Highly Active and Durable Ultrasmall Pd Nanocatalyst Encapsulated in Ultrathin Silica Layers by Selective Deposition for Formic Acid Oxidation
Bibliographic record
Abstract
The low performance of palladium (Pd) is a considerable challenge for direct formic acid fuel cells in practical applications. Herein, we develop a simple strategy to synthesize a highly active and durable Pd nanocatalyst encapsulated in ultrathin silica layers with vertically aligned nanochannels covered graphene oxides (Pd/rGO@pSiO 2 ) without blocking active sites by selective deposition. The Pd/rGO@pSiO 2 catalyst exhibits very high performance for a formic acid oxidation (FAO) reaction compared with the Pd/rGO without protective silica layers and commercial Pd/C catalysts. Pd/rGO@pSiO 2 shows an FAO activity 3.9 and 3.8 times better than those of Pd/rGO and Pd/C catalysts, respectively. The Pd/rGO@pSiO 2 catalysts are also almost 6-fold more stable than Pd/C and more than 3-fold more stable than Pd/rGO. The outstanding performance of our encapsulated Pd catalysts can be ascribed to the novel design of nanostructures by selective deposition fabricating ultrasmall Pd nanoparticles encapsulated in ultrathin silica layers with vertically aligned nanochannels, which not only avoid blocking the active sites but also facilitate the mass transfer in encapsulated catalysts. Our work indicates an important method to the rational design of high-performance catalysts for fuel cells in practical applications.
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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".