High performance resonance Raman spectroscopy using volume Bragg gratings as tunable light filters
Bibliographic record
Abstract
We designed a near infrared tunable resonance Raman spectroscopy system based on a tandem of thick volume Bragg gratings (VBGs). VBGs are here the constituents of two light filtering units: a tunable laser line filter (LLF) and a tunable notch filter (NF). When adapted in a micro-Raman setup with a single stage monochromator (1800 gr/mm grating), the tandem of LLF and NF allowed measurements of Raman signals down to +/-20 cm(-1). The good performance and fast tunability of the VBG Raman system was demonstrated on a sulfur powder and on a bulk single-walled carbon nanotube sample through a series of 22 Stokes and anti-Stokes spectra recorded at excitation wavelengths between 800 and 990 nm. The main drawbacks of the setup are the limited spectral range to the near infrared and the small angular acceptance of the filters (approximately 1 mrad), which causes mainly attenuation problems with the NF. The impact of the main limitations is discussed and solutions are provided.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".