The Influence of Surface Hydroxylation Media on the Luminescence and Electronic Structure of Cr<sup>3+</sup>-Doped Zinc Gallate Nanoparticles
Bibliographic record
Abstract
Near-infrared (NIR) persistent luminescent (PersL) nanomaterials, such as Cr 3+ -doped zinc gallate (CZGO), have shown strong potential for biologically based imaging owing to their unique optical properties. For intravenous injection, maintaining high dispersibility in aqueous media is important, and surface hydroxylation is a common strategy to improve colloidal stability. Prior studies claimed that both acidic and basic treatments can hydroxylate CZGO surfaces. It is unclear why hydroxylation can be achieved in aqueous media with drastically different chemical environments and whether these different hydroxylation routes lead to comparable optical performances and long-term colloidal stability. In this work, CZGO nanoparticles were hydroxylated in acidic, neutral, and basic conditions, where their morphology, dispersibility and luminescence were compared. We found that a stable colloidal dispersion can be successfully produced in all media used, but acid-treated samples exhibited the strongest and longest-lasting luminescence. X-ray photoelectron spectroscopy (XPS) revealed distinct surface chemistry depending on the hydroxylation medium, enabling direct correlation between the treatment environment and luminescence behaviors. These findings clarified the unsolved questions about how hydroxylation conditions influence both surface chemistry and optical performance, which provided insights for optimizing PersL nanoparticles for biomedical 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".