Diving into Unknown Waters: Water-Soluble Clickable Au<sub>13</sub> Nanoclusters Protected with N-Heterocyclic Carbenes for Bio-Medical Applications
Bibliographic record
Abstract
The use of gold nanoclusters in biomedical applications has been steadily increasing in recent years. However, water solubility is a key factor for these applications, and water-soluble gold nanoclusters are often difficult to isolate and susceptible to exchange or oxidation in vivo. Herein, we report the isolation of N-heterocyclic carbene (NHC)-protected atomically precise gold nanoclusters functionalized with triethylene glycol monomethyl ether groups. These clusters are highly luminescent and water soluble and are shown to be stable in biological media. Importantly, the core structure, stability, and high quantum yield of the nanoclusters were conserved after backbone modification. Depending on the nature of the halide group, clusters have high stability in simulated biofluids and resist attack by glutathione. In vivo studies show that no abnormal cellular morphology is introduced in the kidney, liver, or spleen of mice treated with [Au 13 (NHC) 5 Br 2 ]Br 3 nanoclusters protected by 1,8-dimethylnaphthyl-linked NHCs. This cluster has a blood elimination half-life of 0.68 h. Functionalization of the wingtip groups of the cluster with azide groups is demonstrated, and complete reaction of all 10 azide groups with strained alkynes is shown, highlighting the potential of these clusters in biological settings.
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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".