Broadly tunable high-energy spectrally focused CARS microscopy with chemical specificity and high resolution for biological samples
Bibliographic record
Abstract
The current trend in laser sources for Coherent Anti-Stokes Raman Scattering (CARS) microscopy consists of picosecond optical parametric oscillators (OPO)s and femtosecond-pumped fiber supercontinuum sources. While both methods are proven CARS performers, restricted wavelength tuning range and low power limit the Raman lines and types of samples that may be practically interrogated. To address these limitations, we present a novel, highly tunable spectrally focused femtosecond Optical Parametric Amplifier (OPA) and microscope system optimized for CARS microscopy. The laser source consists of an amplified ytterbium fiber laser driving a pair of OPAs producing two outputs that produce tunable femtosecond pulses from 650 to 1300nm. Each OPA may be tuned independently of the other over its entire range, allowing the addressing of any arbitrary wavenumber from 0 to 7700 cm-1. Additionally, the complete freedom of tuning allows one beam to be set at the optimal wavelength for a complementary technique, such as twophoton fluorescence or second harmonic, while the second beam is then tuned to the desired wavenumber difference for CARS. The femtosecond pulses are chirped out to the picosecond regime, reducing non-resonant background and providing improved spectral resolution. Typically, OPA systems are limited to kHz repetition rates, making them impractical for imaging applications. In contrast, our OPA system is driven at 1 MHz, providing a sufficient pulse rate for high-resolution imaging at rates of 1-2 frames per second. The 1 MHz rate preserves good pulse energy while reducing average power, thus limiting sample photo damage.
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".