Temperature-dependent trap engineering in gallate long afterglow phosphor for advanced information encryption and anti-counterfeiting
Bibliographic record
Abstract
Long-persistent luminescence (LPL) materials have attracted significant attention in the fields of information encryption and anti-counterfeiting due to their unique afterglow emission under darkness. However, accurately modulating afterglow behavior as well as trap states of LPL materials is highly tricky. Herein, the temperature-dependent trap engineering in Cu 2+ -doped SrGa 2 O 4 (SGOC) phosphors for tunable afterglow emission is demonstrated. By changing the sintering temperature, the luminescence and afterglow performances of SGOC can be well tuned. A series of characterizations confirmed that increasing calcination temperature results in the formation of a higher concentration of oxygen vacancy defects in SGOC, which can bring out more traps in its band gap and move the trap position to a deeper region, so as to achieve longer and stronger afterglow performance. The theoretical calculations support the role of Cu 2+ in narrowing the bandgap and enhancing electron localization in SGOC for red luminescence and afterglow emission. By virtue of their distinct afterglow and luminescence performance, the SGOC phosphors obtained at different calcination temperatures for advanced anti-counterfeiting and time-resolved information encryption were demonstrated. The temperature-dependent trap engineering proposed in this work can inspire some new concepts in exploring LPL materials with multi-mode emission for advancing information anti-counterfeiting and encryption applications. The temperature-dependent trap engineering in Cu 2+ -doped SrGa 2 O 4 phosphors for tunable afterglow emission is demonstrated, which exhibits great prospects in information encryption and anti-counterfeiting applications.
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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".