Absorption measurements in microfluidic devices using ring-down spectroscopy
Bibliographic record
Abstract
When monitoring separation events in microfluidic devices, one frequently needs to detect small amounts of analyte in picolitre sized volumes with a time response of milliseconds. Fluorescence detection is typically the method of choice due to its very high sensitivity and fast response. However, since many analytes are not naturally fluorescent, labelling protocols may have to be introduced and thereby increase the complexity of the analysis. Here, we present an alternative method that is based on optical absorption, or more specifically on the ring-down time of an optical signal in a cavity or loop made of waveguide material. This optical decay constant changes as small liquid samples containing absorbing species are introduced into a fiber-optic loop. It is demonstrated that one can obtain the optical decay constant using a continuous wave laser beam that is intensity modulated and then coupled into an optical fiber loop. The inherent exponential decay in the fiber loop introduces a phase shift of the light emitted from the loop with respect to the pumping beam. By measuring this phase shift, one can readily determine the concentration of the analyte introduced between the two fiber ends and a model is established to describe the relationship. It is demonstrated that this technique, dubbed "phase-shift fiber-loop ring-down spectroscopy" (PS-FLRDS), is well suited as an absorption detector for any flow system in which the optical absorption path is limited by the instrument architecture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".