Self‐Supported Worm‐like PdAg Nanoflowers as Efficient Electrocatalysts towards Ethylene Glycol Oxidation
Bibliographic record
Abstract
Abstract Advancing the intrinsic catalytic performances of electrocatalysts is believed to be an efficient approach for alleviating the energy crisis and environmental pollution. However, many drawbacks of the newly established catalysts such as large size, poor corrosion resistance, and anti‐poisoning make them exhibit limited electrocatalytic performances. To address these challenges, we herein embody the advantages of both composition and morphology to successfully fabricate self‐supported worm‐like PdAg nanoflowers (PdAg NFs) with the assistance of cetyltrimethylammonium bromide. Such special PdAg NFs with a high active surface area of 42.36 m2 g−1 display much better electrocatalytic performances with mass and specific activities of 5545 mA mg−1 and 12.82 mA cm−2 towards ethylene glycol oxidation, which are 6.9 and 2.1 times higher than that of commercial Pd/C, respectively. Our efforts in this work may open up a new way to maximize the electrocatalytic performance of catalysts by designing and adjusting their morphologies. We also trust that the electrocatalysts fabricated in this work can be applied as potential anodic catalysts and beyond.
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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".