Advanced fiber optic fluorescence turn-on molecular sensor for highly selective detection of copper in water
Bibliographic record
Abstract
In this article a novel advanced fiber-optic fluorescent sensor is demonstrated. The sensor is based on collection of the fluorescence from the sidewall of the multimode optical fiber which is partly de-cladded and covered by the sample under the test (SUT). The most part of the fluorescent intensity is carried by the leaky rays which are inaccessible in traditional evanescent-wave fluorescence fiber sensors. In the proposed structure, some part of a refracting power is collected in the de-cladded segment and used to excite the lower order lossless modes in cladded part by a sidewall mode mixer. In addition to the higher level of fluorescence collection, the architecture allows us to multiplex several different channels along one fiber, since we use only a small segment of the normal (not tapered) sidewall for each channel.A highly efficient fluorescence turn-on molecular probe is applied to this advanced fiber-optic structure, for sensitive and selective detection of Cu+2 in water. The fluorescence turn-on molecular probe is a mixture of a fluorophore polymer P1and M1 as a Cu receptor and a fluorescent quencher. The P1 is used as an indicator which generates the fluorescence centered at the wavelength of 650 nm and then, with a proper amount of M1 solution, the fluorescence is quenched up to 53% of its maximum intensity. The P1-M1 pair is broken by absorption of Cu with the M1 and the fluorescence is released again. This turn-on effect is used for detection of Cu with a low detection limit of 0.02689 g/ml.
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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 | 0.000 |
| 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".