Fibered Optical Sensor Based on the Integration of Quantum Dots on a Polymer Tip
Bibliographic record
Abstract
In this article, we report on a novel approach developed for the fabrication of luminescent optical fibered tips containing quantum dots (QDs). It is based on a sequential process involving photopolymerization of pentaerythritol triacrylate through an optical fiber to create a first polymer tip and then a second tip based on diazonium salt chemistry for surface functionalization and QD immobilization via electrostatic interactions. The resulting luminescent tip has a radius of curvature of 60.7 nm and exhibits the same luminescence spectrum as colloidal QDs. To evaluate the potential of this QD-tip structure in sensing applications, it was immersed in an ethanol solution of ZnO nanoparticles of various concentrations (7–35 mg/mL), which resulted in QD luminescence quenching. A 17.9-fold quenching of the luminescence intensity was achieved at a maximum concentration of 35 mg/mL. The change in the photoluminescence lifetime was proposed as a sensing criterion since it is more sensitive to the environment than the luminescence intensity. The calibration curve was obtained, showing the potential of such type of tips as sensors for a new nondestructive method for the determination of nanoparticle concentration in solution.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".