Sensing of Halocarbons Using Femtosecond Laser-Induced Fluorescence
Bibliographic record
Abstract
A femtosecond laser-induced clean fluorescence technique was explored as a means to monitor halogenated alkanes in the atmosphere. Characteristic difluorocarbene radical (CF2) fluorescence in the UV-vis can be generated inside a femtosecond laser-induced filament for different halocarbons. We show that, due to different dissociation and excitation kinetics leading to fluorescence emission, it is possible to temporally resolve the characteristic fluorescence of CF2-containing halocarbons from that of background species, therefore enhancing the signal-to-noise ratio. Laboratory-scale experiments demonstrate the potential use of femtosecond laser-induced clean fluorescence for the remote sensing of halocarbons in the atmosphere. The combination of this detection strategy with LIDAR could allow the long-range monitoring of several atmospheric species with a single laser source, eventually leading to a better understanding of chemical and dynamic processes affecting global warming, ozone loss, tropospheric pollution, and weather prediction.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".