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Record W3135559555 · doi:10.1117/12.2584331

Femtosecond laser welding of silica glass fiber for robust Bragg grating sensing in high temperature environment

2021· article· en· W3135559555 on OpenAlexaff
Oleg B. Vorobyev, Young Hwan Kim, Jianzhao Li, Michael Bakaic, Nicholas Burgwin, Abdullah Rahnama, Peter R. Herman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsFibics (Canada)University of Toronto
Fundersnot available
KeywordsMaterials scienceFiber Bragg gratingWeldingFemtosecondPHOSFOSFiber laserOptical fiberLaserLaser beam weldingHard-clad silica optical fiberOpticsComposite materialCladding (metalworking)OptoelectronicsFiber optic sensorPlastic optical fiberFiber

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.194
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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