3D-nanoarchitectured Pd/Ni catalysts prepared by atomic layer deposition for the electrooxidation of formic acid
Bibliographic record
Abstract
Three-dimensionally (3D) nanoarchitectured palladium/nickel (Pd/Ni) catalysts, which were prepared by atomic layer deposition (ALD) on high-aspect-ratio nanoporous alumina templates are investigated with regard to the electrooxidation of formic acid in an acidic medium (0.5 M H2SO4). Both deposition processes, Ni and Pd, with various mass content ratios have been continuously monitored by using a quartz crystal microbalance. The morphology of the Pd/Ni systems has been studied by electron microscopy and shows a homogeneous deposition of granularly structured Pd onto the Ni substrate. X-ray diffraction analysis performed on Ni and NiO substrates revealed an amorphous structure, while the Pd coating crystallized into a fcc lattice with a preferential orientation along the [220]-direction. Surface chemistry analysis by X-ray photoelectron spectroscopy showed both metallic and oxide contributions for the Ni and Pd deposits. Cyclic voltammetry of the Pd/Ni nanocatalysts revealed that the electrooxidation of HCOOH proceeds through the direct dehydrogenation mechanism with the formation of active intermediates. High catalytic activities are measured for low masses of Pd coatings that were generated by a low number of ALD cycles, probably because of the cluster size effect, electronic interactions between Pd and Ni, or diffusion effects.
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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".