Lanthanide-Doped Upconversion Nanoparticles: Exploring A Treasure Trove of NIR-Mediated Emerging Applications
Bibliographic record
Abstract
Lanthanide-doped upconversion nanoparticles (UCNPs) possess the remarkable ability to convert multiple near-infrared (NIR) photons into higher energy ultraviolet-visible (UV-vis) photons, making them a prime candidate for several advanced applications within the realm of nanotechnology. Compared to traditional organic fluorophores and quantum dots (QDs), UCNPs possess narrower emission bands (fwhm of 10-50 nm), large anti-Stokes shifts, low toxicity, high chemical stability, and resistance to photobleaching and blinking. In addition, unlike UV-vis excitation, NIR excitation is nondestructive at lower power intensities and has high tissue penetration depths (up to 2 mm) with low autofluorescence and scattering. Together, these properties make UCNPs exceedingly favored for advanced bioanalytical and theranostic applications, where these systems have been well-explored. UCNPs are also well-suited for bioimaging, optically modulating chemistries, forensic science, and other state-of-the-art research applications. In this review, an up-to-date account of emerging applications in UCNP research, beyond bioanalytical and theranostics, are presented including optogenetics, super-resolution imaging, encoded barcodes, fingerprinting, NIR vision, UCNP-assisted photochemical manipulations, optical tweezers, 3D printing, lasing, NIR-II imaging, UCNP-molecule nanohybrids, and UCNP-based persistent luminescent nanocrystals.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".