Structural and optical properties of Nd:YAB-nanoparticle-doped PDMS elastomers for random lasers
Bibliographic record
Abstract
)-nanoparticle-doped PDMS elastomer films for random lasing (RL) applications. Nanoparticles with Nd ratios of x = 0.2, 0.4, 0.6, 0.8, and 1.0 were prepared and then incorporated into the PDMS elastomer to control the optical gain density and scattering center content over a wide range. The morphology and thermal stability of the elastomer composites were studied. A systematic investigation of the lasing wavelength, threshold, and linewidth of the laser was carried out by tailoring the concentration and optical gain of the scattering centers. The minimum threshold and linewidth were found to be 0.13 mJ and 0.8 nm for x = 1 and 0.8. Furthermore, we demonstrated that the RL intensity was easily tuned by controlling the degree of mechanical stretching, with strain reaching up to 300%. A strong, repeatable lasing spectrum over ~ 50 cycles of applied strain was observed, which demonstrates the high reproducibility and robustness of the RL. In consideration for biomedical applications that require long-term RL stability, we studied the intensity fluctuation of the RL emission, and confirmed that it followed Lévy-like statistics. Our work highlights the importance of using rare-earth doped nanoparticles with polymers for RL 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".