La, Ni-Based Metal Oxides As Bifunctional Catalysts for the Oxygen Reduction and Evolution Reactions in Alkaline Medium
Bibliographic record
Abstract
The oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are the fundamental processes occurring at the air electrode of metal-air batteries and reversible fuel cells. [1,2] However, the performance of those devices is hindered by the sluggish kinetics of both OER and ORR, and by the low stability and high cost of the catalysts. Here, we will present our recent studies on the one-dimensional (1D) La, Ni-based metal oxide [3] bifunctional catalysts which are characterized by excellent OER and ORR electrocatalytic activity and outstanding stability. The catalysts were prepared by hydrothermal method and, by controlling the KOH concentration, 1D hydroxide precursors with different aspect ratios were synthesized. By using graphene (G) as the supporting material, a variety of La, Ni-based metal oxides such as Ni+La2O3/G, LaNiO3, and La2NiO4+NiO/G were synthesized, Figure 1. Electrochemical characterization of the catalysts showed that the aspect ratio of the materials has a significant effect on their electrocatalytic activity. Namely, Ni+La2O3/G nanorods have proven to be electrochemically active for ORR with a half-wave potential (E1/2) of 0.715 V vs RHE, and for OER with 1. 574 V vs RHE at 10.0 mA cm−2 (E10) in 0.1 M KOH. The performance of Ni+La2O3/G is close to that of commercial Pt/C (E1/2=0.830V) which is the most efficient catalyst of ORR, and significantly higher than commercial RuO2 (E10=1.633 V) and IrO2 (E10=1.663 V) for OER. The unique structural, morphological and electrocatalytic properties of the Ni+La2O3/G catalysts will be presented and discussed. 1. Sui, S.; Wang, X.; Zhou, X.; Su, Y.; Riffat, S.; Liu, C.-j., A comprehensive review of Pt electrocatalysts for the oxygen reduction reaction: Nanostructure, activity, mechanism and carbon support in PEM fuel cells. Journal of Materials Chemistry A 2017, 5 (5), 1808-1825. 2. Cheng, F.; Shen, J.; Peng, B.; Pan, Y.; Tao, Z.; Chen, J., Rapid room-temperature synthesis of nanocrystalline spinels as oxygen reduction and evolution electrocatalysts. Nature chemistry 2011, 3 (1), 79. 3. Singh, Sarika, Daria Zubenko, and Brian A. Rosen. "Influence of LaNiO3 shape on its solid-phase crystallization into coke-free reforming catalysts." ACS Catalysis 2016, 7 4199-4205. 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 | 0.000 | 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".