Investigation of energy transfer between semiconducting polymer dot donors and hydrophilic and hydrophobic Cy5 acceptors
Bibliographic record
Abstract
Semiconducting polymer dots (Pdots) are rapidly emerging fluorescent probes for bioanalysis. Pdots have extraordinarily strong absorption and bright emission compared to other commonly used fluorescent probes, making them very attractive for applications involving Förster resonance energy transfer (FRET). Here, we investigated two FRET systems with green-emitting poly(9,9-dioctylfluorene-alt-benzothiadiazole) (F8BT) Pdots as donors and two different Cyanine 5 (Cy5) dyes as acceptors. A hydrophilic sulfo-Cy5 dye was directly conjugated to the Pdot surface using carbodiimide chemistry, and a hydrophobic Cy5 dye was observed to spontaneously partition into the core of the Pdot. FRET was observed to depend on the acceptor dye concentration with both systems, and was characterized using a combination of fluorescence emission spectra, excitation spectra, and lifetime measurements. Much stronger quenching of Pdot emission and FRET-sensitized acceptor dye emission were observed for the hydrophobic Cy5 system, and these trends were attributed to reduced donor-acceptor distances in comparison to the hydrophilic sulfo-Cy5 system. Current limitations in the experimental format are discussed. The results show that Pdots are effective FRET donors for acceptor dyes located both within and at the surface of Pdots.
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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".