Spectral focusing-based stimulated Raman scattering microscopy using compact glass blocks for adjustable dispersion
Bibliographic record
Abstract
Spectral focusing is a well-established technique for increasing spectral resolution in coherent Raman scattering microscopy. However, current methods for tuning optical chirp in setups using spectral focusing, such as glass rods, gratings, and prisms, are very cumbersome, time-consuming to use, and difficult to align, all of which limit more widespread use of the spectral focusing technique. Here, we report a stimulated Raman scattering (SRS) configuration which can rapidly tune optical chirp by utilizing compact adjustable-dispersion TIH53 glass blocks. By varying the height of the blocks, the number of bounces in the blocks and therefore path length of the pulses through the glass can be quickly modulated, allowing for a convenient method of adjusting chirp with almost no necessary realignment. To demonstrate the flexibility of this configuration, we characterize our system’s signal-to-noise ratio and spectral resolution at different chirp values and perform imaging in both the carbon-hydrogen stretching region (MCF-7 cells) and fingerprint region (prostate cores). Our findings show that adjustable-dispersion glass blocks allow the user to effortlessly modify their optical system to suit their imaging requirements. These blocks can be used to significantly simplify and miniaturize experimental configurations utilizing spectral focusing.
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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) | 0.003 | 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".