Light-Generating CdSe/CdS Colloidal Quantum Dot-Doped Plastic Optical Fibers
Bibliographic record
Abstract
Colloidal quantum dots (cQDs) are now a mature nanomaterial with optical properties customizable through varying size and composition. However, their use in optical devices is limited as they are not widely available in convenient forms such as optical fibers. With advances in polymerization methods incorporating nanocrystals, nanocomposite materials suitable for processing into high quality hybrid active fibers can be achieved. We demonstrate a plastic optical fiber fabrication method which ensures homogeneous dispersion of cQDs within a polymer core matrix. Loading concentrations between 10 11 and 10 13 CdSe/CdS cQDs per cm 3 in polystyrene were electronically imaged, confirming only sporadic subwavelength aggregates. Rayleigh scattering losses are therefore dominant at energies below the semiconductors’ band gap, but are overtaken by a sharp CdS-related absorption onset around 525 nm facilitating cQD excitation. The red-shifted photoluminescence emission is then minimally reabsorbed along the fiber with a spectrum barely affected by the polymerization and a quantum yield staying at ∼65% of its initial value. The latter, along with the glass transition temperature and refractive index, is independent of the cQD concentration, hence yielding a proportionally increasing light output. Our cQD-doped fibers are photostable to within 5% over days showing great promise for functional material applications.
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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 | 0.000 | 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".