Synthesis and characterization of tetraphenylethylene-functionalized <i>N</i>-heterocyclic carbene-stabilized gold nanoparticles with aggregation-induced emission
Bibliographic record
Abstract
Gold nanoparticles (AuNPs) have found use in broad range of applications such as in catalysis and nanomedicine. Despite the fact that thiol-based AuNPs have been widely studied, they suffer from relative instability in various conditions, such as high and low temperatures, pH variations, and are prone to oxidation. Over the last decade, N-heterocyclic carbenes (NHCs) have been under spotlight as suitable ligands to stabilize metal nanoparticles and surfaces. Although NHC-functionalized AuNPs have been shown to outperform their thiol-based analogs in terms of stability, their applications in nanomedicine have not been realized. Hybrid nanomaterials, such as AuNPs tagged with π-conjugated molecules with aggregation-induced emission (AIE) property, are promising candidates to develop fluorescent materials for cellular imaging. The combination of NHC-stabilized AuNPs with AIE to form stable, fluorescent hybrid AuNPs is of significant interest to open the door to develop new NHC-based nanomaterials. Herein, we report the synthesis and characterization of water-soluble fluorescent NHC-decorated AuNPs for potential applications in nanomedicine. Their stability in biologically relevant conditions is investigated.
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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".