Efficient Anodes for Direct Methanol and Formic Acid Fuel Cells: the Synergy Between Catalyst and Three-Dimensional Support
Bibliographic record
Abstract
The synergy between three-dimensional support (graphite felt uncompressed thickness 2 mm , GF) and Pt – Ru catalyst preparation method afforded a four times reduction of the catalyst load in direct methanol fuel cell (DMFC) anodes while improving the power density compared to both the catalyst-coated membrane (CCM) and catalyst-coated diffusion layer designs (CCDL). In DMFC experiments the Pt – Ru ( 10 g m − 2 ) ∕ GF anode generated a maximum power density of 741 W m − 2 at 333 K compared to 703 and 278 W m − 2 obtained with CCM and CCDL, respectively, both with 40 g m − 2 Pt – Ru load. In direct formic acid fuel cell experiments at 333 K and 1 M HCOOH concentration, the maximum power density using the Pt – Ru ( 10 g m − 2 ) ∕ GF anode reached 860 W m − 2 , compared to 526 W m − 2 with CCM. A micellar solution composed of the nonionic surfactant Triton X-102 and an aqueous phase containing H 2 PtCl 6 and ( NH 4 ) 2 RuCl 6 was utilized for the galvanostatic electrodeposition of Pt – Ru nanoparticles ( 5 – 10 nm crystallite size), assuring excellent catalyst penetration throughout the GF thickness as demonstrated by surface analytical techniques. The type of micellar media employed for electrodeposition had an impact on the crystallographic features of the Pt – Ru catalyst and conversely on the electrocatalytic activity. The highest activity was observed for Pt – Ru with the largest fraction of Pt [1,1,1] and [3,1,1] crystal facets and bimetallic Pt – Ru surface structure as opposed to alloy. The present work opened up a venue for “designing” the catalytic surface and exploiting the catalyst/three-dimensional support interaction for direct liquid fuel cell anodes.
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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 | 0.001 | 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".