Multiband MgGeO<sub>3</sub>-Based Persistent Luminescent Nanophosphors for Dynamic and Multimodal Anticounterfeiting
Bibliographic record
Abstract
Persistent luminescence (PersL) materials are excellent candidates in the dynamic and multimodal anticounterfeiting field. Compared to commercially available micrometer-sized PersL phosphors, nanosized PersL materials could blend more easily with solvents and allow printing patterns with fine details. MgGeO 3 is one of the frequently employed lattice hosts for PersL phosphors. It can accommodate divalent ions such as Mn 2+ to produce deep-red PersL. To date, the only reported method of synthesizing nanosized Mn-doped MgGeO 3 (MGO:Mn) is the sol–gel method. The synthesis product has a wide particle size distribution and suffers severe aggregation. In this work, MGO:Mn nanorods are synthesized, for the first time, with a uniformly distributed morphology. These nanorods exhibit more intense and longer-lasting PersL. A detailed comparative study between the MGO:Mn nanorods developed in this work and the MGO:Mn particles prepared by the sol–gel method is performed to identify the origin of the improved PersL property. We also demonstrate that the afterglow duration of the MGO:Mn nanorods can be further modulated by adding co-dopants such as Yb 3+, Eu 3+, and Li + . The Yb 3+ dopant also introduces a second PersL emission band in the near-infrared region of ∼1000 nm. Using a combination of these MGO-based PersL nanorods, dynamic and multimodal anticounterfeiting can be achieved.
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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".