Metal-enhanced fluorescence emission and quenching protection effect with a host–guest nanophotonic-supramolecular structure
Bibliographic record
Abstract
The functionalization of the nanoparticle’s (NP) surface is one method for tuning their overall properties to fit targeted applications. We developed a nanosensor based on the specific supramolecular interactions between ß-cyclodextrin (ßCD) nanocavities and organic molecules of biological interests using the metal-enhanced fluorescence effect (MEF) as the detection signal. We grafted ßCD, a typical macrocyclic host molecule that interacts specifically with different organic molecules and changes their physical properties (such as their fluorescence emission intensity), on gold NPs. To evaluate this nanosensor and the effect of the metallic core, we worked with a typical organic molecule, Rhodamine B (RhB), that has a strong association constant with ßCD (5700 M − 1) and is well-known to be quenched in the presence of cyclodextrins (CDs). The results show that, by grafting ßCD on gold NPs, it is possible to increase the sensitivity of RhB detection by 70%, 80%, and 294% when compared with solutions in (1) a phosphate buffer, (2) with ßCD, and (3) with Au NPs, respectively. These results show that the use of a supramolecular system attached to a metallic NP can interact specifically with a dye to enhance its fluorescence emission through the MEF effect. Moreover, this type of nanosystem can overcome the quenching of the signal by the matrix, such in the case of RhB with CDs. Eventually, this concept could be extended to other dyes with different quenching effects. For this reason, this type of nanosensor system could be used in the future to protect and enhance the dye emission of fluorophores in different biological media.
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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".