Should a franck-condon or a curve-crossing picture be applied to ion-target collisional activation? A study of keV CO<sub>2</sub><sup>+·</sup>/He collisions by emission spectroscopy
Bibliographic record
Abstract
Collision-induced photon emissions (CIE) were observed for keV CO(2)(+*)/He collisions from 190 to 1020 nm. The emissions were assigned to the Deltanu=0 band of the CO(2)(+*) B (2)Sigma(u)(+) --> X (2)Pi(g) electronic transition and the Deltanu= +3, +2, +1, 0, -1, -2, -3 vibrational transition progression in the CO(2)(+*) A (2)Pi(u) --> X (2)Pi(g) electronic transition. The other peaks arise from the emissions of excited O* fragment atoms and the target gas. The relative intensities of the CO(2)(+*) and O* emissions are independent of the ion translational energy above 3 keV, supporting the curve-crossing mechanism for collisional excitation. Investigation of the relative intensities within the A (2)Pi(u) --> X (2)Pi(g) emission of CO(2)(+*) indicates that the vibrational distribution is well described by the Franck-Condon principle at high collision energy, a consequence of short collision time but not necessarily an indication of vertical transitions. Below 3 keV ion translational energy, vibrational excitation in the A (2)Pi(u) electronic state was observed. The observation is consistent with the explanation that the reaction occurs at small impact parameters, in which short-range, repulsive interactions between the projectile and the target result in direct translational-vibrational excitation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".