In Situ Collection of Nanoparticles during Femtosecond Laser Machining in Air
Bibliographic record
Abstract
Nanoparticles generated during laser material processing are often seen as annoying side products, yet they might find useful application upon proper collection. We present a parametric study to identify the dominant factors in nanoparticle removal and collection with the goal of establishing an in situ removal method during femtosecond laser machining. Several target materials of different electrical resistivity, such as Cu, Ti, and Si were laser machined at a relatively high laser fluence. Machining was performed under three different charge conditions, i.e., machining without an externally applied charge (alike atmospheric pulsed laser deposition (PLD)) was compared to machining with a floating potential and with an applied field. Thereby, we investigated the influence of three different charge conditions on the behavior of laser-generated nanoparticles, in particular considering plume deflection, nanoparticle accumulation on a collector plate and their redeposition onto the target. We found that both strategies, machining under a floating potential or under an applied field, were effective for collecting laser-generated nanoparticles. The applied field condition led to the strongest confinement of the nanoparticle plume and tightest resulting nanoparticle collection pattern. Raster-scanning direction was found to influence the nanoparticle collection pattern and ablation depth. However, the laser-processed target surface remained unaffected by the chosen nanoparticle collection strategy. We conclude that machining under a floating potential or an applied field is a promising setup for removing and collecting nanoparticles during the machining process, and thus provides an outlook to circular waste-free laser process design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".