Gradient defects mediate negative thermal quenching in phosphors
Bibliographic record
Abstract
Luminescent materials often suffer from thermal quenching (TQ), limiting the continuation of their applications under high temperatures up to 473 K. The formation of defect levels could suppress TQ, but rational synthesis and deep understanding of multiple defects-regulated luminescent materials working in such a wide temperature range still remain challenging. Here, we prepare a negative thermal quenching (NTQ) phosphor LiTaO3 : Tb3 + by introducing gradient defects VTa5−, TbLi2+, and ( VTaTbLi)3 − as identified by advanced experimental and theoretical studies. Its photoluminescence significantly becomes intense with rising temperatures and then slowly increases at 373 to 473 K. The mechanism studies reveal that gradient defects with varied trapping depths could act as energy buffer layers to effectively capture the carriers. Under thermal disturbance, the stored carriers could successively migrate to the activators in consecutive and wide temperature zones, compensating for TQ to enhance luminescence emission. This study initiates the synthesis of multi-defect NTQ phosphors for temperature-dependent applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".