Metal nanoparticle collection and in-flight functionalization for circular femtosecond laser micromachining
Bibliographic record
Abstract
Femtosecond pulsed laser micromachining is an advanced machining technique where material is ablated from a surface to produce desired structures.This process generates nanoparticles, which in industrial settings become trapped in a high efficiency particulate air filter.Therefore, this project focused on recovering nanoparticles by collecting the ejected 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.Tunable process parameters were the flowrate in the suction line and the laser machining stage velocity.For a fixed laser fluence of 4.5 times the ablation threshold, three suction flowrates (0, 0.57, and 1.13 m 3 /h) and stage velocities (1, 5, and 10 mm/s) were considered to determine optimal collection parameters for an applied potential of 2.0 kV.Using copper as an initial target, the collection efficiency was determined by comparing the masses of collected and ablated material.We found also allowing me to work independently and explore research topics that I am passionate about.Sylvain, thank you for teaching me about what it means to "get your hands dirty" in the lab and taking our ideas from the drawing board to an actual setup.The lessons I have learned from you both will serve me well into my future engineering career and for that I am eternally grateful.The financial assistance given by the
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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) | 0.002 | 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".