Part <scp>III</scp> : <scp> NiMoO <sub>4</sub> </scp> nanostructures synthesized by the solution combustion method: The influence of material synthesis parameters on the electrocatalytic activity toward the oxygen evolution reaction in an alkaline medium
Bibliographic record
Abstract
Abstract The oxygen evolution reaction (OER) is a critical step in water electrolysis and has long been recognized as the primary bottleneck of the process, owing to its inherently sluggish kinetics and significant energy demands when compared to the hydrogen evolution reaction (HER). Transition metal oxides have been identified as promising electrocatalytic materials for the OER, attributed to their low cost, high catalytic activity, thermodynamic stability, and ease of synthesis and scalability. Among these, NiMo‐oxide‐based materials exhibit particularly advantageous electrochemical and structural properties, making them strong candidates for OER electrocatalysis. In our previously published study (Part I of the series), NiMo‐oxide nanostructures with varying physicochemical properties and microstructures were synthesized via the solution combustion method by systematically modifying key experimental parameters and subsequently tested as HER electrocatalysts. In the current study, the electrocatalytic performance of these materials was thoroughly investigated in the context of the OER in an alkaline medium. The results demonstrated that the β‐NiMoO 4 phase exhibited superior OER activity compared to the α‐phase. Notably, the in‐house catalyst outperformed the benchmark IrO 2 , achieving a lower overpotential at 10 mA cm −2 (289 mV vs. 429 mV for IrO 2 ) and a Tafel slope of 47 mV dec −1 . Furthermore, the catalyst demonstrated exceptional stability during the 24 h polarization test. The observed enhancement in long‐term performance was attributed to the formation of NiOOH and the increased surface area of the electrocatalytic layer.
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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".