Control of Bond Excitation and Dissociation in HCN Using Laser Pulses
Bibliographic record
Abstract
The potential for selectively controlling the excitation and dissociation of the two bonds of the linear triatomic molecule HCN using short laser pulses is studied. We show that intramolecular vibrational redistribution (IVR) has strong influence on the controlling process: at low excitation level, IVR is slow and it is easy to control the excitation of either bond, whereas at higher exitation, IVR causes rapid leakage into the unexcited bond, making control more difficult. Simple linearly chirped pulses are effective in exciting and dissociating the weaker C−H bond. In an attempt to excite the stronger C−N bond, we found that although linearly chirped pulses can transfer energy to the C−N bond, they cannot compete with the much faster internal energy redistribution, and the C−N bond cannot be dissociated because the weaker C−H bond will always break first. To overcome this problem, we construct an optimization scheme and use it to find pulses that do effect C−N dissociation. These pulses however are very short (less than 0.5 ps) and too intense (with brief peaks in the low 10 15 W/cm 2 range) so that the molecule will be ionized before dissociation into neutral fragments can occur.
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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 | 0.000 |
| 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) | 0.001 | 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".