Characterization of Pt Nanoparticles Deposited onto Carbon Nanotubes Grown on Carbon Paper and Evaluation of This Electrode for the Reduction of Oxygen
Bibliographic record
Abstract
Multiwalled carbon nanotubes (MWCNTs) were grown on the fibers of a commercial porous carbon paper used as carbon-collecting electrodes in fuel cells. The tubes were then covered with Pt nanoparticles in order to test these gas diffusion electrodes (GDEs) for oxygen reduction in H2SO4 solution and in H2/O2 fuel cells. The Pt nanoparticles were characterized by cyclic voltammetry, transmission electron microscopy, and X-ray photoelectron spectroscopy. The majority of the Pt particles are 3 nm in size with a mean size of 4.1 nm. They have an electrochemically active surface area of 60 m2/g Pt for Pt loadings of 0.1-0.45 mg Pt/cm2. Although the electroactive Pt surface area is larger for commercial electrodes of similar loadings, Pt/MWCNT electrodes largely outperform the commercial electrode for the oxygen reduction reaction in GDE experiments using H2SO4 at pH 1. On the other hand, when the same electrodes are used as the cathode in a H2/O2 fuel cell, they perform only slightly better than the commercial electrodes in the potential range going from approximately 0.9 to approximately 0.7 V and have a lower performance at lower voltages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".