Interrelated functionalities of hierarchically CNT/CeO2/Pt nanostructured layers: synthesis, characterization, and electroactivity
Bibliographic record
Abstract
We present a free-standing catalyst layer comprising current collector/CNTs (catalyst support)/CeO(2)/Pt (catalyst) nanostructured layers, each layer constructed upon the one below it. FESEM and TEM showed that a CeO(2) layer has a fluffy morphology recalling the texture of cotton, whereas Pt nanoparticles assemble into cauliflower or broccoli-like arrangement. New insights have been gained into the effect of CeO(2) on the structural properties of the beneath CNTs layer and on the above Pt layer. First, by means of Raman analysis, it was found that interaction of CeO(2) with CNTs induced a decrease in the crystallinity of the latter. Second, by TEM and XPS analyses, it was observed that the size of Pt nanoparticles in the CNT/CeO(2)/Pt structure was inferior to that in the CNT/Pt, implying that CeO(2) influenced the dispersion quality of Pt nanoparticles. For the first time, it is observed that CeO(2) supported CNTs undergo oxidation/reduction reactions at low potentials in the ethanol electrolyte. The electrochemical analysis showed that entities produced from those redox processes are surface adsorbed/desorbed species most likely hydroxides. This unexpected electroactivity is due to the beneath CNTs that boosted the conductivity of CeO(2). Such improved conductivity of CeO(2) has fostered the electron-transfer kinetics of ethanol at Pt as demonstrated by the decreased overpotential required to oxidize ethanol and by the specific mass activity, which was greater than that of CNT/Pt.
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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".