Robust Water-Soluble Gold Nanoparticles via Polymerized Mesoionic <i>N</i>-Heterocyclic Carbene–Gold(I) Complexes
Bibliographic record
Abstract
Gold nanomaterials have widespread applications across multiple areas of science and technology. Sulfur-containing ligands (thiols and thioethers) have been traditionally used as ligands to protect and functionalize these materials. N-Heterocyclic carbenes (NHCs) have recently emerged as organic alternatives to thiols in stabilizing gold nanoparticles (AuNPs) and flat surfaces. In fact, gold-containing materials decorated with NHCs have been shown to withstand a variety of harsh conditions. However, such materials still suffer from limited stability in the presence of thiols, such as the biologically relevant glutathione, in aqueous media. Here, we report the synthesis and application of polymeric mesoionic NHC–Au(I) complexes as precursors to polyNHC-stabilized AuNPs. Using copper-catalyzed alkyne–azide cycloaddition polymerization of diazide- and dialkyne-containing monomers, we directly install 1,2,3-triazole groups, as precursors to mesoionic carbenes, on the backbone of the resulting polymers. This effectively eliminates the need to presynthesize NHC–Au(I)-containing monomers to access this class of polymers. Using these polymers as the substrate, the resulting robust AuNPs, protected by a catenated network of NHCs, demonstrate exceptional stabilities in aqueous media under various conditions, particularly against high concentrations of glutathione (up to 6 mM) for extended periods of time (up to 10 days). Moreover, the use of the macromolecular substrate, compared to small NHC–Au complexes used thus far yielding relatively small AuNPs (∼5 nm), results in the formation of larger AuNPs (∼12 nm). Such enhanced stabilities in aqueous media together with their larger diameters make these materials promising for potential applications in nanomedicine. To highlight their multifunctionality, we also demonstrate their catalytic activity in the reduction of 4-nitrophenol.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".