Composition Control in Molecular Cluster-Aggregates: A Toolbox for Optical Output Tunability <i>via</i> Energy Transfer Pathways
Bibliographic record
Abstract
Composition control is a powerful tool for obtaining high-performance lanthanide (Ln) luminescent materials with adjustable optical outputs. This strategy is well-established for hierarchically structured nanoparticles, but it is rarely applied to molecular compounds due to the limited number of metal centers within a single unit. In this work, we present a series of molecular cluster-aggregates (MCAs) with an icosanuclear core {Ln 2 Eu 2 Tb 16 } (Ln = Ce, Pr, Nd, Sm, Gd, Dy, Ho, Er, Tm, and Yb) in which we explore composition control, akin to nanoparticles, to modulate the optical output. More specifically, we target to understand how the presence of a third Ln III doping ion would impact the well-known Tb III → Eu III energy transfer and the ratiometric optical thermometry performance based on the Tb III /Eu III pair. Photophysical properties at room and at varying temperatures were investigated. Based on experimental data and well-established intrinsic features, such as spin–orbit coupling strength and Ln III 4 f energy levels’ structure, we discuss the possible luminescent processes present in each MCA and provide insight into qualitative trends that can be rationally correlated throughout the series.
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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".