Comparative Oxygen Evolution Reaction performance of cobalt oxide electrocatalyst in combination with various metal ions MCo<sub>2</sub>O<sub>4</sub> (M= Mn<sup>2+</sup>, Cu<sup>2+</sup>, Co<sup>2+</sup>, Zn<sup>2+</sup>, Fe<sup>2+</sup>, Mg<sup>2+</sup>)
Bibliographic record
Abstract
Abstract Oxygen evolution reaction (OER) supported by electrocatalyst is very important reaction in electrochemical system e.g. air-battery based energy storage devices, water splitting, and photo electrochemical cells. Therefore developing inexpensive, non-hazardous, noble metal free, transition metal oxide based electrocatalyst is necessary for energy application and environmental sustainability. MCo2(III)O4 based oxides in combination of various metal ions (Mn2+, Cu2+, Co2+, Zn2+, Fe2+, Mg2+) are studied as OER electrocatalyst in both acidic and basic medium. When deposited on a glassy carbon current collector the comparative LSV polarization plots revealed that in acidic medium FeCo2O4 is the best OER performing electrocatalyst, showing onset potential +1.62 V vs RHE with current 1.66 mA/cm2, while in basic medium it is MnCo2O4 that preforms the best, showing an onset potential +1.53 V vs RHE with OER current density 2.06 mA/cm2. When nickel foam was used as the current collector, Co3O4 shows the best OER performance, with an onset potential 1.508 V vs RHE and OER current 159 mA/cm2 in acidic medium. However in the basic medium the substrate nickel foam outperforms all the oxides combinations with different metal ions due to partially oxidized NiO at nickel foam, showing onset OER potential +1.58 V vs RHE and OER current density 13mA/cm2. No correlation was found between the rates of OER and the bond dissociation energies of the respective metal-oxygen bonds nor the metal-hydroxide bond strength.
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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".