High-Performance Osmium Nanoparticle Electrocatalyst for Direct Borohydride PEM Fuel Cell Anodes
Bibliographic record
Abstract
Carbonaceous direct fuel cells are hampered by sluggish anode kinetics associated with CO poisoning and therefore typically require a high load of costly Pt-based electrocatalysts. Direct borohydride fuel cells (DBFCs) have an inherent advantage due to the absence of CO and are characterized by high thermodynamic specific energy . Here we show for the first time, using fundamental electrochemical methods combined with fuel cell experiments, that osmium nanoparticles are kinetically superior and stable catalysts for borohydride electro-oxidation compared to Pt and PtRu. Osmium favors the direct oxidation of by a total of seven electrons as opposed to in situ hydrogen generation. The complex network of reactions involved in the oxidation of to is analyzed in the context of the experimental data. The current densities obtained with DBFC at with 20% anode were at and at .
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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".