Atomic-Layer-Deposition Derived Pt subnano Clusters on the (110) Facet of Hexagonal Al<sub>2</sub>O<sub>3</sub> Plates: Efficient for Formic Acid Decomposition and Water Gas Shift
Bibliographic record
Abstract
The Pt subnano clusters dispersed on the (110) facet of regularly shaped hexagonal Al 2 O 3 plates were fabricated via an atomic layer deposition approach. The resulting material contains Pt loading as low as 0.07 wt %; the interfacial structure exhibits nearly full CO conversion for water gas shift reaction at 210 °C, and the turnover frequency (TOF) of CO is as high as 2.1 s –1, outperforming most of the systems reported. The same interfacial structure was also found to be highly active for catalytic decomposition of formic acid (FA), with full FA conversion (with little CO product) and the TOF being 1.02 s –1 . Further characterizations together with density functional theory simulations elucidate that the superior catalytic performances are attributed to the unique interfacial structure and the synergism between the small Pt clusters and the Al 2 O 3 (110) substrate, leading to lower energy barriers for the *COOH intermediate formation over the ultrafine Pt ensembles and the hydroxylation over the Al 2 O 3 (110) substrate close to the Pt entities. Both are favorable for the evolution of *COOH intermediate and the reaction between *COOH and neighbor OH species. The current study provides insights into the effectiveness in generating high-performance catalytic material for clean energy production and modulation through precise control of the metal entity dimension and the oxide substrate engineering to achieve specific facet exposure.
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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) | 0.001 | 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".