Inside-Out/Outside-In Tunability in Nanosized Lanthanide-Based Molecular Cluster-Aggregates: Modulating the Luminescence Thermometry Performance via Composition Control
Bibliographic record
Abstract
Modulating the optical property of a material via structural modification is a powerful tool for obtaining the desired optical output. If a material can be tuned inside (core) and outside (outer shell), then the degree of control is greater toward application. Herein, we present a lanthanide-based nanosized molecular cluster aggregate (MCA) that allows fine-tuning of the inner core via composition control akin to nanoparticles. At the same time, the tunable outer shell enables light-harvesting properties similar to molecular systems. As such {Eu 4 Tb 16 }, {Eu 3 Gd 5 Tb 12 }, {Eu 2 Gd 10 Tb 8 }, and {Eu 1 Gd 15 Tb 4 } compositions were synthesized, and their photophysical properties were investigated in solution and in the solid state. Controlling the composition and spacing of the emitter ions with the optically silent Gd III ions results in a decrease in the Tb III → Eu III energy-transfer process efficiency. Consequently, ratiometric luminescence thermometry performance is fine-tuned to reach a maximum relative sensitivity of 4.17% °C –1 at 36 °C for the {Eu 4 Tb 16 } MCA. This study demonstrates that the optical properties are intrinsic to individual MCA species rather than a collective intermolecular effect. The color change observed close to room temperature for {Eu 2 Gd 10 Tb 8 } suggests potential applications such as multistage anticounterfeiting technology.
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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) | 0.000 | 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".