Core or Shell? Er<sup>3+</sup> FRET Donors in Upconversion Nanoparticles
Bibliographic record
Abstract
Upconversion nanoparticles (UCNPs) are of high interest for biosensing because of their unique near‐infrared‐excitation and visible‐emission features. An emerging field within UCNP biosensing is the detection of biological interactions through Förster resonance energy transfer (FRET). However, the relatively large size, the distribution of emitting lanthanide ions within the nanoparticle, the unknown photoluminescence (PL) quantum yields (QY) of these emitting ions, and the many available core–shell architectures make the interpretation of UCNP‐based FRET data extremely difficult. Here, we present a detailed spectroscopic study of three types of NaGdF4:Er3+,Yb3+ UCNPs with and without shells and lanthanide‐ion doping in the cores or the shells. The different architectures strongly influence the brightness and PL lifetimes of the UCNPs, which are important properties for FRET to Cy3.5 dyes attached to the UCNP surfaces through DNA. Analysis of the FRET‐sensitized dye PL decays allows the determination of the FRET efficiencies, which, in turn, can be used to estimate donor–acceptor distances, Förster distances, and Er3+ donor QYs, all of which are difficult to assess by other methods.
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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 | 0.001 |
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".