Radiofrequency Plasma‐Assisted Pulsed Laser Deposited Pt/TiO<i><sub>x</sub></i>N<i><sub>y</sub></i> Coatings on Multi‐Walled Carbon Nanotubes as Gas Diffusion Electrodes for the Oxygen Reduction Reaction
Bibliographic record
Abstract
Abstract This study reports on the fabrication and characterization of novel multilayered electrocatalyst nanostructures for the oxygen reduction reaction (ORR). Thin titanium oxynitride (TiOxNy) coatings are deposited on a forest of multi‐walled carbon nanotubes (MWCNTs), directly grown on a stainless‐steel mesh, by capacitively coupled radiofrequency plasma‐assisted pulsed laser deposition (RF‐PAPLD) in a N2 environment. The resulting high‐surface‐area binder‐free electrode is further coated with a low quantity of well‐dispersed Pt nanoparticles (NPs) by PLD in an inert atmosphere. High‐speed imaging of the laser‐induced plasma expansion provided evidence of a higher kinetics of the expanding plume at higher RF plasma powers, changing the morphology of the TiOxNy coatings. X‐ray photoelectron spectroscopy demonstrated that the coatings are homogeneous throughout their thickness, where the TiN, TiON, and TiO bonds exist in all samples. The oxygen content of the coating increases with the RF plasma power. Both TiOxNy/MWCNT and Pt/TiOxNy/MWCNT nanostructures are tested in a gas diffusion electrode setup to evaluate their activity in the ORR. The results showed the superior activity of the Pt/TiOxNy‐0.03 Torr‐30 W/MWCNT, reaching the highest current density of 180 mA cm−2, while the commercial Pt/C electrode with the same Pt loading yielded 105 mA cm−2 at the potential of 0.5 V.
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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) | 0.001 | 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".