Ni<sub>x</sub>Fe<sub>100-x</sub> for urea and oxygen evolution: a matter of compromise
Bibliographic record
Abstract
The combination of aqueous electrolysis, either for hydrogen generation or CO2 conversion, with wastewater treatment offers an elegant way to tackle issues associated with our energy transition and the need for clean drinking water. However, it requires an anode capable of doing both the oxidation of the targeted pollutant and the oxygen evolution reaction (OER), as most pollutants are present in too low concentration to be practical for industrial electrolysis. In this study, we focussed on the oxidation of urea on NixFe100-x catalysts. These catalysts were prepared by pulsed laser ablation in liquid, a versatile and green technique to prepare electrocatalysts. Transmission electron microscopy of the nanoparticles indicates the production of monodisperse nanoparticles, with an average diameter increasing from 7.8 ± 2.8 to 19.7 ± 3.9 nm with a higher iron fraction. The composition could be controlled between pure Ni and NiFe bimetallic nanoparticles with up to 56 ± 3% of iron, by controlling the composition of the target. A brief optimisation of the electrode preparation (loading, catalyst-to-carbon ratio) yielded an optimum at about 30 µg/cm2 of catalyst with a catalyst-to-carbon ratio of 20:80. During the electrocatalytic tests, Ni was the best catalyst for urea oxidation, with a maximum peak current of 619 mA/mg. However, Ni75Fe25 was the best OER catalyst, showing a peak current of 1150 mA/mg. The difference increased further during CA at 0.5 V, during which Ni75Fe25 outperformed pure Ni by almost a factor of 3 after 30 min.
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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 | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".