Electrooxidation of Ammonia at Tuned (100)Pt Surfaces by using Epitaxial Thin Films
Bibliographic record
Abstract
Abstract PtxNi100−x thin films (72≤x≤100, 10–25 nm thick) were deposited on MgO (100) substrates by using pulsed laser deposition. As shown from X‐ray diffraction analysis, the formation of a Pt(Ni) solid solution was observed over the Ni range investigated. All PtNi‐based films showed epitaxial growth in the (100) direction at 350 °C, which is in contrast to pure Pt films, where a Ni seeding layer was required to obtain epitaxial deposits at this temperature. X‐ray photoelectron spectroscopy depth‐profile analyses showed a Ni enrichment at the PtNi/MgO interface, which may be at the origin of the epitaxial growth in the alloys. After immersion in acidic media, Ni atoms are totally dissolved from the first ten atomic layers of the PtNi films, forming pure Pt electrodes. On the basis of underpotential‐deposited hydrogen electrochemical analyses, a (100) preferential surface orientation of the crystallites originating from epitaxial growth was confirmed on both Ptseeded and PtxNi100−x films. It was shown that the fraction of (100) terraces and terrace edge sites are a determining factor in the electrooxidation of NH3. The highest electrocatalytic activity was observed with the Ptseeded film.
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".