Femtosecond laser welding of silica glass fiber for robust Bragg grating sensing in high temperature environment
Bibliographic record
Abstract
Femtosecond laser welding was extended to optical silica fiber (SMF-28) by focusing through fused silica substrates and ferrules to form all-glass weld seams. Laser radiation was focused into the fiber cladding to create a welding zone, which drove molten glass to fill as much as a 3 𝜇𝑚 gap around a contact line to form a crack free pseudo-continuous welding seams along the contact line. The strong weld seams up to 30 𝜇𝑚 wide were generated in fiber-to-plate and fiber-to-ferrule geometries without inducing photochemical or thermal degradation of a fiber Bragg grating (FBG) positioned only 62.5 𝜇m from the weld zone. Welding was optimized by real-time monitoring of the FBG thermo-optical shift during laser scanning. Four-point bending tests confirmed a high mechanical strength while thermal annealing showed stable mechanical and FBG responses up to 1000 ˚C. Femtosecond laser writing and welding thus demonstrated a flexible means for photonics fabrication and packaging of FBGs, enabling reliable, high frequency vibration sensing suited for high temperature and strain environments.
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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) | 0.000 | 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".