Digital interference microscopy and density reconstruction of picosecond infrared laser desorption at the water-air interface
Bibliographic record
Abstract
Material ablation and evaporation using pulsed infrared lasers pose promising approaches for matrix-free laser desorption ionization and in laser surgery. For the best results, key parameters such as laser wavelength, pulse duration, and pulse energy need to be carefully adjusted to the application. We characterize the dynamics at the water-air interface induced by a 10 ps infrared laser tuned to the water absorption band at 3 μm, a parameter set facilitating stress confined desorption for typical absorption depths in biological samples and tissue. By driving the ablation faster than nucleation growth, cavitation induced sample damage during the ablation process can be mitigated. The resultant explosive ablation process leads to a shock front expansion and material ejection which we capture using off-axis digital interference microscopy, an interference technique particularly useful for detecting the phase shift caused by transparent objects. It is demonstrated that the method can yield local density information of the observed shock front with a single image acquisition as compared to the usually performed fit of the velocity extracted from several consecutive snapshots. We determine the ablation threshold to be (0.5±0.2) J cm−2 and observe a significant distortion of the central parts of the primary shock wave above approximately 2.5 J cm−2. The differences in plume shape observed for higher fluences are reflected in an analysis based on shock wave theory, which shows a very fast initial expansion.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".