<i>In situ</i>nanojoining of Y- and T-shaped silver nanowires structures using femtosecond laser radiation
Bibliographic record
Abstract
We report the in situ joining of spatially separated silver nanowires without additional filler material by controlled irradiation with femtosecond laser pulses. Nanojoining under these conditions arises from highly localized heat generation in the vicinity of the gap between adjacent silver nanowires. Melting, followed by the flow of silver into the gap, is optimized by adjusting the direction of laser polarization relative to gap geometry. Our results show that melting of silver occurs on both nanowires in the vicinity of the gap between the two components. Successful formation of a joint is found to be a function of the angle between the long axis of the nanowires and the gap distance. Finite element simulations show that the strong localized electric field generated by optical excitation determines the location and the morphology of the resulting bond. Light coupling and the resulting emission properties of these Y-shaped nanowire structures have been simulated and are compared to similar structures where the gap remains open. It is suggested that joined Y-shaped couplers will have a higher switching ratio between emitted nanowire ends than those occurring in open-gap structures. Nanojoining induced by localized heating under strong field excitation may enable the production of robust branched metal nanowire structures for optical applications.
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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".