Near-infrared light-triggered drug release from UV-responsive diblock copolymer-coated upconversion nanoparticles with high monodispersity
Bibliographic record
Abstract
The preparation of a new near-infrared (NIR) light-responsive nanocarrier for controlled drug release is demonstrated. Upconversion nanoparticles (UCNPs) were coated with an amphiphilic diblock copolymer through surface-initiated atom transfer radical polymerization, in which the inner block is hydrophobic, ultraviolet (UV)-sensitive poly(4,5-dimethoxy-2-nitrobenzyl methacrylate) (PNB), and the outer block is hydrophilic poly(methoxy polyethylene glycol monomethacrylate) (POEG). The resulting polymer/UCNP nanocarrier is thermally stable in water over a wide temperature range (5-70 °C) and is uniform in size (120 nm hydrodynamic diameter, polydispersity index <0.1). The diblock copolymer self-assembly on the surface of each UCNP occurs in aqueous solution, which allows encapsulation of antitumor drugs like doxorubicin (DOX) by the hydrophobic "micelle-like" core of PNB surrounding the NIR-sensitive UCNP. Under 980 nm laser exposure, the UV light emitted by the single UCNP is absorbed by the PNB inner layer, which results in cleavage of o-nitrobenzyl groups and formation of carboxylic acid groups. The increasing hydrophilicity of the diblock copolymer resulting from the NIR light-triggered photochemical reaction can thus disrupt the nanocarrier and leads to the release of DOX molecules. This diblock copolymer self-assembly-based approach to constructing NIR light-responsive nanocarriers of well-defined structures is general and offers possibilities for photocontrolled drug delivery.
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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".