Fiber-loop ring-down spectroscopy: A sensitive absorption technique for small liquid samples
Bibliographic record
Abstract
Cavity ring-down spectroscopy has proven to be a very sensitive gas-phase spectroscopic technique, suitable to record either very weak transitions of abundant gases or stronger transitions of trace gases. Here, an adaptation of the ring-down measurement principle to optical waveguides is presented. Fiber-loop ring-down spectroscopy (FLRDS) allows for the measurement of absorption spectra of minute quantities of liquid solutions. An optical fiber is wound into a loop using a fiber splice connector. A nanosecond laser light pulse (λ∼810 nm) is coupled into the loop and the light pulses are detected using a photomultiplier detector. It is found that once the light is coupled into the fiber it experiences very little loss and the light pulses do a large number of round trips before their intensity is below the detection threshold. The characteristic ring-down time is obtained by exponential fitting of the envelope of the wave form. This method is well suited to characterize low-loss processes in fiber optic transmission independent from power fluctuations of the light source. The strengths of the technique are demonstrated by characterization of a variety of loss processes—in particular by the measurement of the absolute loss of the optical fiber and of the fiber connector, losses due to macrobending of a section of the fiber loop, as well as losses due to lateral and longitudinal displacement in the fiber–fiber connection. Furthermore, it is shown that FLRDS is useful as an absorption spectroscopic technique for very small sample volumes and may be applied as an absorption detection method in analytical chemistry devices. A crude 47 μm channel in polydimethylsiloxane polymer was fabricated between the fiber end facets and the dye 1,1′-diethyl-4,4′-dicarbocyanine iodide (DDCI) was introduced into the channel. From the concentration dependence of ring-down time the sample volume was determined as 700 pL and the detection limit as about 10−10 mol, or 7×10−8 g of DDCI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".