Femtosecond laser direct-write of optofluidics in polymer-coated optical fiber
Bibliographic record
Abstract
Multifunctional lab in fiber technology seeks to translate the accomplishments of optofluidic, lab on chip devices into silica fibers. a robust, flexible, and ubiquitous optical communication platform that can underpin the ‘Internet of Things’ with distributed sensors, or enable lab on chip functions deep inside our bodies. Femtosecond lasers have driven significant advances in three-dimensional processing, enabling optical circuits, microfluidics, and micro-mechanical structures to be formed around the core of the fiber. However, such processing typically requires the stripping and recoating of the polymer buffer or jacket, increasing processing time and mechanically weakening the device. This paper reports on a comprehensive assessment of laser damage in urethane-acrylate-coated fiber. The results show a sufficient processing window is available for femtosecond laser processing of the fiber without damaging the polymer jacket. The fiber core, cladding, and buffer could be simultaneously processed without removal of the buffer jacket. Three-dimensional lab in fiber devices were successfully fabricated by distortion-free immersionlens focusing, presenting fiber-cladding optical circuits and progress towards chemically-etched channels, microfluidic cavities, and MEMS structure inside buffer-coated fiber.
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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".