Theoretical Analysis of the Micro-optic Bottle Resonator and its Applications
Bibliographic record
Abstract
The micro-optic resonators confines light in a small volume resonating cavity and sustains a high-quality factor.Among all micro resonator morphologies, the micro-optic bottle resonator has a 3-D mode confinement geometry.In this thesis, MBR is profiled by azimuthal sculpting of a pair of rings along the glass whisker's perimeter.The structure is examined using a numerical solver optimized for the cylindrical symmetry of such resonators.The modal space and field profiles are computed as a function of ring spacing and demonstrate that multiple glass region confined states are available and can generally be thought of as Whispering Gallery Modes.Additional computational results are presented when the structure is configured as a sensor, suitable for measuring a specific constituent such as index of refraction, temperatures, gases, and chemicals.Attempts were underway to fabricate the MBR's geometry, but the work has remained incomplete due to COVID-19.In this thesis from designing the MBR (reduced-size) to implementing its fundamental mode WGM resonance wavelength into various sensor-related applications (mentioned above) are the work of the author.Whereas for the computational needs FFB mode solver was built by the supervisor (Dr.Gauthier).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".