Molecular dissociation and vibrational excitation in the surface scattering of (N2)n and (O2)n clusters
Bibliographic record
Abstract
Theoretical studies have predicted that the extreme conditions produced within a cluster during cluster-surface scattering could catalyze multicenter reactions with large activation barriers. However, recent experimental results did not reveal vibrational excitation or molecular dissociation in the scattering of molecular van der Waals clusters on a graphite surface. Building on our previous investigations of translational and rotational excitation, we carried out a detailed study of the mechanisms of energy transfer to the vibrational degrees of freedom of the products of (N2)n and (O2)n cluster-surface scattering by means of molecular dynamics simulations. Our results indicate that the monomer product vibrational energy distributions are best fit by a sum of two Boltzmann distributions, which suggests that two distinct thermal-like processes of vibrational excitation may be occurring during cluster scattering. The cold component of the distribution was shown to involve monomers originating from the cluster interior while the hot component of the distribution is made up of monomers essentially lying at the outskirts of the cluster at surface impact. Under current experimental conditions, cluster products are found to be only slightly vibrationally excited. Only a small fraction of the incident cluster kinetic energy is transferred to the monomer product vibrational modes, such that molecular dissociation is not possible under typical experimental conditions, and a much larger incident kinetic energy is required to obtain a significant probability of surface-induced monomer dissociation. Furthermore, our results indicate that increasing cluster size does not catalyze, but rather hinders monomer vibrational excitation, and enhances vibrational relaxation. Our findings suggest the existence of an optimal cluster size for experimental studies of cluster-catalyzed reactions.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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)
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".