(Invited) A Frequency Domain Optofluidics Dissolved Oxygen Sensor with Enhanced Sensitivity for Water Monitoring
Bibliographic record
Abstract
Dissolved oxygen (DO) is an important indicator for water quality and is also used in point of care applications. Fluorescence signal from Ruthenium (Ru(dpp)3 2+ ) based fluorophores can be quenched by oxygen such that the reduction of fluorescence signal is proportionally to oxygen concentration. This mechanism allows an optical method to measure DO in liquid. Although optical DO measurements offer high sensitivity and stability, they are also expensive, bulky, and difficult to use due to its complex design and requirement for specialized optical instruments such as light sources and spectrometers. Recently, the advances in microfabricated optical component including waveguides, lenses and mirrors, light sources, and photo detectors enable the integration of optical sensing and microfluidics sampling platform. We report the development of a total internal reflection assisted (TIRA) optical DO sensor for sensitivity enhancement. The excitation light is conducted down the water channel direction in the microfluidic device, while being confined within glass slide by total internal reflection. In addition to steady state intensity measurements, oxygen quenching is measured in the frequency domain using modulated light sources and synchronized detection systems. Experimental results show that optical sensitivity can be increased up to an order of magnitude in TIRA sensors. These results suggest the potential of TIRA scheme as a sensing and characterizing platform for optofluidic sensors.
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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 | 0.001 |
| 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".