Nanoscale thin films of niobium oxide on platinum surfaces: creating a platform for optimizing material composition and electrochemical stability
Bibliographic record
Abstract
A nanoscale thin film of niobium oxide on a platinum substrate was evaluated for its influence on the electronic and chemical properties of the underlying platinum towards the oxygen reduction reaction with applications to proton exchange membrane fuel cells. The nanoscale thin film of niobium oxide was deposited using atomic layer deposition onto the platinum substrate. A film of niobium oxide is a chemically stable and electronically insulating material that can be used to prevent corrosion and electrochemical degradation when layers are several nanometers thick. These layers can be insulating if sufficiently thick and may not be sufficient to protect the platinum from corrosion if too thin. An ∼3 nm thin film of niobium oxide was fabricated on the platinum surface to determine its influence on the electronic and chemical properties at the interface of these materials. The atomic layer deposition process enabled a precise control over the material composition, structure, and layer thickness. The niobium oxide film was evaluated using cyclic voltammetry and electrochemical impedance spectroscopy to evaluate whether a balance could be found between the inhibition of platinum degradation and electronic insulation of the platinum for use in proton exchange membrane fuel cells. The 3 nm thin niobium oxide film was found to be sufficiently thin to permit electronic conductivity while reducing the incidence of platinum dissolution.
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".