The Fluoride Host: Nucleation, Growth, and Upconversion of Lanthanide‐Doped Nanoparticles
Bibliographic record
Abstract
The rapid ascent of nanoscience has garnered significant attention in recent years. Much of the interest generated has dealt with the integration of nanoparticles in various applications ranging from automotive and textiles to bioimaging and nanomedicine. In order for the realization of this potential, their synthesis and chemistry need to be thoroughly understood. One particularly interesting class of nanoparticles comprises a lanthanide‐doped inorganic matrix. Due to their physicochemical and optical properties, these lanthanide‐doped nanoparticles are undergoing widespread investigation in many fields, particularly for in vitro and in vivo imaging, as well as theranostics. They offer significant advantages in biological applications, particularly the extension of the system applicability to deep tissue regions of the body, a reduced scattering of the excitation wavelength, reduction of autofluorescence, and decrease in thermal loading and photodamage to the system under study. Specifically, lanthanide‐doped fluoride hosts are being propelled to the forefront of the current research efforts as they offer several advantages relative to other studied upconverting host materials. This review will take an in‐depth look at lanthanide‐doped upconverting fluoride nanoparticles with a particular emphasis on the synthesis, nucleation, and growth mechanisms and, finally, the potential to tailor particle properties.
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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".