Frequency-Resolved Modulation Excitation Spectroscopy Methodology for Identifying Surface Reaction Species in Ethanol Oxidation on Gold Catalysts
Abstract
:1. Introduction
2. Results and Discussion
2.1. Ethanol Oxidation Catalytic Activity Results
2.2. Operando Modulation Excitation–Diffuse Reflectance Infrared Spectroscopy–Phase Sensitive Detection (ME–PSD–DRIFTS)
2.2.1. Time-Domain Spectra
2.2.2. MS Signal Response
2.2.3. Time and Frequency-Domain Absorbance IR Spectra
2.2.4. Phase-Domain Contour Plots During ME–PSD–DRIFTS
2.2.5. Phase–Domain Trace Plots During ME–PSD DRIFTS with PSD at the Fundamental Frequency (1f0)
- C-H stretching (C-H). (i) During EtOH modulation (Figure 7), 2976, 2909 cm−1 (on Au/SiO2); 2981, 2925, 2869 cm−1 (on Au/TiO2); 2963, 2937 cm−1 (on Au/ZnO); and 2870, 2926 cm−1 (on Au/SrTiO3). (ii) During O2 modulation (Figure 8), 2966, 2874 cm−1 (on Au/TiO2); 2963, 2896 cm−1 (on Au/ZnO); and 2960, 2873 cm−1 (on Au/SrTiO3).
- Hydroxyls stretching (-OH). (i) During EtOH modulation (Figure 7), 3673 cm−1 (on Au/SiO2); 3690 cm−1 (on Au/TiO2); and 3690 cm−1 (on Au/SrTiO3). (ii) During O2 modulation (Figure 8), ~3700 cm−1 (on Au/SiO2); 3690 cm−1 (on TiO2); ~3700 cm−1 (on Au/ZnO); and ~3700 cm−1 (on Au/SrTiO3). These signals could arise from adsorbed ethanol and/or support metal hydroxyls [52,53,54,55,56,61].
- Among other relevant peaks, broad shoulders in the range of 3500–3000, 2500–2000, and ~1275 cm−1 are indicative of ethanol, water, and hydrogen being bonded to the surface hydroxyl groups (OH), trapped electrons (especially during O2 modulations on Au/TiO2 and Au/ZnO), and carbonates, respectively.
2.2.6. Phase-Domain Trace Plots During ME-PSD-DRIFTS with PSD at 0.07f0, 0.5f0, 2f0, and 3f0
2.3. Modulation Excitation–Phase Sensitive Detection–Diffuse Reflectance UV–Vis Spectroscopy (ME–PSD–DRUV–Vis)
2.4. Possible Reaction Intermediates and Reaction Scheme During Ethanol Oxidation
- Reactant adsorption. Ethanol oxidation reaction on metal oxide supported gold catalysts proceeds through the dissociative adsorption of ethanol on the catalyst surface (Figure 7 and Figure 8), more likely on Au. This was demonstrated by DFT calculations on Au5/TiO2(101) [68]. The preferable adsorption of O2 at the Au–support interface, as mentioned in Section 2.3, was also evidenced by an in situ gold maximum plasmon peak shift (MaPPS) methodology, described in Refs. [68,69] (Scheme 1, steps 1 and 5).
- Ethanol dehydrogenation. Adsorbed ethanol dehydrogenates to form ethoxy species (at 1000–1200 cm−1 described in Section 2.2.4) in a fast elementary step, which is likely equilibrated and forms H species. This proton can transfer charge to Au, as evidenced from the signals due to trapped electrons at 2000–2500 cm−1 in Figures S23 and S24 (Scheme 1, step 2).
- Oxygen reaction at the interface. The H species formed on gold can diffuse rapidly to the Au–support interface, where it can react with O2 (Section 2.3) to form hydroperoxyl species (OOH) in a fast step (Scheme 1, step 5), as proposed recently via a charge-transfer spectrokinetic analysis (CT–SKAn) methodology [68]. This fast reaction is also evidenced in phase-domain plots obtained during O2 modulation in Section 2.2.6. O2 activation at the metal-support interface in the presence of charge transfer from Au can also occur dissociatively to form On− and OH species (Scheme 1, step 6). The CT–SKAn methodology recently showed that Au/TiO2, OOH, On−, and OH are kinetically relevant reaction intermediates for ethanol oxidation [68].
- Acetaldehyde formation. Adsorbed ethoxy species can be oxidized at the Au–support interface with activated O2 species, most likely with atomic oxygen [32,68] to form acetaldehyde and H2O, which are adsorbed at the surface, as evidenced from bands in the 1700–1740 cm−1 and 3300–3500 cm−1, respectively, as detailed in Section 2.2.6 (Scheme 1, steps 3, 9).
- Acetic acid formation. The ME–PSD–DRIFTS results point to the further oxidation of acetaldehyde to acetic acid (Scheme 1, step 7), primarily on Au/SrTiO3, which more strongly adsorbs acetaldehyde (Scheme 1, step 3) on the catalyst surface. MES spectral dynamic changes point to adsorbed acetaldehyde undergoing different surface processes. In one process, weakly adsorbed acetaldehyde desorbs to the bulk phase via a fast elementary step (Scheme 1, step 4). In the second process, strongly bonded acetaldehyde is oxidized to acetic acid in a slow step, as evidenced from MS (Section 2.2.2) and discussed in Section 2.2.6 (Scheme 1, step 7). Au/SrTiO3, which exhibited the highest activity for acetate (acetic acid and ethyl acetate) formation, also showed the rapid desorption of these species (Scheme 1, steps 8 and 13), preventing combustion to CO2 (Section 2.2.6, Scheme 1, steps 10 and 11).
- ME–PSD–DRIFTS also indicated strong CO2 adsorption on all catalysts, along with a strong affinity for water (Scheme 1, step 9), further evidenced by the slow water desorption (Scheme 1, step 12) detected by MS (Section 2.2.2).
3. Materials and Methods
3.1. Materials
3.2. Catalyst Preparation
3.3. Catalyst Characterization
3.3.1. ME–PSD Experimental Setup
3.3.2. ME–PSD–DRIFTS Experimental Conditions During Ethanol Modulation
3.3.3. ME–PSD–DRIFTS Experimental Conditions During Oxygen Modulation
3.3.4. Modulation Excitation–Phase Sensitive Detection Methodology
- The reactant concentration is varied in a periodic fashion while simultaneously collecting infrared or UV–Vis spectra.
- IR and/or UV–Vis spectra are swiftly and consistently acquired at regular intervals (~1–2 spectra per second) to capture surface alterations coinciding with periodic changes in gas concentrations. The spectra are plotted in what is called the time domain.
- These time-domain spectra are processed via the Fourier transform (FT) equation, transforming time-domain data, f(t), into the frequency domain, F(ω).
- At this stage, one sorts the frequencies in ascending order (i.e., with the fft shift function in Python) and selects a frequency or range of frequencies of interest to filter the different species contributions to the spectra. Usually, the fundamental frequency (e.g., 1/90 Hz) is chosen to filter out reactive species that respond to that modulation frequency.
- Then, the filtered data in the frequency domain, F(ω), is processed via the inverse Fourier transform (IFT) to recreate the original data into an averaged f(t), where this t corresponds to the time of a single modulation period.The above IFT equation is solved for discrete datasets via its inverse discrete Fourier transform (IDFT). We perform this transformation within Python using the “ifft” function, which effectively reverts the “filtered” data in the frequency domain back into the time domain of a single period (i.e., phase domain).
- The resulting “phase domain” dataset represents an average across all periods and in this work, for simplicity, it is plotted in time units. The specificity of the spectral response will depend on the frequency or frequency range chosen for filtering. When the filtered frequency is the fundamental one, the data obtained in the “phase domain” correspond to the sinusoidal contribution to the waveform, irrespective of the waveform shape of the system species.
3.4. Catalytic Activity Testing
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AcOH | Acetic acid |
DFT | Discrete Fourier Transform |
EtOH | Ethanol |
EtOAc | Ethyl acetate |
FBR | Fixed Bed Reactor |
FFT | Fast Fourier Transform |
FT | Fourier Transform |
FTIR | Fourier Transform Infrared |
IDFT | Inverse Discrete Fourier Transform |
IR | Infrared |
MaPPS | Maximum Plasmon Peak Shifts |
ME | Modulation Excitation |
ME–PSD–DRIFTS | Modulation Excitation–Phase Sensitive Detection–Diffuse Reflectance Infrared Fourier Transform Spectroscopy |
ME–PSD–DRUV–Vis | Modulation Excitation–Phase Sensitive Detection–Diffuse Reflectance Ultraviolet Visible Spectroscopy |
MES | Modulation Excitation Spectroscopy |
MS | Mass spectrometry |
NP | Nanoparticle |
NTP | Normal temperature and pressure |
PD | Phase Domain |
PSD | Phase Sensitive Detection |
RAcetaldehyde | Production rate of acetaldehyde (mol/gcat/s) |
RAcetates | Production rate of acetates, acetic acid + ethyl acetate (mol/gcat/s) |
SAcH | C2-carbon selectivity to acetaldehyde |
SAcetates | C2-carbon selectivity to acetates, acetic acid + ethyl acetate |
TRIC | Transition Relative Intensity Changes |
UV–Vis | Ultraviolet-Visible |
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Patil, B.S.; Torres-Velasco, A.; Bravo-Suárez, J.J. Frequency-Resolved Modulation Excitation Spectroscopy Methodology for Identifying Surface Reaction Species in Ethanol Oxidation on Gold Catalysts. Catalysts 2025, 15, 346. https://doi.org/10.3390/catal15040346
Patil BS, Torres-Velasco A, Bravo-Suárez JJ. Frequency-Resolved Modulation Excitation Spectroscopy Methodology for Identifying Surface Reaction Species in Ethanol Oxidation on Gold Catalysts. Catalysts. 2025; 15(4):346. https://doi.org/10.3390/catal15040346
Chicago/Turabian StylePatil, Bhagyesha S., Alejandra Torres-Velasco, and Juan J. Bravo-Suárez. 2025. "Frequency-Resolved Modulation Excitation Spectroscopy Methodology for Identifying Surface Reaction Species in Ethanol Oxidation on Gold Catalysts" Catalysts 15, no. 4: 346. https://doi.org/10.3390/catal15040346
APA StylePatil, B. S., Torres-Velasco, A., & Bravo-Suárez, J. J. (2025). Frequency-Resolved Modulation Excitation Spectroscopy Methodology for Identifying Surface Reaction Species in Ethanol Oxidation on Gold Catalysts. Catalysts, 15(4), 346. https://doi.org/10.3390/catal15040346