Lanthanide Ion Doped Upconverting Nanoparticles: Synthesis, Structure and Properties
Bibliographic record
Abstract
Lanthanide doped upconverting nanoparticles (UCNPs) have emerged as a new class of luminescent materials, with major discoveries and overall significant progress during the last decade. Unlike multiphoton absorption in organic dyes or semiconductor quantum dots, lanthanide doped UCNPs involve real intermediate quantum states and convert infrared (IR) into visible light via sequential electronic excitation. The relatively high efficiency of this process even at low radiation flux makes UCNPs particularly attractive for many current and emerging areas of technology. The aim of this article is to highlight several recent advances in this rapidly growing field, emphasizing the relationships between structure and properties of UCNPs. Additionally, various strategies developed for the synthesis of UCNPs with a focus on the various synthetic approaches that yield high-quality monodisperse samples with controlled size, shape and crystalline phase are reviewed. Emerging synthetic approaches towards designed structure to improve the optical and electronic properties of UCNPs are discussed. Finally, recent examples of applications of UCNPs in biomedical and optoelectronics research, giving our own perspectives on future directions and emerging possibilities of the field are described.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".