Structural Characteristics and Eutaxy in the Photo-Deposited Amorphous Iron Oxide Oxygen Evolution Catalyst
Bibliographic record
Abstract
A central challenge for hydrogen generation via electrolytic water splitting is the identification of efficient oxygen evolution reaction (OER) catalysts; a key aspect of the challenge hinges on an ability to relate atomic-scale structure to observed activities. Amorphous iron-based oxide(hydroxides) prepared by photochemical metal organic decomposition (PMOD) are proven OER catalysts, but their atomistic structures have been elusive. Here, a combination of powder diffraction and pair distribution function (PDF) analyses enables the formulation of a set of structural characteristics that capture the salient features of amorphous iron oxide(hydroxide) ( a -FeO x ), a model compound for this class of materials. a -FeO x contains only octahedrally coordinated iron atoms, which form clusters of both edge- and corner-sharing octahedra. A degree of “eutaxy” with predominantly ABC-type anion stacking persists at length scales beyond the dimensions of cluster domains–consistent with thermally induced crystallization into the defect spinel γ-Fe 2 O 3 . Evidence for considerable octahedral irregularities suggests the presence of a large number of bridging and terminal hydroxyl or water ligands, which would provide a high concentration of potential active sites. The structural features of a -FeO x are reminiscent of other first-row transition metal oxyhydroxide OER catalysts that comprise layers of edge-sharing octahedral ions capable of electron transfer and ligand association/dissociation. In keeping with ABC anion stacking, however, the title compound more closely resembles a highly defective spinel lattice rather than a layered hydroxide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".