(Keynote) Oxygen Reduction and Evolution Reactions on Faceted Mn<sub>x</sub>Co<sub>1-X</sub>Fe<sub>2</sub>O<sub>4</sub> Nanoparticles Prepared By Induction-Coupled Plasma
Bibliographic record
Abstract
Substantial research efforts are directed toward the development of precious metal-free catalysts for fuel cells, metal-air batteries and electrolyzers, because the deployment of these technologies requires the synthesis of low-cost catalysts with controlled properties in large quantities. Mixed spinel oxides are a promising class of catalytic materials for the oxygen evolution (OER) and oxygen reduction (ORR) reactions, and thus several works have been devoted to the understanding of the structure – composition – activity relationships. Examples include the correlation between activity of spinel oxides and the covalency of the bond formed between the oxygen species and the metal cation in the octahedral sites1, or the possible role of metal cations in the tetrahedral sites in the oxygen electrocatalysis2. However, studies highlighting the importance of the facet engineering on the electrocatalytic activity of spinel oxides3 are still rare. Here, we will present our recent work on facetted nanoparticles MnxCo1-xFe2O4 (0 ≤ x ≤ 1) prepared by thermal plasma induction; a one step4, cost-effective and scalable method allowing the synthesis of large amounts (up to 30 g h-1 for 50 kW units) of uniform and crystalline nanoparticles5. Structural characterization by X-ray diffraction and TEM-EELS analysis showed that the mixed ferrite nanoparticles are formed by individual nanocrystals with well-defined truncated octahedron shape and with {100} and {111} facets mainly exposed, have high crystallinity, a median particle size of 40 nm and a homogeneous composition down to the atomic level. Electrochemical studies conducted in 0.1M KOH using the rotating disk electrode showed the highest activity was found for carbon black + Mn0.5Co0.5Fe2O4 composite electrode: half-wave potential for oxygen reduction 140 mV more negative than that of 20 wt% Pt/C; 420 mV oxygen evolution overpotential at 10 mAcm-2 (identical to IrO2/C). These are among the best performances reported for ferrites considering the size of the nanocrystals. Computational studies using density functional theory used to study the adsorption of oxygen molecule on the {100} and {111} facets of both CoFe2O4 and Mn0.5Co0.5Fe2O4 demonstrated that the Mn0.5Co0.5Fe2O4 {111} surface appears to have a highest affinity for the O2 molecule. The sites with lowest adsorption energy on each surface were identified and further analysis of O-O bond length and frequency as well as the Mulliken charge and populations confirmed the activation of the adsorbed O2 molecule. The energy diagrams of the ORR and OER computed for both Mn0.5Co0.5Fe2O4 {100} and {111} surfaces also pointed for a higher electrocatalytic activity for the {111} facet. References [1] Y. Zhou, S. Sun, C. Wei, Y. Sun, P. Xi, Z. Feng, Z. J. Xul, Adv. Mater. 31, 1902509 (2019). [2] A. Bergmann, E. Martinez-Moreno, D. Teschner, P. Chernev, M. Gliech, J. Ferreira de Araujo, T. Reier, H. Dau and P.Strasser, Nature Commun.,6 8625(2015). [3]J. Xiao, Q. Kuang, S. Yang, F. Xiao, S. Wang, L. Guo, Sci. Rep.,3, 1 (2013). 4 J. Chen et al, in preparation. [4] A.Y. Li, N. Dumaresq, A. Segalla, N. Braidy, A. Moores, ChemCatChem.,11, 3958,(2019). Acknowledgements This work was financially supported by the Centre Québécois des Matériaux Fonctionnels (CQMF, program “Projets de recherche collaboratives”) and by Natural Sciences and Engineering Research Council of Canada (NSERC, Discovery Grants of A.C.T and N.B.). N.B. also wish to acknowledge the funding support from Canada Research Chairs program and the Plateforme de Recherche et d’Analyse des Matériaux of Université de Sherbrooke (PRAM). A.C.T. and S.M. acknowledge the support from Compute Canada (www.computecanada.ca) supercomputing facility. J.C. acknowledges the financial support from Fonds de Recherche du Québec – Nature et Technologie (FRQNT, PhD scholarship “Énergie #286570”).
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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) | 0.028 | 0.007 |
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".