Nanoparticle collection during femtosecond laser micromachining
Bibliographic record
Abstract
• Metal nanoparticle collector consisting of a rod electrode and gas suction line. • Recovery of metal nanoparticles during laser micromachining is assessed. • Pulsed laser interactions with ejected material influence recovery efficiency. • Laser fluence dependant dynamics of ejected nanoparticles affect material recovery. • Dependency of material composition on collection efficiency is observed. Femtosecond pulsed laser micromachining is a technique where material is ablated from a surface to produce desired structures. This process generates nanoparticles, which in industrial settings become trapped in a particulate air filter. This work focused on recovering nanoparticles at the point of ablation. The goal was to optimize operating settings for a nanoparticle collector consisting of a rod-shaped electrode contained in a tube that is connected to a suction line. For a fixed laser fluence, three suction flowrates and stage velocities were considered to determine optimal collection parameters. Using copper as an initial target, the collection efficiency was determined by comparing the masses of collected and ablated material. We found that the highest stage velocity led to the best collection efficiency due to reduced interaction between laser pulses and the expanding nanoparticle plume. An intermediate suction flowrate was found to be optimal, balancing attraction of the plume with effective collection. The effect of laser fluence was also investigated. Fluence-dependent dynamics of ejected material led to disparities in collection efficiency. The effect of target material composition was investigated by comparing the collection of pure metals and alloys. A dependency of the collection efficiency on the material composition was observed.
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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 | 0.001 |
| 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".