Measure for optical robustness of directly bonded glass-to-glass joint using its interaction with damage induced by fs laser
Bibliographic record
Abstract
To produce more powerful compact ultrafast lasers, research aims at improving the quality of bonds between components inside the laser cavity. Increasing bond robustness under optical irradiation helps the bonds to survive the high energy pulses that these lasers are designed to produce. A measure for such robustness is reported here to support work toward improved bonding processes for such lasers. We produced bonds between pairs of optical grade fused silica glass cylinders using a wet direct bonding procedure. We evaluated these bonds using conventional microscopy, including scanning electron microscopy (SEM) and optical microscopy, without quantifiable results. The bond interface was not discernible through conventional SEM imaging, even after cross sectioning and polishing. The majority of the interface was also undetectable in optical micrographs, except for some limited areas of interfacial disturbance. To obtain quantifiable results for optical robustness, we used an 800 nm femtosecond laser to produce filament-shaped damage from a focal spot moving across the interface. Microscopy of the damage showed its interaction with the interface, the presence of which caused a ≈0.130 to ≈0.230 mm long interruption in the damage line. The exact value depended not only on laser power but also interface quality, and thereby quantified the optical robustness. The reported method proved more sensitive in detecting bonds of fused silica samples compared to other visualization techniques used. Our results suggest a nuanced understanding of bonded glass joints-mechanically sound, yet with limited optical robustness under specific laser conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".