Chirp modulation stimulated Raman scattering microscopy
Bibliographic record
Abstract
Coherent Raman Microscopy (CRM), a nonlinear optical version of Raman microscopy, offers rapid, chemical-specific, label-free imaging. Unfortunately, competing background optical processes limit its sensitivity and contrast in many materials. Existing modulation transfer schemes developed to reduce these are based on well-known concepts in linear signal processing, such as amplitude, polarization or frequency modulation. No existing schemes simultaneously remove all background types in CRM, resulting in sample-dependent sensitivity and contrast. Here we propose and demonstrate a novel CRM modulation scheme, based on rapid modulation of the higher order optical phases of the input beams, which removes all non-Raman background signals: Chirp-Modulation Stimulated Raman Scattering (CM-SRS). The modulation of higher order phases, unimportant in linear optics, is remarkably effective in nonlinear optical spectroscopy and microscopy. We exemplify this concept through a modulation of - exclusively - the relative sign of the quadratic phase (linear chirp) of the input lasers, keeping all other laser parameters fixed. We show that CM-SRS removes all non-Raman backgrounds, even in samples near electronic resonances which can stymie other modulations schemes. Importantly, this technique remains linear in both Raman oscillator strength and concentration, allowing for high sensitivity, quantitative studies. We present applications of CM-SRS to traditionally challenging samples such as plant materials. We also use the high sensitivity and background removal afforded by CM-SRS to demonstrate the monitoring of small molecule pharmacokinetics in single living cells.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".