Upconverting Nanoparticles Coated with Light-Breakable Mesoporous Silica for NIR-Triggered Release of Hydrophobic Molecules
Bibliographic record
Abstract
Upconverting nanoparticles (UCNPs) doped with Yb 3+ and Tm 3+ are near-infrared (NIR) to ultraviolet (UV) transducers that can be used for NIR-controlled drug delivery. However, due to the low quantum yield of upconversion, high laser powers and long irradiation times are required to trigger this drug release. In this work, we report the one-step synthesis of a nanocomposite consisting of a LiYbF 4:Tm 3+ @LiYF 4 UCNP coated with mesoporous UV-breakable organosilica shells of various thicknesses. We demonstrate that a thin shell accelerates the breakage of the shell at 1 W/cm 2 NIR light exposure, a laser power up to 9 times lower than that of conventional systems. When the mesopores are loaded with hydrophobic vitamin D 3 precursor 7-dehydrocholesterol (7-DH), shell breakage results in subsequent cargo release. Its minimal toxicity in HeLa cells and successful internalization into the cell cytoplasm demonstrate its biocompatibility and potential application in biological systems. The tunability of this system due to its simple, one-step synthesis process and its ability to operate at low laser powers opens up avenues in UCNP-powered NIR-triggered drug delivery toward a more scalable, flexible, and ultimately translational option.
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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".