Simultaneous and Continuous Multiple Wavelength Absorption Spectroscopy on Nanoliter Volumes Based on Frequency-Division Multiplexing Fiber-Loop Cavity Ring-Down Spectroscopy
Bibliographic record
Abstract
We demonstrate a method for measuring optical loss simultaneously at multiple wavelengths with cavity ring-down spectroscopy (CRD). Phase-shift CRD spectroscopy is used to obtain the absorption of a sample from the phase lag of intensity modulated light that is entering and exiting an optical cavity. We performed dual-wavelength detection by using two different laser light sources and frequency-division multiplexing. Each wavelength is modulated at a separate frequency, and a broadband detector records the total signal. This signal is then demodulated by lock-in amplifiers at the corresponding two frequencies allowing us to obtain the phase-shift and therefore the optical loss at several wavelengths simultaneously without the use of a dispersive element. In applying this method to fiber-loop cavity ring-down spectroscopy, we achieve detection at low micromolar concentrations in a 100 nL liquid volume. Measurements at two wavelengths (405 and 810 nm) were performed simultaneously on two dyes each absorbing at mainly one of the wavelengths. The respective concentrations could be quantified independently in pure samples as well as in mixtures. No crosstalk between the two channels was observed, and a minimal detectable absorbance of 0.02 cm(-1) was achieved at 405 nm.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".