Surface Nanodroplet‐Confined Engineering of Gold (I) ‐Thiolate Nanostructures
Bibliographic record
Abstract
Abstract Gold(I)‐thiolate complexes have served as primary building blocks for diverse Au nanostructure synthesis strategies. A delicate approach to characterize and control Au(I)‐thiolate motif formation and assembly on the surface is needed as it can potentially solve challenges associated with utilizing gold nanomaterials in many applications. Here, the controllable generation of flower‐shaped surface gold nanostructures (FSGNs) is demonstrated by manipulating the formation‐assembly process of Au(I)‐dodecanethiolate motifs within nanoscale surface droplets. The morphology and structure of the resulting Au nanostructures are governed by internal convection flows and interfacial energy, modulated by the nanodroplet composition and substrate wettability. The obtained FSGNs are proven to act as versatile scaffolds for the selective generation of Au spiky nanostars. These FSGNs can also be utilized to functionalize nanodroplet‐based reactors, boosting the fluorescent intensity of Nile red (NR) fluorophores and decomposing NR via catalytic reaction. Remarkably, with FSGN functionalized droplets smaller than a radius of 500 nm, the decomposition rate of NR can reach ≈0.01 s−1. These results highlight a miniaturized, controllable, and automated method for the in situ production of 3D gold nanostructures on substrates, offering prospects for fast surface nanostructure fabrication and efficient environmental pollutant treatment.
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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".