Polarization conversion in soft glass fluoride and chalcogenide fibers for mid-infrared applications
Bibliographic record
Abstract
Abstract Mid-infrared (MIR) technologies are crucial for applications from chemical sensing to precision medical surgery. Effective polarization control is essential to enhance the functionality of fiber systems. Current solutions for polarization control in the MIR primarily involve free-space devices and components. In this work, we take a step forward and experimentally demonstrate polarization conversion within soft glass fluoride and chalcogenide fibers using a commercially available in-line polarization controller (PC). Our experiments using a single PC show a polarization extinction ratio (PER) of 20.7 dB in a ZBLAN fiber (FiberLabs) with a coating of urethane acrylic resin. Cascading two PCs enhances the PER to 39.1 dB while reducing the required compressive force and, thus, increasing the fiber lifetime. Chalcogenide fibers (As2Se3, As2S3, Ge20Se60Te20) are coated with polymethyl methacrylate and tested using a single PC. Thanks to the higher strain-optic coefficients of chalcogenide glass, these fibers exhibited exceptional PER values, reaching 39.3 dB for As2Se3, 41.4 dB for As2S3, and 38.3 dB for Ge20Se60Te20. The polymer coatings of the ZBLAN and chalcogenide fibers effectively protect them from compressive force and twisting, enabling them to endure more than 30 cycles of compression and decompression without breakage. Stability test conducted over 12 h with ZBLAN fiber demonstrated that the achieved polarization state remains stable, with maximum deviations due to environmental factors estimated to be less than 2%. This work is the first proof that in-line polarization control using soft glass fibers is achievable, paving the way toward the development of all-fiber MIR systems.
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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".