Engineering Morphology and Ni Substitution of Ni<i><sub>x</sub></i>Co<sub>3–<i>x</i></sub>O<sub>4</sub> Spinel Oxides to Promote Catalytic Combustion of Ethane: Elucidating the Influence of Oxygen Defects
Bibliographic record
Abstract
Transition metal oxides are potential alternatives to noble metal catalysts for oxidation reactions. Co-based spinel oxides, in particular, have attracted significant attention. Herein, Ni x Co 3– x O 4 catalysts were synthesized to elucidate the influence of oxygen vacancies on catalyst activities and reaction mechanisms for ethane combustion. A correlation between the activity and the population and properties of O defects was developed, with an increased number of O defects typically resulting in higher activity. Also, the shape-induced facet effect is related to the amount of Ni that is incorporated into the octahedral sites of Co oxide. The substituted Ni atoms altered the redox ability of Ni x Co 3– x O 4 by changing O vacancy formation and C–H bond dissociation. The NiCo 2 O 4 -TM catalyst (6.0 mmol mL –1 h –1 ) exhibits the highest activity for ethane oxidation compared with NiCo 2 O 4 -PC (0.5 mmol mL –1 h –1 ) and NiCo 2 O 4 -OL catalysts (1.3 mmol mL –1 h –1 ) at 330 °C, and its activation energy (E a ) is 70.9 kJ mol –1 . No activity decay is observed after the initial transition stage of the reaction in a long-term stability test up to 500 h on the NiCo 2 O 4 -TM-coated monolith, either with or without water addition. A vacancy-mediated pathway was proposed according to in situ diffuse reflectance infrared Fourier transform (DRIFT) and density functional theory (DFT) calculations over the NiCo 2 O 4 (311) facet. Findings from this study expand our understanding of the facet-dependent catalytic behavior and ultimately enable the rational design of high-performance catalysts.
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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) | 0.000 | 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".