Maskless Patterning of Metal Outflow in Alternating Metal/Ceramic Multiple Nanolayers by Femtosecond Laser Irradiation
Bibliographic record
Abstract
Abstract In this work, solid-state metal transport from internal metal nanolayers onto the surface of metal/ceramic nanomultilayers (NMLs) has been directed in a controlled way by femtosecond (fs) laser irradiation and subsequent low-temperature thermal annealing. Laser-irradiation-induced modifications of the NML microstructures and stress states can be limited within the first few top nanolayers due to the focused laser energy input at the metal/ceramic interface by exploiting the local plasmonic effect. Accompanied laser peening can further refine the crystallites and introduce compressive stress at the laser-irradiated region, which reduces the activation energies for vacancy formation and migration of metal atoms in the nanoconfinement. Patterned Cu surface nanostructures (outflow) appear selectively along the laser path after air annealing at temperatures down to 360 °C. For the solid-state diffusion of Cu in confinement, in-plane metal transport along the Cu–AlN interfaces is much faster than the outward short-circuit diffusion of Cu across the AlN barrier layers. Localized metal outflow is accompanied by the collapse and sintering of the remaining AlN barrier layers, under the influence of the acting capillary forces, which may further accelerate the metal transport. This laser-induced maskless patterning of metal outflow is applicable not only in Cu/AlN NMLs but also in Ag/AlN NMLs, assisted by subsequent low-temperature annealing.
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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".