Synergistic combination of Pd nanosheets and porous Bi(OH)3 boosts activity and durability for ethanol oxidation reaction
Bibliographic record
Abstract
Highly active and durable Pd-based electrocatalysts for ethanol oxidation reaction (EOR) play a crucial role in the commercialization of direct ethanol fuel cells (DEFCs). However, the poisonous intermediates (especially adsorbed CO species (CO ad )) formed during the EOR process can easily adsorb and block the active sites on Pd electrodes, which in turn limits the catalytic efficiency. Hence, we present a series of Pd-based composites with a strong coupling interface consisting of Pd nanosheets and amorphous Bi(OH) 3 species. The incorporation of Bi(OH) 3 can induce an electron-rich state adjacent to the Pd sites and effectively separate the Pd ensemble, leading to excellent CO tolerance. The optimal Pd-Bi(OH) 3 NSs catalyst manifests a mass activity of 2.2 A·mg Pd −1 , which is 5.7 and 2.0 times higher than that of Pd NSs and commercial Pd/C catalyst, respectively. Further CO-stripping experiments and CO-DRIFTS tests confirm the excellent CO tolerance on Pd-Bi(OH) 3 NSs electrode, leading to the enhanced EOR durability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".