Electrochemical Characterization of Pd-Based Catalysts Deposited on Yittria Stabilized Zirconia Solid Electrolyte for Methane Oxidation Reaction
Bibliographic record
Abstract
Methane, the main constituent of natural gas, has received increased attention since it has a higher energy density than other fossil fuels while producing less particulates containing sulphur and nitrogen. However, although methane emits less CO2 than other fossil fuels, it has 28 times more global warming potential [1]. Therefore, complete methane oxidation via heterogeneous catalysis that prevents escaping unburned methane from the engine exhaust is a key solution to this issue. Palladium is the most effective conventional noble metal for methane catalytic oxidation, which is susceptible to deactivation, especially in the presence of water. The stability and activity of a monometallic Pd catalyst could be improved upon adding a second less expensive and more abundant non-noble metal or metal oxide[2]. The interaction of Pd nanoparticles and the second metal can be characterized using electrochemical means to understand the catalytic and electrocatalytic activity of catalysts[3,4]. In this work, the electrochemical behaviour of PdM (M= Co, Sn, Fe and ZnO) nanoparticles deposited on YSZ solid electrolyte was evaluated for complete methane oxidation and compared to free-standing Pd catalyst. To this end, the nanoparticles were synthesized via the polyol method and tested for their open circuit catalytic oxidation and electrochemical performance in a temperature range from 320 to 400 °C under reducing, stoichiometric and oxidizing reaction conditions. The light-off experiments in open circuit condition revealed that the presence of Sn increased the catalytic rate of the reaction more than two other metals, Co and Fe, which resulted in lowering the reaction rate per unit mass of active catalyst. Furthermore, ZnO-supported Pd demonstrated the highest catalytic reactivity due to metal-support interactions. Lastly, the results demonstrate a higher level of catalytic reactivity with higher temperatures and oxygen partial pressures (pO2). Using Tafel plot calculations from the linear sweep voltammetry measurements, the exchange current density (io) was obtained for each catalyst, as well as the apparent activation energy of the reaction in the individual condition. It was found that higher exchange current density corresponds to lower open circuit catalytic rates. The catalyst was characterized thoroughly by several physicochemical techniques, such as TEM, SEM, XRD and ICP-MS. References [1] J. A. Arminio-Ravelo and M. Escudero-Escribano, “Strategies toward the sustainable electrochemical oxidation of methane to methanol,” Curr. Opin. Green Sustain. Chem., vol. 30, p. 100489, Aug. 2021, doi: 10.1016/J.COGSC.2021.100489. [2] K. Persson, A. Ersson, K. Jansson, N. Iverlund, and S. Järås, “Influence of co-metals on bimetallic palladium catalysts for methane combustion,” J. Catal., vol. 231, no. 1, pp. 139–150, Apr. 2005, doi: 10.1016/J.JCAT.2005.01.001. [3] P. Vernoux and C. G. Vayenas, Eds., “Recent Advances in Electrochemical Promotion of Catalysis,” vol. 61, 2023, doi: 10.1007/978-3-031-13893-5. [4] H. A. E. Dole, A. C. G. S. A. Costa, M. Couillard, and E. A. Baranova, “Quantifying metal support interaction in ceria-supported Pt, PtSn and Ru nanoparticles using electrochemical technique,” J. Catal., vol. 333, pp. 40–50, Jan. 2016, doi: 10.1016/J.JCAT.2015.10.015.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".