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Record W2067926718 · doi:10.1117/12.778079

Optimization of coherent anti-Stokes Raman scattering microscopy using photonic crystal fiber

2007· article· en· W2067926718 on OpenAlexaff
Sangeeta Murugkar, Yury Logvin, Craig Brideau, Andrew Ridsdale, Peter K. Stys, Hanan Anis

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOpticsPhotonic-crystal fiberMaterials scienceRaman scatteringBandwidth-limited pulseFemtosecondPulse durationFemtosecond pulse shapingRaman spectroscopyCoherent anti-Stokes Raman spectroscopyFiber laserUltrashort pulseLaserWavelengthOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

We have recently demonstrated coherent anti-Stokes Raman scattering (CARS) microscopy and multiplex CARS spectroscopy of lipid-rich structures based on a single femtosecond Ti:sapphire laser. A nonlinear photonic crystal fiber (PCF) with two closely lying zero dispersion wavelengths is used to generate the Stokes pulse. Further optimization in terms of higher spectral resolution in the CARS spectra can be achieved by adding a second PCF to the pump arm to produce a spectrally compressed picosecond pulse. Theoretical predictions from modeling the propagation of the negatively chirped pump pulse in the PCF, are compared with experimental results. The effect of pulse duration, peak power and the length of the PCF in determining the bandwidth of the spectrally compressed pump pulse are considered. It is shown that for higher average output power and constant pulse duration, it is desirable to use shorter length of the PCF for attaining transform limited spectral width of the pump pulse.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.282
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSpectroscopy Techniques in Biomedical and Chemical ResearchFrench-language works237,207