Photophysical Properties of Dye-Doped Silica Nanoparticles Bearing Different Types of Dye−Silica Interactions
Bibliographic record
Abstract
Photophysical properties of three types of dye-doped silica nanoparticles (NPs) with different dye−silica interactions have been investigated. In two cases the dye−silica interactions are noncovalent, where tris(2,2′-bipyridine)ruthenium(II) chloride (Rubpy) is attracted to the silica network electrostatically and tetramethylrhodamine-dextran (TMR-Dex) is trapped inside the silica matrix through spatial/steric hindrance. In the third case, tetramethylrhodamine-5-isothiocyanate (TRITC) modified with 3-aminopropyltriethoxysilane (APTES) to form TMR-APTES is bound to the silica matrix covalently. Although in all three types of architectures absorption, excitation, and emission spectra show only small red-shifts (<5 nm) as compared with free dye in water, excited state emission lifetimes, quantum yields, and anisotropies vary significantly and in quite different ways between the three architectures. All three types of interactions facilitate effective encapsulation of dye within a silica network. However, covalent bonding possesses a notable advantage over the other two types of interactions as it results in a large reduction of a nonradiative relaxation rate of the embedded dye (TMR-APTES) and, thus, a large (∼3.55-fold) increase of its quantum yield.
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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 | 0.000 |
| 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".