NIR FRET Luminescence in Rhenium Complex and Dye Co‐Loaded Polymer Nanoparticles
Bibliographic record
Abstract
Abstract Photoactive transition‐metal complexes are luminophores combining high photostability and long luminescence lifetimes. However, reduced optical performance in aqueous solutions has limited their use in biological systems. Herein, the physicochemical and photophysical properties and bioimaging compatibility of Re diimine complexes and near‐infrared (NIR) emitting Cy5 dyes coencapsulated in polymer nanoparticles (NPs) are investigated. By varying the polymers, NPs with sizes from 20 to 70 nm and encapsulating ≤ 40 wt.% of Re complexes, i.e., ≈11 000 Re complexes per NP, are obtained. The photoluminescence (PL) quantum yields of the Re complexes increase eightfold to ≈50% upon encapsulation (vs 6–7% in acetonitrile), resulting in PL brightness up to 108 m−1 cm−1 and PL lifetimes of 3–4 µs. Coencapsulation of Cy5 yields very bright NIR emission upon Re complex excitation. Very close Re‐to‐Cy5 donor–acceptor distances down to ≤2 nm and FRET efficiencies over 90% are confirmed by PL lifetime measurements. The Re‐Cy5 NPs enter mammalian cells for high‐contrast PL imaging in both visible and NIR. This detailed characterization provides a better understanding of the photophysical properties of the transition‐metal‐dye FRET NPs and presents a vital step toward the efficient design of a new class of bright luminescent NP probes.
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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".