Synthesis and Electrochemical Study of Mesoporous Nickel-Cobalt Oxides for Efficient Oxygen Reduction
Bibliographic record
Abstract
Development of a cost-effective and efficient electrocatalyst for the sluggish oxygen reduction reaction (ORR) is a crucial challenge for clean energy technologies. In this study, we have synthesized various Ni and Co oxide (NCO) nanomaterials via a facile coprecipitation, followed by the calcination method. The morphology of the formed NCO nanomaterials was controlled by varying the percentage of the Ni and Co precursors, leading to the formation of a template-free mesoporous spinel phase structure of NixCo3–xO4. It was found that the number of the octahedral site cations and the defect sites with lower oxygen in the spinel oxides can be tunable by taking appropriate ratios of the Ni and Co precursors. The optimized NCO nanomaterial exhibits superior electrocatalytic activity compared to the mono-metal oxides of NiO and Co3O4 with over 3 times higher current density and ∼0.250 V lower onset potential toward ORR in a 0.1 M KOH solution. Scanning electrochemical microscopy was utilized in mapping the activity of the catalyst and monitoring the ORR products, further confirming that a four-electron transfer pathway was facilitated by the NCO nanomaterial. Moreover, the developed mesoporous NCO nanomaterial exhibits a high methanol tolerance capability and long-term stability when compared to the commercial state-of-the-art Pt/C electrocatalyst. The improvement of the catalytic activity and stability of this advanced NCO nanomaterial toward ORR may be attributed to the facile accessible mesoporous structure, and the abundance of octahedral site cations and defective oxygen sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".