Novel Mesoporous Carbon Supports for PEMFC Catalysts
Bibliographic record
Abstract
Over the past decade; a significant amount of research has been performed on novel carbon supports for use in proton exchange membrane fuel cells (PEMFCs). Specifically, carbon nanotubes, ordered mesoporous carbon, and colloid imprinted carbons have shown great promise for improving the activity and/or stability of Pt-based nanoparticle catalysts. In this work, a brief overview of these materials is given, followed by an in-depth discussion of our recent work highlighting the importance of carbon wall thickness when designing novel carbon supports for PEMFC applications. Four colloid imprinted carbons (CICs) were synthesized using a silica colloid imprinting method, with the resulting CICs having pores of 15 (CIC-15), 26 (CIC-26), 50 (CIC-50) and 80 (CIC-80) nm. These four CICs were loaded with 10 wt. % Pt and then evaluated as oxygen reduction (ORR) catalysts for use in proton exchange membrane fuel cells. To gain insight into the poorer performance of Pt/CIC-26 vs. the other three Pt/CICs, TEM tomography was performed, indicating that CIC-26 had much thinner walls (0–3 nm) than the other CICs and resulting in a higher resistance (leading to distributed potentials) through the catalyst layer during operation. This explanation for the poorer performance of Pt/CIC-26 was supported by theoretical calculations, suggesting that the internal wall thickness of these nanoporous CICs is critical to the future design of porous carbon supports.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".