Role of Initial Vibrational and Rotational Reactant Excitation for the Reaction Dynamics of H<sub>2</sub>(ν<sub>0</sub>,<i>J</i><sub>0</sub>) with Si<sup>+</sup>(<sup>2</sup>P)
Bibliographic record
Abstract
Molecular dynamics simulations with semiempirical quantum chemistry have been used to investigate the reaction dynamics of ground-state silicon ions with molecular hydrogen for translational impact energies between 0.5 and 10 eV. The validity of the employed PM3 method is demonstrated in comparison to high-level ab initio CCSD(T) calculations. Reaction cross sections for both SiH + (ν, J ) formation and complete dissociation are determined for different initial vibrational and rotational excitation states of the H 2 (ν 0, J 0 ) molecules. Heating the commonly used room temperature hydrogen plasma to a maximum possible experimental value of 1000 K only results in a very modest increase of the reactive cross section for SiH + production by about 25%. The use of selective laser excitation of the reactants, however, permits us to increase the reactivity drastically. In this latter case, initial rotational laser excitation enhances the reactivity of our system as much as vibrational excitation, illustrating that only the amount of the initial excitation and not its precise nature influences the reaction dynamics. Furthermore, we show how the initial vibrational, rotational, and translational energies of the H 2 reactants control the final energy distributions of the SiH + products. The mechanisms leading to the observed reaction dynamics are discussed.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".