Suppression of Eu<sup>2+</sup> Luminescence Loss
Bibliographic record
Abstract
Abstract Owing to the intriguing luminescence properties, Eu2+ is one of the most desirable activators for next‐generation lighting devices. Yet the application of Eu2+‐doped phosphors is limited because of the drawback of inferior luminescence efficiency. Understanding of this issue is generally from perspectives of frame structural rigidity and electronic band structure, while the lack of persuasiveness of this paradigm is frequently noticed. Herein, the analysis is conducted from a fresh view to investigate the outstanding luminescence properties of Eu2+‐doped SrMgP2O7 owning the narrow‐band emission and near‐unity quantum yield. The structural rigidity of materials is elaborately evaluated, and the influence of ionization on the luminescence efficiency of material is carefully discussed. Efforts are also made to assess the effect of vibronic coupling on 4f−5d transition of Eu2+ and clarify the excitation energy transfer route by using X‐ray spectroscopy. Based on these discussions, the synergy of weak electron−vibration interaction and inactive ionization causes the suppression of Eu2+ luminescence loss, which is associated with highly rigid local coordination and strong binding of Eu2+ to its valence electrons in the system, respectively. This work provides insight into the luminescence mechanism of Eu2+, which benefits the exploration of novel phosphors with superior luminescence features.
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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".