N-Containing Carbon Nanospheres As PEFC Catalyst Support Prepared from Nature Inspired Precursor
Bibliographic record
Abstract
Apart from superior electrocatalytic activity for ORR, nitrogen containing carbon materials are also well-known for their ability to anchoring Pt nanoparticles. It was hypothesized that polydopamine (PDA), an intrinsically N-containing nature inspired polymer, may be able to stabilize platinum nanoclusters due to the binding ability between its amino and catechol groups with Pt precursor. In addition, the inherent fluorescence property of PDA makes it a suitable candidate to study catalyst layer micro-structure and pore size distribution by employing fluorescence microscopy. In this study, PDA was used as a precursor to synthesize ordered carbon nanospheres (100~150 nm) as a catalyst support for polymer electrolyte membrane fuel cells (PEFCs). Morphology of the catalyst was investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and fluorescence microscopy. TEM micrograph (Figure 1) indicated that well-dispersed and uniformly distributed Pt nanoparticles (2-3 nm) was deposited on the PDA based carbon nanospheres (cPDA) as theorized. Electrochemical activity including reaction kinetics of the catalyst was tested in both aqueous and polymer electrolyte (fuel cell condition) media. The performance of the catalyst in a fuel cell was studied at different operating conditions (temperature, relative humidity) and compared with that of the conventional vulcan carbon-based catalyst. Pt durability was assessed in both liquid electrolyte (0.5M H2SO4) and FC condition by conducting US department of energy (DOE) recommended accelerated stress test (AST) protocols. The presentation will share the details of catalyst synthesis and characterization as well as the results of its performance in a fuel cell. Figure 1
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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) | 0.001 | 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".