Enhanced Persistent Luminescence from Cr3+-Doped ZnGa2O4 Nanoparticles upon Immersion in Simulated Physiological Media
Bibliographic record
Abstract
Near-infrared persistent luminescence (PersL) nanoparticles (NPs) have great potential in biomedical applications due to their ability to continuously emit tissue-penetrating light. Despite numerous reports on the distribution, biological safety and other consequences of PersL NPs in vitro and in vivo, there has been a lack of studies on the optical properties of these NPs in the physiological environment. In light of this, we investigated the effects of short-term immersion of the prominent Cr3+-doped ZnGa2O4 (CZGO) NPs in a simulated physiological environment for up to 48 h. This paper reports the changes in the structural and optical properties of CZGO NPs after their immersion in a phosphate-buffered saline (PBS) solution for pre-determined time intervals. Interestingly, the luminescence intensity and lifetime noticeably improved upon exposure to the PBS media, which is unusual among existing nanomaterials explored as bioimaging probes. After 48 h of immersion in the PBS solution, the CZGO NPs were approximately twice as bright as the non-immersed sample. X-ray spectroscopic techniques revealed the formation of ZnO, which results in an improvement in observed luminescence.
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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".