Single-atom Catalysis Using Pt/Graphene Achieved through Atomic Layer Deposition
Bibliographic record
Abstract
Platinum-nanoparticle-based catalysts are widely used in many important chemical processes and automobile industries. Downsizing catalyst nanoparticles to single atoms is highly desirable to maximize their use efficiency, however, very challenging. Here we report a practical synthesis for isolated single Pt atoms anchored to graphene nanosheet using the atomic layer deposition (ALD) technique. ALD offers the capability of precise control of catalyst size span from single atom, subnanometer cluster to nanoparticle. The single-atom catalysts exhibit significantly improved catalytic activity (up to 10 times) over that of the state-of-the-art commercial Pt/C catalyst. X-ray absorption fine structure (XAFS) analyses reveal that the low-coordination and partially unoccupied densities of states of 5d orbital of Pt atoms are responsible for the excellent performance. This work is anticipated to form the basis for the exploration of a next generation of highly efficient single-atom catalysts for various 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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".