Laser ablation of iron: A comparison between femtosecond and picosecond laser pulses
Bibliographic record
Abstract
In this study, a comparison between femtosecond (fs) and picosecond (ps) laser ablation of electrolytic iron was carried out in ambient air. Experiments were conducted using a Ti:sapphire laser that emits radiation at 785 nm and at pulse widths of 110 ps and 130 fs, before and after pulse compression, respectively. Ablation rates were calculated from the depth of craters produced by multiple laser pulses incident normally to the target surface. Optical and scanning electron microscopy showed that picosecond laser pulses create craters that are deeper than those created by the same number of femtosecond laser pulses at the same fluence. Most of the ablated material was ejected from the ablation site in the form of large particles (few microns in size) in the case of picosecond laser ablation, while small particles (few hundred nanometers) were produced in femtosecond laser ablation. Thermal effects were apparent at high fluence in both femtosecond and picosecond laser ablation, but were less prevalent at low fluence, closer to the ablation threshold of the material. The quality of craters produced by femtosecond laser ablation at low fluence is better than those created at high fluence or using picosecond laser pulses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".