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Record W6959418450 · doi:10.1021/ac103210w.s001

Simultaneous and Continuous Multiple Wavelength Absorption Spectroscopy on Nanoliter Volumes Based on Frequency-Division Multiplexing Fiber-Loop Cavity Ring-Down Spectroscopy

2016· article· en· W6959418450 on OpenAlexaff

Bibliographic record

VenueFigshare · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Taxonomy and Phylogenetics
Canadian institutionsQueen's University
Fundersnot available
KeywordsWavelengthSpectroscopyAbsorbanceLaserDetectorAbsorption spectroscopyAbsorption (acoustics)BroadbandMultiplexingOptical amplifier

Abstract

fetched live from OpenAlex

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<sup>−1</sup> 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.001

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.

Opus teacher head0.021
GPT teacher head0.216
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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