A single multifunctional nanoplatform based on upconversion luminescence and gold nanorods
Bibliographic record
Abstract
Lanthanide-doped upconverting nanoparticles (UCNPs), which convert near-infrared (NIR) light to higher energy light have been intensively studied for theranostic applications. Here, we developed a hybrid core/shell nanocomposite with multifunctional properties using a multistep strategy consisting of a gold nanorod (GNR) core with an upconverting NaYF4:Er3+, Yb3+ shell (GNR@NaYF4:Er3+, Yb3+). To use a single excitation beam, the GNR plasmon was tuned to ∼650 nm, which is resonant with the upconverted red Er3+ emission emanating from the 4F9/2 excited state. Thus, under laser irradiation at 980 nm, the intensity ratio of the upconverted green emission (arising from the 2H11/2 and 4S3/2 excited states of Er3+) showed a remarkable thermal sensitivity, which was used to calculate the temperature change due to rapid heat conversion from the GNR core. The red upconversion emission of the GNR@NaYF4:Er3+, Yb3+ core/shell nanocomposite decreased compared with the NaYF4:Er3+, Yb3+ nanoshell structure (without a GNR core), which indicates that energy transfer from NaYF4:Er3+, Yb3+ to the GNR takes place, subsequently causing a photothermal effect. The anticancer drug, doxorubicin, was loaded into the GNR@NaYF4:Er3+, Yb3+ nanocomposites and the drug release profile was evaluated. In particular, the release of doxorubicin was significantly enhanced at lower pH and higher temperature caused by the photothermal effect. This multifunctional nanocomposite, which is suitable for local heating and controlled drug release, shows strong potential for use in cancer therapy.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".