Experimental Investigation of Visible Diffraction in Tilted Fibre Bragg Gratings
Bibliographic record
Abstract
We present an analysis on the visible diffraction patterns of Tilted Fiber Bragg Gratings (TFBG), with applications to blue-laser fiber sensors.On the basis of our current understanding of a visible diffraction phenomenon called sidetapping, or outtapping, we compare theoretical predictions with experimental results.In order to compare theory to experiment we obtain empirical observations of diffraction angles.A 1550nm-TFBG is connected to a Coherent Spectrum-70C laser source and measurements are taken for several wavelengths including red (647.1nm),yellow (568.2nm),green (514.5nm),blue (488.0nm) and violet (457.9nm) to show that our results are in definite agreement with theory.There are other variables that affect and govern this diffraction behaviour so a comprehensive parameter study is used to relate the input variables to output variables.Output variables are power and longitudinal angles of each radiating diffraction order.Input variables would include the type of fiber used, grating pitch, grating tilt, and wavelength.With information gathered from the input-output analysis, we continue by studying blue light diffraction.Different devices are compared to gain insight into the optimal conditions for a blue light TFBG sensor.The diffraction of blue light in particular is exploited to gain insight on specialized fiber nano-coatings used in biochemical sensors.To start we review the existing methods of TFBG diffraction in the visible spectrum, including Coupled Mode analysis and a brief overview of alternate methods.Generally, depending on the wavelength and other factors there may or may not be coupling to radiation modes.The specific angles of these radiation modes with respect to the fiber axis, called longitudinal angles, are determined by theoretical phase-matching conditions related to the incoming beam wavelength.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".