The Effect of Fe Dopant Location in Co(Fe)OOH<sub>x</sub> Nanoparticles for the Oxygen Evolution Reaction
Bibliographic record
Abstract
The addition of iron (Fe) can in certain cases have a strong positive effect on the activity of cobalt and nickel oxide nanoparticles in the electrocatalytic oxygen evolution reaction (OER). The reported optimal Fe dopant concentrations are, however, inconsistent, and the origin of the increased activity due to Fe dopants in mixed oxides has not been identified so far. Here, we combine density functional theory calculations, scanning tunneling microscopy, and OER activity measurements on atomically defined Fe-doped Co oxyhydroxide nanoparticles supported on a gold surface to establish the link between the activity and the Fe distribution and concentration within the oxyhydroxide phase. We find that addition of Fe results in distinct effects depending on its location on edge or basal plane sites of the oxyhydroxide nanoparticles, resulting in a nonlinear OER activity as a function of Fe content. Fe atom substitution itself does not lead to intrinsically more active OER sites than the best Co sites. Instead, the sensitivity to Fe promoter content is explained by the strong preference for Fe to locate on the most active edge sites of oxyhydroxide nanoparticles, which for low Fe concentrations stabilizes the particles but in higher concentrations leads to a shell structure with less active Fe on all edge positions. The optimal Fe content thereby becomes dependent on nanoparticle size. Our findings demonstrate that synthesis strategies that adjust not only the Fe concentration in mixed oxides but also its distribution within a catalyst nanoparticle can lead to enhanced OER performance.
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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".