Investigation of Heterogeneous Catalysts by an Electrochemical Method: Ceria and Titania-Supported Iridium Nanoparticles for Ethylene Oxidation
Bibliographic record
Abstract
Electrochemical promotion of catalysis (EPOC) is a well-established phenomenon in heterogeneous catalysis for enhancing catalytic activity through the application of a small electrical stimulus between the catalyst-working and counter electrode deposited on a solid electrolyte (e.g., yttria-stabilized zirconia (YSZ))1. This electrical stimulus causes the backspillover of ionic species, in this case O2-, from the solid electrolyte to the catalyst surface. A resulting change in catalytic activity is due to the modification of the electronic properties of the catalyst. The extent to which these properties can be modified depends on several factors, such as temperature, type of catalyst material and solid electrolyte, catalyst morphology, e.g., particle size and dispersion, ionic conductivity, and gas phase composition 2,3. EPOC is an interfacial phenomenon where two-phase boundaries, i.e., catalyst/gas and catalyst/solid electrolyte, as well as the three-phase boundary (tpb), i.e., electrolyte/catalyst/gas phase play a key role in the reaction. Recent EPOC studies have shown catalytic enhancement of highly dispersed, nano-structured catalysts through the use of mixed ionic electronic conducting (MIEC) materials, i.e., CeO2 4 and TiO2 5, which are in contact with the YSZ solid-electrolyte. This approach allows for considerable reduction of the mass of metal catalyst required, while maintaining the electrical conductivity of the working electrode needed to complete the electrochemical cell 6,7. Both CeO2 and TiO2 are considered reducible supports. More specifically, CeO2, due to its non-stoichiometry, has the ability to undergo conversion between Ce4+ and Ce3+ quite easily 7. TiO2 has also been known to strongly influence the performance of the supported metal catalysts due to this reducibility 8. These properties make the use of ceria- and titania-containing catalysts of interest for many catalytic applications. This study investigates the complete oxidation of ethylene over low particle size (1.1 nm) ruthenium and (1.0 nm) iridium nanoparticles supported on CeO2 and TiO2 under open (o.c.) and closed circuit conditions. Furthermore, detailed electrochemical characterization of the Ir- and Ru- based catalysts was carried out using a steady-state polarization technique. The results obtained were correlated with their o.c. catalytic performance. The discussion of this study includes the effect of temperature and applied potential on the catalytic activity of the supported and freestanding Ir and Ru catalysts. Ru and Ir nanoparticles, synthesized using a polyol reduction method, were supported on CeO2 and TiO2 resulting in a 1 wt% catalyst loading (RuNPs/CeO2, IrNPs/CeO2, RuNPs/TiO2 and IrNPs/TiO2). The supported and free-standing nanoparticle catalysts were deposited on one side of a YSZ solid electrolyte disk in order to apply polarization 4. Gold counter and reference electrodes were applied on the opposite side of YSZ disk 9. Figure 1 shows representative polarization curves of the Ir/CeO2 catalyst at various temperatures. Positive current density (i) is due to electrochemical oxidation of ethylene and oxygen evolution at the tpb, whereas anodic current density is due to O2 electro-reduction. As can be seen, the rate of both positive and negative current densities (i) increases with temperature, indicating an increase in the reaction rates at the tpb as a result of an increase in electrolyte conductivity. i was also found to increase from 1.26, 3.5 to 29.5 µA.cm-2 for 350, 375 and 400 oC, respectively. As a result, this indicates that as temperature is increased, there is more available O2- at the tpb. A combination of open circuit catalytic and electrochemical measurements was used to evaluate and understand the catalytic performance of highly-dispersed Ru- and Ir-based, free-standing, and CeO2 and TiO2-supported catalysts with regards to the effect of O2- ions from the support. The presence of CeO2 and TiO2 was shown to play a significant role in enhancing the catalytic activity of Ru and Ir nanoparticles. Acknowledgment The financial support from Natural Science and Engineering Research Council (NSERC) is acknowledged. References 1. B. S. Vayenas Costas G., Bebelis Symeon, Pliangos Costas, Electrochemical Activation of Catalysis, (2001). 2. S. Brosda, J. Catal., 208, 38–53 (2002). 3. P. Vernoux et al., Chem. Rev., 113, 8192–8260 (2013). 4. H. A. E. Dole, A. C. G. S. A. Costa, M. Couillard, and E. A. Baranova, J. Catal., 333, 40–50 (2016) 5. E. I. Papaioannou et al., J. Appl. Electrochem., 40, 1859–1865 (2010). 6. Kambolis et al., ECS Trans., 45, 535–541 (2012). 7. H. A. E. Dole, L. F. Safady, S. Ntais, M. Couillard, and E. A. Baranova, J. Catal., 318, 85–94 (2014). 8. S. J. Tauster, Acc. Chem. Res., 20, 389–394 (1987). 9. H. A. E. Dole, L. F. Safady, S. Ntais, M. Couillard, and E. A. Baranova, ECS Trans., 61, 65–74 (2014). Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".