Equal Rights for Activators – Ytterbium to Terbium Cooperative Sensitization in Molecular Upconversion
Bibliographic record
Abstract
Abstract Molecular scaffolds are ideal for investigating upconversion (UC) at the highest spatial resolution and to create precisely controllable luminescent materials. Such control may be the key to overcoming the limitations of brightness and reproducibility found in UC micro‐ and nanoparticles. Cooperative UC can significantly increase luminescence brightness and bulk studies showed that highest efficiencies can be obtained by sensitizer‐to‐activator ion ratios ≥ 2, that is, via high probabilities of sensitizing the emitting lanthanide ion. Using nonanuclear molecular complexes, the authors demonstrate both experimentally and theoretically that interion distances are more relevant and that the highest UC efficiencies are actually attained for sensitizer‐to‐activator ion ratios around 1. By modeling accretive and cooperative sensitization UC, energy migration, and fitting experimental data, it is revealed that cooperative sensitization is predominant for the determination of UC luminescence intensities, whereas energy migration defines UC luminescence kinetics. The implementation of interion distances and different energy transfer mechanisms into advanced modeling of experimental UC data will be paramount for designing brighter and better UC materials.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.176 | 0.041 |
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".