Comparing the Support Effects of Graphene Nanosheets (GNs) and N-doped GNs with Respect to Anti-Poisoning Performance of Pt Catalysts
Bibliographic record
Abstract
It is of great significance to precisely evaluate the anti-poisoning performance (APP) of anode catalysts for development of direct liquids fuel cells. By controlloing the similarity of Pt deposited on graphene nanosheets (GNs) and N-dopped GNs (NGNs), Pt/GNs and Pt/NGNs were prepared as model catalysts for APP evaluation. Cyclic voltammograms (CVs) of Pt/GNs and Pt/NGNs for formate and methanol oxidation showed that the APP differences of two catalysts were hardly distinguished only from CVs because the activities of the two catalysts were similar. By further analyzing the i-t data, Pt/NGNs was proved to be evidently superior to Pt/GNs in terms of APP, verifying the necessity of APP analysis for comprehensive catalysts evaluation. Furthermore, by analyzing Raman spectra, CO stripping and X-ray photoelectron spectroscopy, stronger interaction between Pt and NGNs was shown to produce weaker adsorption of poisoning species on NGNs-supported Pt, which is benificial for enhancing APP of Pt/NGNs. We believe this understanding can shed light on future work toward rational supports engineering for APP improvement.
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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 | 0.001 |
| 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".