Recent insights into upconverting nanoparticles: spectroscopy, modeling, and routes to improved luminescence
Bibliographic record
Abstract
The development of reliable and reproducible synthetic routes that produce monodisperse lanthanide-doped upconverting nanoparticles has resulted in an appreciable need to determine the mechanisms which govern upconversion luminescence at the nanoscale. New experimental and theoretical evidence explicates the quenching phenomena involved in the low luminescence efficiencies. A deeper understanding of the role of surfaces and defects in the quenching mechanisms and the properties of upconverting nanoparticles are of fundamental importance to develop nanomaterials with enhanced luminescence properties. Herein, we summarize the most recent spectroscopic investigations, which have enabled the scientific community to ascertain that the predominant source of quenching involved in the luminescence of lanthanide-doped upconverting nanoparticles can be attributed to surface-defects. Modeling of these mechanisms in nanomaterials supports the experimental findings and yields further insights into the surface phenomena, providing a predictive tool to improve the luminescent efficiencies in nanomaterials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".