface methoxy M-OCH, [3-g]. In many systems, predosing the transition metal substrate with oxygen promotes the formation of surface methoxy and shifts thermally induced decomposition to substrate temperatures higher than that observed on the oxygen-free surface. Because oxide substrates are often used in partial oxidation synthesis, or can be expected to form on the transition metal under typical catalytic conditions, the adsorption of methanol on well characterized oxide substrates is relevant in establishing the adsorbate properties of methanol and the methoxy intermediate. Correspondence NE 685886304,
to: M.A. USA.
0368-2048/92/$05.00
0
Langell,
Department
1992 Elsevier
Science
of Chemistry,
Publishers
University
B.V.
of Nebraska,
All rights reserved.
Lincoln,
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High resolution electron energy loss spectroscopy (HREELS) has been able to determine unequivocally the form of adsorbed methanol on a variety of metal substrates [3-141. In HREELS, a monoenergetic beam of z 5 eV is directed at the substrate surface and the adsorbate vibrational spectrum is measured by energy analyzing the electron loss spectrum within 500meV (4000 cm-l) of the elastically scattered beam [15]. When HREELS is applied to oxides [16-201 and other compounds 121,221 with significant ionic character, the substrate lattice itself participates in the vibrational loss processes through the excitation of intense transverse optical vibrational modes known as Fuchs-Kliewer phonons [23]. The phonons, which are strongly coupled to the incoming electron beam, yield an intense single loss peak of up to 80% of the elastic peak intensity followed by a series of higher multiple losses with intensities decreasing in Poisson distribution [24]. Because adsorbate vibrational modes are only w 1% of the elastic peak intensity, the phonon structure totally obscures all but the highest frequency adsorbate vibrational losses. A potential solution to the phonon problem was proposed by Cox et al. [17]. Their solution attempts to deconvolute the higher multiple losses or “harmonics” by using information from the single loss phonon peak, thus freeing a substantial portion of the HREEL spectral range for adsorbate vibrational analysis. The logarithmic deconvolution algorithm, which is explained further in the text below, is applied here successfully to methanolexposed stoichiometric and HZ-reduced NiO(lO0) surfaces to establish the presence of methoxy adsorbate species by identification of its characteristic adsorbate vibrational losses. The algorithm is not without problems, however, and several caveats in its application are also discussed. EXPERIMENTAL
All analyses were performed in a 40 liter ultrahigh vacuum (UHV) bell jar ion pumped to a base pressure of 4 x lo-‘Pa. The system is equipped with a Physical Electronics 15-225G double pass cylindrical mirror analyzer (CMA) for AES and X-ray photoelectron spectroscopy (XPS), four grid retarding optics for low energy electron diffraction (LEED), a 2 keV inert ion bombardment gun of in-house design and a UT1 100 C quadrupole mass spectrometer for residual gas analysis. Auger electrons were generated with a 2 keV electron gun and were mode lock amplified by modulating the CMA at 12 kHz with a sine wave of 2 eV amplitude. XPS spectra, initiated with either an aluminum (Al Ka; 1486.6 eV) or magnesium (Mg Ka; 1253.6eV) anode, were obtained in a pulse count mode at constant CMA pass energy, specifics for which are given in the relevant figure captions. All XPS spectra are referenced to the NiO(100) 0 1s peak, previously cali-
K. W. Wulser, M.A. Langell]J. Electron Spectrosc. Relat. Phenom. 59 (1992) 22%241
-1000
I
0
I
loo0
I
2ooo
225
I
3ooo
4 00
Electron Energy Loss, in cmml Fig. 1. HREEL spectra of NiO(IO0) (a) showing the well developed phonon spectrum and (b) after deconvolution.
brated to be at 529.4 eV [25] and are reported with a precision of 0.1 eV unless otherwise noted. The HREEL spectrometer is also mounted on the bell jar and was built in-house of nonmagnetic materials. A set of Helmholtz coils surrounds the chamber and nulls out stray magnetic fields to -c 10mGauss in the region of HREELS analysis. A mu-metal shield surrounds the spectrometer and the sample region to further homogenize the field. The HREEL spectrometer is a single pass 127O sector design (designed by J. Gland, University of Michigan, with blueprints adapted by J-M. White, University of TexasAustin) [26] that provides a monoenergetic primary beam adjustable from l-10 eV at O-l-l.OnA beam current. Scattered electrons are detected specularly at 60° relative to the surface normal in a pulse count mode. When operating on metal substrates the spectrometer can achieve a resolution of 6-7 meV, measured as the full width half maximum (FWHM) of the elastically scattered peak. However, when measurements are performed on the semiconducting NiO(100) sample, a resolution of lO-15meV is more commonly obtained. A typical HREEL spectrum of a clean stoichiometric NiO(100) surface is shown in Fig. 1, which is discussed more fully in the text below. The NiO(lo@ sample, a single crystal of approximately 0.5 mm thickness and 0.5 cm diameter, was cut from a boule grown by the Czochralski method (kindly supplied by N. Peterson, Materials Science Division, Argonne National Laboratory). The sample was oriented to within lo of the (100)
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plane, as determined by Laue X-ray diffraction, and was polished with successively finer grades of alumina to a final 0.03 pm particle size. When first mounted in the UHV bell jar, the surface was covered with multilayers of a defect-riddled higher oxide contaminant, which were removed only after multiple cycles of argon ion bombardment @PA cmp2Ar+ at 2 keV) and oxygen annealing (798K at 7 x 10-5Pa). The final cycle was followed by a 10min UHV anneal at 798 K. The integrity of the stoichiometric NiO(100) surface was established by AES, XPS, LEED and HREELS. With present AES settings and CMA resolution, we obtain an AES O/Ni = 2.5 peak-to-peak ratio using the 0 KL,L,/Ni L,M,,,M,,, AES transitions and observe no AES peaks attributable to surface contamination. Although AES is sensitive to changes in oxygen concentration, we advise caution in its use as a primary measure of surface stoichiometry for metal oxides such as NiO(100). Although deviations from the appropriate O/Ni AES ratio indicate a definite change in average composition, obtaining the “appropriate” relative Auger signals for oxygen and nickel do not insure stoichiometry because of the three dimensional nature of the measurement. To insure that the surface is NiO(-iOO>,the XPS must have a single Is oxygen component (529.4eV) and a nickel 2p region appropriate to nickel oxide (2p,,, at 854.2eV, 2p,,, at 871.8eV), with correct shake-up structure [25,27]. The HREEL spectrum must also show a well developed and intense Fuchs-Kliewer phonon spectrum, with higher harmonics clearly visible and with the fundamental phonon energy as expected for NiO. Our value at 564 cm-l (70.5 f 1.0 meV) compares well with previously reported values [17]. It is only when these conditions are met that the surface characteristics, including adsorption behavior, become truly reproducible. RESULTS
Deconvolution algorithm applied to the NiU(lO0) adsorbate-free surface A representative NiO(100) Fuchs-Kliewer phonon spectrum is given in Fig. l(a), plotted as a function of loss energy from the elastically scattered peak. Surface scientists interested in gas adsorption phenomena generally report HREELS adsorbate loss models in wavenumbers (cm-l), continuing a precedent set in analytical IR spectroscopy for fingerprint identification of molecular species. To facilitate comparison with characteristic adsorbate spectra reported in the literature, we retain the wavenumber tradition. The data can, however, readily be converted to meV by dividing the loss “energy” by the conversion factor of 8.065 cm-lmeV1. The spectrum is taken with 5.0eV primary beam energy and shows a typical resolution of 100 cm-‘, measured as the FWHM of the elastic peak.
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The resolution is comparable to [17,22] or better than [US] that previously reported for HREEL phonon spectra of nickel oxide, although it is somewhat broadened over the achievable resolution of our spectrometer, owing to the limited conductivity of the NiO surface. The multiple loss structure is similar in form to previously reported spectra with a single loss energy of 564cm-' (70.5f 1.0meV) and a higher loss progression at integral multiples of the single loss value. Because 300 K thermal energies are sufficient to excite detectable amounts of Fuchs-Kliewer phonons, a small gain peak is also observed and is reported as a negative loss in Fig. l(a). The measured (single gain)/(single loss) intensity ratio of 0.068 is close to that of 0.0653calculated assuming a Boltzmann distribution _Igain I
=
e-‘h”/RT)
(1)
1OSS
In principle, all peak shape and energy information on the multiple loss phonons is contained in the single phonon loss. If the surface vibrations are assumed to be harmonic in nature, the relative intensities of the progression can be shown to follow a Poisson distribution [24]. Thus, in the harmonic approximation, the single loss feature determines all information on the higher loss intensities too. Cox and Williams [22]have proposed an algorithm for the removal of the higher loss phonons based on the harmonic approximation and have applied it to several oxide surfaces [17,18,22]. We follow their argument in the next paragraph. The HREEL spectrum s(w) can be written as a self-convolution of the fundamental phonon spectrum p(o) s(w)
=
i(0) @
[s(o) +
&.))
+
p(o) $ p(a) + Ia) @“p .
@ph) + . . . .
(2)
1
where the symbol @ represents convolution, i(w) is the instrument response function and 6(O) is the elastic contribution whose peak shape is determined entirely by i(w). Fourier transformation of eqn. (2) into the time domain gives S(z)
=
I(s)
[
P(rj2 p(+3 1 + p(z) + 7 + QI + . . . .
.
1
(3)
where the quantity in square brackets in eqn. (3) is simply a logarithmic expansion of P(T)
S(z) =
I((z)e-S~)
(4)
Rearranging eqn. (4)and taking the logarithm of each side yields P(z)
=
In
[
E
1
(5)
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which allows the vibrational single loss spectrum to be obtained deconvoluted from the higher loss functions p(w)
=
9-l
In 1
Ea>
(6)
Application of eqn. (6) to the NiO(100) HREELS data of Fig. l(a) results in the spectrum shown in Fig. l(b). The elastic peak has been used to approximate the instrument response function, as suggested by Cox and Williams [22], and in generating Fig. l(b) we have taken i(w) to be a Gaussian function with FWHM equal to that of the elastic peak, and with an exponential tail added similar in form to that applied in XPS analysis [28]. The form of the instrument response function is discussed more fully below. The elastic peak itself is removed by use of eqn. (6), and data to the left of the zero loss position is discarded prior to performing the forward Fourier transform. Thus, no elastic peak appears in the deconvoluted spectrum and no information is given below zero loss. Discarding information from the gain peak is necessary because Fourier transform methods typically expect cyclic functions, requiring that the data be wrapped around [29] the end of the spectrum to produce a symmetric function that begins and ends with the elastic peak. The Cooley-Tukey fast Fourier transform algorithm [30] was used in the present series of analyses. Removal of higher order phonon structure is not perfect and some intensity from the double excitation peak is clearly visible in Fig. l(b). Upon expansion of the data along the ordinate, it can be seen that the algorithm actually overcompensates slightly for the triple excitation loss and even allows some of the quadrupole loss intensity to remain. Nevertheless, the higher order loss peaks have been greatly suppressed, allowing adsorbate vibrational features to be competitive in intensity over a much wider spectral range than is possible without deconvolution. Unfortunately, the instrument response function is not easily obtainable and is generally not known exactly. Thus, its approximation was made above as a Gaussian with FWHM equal to that of the elastic peak. We examine below the effect of varying the instrument response function on the resulting deconvoluted spectrum. A Gaussian-Lorenztian peak shape of Sherwood’s [31]formulation was fit to the high energy side of the elastic peak to generate the instrument response function GL(r), where e-A(‘-M)lnZ G&e
and
=
1
+
AM
(7)
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In eqns. (7) and (8), M is the mixing ratio of Gaussian to Lorentzian contributions with M = 0 being full Gaussian, x0 is the center of the elastic peak and [FWHM] is the value of the FWHM of the peak (see figure captions). An optional tailing function to compensate for charge-induced tailing and other loss processes is fit to the low energy side of the peak. In this case the tail is generated by a simple exponential decay function, as shown in eqns. (9) and (10). Y(X) =
H[GL(x)
+ (1 -
GL(x))T(x)]
(9)
where Nis the spectral peak height and Z’(X), the tailing function, is given by
GL(x) is given by eqn. (8), H, is the maximum height of the tail, Eis the slope of the tail, E1 and E, control the shape of the tail and Y(X) is the total envelope of the Gaussian-Lorenztian plus tail to be used as the instrument response function, Deconvblution is first performed on spectra which have been fit with only Gaussian-Lorentzian peaks (no tail), as suggested by Cox et al. [173. GL(x) instrument response functions with several different halfwidths are defined as broad ([FWHM = 1.4 x EFWHM],Lstic), narrow ([FWHM] = 0.6 x P’WHM],L,tic 1, or matching (IFWHM] = [FWHM],l,,tii,), depending upon their relationship to the observed width of the elastic peak. The results of using narrow, wide, and matching width peaks are shown in Fig. 2. In all three cases, the frequency range below the phonon single loss is distorted somewhat by incorrect removal of the tailing. The higher frequency ranges of the matched (Fig. 2(a)) and broad (Fig- 2(b)) instrument response functions are quite similar in appearance and exhibit reasonably flat backgrounds with some residual multiple excitation intensity left in the spectrum. An interesting effect of the algorithm is the alternate under- and over-removal of the multiple excitation intensity as seen in Figs. 2(a) and 2(b). If too narrow an instrument response function is used (Fig. 2(c)), the spectrum is considerably distorted in the lower energy region immediately following the primary phonon loss, due to incomplete removal of the multiple losses. Too broad an instrument response function (Fig. 2(b)) oversmooths the spectrum and also emphasizes the intensities of higher energy features, thus giving a deconvoluted spectrum with greater residual multiple excitation intensity. Note the lower magnification of Fig. 2(b) relative to Figs. 2(a) and 2(c). Although oversmoothing and overemphasis of higher energy loss features are apt to obscure potentially useful adsorbate loss regions, they are not likely to result in features mistaken as adsorbate
230
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1
2000
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I
3000
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I
Electron Energy Loss, iti cm-’ Fig. 2. Deconvoluted results using Gaussian-Lorenztian peaks with (a) matching FWHM, (b) broad FWHM, and (c) narrow FWHM. (In these spectra, E1 = 2, E, = 0.3 and M = 0.3. The other parameters vary with the fit.)
losses. Such is not the case when deconvolution is attempted with a narrower GL peak than that of the observed elastic peak. The results of fitting the spectra with tailed functions that more closely match the observed elastic peak shapes are shown in Fig. 3. These deconvoluted spectra illustrate the effect of varying the tailing function upon the deconvolution procedure. The traces from top to bottom show the deconvoluted results using tails with an under-compensated fit, an over-compensated fit and a “best” fit. The best fit was chosen with parameters which most closely approximate the shape of the observed elastic peak, as shown in Fig. 4. The over-compensated tail has a slope 1.1 times greater than the best fit slope, whereas the slope of the under-compensated is 0.9 times less, as shown in Fig. 4. The results show that the major effect of the poor fits is to distort the spectra region between the elastic and phonon peak. In the higher frequency range it is apparent that the fitted spectrum with over-tailing has removed too much intensity, causing an overall upward slope to be superimposed on the spectrum. This distortion tends to “smear” adsorbate
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30x I 1000
I
2000
I
3ooo
4
Electron Energy Loss, in cm’l Fig. 3. Deconvoluted results using instrument response functions with (a) best fit tailing, (b) excessive tailing, and (c) insufficient tailing. (In these spectra, E, = 2, E, = 0.3 and M = 0.3. The other parameters vary with the fit.)
vibrational loss peaks together, making positive identification of the number and positions of overlapping vibrational modes difficult. These results show that the algorithm is quite robust with regard to the choice of instrument response function as long as [FWHM] is at least as large as the observed [FWHM] of the elastic peak. It is thus not necessary to know i(m) exactly, provided its width is not severely underestimated. Distortions introduced by approximating the function as a symmetric mixed Gaussian-Lorenztian can be further ameliorated by more closely matching the observed peak shapes with a “tailed” loss function. Effect of stoichiometry on the phonon structure and deconvolution To investigate the effect that changes in the NiO(100) surface have on the success of the deconvolution process, we have slowly reduced the NiO(100) surface by heating it at 625 K under 1.3 x 10e4 Pa hydrogen for 1 h, causing the AES O/N ratio to decrease to 60% of the stoichiometric surface value. Surfaces reduced in this manner have been extensively analyzed in previous pubications [22,32]. Under these conditions, the surface remains
K. W. Wulser, MA.
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1 W
a)
D
0
800
1600
2400
3200
4000
Electron Energy Loss, in cm-l Fig. 4. Spectra showing size of tails used: (a) matched tail; (b) overcompensated (c) undercompensated tail. (In these spectra, E1 = 2, E, = 0.3 and M = 0.3. The parameters vary with the fit.)
tail; other
sufl?ciently well ordered to maintain LEED of comparable quality to the stoichiometric NiO(100) surface with no additional diffraction features detectable for a considerable period of time into the reduction. During reductiori, lattice oxygen is removed from the near surface in a three dimensional fashion. The depth of the altered layer depends upon reduction time and substrate temperature, and under present conditions has been shown to extend into the crystal surface for several hundred Angstrijms [25]. The net effect of the reduction on the phonon spectrum is to decrease its intensity relative to the elastically scattered peak (Fig. 5(a)) with only minor changes in phonon peak shape and no detectable change in the peak position. Deconvolution (Fig. 5(b)) again yields reasonably satisfactory results. Fuchs-Kliewer phonons are long range phenomena and, under the present conditions of analysis, can be expected to penetrate w 100 A into the nickel oxide surface [15]. It is therefore expected that the phonon spectrum is not very sensitive to small changes in the near surface region anticipated for gas absorption studies. However, even significant changes
K. W. Wulser, M.A. LangelllJ. Electron Spectrosc. Relat. Phenom. 59 (1992) 22&241
233
00
Electron Energy Loss, in cm” Fig. 5. Hydrogen reduced NiO(100) HREEL spectra (a) prior and (b) post deconvolution.
in the phonon spectrum should not invalidate the use of eqn. (6) in the phonon deconvolution process. It should be emphasized that the algorithm given by eqn. (6) results in a true deconvolution (not a spectral subtraction) of the higher order phonon losses and that all information for the deconvolution resides in the elastic and single phonon loss peaks of the spectrum to be deconvoluted. No “standard spectrum” of the surface is required and any changes in the phonon spectrum that might be brought about by different ambient conditions, surface pretreatment or gas adsorption should be entirely self-correcting. HREELS
from CH,OH
adsorptiort
Nickel oxide adsorbs gases only very slowly under conditions typically attainable with UHV-based methods of analysis. To obtain detectable quantities of methanol under the ambient temperature of 298K, it is necessary to valve the chamber contining the NiO(100) sample from the pumping station and to backfill the chamber with near saturation pressures (z 17 kPa) of CH,OH. The HREEL spectrum obtained after 30 min or 0.2 teralangmuirs (TL) (1 L is a convenient unit of gas exposure equal to 1 x 10-“Torr s, equivalent to 1.33 x lo-* Pas), of methanol exposure is shown in Fig. 6(a). The overall intensity of the vibrational spectrum is diminished by about 50% over that obtained from the clean NiO(100) surface (Fig. l(a)) and upon expansion of the ordinate it is clear that
234
K. W. Wulser, M.A. LangelllJ. Electron Spectrosc. Relat. Phenom. 59 (2992) 22%241
TABLE
1
Adsorbate Vibrational
vibrations modes
‘CHB
6 CAB+ “c-o “C-O s C%
for methoxy
species (in cm-*) CH,OH
CD, OD
3056 2527 1461 1066”
2075 2236 1387 922
’By difference.
additional vibrational features have appeared as a result of the exposure. The exact number and positions of the new features, however, are difficult to ascertain because they are convoluted with the Fuchs-Kliewer phonons. Upon deconvolution of the multiple excitations (Fig. 6(b)), the adsorbate features are made more obvious. All adsorbate-related vibrations can be assigned to one of three species, the specific assignments for two of which, adsorbed methoxy (CH,O;,,) and adsorbed water, are listed in Tables 1 and 2, respectively. The third adsorbate is CO,,,, indicated by the C-O stretch at 2030cm-l and unavoidable as a contaminant in the extemely long exposure necessary to generate methoxy in sufficient quantities to be detectable by HREELS. The molecularly adsorbed water yields peaks at 3569,1677 and 844 cm-l, characteristic of stretching, scissoring and rocking H,O normal modes, respectively, [9,33,34]. At 3569 cm-‘, the value for the stretch is sufficiently high to indicate either isolated H,O species or only very weak hydrogen bonding among the H,O adsorbates [9]. At least some of the surface water might be attributed to adsorption of ambient water vapor, which builds up as a background contaminant under the long methanol exposures used here. However, condensation of surface hydroxyl groups generated in the dissociative adsorption of methanol cannot be ruled out as a contributing route to surface water production. The other adsorbate vibrational features are characteristic of the chemiTABLE
2
Adsorbate vibrations for adsorbed H,O (in cm-‘) Vibrational “Hz0 biZO PHaO
“Obscured.
mode
Hz0
D,O
3569 1677 644
2492 _a _a
K. W. Wulser,
MA.
LangelllJ.
Electron
Spectrosc.
Relat. P&nom.
59 (1992) 223-24X
235
,846
b) J
1461
=1 -1000
0
1ooo2ooo3ooo4ooa
ElectronEnergyLoss, in cm” Fig. 6. HREELS of CH,OH exposed NiO(100) showing (a) phonon and adsorbate vibrational structure and (b) the spectrum after deconvolution of higher phonon modes.
sorbed methoxy species [3-g]. The stretching mode at 3056 cm-l is characteristic of the asymmetric stretch of C-H in methyl functional groups; a symmetric stretch is expected by comparison with gas phase methanol spectra at approximately 2860 cm-l, but is generally not resolved from the asymmetric stretch in HREELS methanol or methoxy adsorbate spectra. The carbon-oxygen stretch at 1461 cm-l is comparable to values observed for methoxy species coadsorbed with oxygen on transition metal substrates [9]. The data suggest that some molecularly adsorbed methanol may also be present, because the hydrocarbon stretching peak (3056 cm-l) appears somewhat distorted towards higher loss energy in the region expected for -OH stretching ( * 3250 cm-‘) from the undissociated alcohol functional group. The dominant form of the adsorbate is, however, clearly in the methoxy form. Unfortunately, the remaining fingerprint mode of the methoxy adsorbate, observed at z 1050cm-’ on transition metal substrates, occurs in the range still obscured by the unremoved portion of the second phonon harmonic, marked with an asterisk in Fig. 6(b). Fortuitously, the mode can be detected indirectly because it appears in a combination band with the methoxy C-O stretch. The BCH3+ vcc overtone has previously been reported in HREELS studies of methanol adsorption for several transition metal surfaces [10,35] and is found at 2527 cm-l in the present set of spectra. The combination band places the remaining methoxy vibrational mode at
K. W. Wulser, M.A.
236
I-~
542
Sk7
Lange&T.
Electron
A2
Spectrosc.
Reheat. Phenom.
59 (1992) 223-241
A7
0 1 s Binding Energy, eV Fig. 7. Oxygen 1s XPS taken with Mg Kcc(1253.6 eV) radiation at 25 eV bandpass. Actual data is shown as discrete points with Gaussian curves fit to the individual components to yield the solid line.
1066 cm-‘, in good agreement with literature values for the methoxy GO stretch. XPS data taken after the HREEL spectrum was acquired confirm the adsorbate identity. Oxygen 1s photoemission (Fig. 7) shows evidence of three distinct surface oxygen species. The NiO(lO0) lattice oxygen peak at 529.4eV is the largest, because the kinetic energy of the photoelectron allows a near surface region of x 50 A [36] to be sampled. The most intense adsorbate 0 1s peak occurs at 531.4 + 0.3 eV, corresponding well to oxygen emission from methoxy adsorbate [37,39]. In addition, a small water peak is found at 533.4 eV. Carbon 1s (Fig. 8) spectra are noisier than that of the oxygen, due to the lower cross section for photoemission from carbon, but clearly yield two distinct peaks with approximate binding energies of 286 and 288 f 1 eV. Based on HREELS and 0 1s XPS data, the peaks are assigned to methoxy and molecularly adsorbed methanol, respectively. Considering the fairly large error in the C Is spectra, the binding energies are in fair agreement with previously reported XP spectra [37-391. As can be seen from a comparison of peak areas in Fig. 7, the methoxy represents about 70% of the alcohol-generated adsorbate. HREELS
from CD,OD adsorption
Support of vibrational assignments for hydrogen-containing molecules is often given by comparison with deuterated spectra. Fig. 9(a) shows the NiO(100) convoluted phonon and adsorbate HREEL spectrum after the substrate was exposed to saturation vapor pressures of deuterated methanol in a manner similar to that described for CH,OH above. Prior to
K. W. Wuker, MA.
LangeU/J. Electron S’ctrosc.
I
I
I
285 280 C 1s Binding Energy, in eV
295
290
Relat. Phenom. 59 (1992) 223-241
237
275
Fig. 8. Carbon Is XPS taken with Mg Ka (1253.6 eV) radiation at 50 eV bandpass. Actual data is shown as discrete points with Gaussian curves fit to the individual components to yield the solid line.
deconvolution, the HREEL spectrum is of comparable quality to the CH,OH results (Fig. 6(a)). Some adsorbate vibrational structure can be discerned, especially after expanding the scale. However, a much richer adsorbate vibrational spectrum is made obvious by the deconvolution of the higher phonon modes (Fig. 9(b)). The spectrum contains -OCD, adsorbate features, assignments for which are given in Table 1. There are also contributions, however, from non-
-1obo
b
lb00
2obo
xi00
4 00
Electron Energy Loss, in crnsl Fig. 9. HREELS of CD,OD exposed NiO(100) showing (a} phonon and adsorbate structure and (b) the spectrum after deconvolution of higher phonon modes.
vibrational
238
K. W. Wulser,
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LangelllJ.
ELectron Spectrosc.
Relat.Ph.enom. 59 (1992) 22&241
deuterated and partially scrambled H/D isotopes of the methoxy adsorbate. The scrambling results from the catalytic action of the NiO(100) surface combined with the natural H,O background of the stainless steel UHV chamber, and indiates that the C-H bond of the surface methoxy is labile on NiO(100). Despite the increased complexity of the spectrum, all peaks can still be accounted for by using vibrational modes of surface methoxy and adsorbed water. After removal of the -OCH, and H, 0 modes previously found in Fig. 6(b), the spectrum consists of modes from their deuterated and partially deuterated analogs. The deuterated methoxy modes characteristic of the adsorbate are found at 922 (de,,) and 1387 (v~_~), in agreement with previously reported values [40]. The peaks at 2075 and 2236 cm-l are due to the symmetric and asymmetric stretches respectively of aliphatic -CD,. The peak at 2236 cm-l presumably also has a contribution from the weaker 6 CD3 + vC-O combination band, although it is not resolved from the more intense aliphatic stretch. The O-D stretch from the deuterated water is found at 2492 cm-l, although the scissoring mode, expected at approximately 1100 cm-’ [41], is now unfortunately obscured by the unremoved portion of the double excitation phonon marked with an asterisk in Fig. 9(b). The remaining feature at 2836 cm-’ is a mixed isotope CDH, aliphatic stretching mode. The symmetric and asymmetric stretches are resolvable in the deuterated vibrational spectrum because of the larger energy spacing between the modes. DISCUSSION
In the present study a Fourier-log deconvolution algorithm has been used to extend the useful frequency range of NiO(100) HREEL adsorbate spectra. The algorithm has good stability towards the choice of instrument response function. When simple Gaussians of different halfwidth are used, it is found that functions which equal or exceed the FWHM of the elastic peak perform well as instrument response functions. However, narrower functions distort the spectrum in the low frequency range to a high degree and could potentially introduce artifacts into the HREEL spectrum. Thus, attempts to improve resolution by Fourier deconvolution techniques must be handled carefully. In an effort to further improve the deconvolution algorithm, tails were added to the instrument response function to compensate for the observed phonon peak shapes. A natural concern of such action is the result of a poor choice in tail fitting parameters. Instrument response functions with tailing that clearly overestimated or underestimated the true peak shape were used to study this effect. Whereas fits with a smaller tail than that observed worked well with the algorithm, primarily causing distortion in
K. W. Wulser,
M.A.
LangelEjJ. Ekctron
Spectrosc.
Reht.
Phenom.
59 (1992) 22%241
239
the frequency range below the fundamental phonon frequency, fits with too much tailing gave rise to distorted spectra with large sloping background. Thus, as with the choice of FWHM of elastic peak, it is safe to remove too little, but not too much, of the expected phonon multiple loss peaks. However, a generous amount of mismatch is needed to cause a significant amount of distortion and provided the peak shapes are fairly closely matched, the deconvolution works reliably. The effect of the stoichiometry of the surface is of concern if the algorithm is to be successfully applied to a wide variety of surface preparations. The phonon spectrum itself decreases noticeably in intensity upon gas adsorption or change in surface stoichiometry, but does not change significantly in peak shape or relative phonon intensities. The primary phonon loss energy (70.5 meV) also remains constant, regardless of surface pretreatment. Specifically relevant to the HREELS deconvolution application, the phonons retain their near-harmonic behavior. Thus information of the higher losses can be obtained from the primary loss regardless of their position, intensity and peak shape correlation with the initial adsorbate free surface. Phonon losses that are incompletely removed are now of comparable or lower intensity than potential adsorbate peaks. Because their positions are accurately known, they can be distinguished as phonon losses in the deconvoluted spectra and thus need not be confused with adsorbate losses. Studies of methanol-exposed NiO( 100) surface yield little distinct change in the phonon-dominated HREEL spectrum when compared to the unexposed NiO(100) phonon spectrum. Although some extra vibrational features can be detected at higher loss energies in the range of the aliphatic stretching, their exact peak positions are not clear. Post deconvolution, a considerably wider range of adsorbate vibrational losses can be sampled. The loss features obtained for this system (Tables 1 and 2) agree well with characteristic losses reported for the surface methoxy species and for coadsorbed water, species that are clearly identified as being present by XPS analysis of the adsorbate surface. Deuterated adsorbate losses shift accordingly. The deuterated spectra are somewhat complicated by the catalytic effects of the substrate, which causes H-D scrambling under adsorption conditions of the hydrogen-rich atmosphere of the stainless steel chamber. CONCLUSIONS
We have shown the power and utility of the Fourier-log deconvolution algorithm when applied to the metal oxide HREEL spectra. The Fourier-log deconvolution algorithm exhibits good stability under a variety of computational and surface conditions and choice of instrument response func-
240
K. W- Wdser,
M.A.
Lungell}J.
Electron
Spectrosc.
Relat. Phenom.
59 (1992) .%%%-~?~1
tional form. The algorithm was used to study the adsorption of methanol on NiO(100) which adsorbs dissociatively on the surface at room temperature to yield methoxy groups, surface hydroxyls, and adsorbed water. ACKNOWLEDGMENT
Acknowledgment is made to the Donors of The Petroleum Research Fund, administered by the American Chemical Society for support of this research. REFERENCES 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
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