Two-Color Two-Photon Excitation Using Femtosecond Laser Pulses
Bibliographic record
Abstract
The use of two-color two-photon (2c2p) excitation easily extends the wavelength range of Ti:sapphire lasers to the UV, widening the scope of its applications especially in biological sciences. We report observation of 2c2p excitation fluorescence of p-terphenyl (PTP), 2-methyl-5-t-butyl-p-quaterphenyl (DMQ) and tryptophan upon excitation with 400 and 800 nm wavelengths using the second harmonic and fundamental wavelength of a mode-locked Ti:sapphire femtosecond laser. This excitation is energetically equivalent to a one-photon excitation wavelength at 266 nm. The fluorescence signal is observed only when both wavelengths are spatially and temporally overlapping. Adjustment of the relative delay of the two laser pulses renders a cross correlation curve which is in good agreement with the pulse width of our laser. The fluorescence signal is linearly dependent on the intensity of each of the two colors but quadratically on the total incident illumination power of both colors. In fluorescence microscopy, the use of a combination of intense IR and low-intensity blue light as a substitute for UV light for excitation can have numerous advantages. Additionally, the effect of differently polarized excitation photons relative to each other is demonstrated. This offers information about different transition symmetries and yields deeper insight into the two-photon excitation process.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".