Afterglow Properties of Silica-Capped Sr2MgSi2O7:Eu,Dy Nanoparticles Prepared by Laser Ablation in Ethanol
Bibliographic record
Abstract
The effect of silica-capping on the afterglow property of Sr 2 MgSi 2 O 7 :Eu,Dy nanoparticles was investigated. Sr 2 MgSi 2 O 7 :Eu,Dy nanoparticles were prepared by laser ablation in liquid. Afterglow nanoparticles were capped with silica using Stber method. A dense silica capping layer was achieved after 4 hours of reaction. Silica-capping of the afterglow nanoparticles improved the particles' afterglow property, which was degraded by nanosizing. Decay curves indicated that initial values of afterglow intensity were increased with silica-capping capacity. Moreover, silica-capping decreased the decay curve constant , which was related to trap parameters. The decrease in the value of resulted in the reduction in the slope of decay curve, and thus the improvement of the afterglow property. We concluded that capping nanoparticles with silica improved the particles' afterglow property by the passivation of surface defects and the prevention of energy transfer to water molecules.
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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".