Antipoisoning Performance of Platinum Catalysts with Varying Carbon Nanotube Properties: Electrochemically Revealing the Importance of Defects
Bibliographic record
Abstract
Abstract To understand the role of functional groups (FGs) and defects in improving the antipoisoning performance of platinum catalysts for formate oxidation, four kinds of supports originating from carbon nanotubes (CNTs) with varying amounts of FGs and varying degrees of defects are discussed. Platinum particles with controlled similarity are deposited onto the four supports to precisely compare the differences between the four supports. The catalysts structures are characterized by XRD, high‐resolution TEM, Raman spectroscopy, and XPS. The electrochemical performances of the four catalysts are characterized by cyclic voltammetry and chronoamperometry methods. The results show that the use of fully unzipped CNTs, with a higher degree of defects and lower amount of FGs, as a support results in the greatest improvement in antipoisoning performance of platinum, relative to oxidized CNTs with a higher amount of FGs and lower degree of defects. These results indicate that CNT defects play a greater role in promoting the antipoisoning performance of supported catalysts than FGs. These results are helpful to guide the design of supports to improve the antipoisoning performance of formate oxidation catalysts.
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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".