Amphiphilic N-Heterocyclic Carbene-Stabilized Gold Nanoparticles and Their Self-Assembly in Polar Solvents
Bibliographic record
Abstract
Soft matter-directed self-assembly of amphiphilic inorganic nanoparticles (NPs) has recently emerged as a promising approach to access NP ensembles with superior collective properties. While thiol-terminated molecules are primarily employed to tether the amphiphilic ligand to the metal, concerns remain regarding the stabilities of the resulting NPs and their corresponding aggregates. As an alternative, we report amphiphilic N-heterocyclic carbene (NHC)-functionalized gold nanoparticles (AuNPs). To accomplish this, an amphiphilic NHC-Au I complex based on an asymmetric triethylene glycol-/dodecyl-functionalized benzimidazole was first synthesized and used to prepare the corresponding stable amphiphilic NHC-decorated AuNPs. The resulting NPs were comprehensively characterized using both solution- and solid-state-based techniques such as proton nuclear magnetic resonance spectroscopy, dynamic light scattering, transmission electron microscopy, thermogravimetric analysis, and X-ray photoelectron spectroscopy. By optimizing the self-assembly behavior of these amphiphilic AuNPs in deionized water, ethanol, and their mixtures, we were able to fine-tune the plasmonic properties of the AuNPs in the wide range of 525–640 nm. Furthermore, when treated with thiols, the ensembles showed greater stability compared to their parent discrete AuNP counterparts at room temperature.
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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".