Vibrationally excited ultrafast thermodynamic phase transitions at the water/air interface
Bibliographic record
Abstract
The extraordinary ability of the hydrogen-bond network of water in the condensed phase to thermalize vibrational excitations within several picoseconds, even under supercritical conditions, offers the possibility of creating highly excited thermodynamic states at water surfaces on ultrafast time scales using vibrationally resonant short infrared laser pulses. We experimentally and numerically studied such states created by depositing ~100 ps long pulses tuned to the 3400 cm(-1) O–H stretch vibration at the water/air interface using time-resolved dark-field imaging and time-resolved optical reflectivity. The results are reasonably well described by using a hydrodynamic ablation model under the assumption of impulsive heat deposition. The large thermoelastic stress amplitudes on the order of 1 GPa created within 100 ps by depositing laser pulses with ~1 J cm(-2) fluence were inferred from the numerical simulations. Stresses of this magnitude drive the excited water layer into a very fast expansion resulting in rapid adiabatic cooling and thorough vaporization within a few nanoseconds. The spatial and temporal lengths scales of the ablation plume are nearly ideal for ejecting molecules into the gas phase with minimum perturbation for applications ranging from mass spectrometry and laser surgery to the development of extremely high pressure molecular beams.
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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".