Chiral discrimination by recollision enhanced femtosecond laser mass spectrometry
Bibliographic record
Abstract
Chiral molecules and their interactions are critical in a variety of chemical and biological processes. Circular dichroism (CD) is the most widely used optical technique to study chirality, often performed in a solution phase. However, CD has low-efficiency on the order of 0.01-1[Formula: see text]. Therefore, there is a growing need to develop high-efficiency chiroptical techniques, especially in gas-phase, to gain background-free in-depth insight into chiral interactions. By using mass spectrometry and strong-field ionization of limonene with elliptically polarized light, we demonstrate an efficient chiral discrimination method that produces a chiral signal of one to two orders of magnitude higher than the conventional CD. The chiral response exhibits a strong dependence on wavelength in the range of 1,300-2,400 nm, where the relative abundance of the ion yields alternates between the two enantiomers. The origin of enhanced enantio-sensitivity in intense laser fields is attributed to two mechanisms that rely on the recollision dynamics in a chiral system: (1) the excited ionic state dynamics mediated either by the laser field or by the recollision process, and (2) non-dipole effects that alter the electron's trajectories. Our results can serve as a benchmark for testing and developing theoretical tools involving non-dipole effects in strong-field ionization of molecules.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".