In-fiber Rayleigh peak suppression for Brillouin spectroscopy
Bibliographic record
Abstract
Brillouin spectroscopy shows great potential for various biomedical applications, but the complexity, high cost, and size of current technologies limit its widespread access. Major complications result from the need to measure weak Brillouin peaks picometers away from the Rayleigh peak, which are larger by many orders of magnitude. We propose and study an ultra-narrow notch filter based on a fiber Bragg grating Fabry-Perot in reflection. L-band and 785 nm devices are prepared, exhibiting good agreement with theory and simulations. Strain tuning enables precise adjustment over nanometers. A suppression of 31 dB is achieved at 785 nm with an intrinsic insertion loss of 2 dB. Brillouin spectra of various samples are acquired, demonstrating the in-fiber module's ability to suppress the otherwise overwhelming Rayleigh light, thus greatly improving measurement capabilities. Overall, this high-performance device is simple, alignment-free, compact, low-cost, tunable, serializable, and its design is flexible, holding promise for democratizing Brillouin spectroscopy and developing field-deployable systems.
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 | 0.000 |
| 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 | 0.001 | 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".