Single-atom Catalysis Using Pt/Graphene Achieved through Atomic Layer Deposition
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- Meta-epidemiology (narrow)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Bench or experimentalConsensus signal: Bench or experimental
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.027
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.214 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Scientific Reports
- Topic
- Electrocatalysts for Energy Conversion
- Field
- Energy
- Canadian institutions
- Ballard Power Systems (Canada)Canadian Light Source (Canada)Brockhouse Institute for Materials ResearchMcMaster UniversityInstitut National de la Recherche ScientifiqueWestern University
- Funders
- National Research Council CanadaWestern Economic Diversification CanadaMcMaster UniversityNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of SaskatchewanCanadian Light Source
- Keywords
- Atomic layer depositionCatalysisGrapheneNanosheetAtom (system on chip)Materials scienceNanoparticleNanotechnologyX-ray absorption fine structureChemical engineeringDeposition (geology)Layer (electronics)ChemistryComputer scienceSpectroscopyPhysicsOrganic chemistry
- Has abstract in OpenAlex
- yes