Ab Initio Study of Stability and Site-Specific Oxygen Adsorption Energies of Pt Nanoparticles
Bibliographic record
Abstract
We have employed ab initio calculations based on density functional theory in order to study stability and oxygen adsorption energies of Pt nanoparticles. For particles with sizes up to 200 atoms and various geometric shapes, we have explored the dependence of cohesive energies on atomic coordination number and on lattice strain effects. A simple empirical relation, which is consistent with the well-known Gibbs−Thomson relation, represents the cohesive energy over the range of considered sizes and shapes. For hemispherical cuboctahedral particles with 37 and 92 atoms, we have generated contour plots of the adsorption energy of atomic oxygen on all nanofacets. These plots furnish the known trend of strongly enhanced oxygen adsorption energies in comparison to extended surfaces. We found that the interplay of geometric effects, involving the periodic arrangement of surface atoms and edge effects on nanofacets, causes the high site-selectivity of Pt−oxygen interaction energies, with the largest adsorption energies found at the edges. Particle relaxation upon oxygen adsorption exhibits a significant influence on adsorption energies. The presented results provide a map of the peculiar site-selectivity of adsorption at Pt nanoparticles, which should be accounted for in building detailed models of reaction mechanisms and reactivity.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".