Upconverting Nanoparticle to Quantum Dot Förster Resonance Energy Transfer: Increasing the Efficiency through Donor Design
Bibliographic record
Abstract
We propose two effective approaches to enhance the Förster resonance energy transfer (FRET) efficiency from near-infrared excited upconverting nanoparticles (UCNPs, namely, LiYF4:Yb3+,Tm3+) to CuInS2 quantum dots (QDs) upon engineering of the donor’s architecture. The study of the particles’ interaction highlighted a radiative nature of the energy transfer among the moieties under investigation when in solution. However, analyses performed on dry powders allowed observing clear evidence of a FRET mechanism. In particular, photoluminescence lifetime measurements showed that FRET efficiency could be effectively increased by both reducing the size of the UCNPs and directly controlling the distribution of the active ions throughout the donor’s volume, i.e., doping them only in the outer shell of a core/shell system. Both strategies resulted at least in a more than doubled FRET efficiency compared to larger core-only UCNPs. Obtained experimental values were compatible with those predicted from geometrical considerations on the active ions’ distribution over the UCNP volume. These results provide a concrete proof of the potential of a UCNP–QD FRET pair when the system is properly designed, hence setting a solid base for the development of robust and efficient all-inorganic probes for FRET-based assays.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".