Graphene‐Supported Substoichiometric Sodium Tantalate as a Methanol‐Tolerant, Non‐Noble‐Metal Catalyst for the Electroreduction of Oxygen
Bibliographic record
Abstract
Abstract For the oxygen reduction reaction (ORR) in acidic medium, a good combination of an active and stable catalyst highly dispersed on a conductive and durable support is required. Moreover, for direct methanol fuel‐cell applications, a suitable tolerance to methanol poisoning is also necessary. In this communication, we report on a new graphene‐supported sodium tantalate (Na2Ta8O21−x) electrocatalyst obtained at high temperature and characterized by significant substoichiometry. The synthesis includes the precipitation of tantalum oxide on a high surface area graphene and subsequent thermal treatment at 900 °C. A novel sodium tantalate phase with oxygen vacancies is thereafter obtained. This catalyst formulation shows promising activity towards the ORR especially in the presence of a high methanol concentration, as evidenced by a high tolerance to methanol poisoning. Accelerated stress tests and chemical leaching experiments also show the remarkable stability of the catalyst. This material presents good perspectives for application in cost‐effective fuel cells.
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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) | 0.000 | 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".