An integrated micro-volume fiber-optic sensor for oxygen determination in exhaled breath based on iridium(iii) complexes immobilized in fluorinated xerogels
Bibliographic record
Abstract
A novel integrated fiber-optic sensor with micro detection volume is developed and evaluated for O(2) determination on a breath-by-breath basis in human health monitoring applications. The sensing element was fabricated by dip-coating an uncladded optical fiber with [Ir(piq)(2)(acac)]-doped hybrid fluorinated ORMOSIL (organically modified silicate) film, which was prepared from 3,3,3-trifluoropropyltrimethoxysilane (TFP-TriMOS) and n-propyltrimethoxysilane (n-propyl-TriMOS). The sensor was then constructed by inserting the prepared optical fiber into a transparent capillary. A microchannel formed between the optical fiber and the capillary inner wall acted as a flow cell for the sample flowing through. The evanescent wave (EW) field produced on the fiber core surface can excite the O(2)-sensitive fluorophores of [Ir(piq)(2)(acac)] to produce emission fluorescence. O(2) can be sensed by its quenching effect on the emission fluorescence intensity. Spectroscopic properties have been characterized by FTIR and fluorescence measurements. Stern-Volmer and Demas models were both employed to analyse the sensor sensitivity, which is 13.0 with the LOD = 0.009% (3σ) and the response time is about 1 s. By integrating the sensing and detection elements on the optical fiber, the novel configuration showed advantages of easy fabrication and low cost. Parameters of sensitivity, response time, repeatability, humidity effect and temperature effect were discussed in detail. The proposed sensor showed potential for practical in-breath O(2) analysis application due to its advantages of easy fabrication, low cost, fast response, excellent hydrophobicity, negligible temperature interference and suitable sensitivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".