TiSi<sub>2</sub>O<sub>x</sub> Coated N-Doped Carbon Nanotubes as Pt Catalyst Support for the Oxygen Reduction Reaction in PEMFCs
Bibliographic record
Abstract
Composite nanostrucutres of TiSi 2 O x coated nitrogen-doped carbon nanotubes (NCNTs) were synthesized by a combination of chemical vapor deposition (CVD) and magnetron sputtering processes. The synthesized nanostructures were used as supports for Pt catalyst for oxygen reduction reaction (ORR) in proton exchange memberane fuel cells (PEMFCs). An amorphous layer of TiSi 2 O x with controlled thicknesses was sputtered on NCNTs and followed by post-treatment at high temperature (1000 °C, An -TiSi 2 O x -NCNTs), inducing TiO 2 nanoparticles of around 5 nm in diameter embedded in the amorphous layer. Further analyses via X-ray absorption spectroscopy of the Ti K edge and Si K edge revealed the Ti atoms were in a TiO 2 rutile environment and the Si atoms were in a SiO 2 environment. Pt nanoparticles with an average diameter of 3 nm were deposited on the composite support, and their electrochemical behaviors toward ORR were studied. It was revealed that, even with lower electrochemical surface area (ECSA), Pt/ An -TiSi 2 O x -NCNTs showed better catalytic activity toward ORR than Pt/NCNT catalysts. The origin of enhanced activity of Pt/ An -TiSi 2 O x -NCNTs was examined by high resolution transmission electron microscopy (HRTEM) and the X-ray absorption near edge structure spectra (XANES) of the deposited Pt nanoparticles.
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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".