Bibliographic record
Abstract
Rare earth doped nanoparticles have the uncharacteristic ability to (up)convert near-infrared (NIR) excitation light to higher energies spanning the UV, visible, and shorter wavelength NIR regions via a multiphoton process known as upconversion. The ability to stimulate luminescent nanoparticles with NIR light has made possible their use in a plethora of biological and medical applications. In fact, the biggest impact of these upconverting nanoparticles (UCNPs) would be in the field of disease diagnostics and therapeutics, now commonly referred to as theranostics. UCNPs that can be excited by low levels of NIR light but that emit higher-energy emission offer an attractive alternative to conventional fluorophores, both as detectable species for labeling and as energy donors for FRET-based bioassays. UCNPs can be excited selectively in the presence of very high levels of conventional fluorophores. Relative to visible and UV radiation, NIR photons have greater tissue penetration and cause much less damage to the specimens under study because NIR radiation is not absorbed strongly by chromophores commonly found in tissues. Thus, NIR excitation of UCNPs in a biological sample does not give rise to background autofluorescence and consequently detection sensitivity can, in principle, be very high. Upconverting labels also have unique potential advantages as energy donors in FRET-based bioassays because NIR excitation will only excite the donor, and not the acceptor or any fluorescent impurities in the sample. Here, we present the synthesis and surface functionalization of various NIR excited UCNPs and demonstrate their potential use in sensing applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".