Fluorogenic Gold Nanoparticle (AuNP) Substrate: A Model for the Controlled Release of Molecules from AuNP Nanocarriers via Interfacial Staudinger–Bertozzi Ligation
Bibliographic record
Abstract
The ability to regulate small-molecule release from metallic nanoparticle substrates offers unprecedented opportunities for nanocarrier-based imaging, sensing, and drug-delivery applications. Herein we report a novel and highly specific release methodology off gold nanoparticle (AuNP) surfaces based on the bioorthogonal Staudinger–Bertozzi ligation. A thiol ligand bearing the molecular cargo, a Rhodamine B dye derivative, was synthesized and used to modify small water-soluble 5 nm AuNPs. Upon incorporation into the AuNP monolayer, we observed efficient quenching of the dye emission, resulting in a very low level of fluorescence emission that provided the baseline from which cargo release was monitored. We examined the ability of these AuNPs to react with azide molecules via Staudinger–Bertozzi ligation on the nanoparticle surface by monitoring the fluorescence emission after the introduction of an organic azide. We observed an immediate increase in emission intensity upon azide addition, which corresponded to the release of the dye into the bulk solution. The 31 P NMR spectrum of the AuNP product also agrees with the formation of the ligation product. Thus this system represents a novel and highly specific release methodology off AuNP surfaces that can have potential applications in drug delivery, sensing, and materials science.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".