Energy-efficient microwelding of copper by continuous-wave green laser: Insights into nanoparticle-assisted absorptivity enhancement
Bibliographic record
Abstract
As a highly reflective material, copper has been difficult to weld with IR lasers. Short-wavelength (400–600 nm) lasers offer promising solutions to copper welding due to significant increases in absorptivity (to > 50 %). In this work, we present an experimental study on copper welding by CW green laser. We unveil that nanoparticles redeposited on the copper surface from the vaporized plume can strongly modify the surface condition and result in significant enhancement of the surface absorptivity. The absorptivity can be theoretically enhanced by nanoparticles up to 82 % compared to the polished copper surface. Assisted by this absorptivity enhancement, the threshold power density (critical laser intensity) for copper melting is reduced by more than 50 %, so that continuous and smooth conductive welding tracks can be generated in energy-efficient manner. In situ deposition of nanoparticles generated upon cooling and oxidation of the vapor plume in the vicinity of the melt zone has been identified as the key mechanism of absorptivity enhancement. XPS measurements of the nanoparticles indicated that Cu2O is the dominant species in the redeposited nanoparticles. This study provides novel insights into the fundamental mechanisms in highly-reflective material welding by short-wavelength lasers and offers an opportunity for energy-efficient high-quality welding of highly-reflective material thin films, which can find numerous applications in consumer electronics and electric vehicles, as well as battery industries. Moreover, it enables a new process-material-environment design route for laser additive manufacturing of nanoparticle-reinforced metal matrix composites.
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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) | 0.001 | 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".