Fabrication and THz loss measurements of porous subwavelength fibers using a directional coupler method
Bibliographic record
Abstract
We report several strategies for the fabrication of porous subwavelength fibers using low density Polyethylene plastic for low-loss terahertz light transmission applications. We also characterize transmission losses of the fabricated fibers in terahertz using a novel non-destructive directional coupler method. Within this method a second fiber is translated along the length of the test fiber to probe the power attenuation of a guided mode. The method is especially suitable for measuring transmission losses through short fiber segments, a situation in which standard cutback method is especially difficult to perform. We demonstrate experimentally that introduction of porosity into a subwavelength rod fiber, further reduces its transmission loss by as much as a factor of 10. The lowest fiber loss measured in this work is 0.01 cm(-1) and it is exhibited by the 40% porous subwavelength fiber of diameter 380 microm. For comparison, the loss of a rod-in-the-air subwavelength fiber of a similar diameter was measured to be approximately 0.1 cm(-1), while the bulk loss of a PE plastic used in the fabrication of such fibers is >or= 1 cm(-1). Finally, we present theoretical studies of the optical properties of individual subwavelength fibers and a directional coupler. From these studies we conclude that coupler setup studied in this paper also acts as a low pass filter with a cutoff frequency around 0.3 THz. Considering that the spectrum of a terahertz source used in this work falls off rapidly below 0.25 THz, the reported loss measurements are, thus, the bolometer averages over the approximately 0.25 THz-0.3 THz region.
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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".