<scp>Au</scp> , <scp>Ag</scp> , and <scp>Cu</scp> Nanostructures
Bibliographic record
Abstract
Au, Ag, and Cu nanostructures (NSs) in ionic liquid (IL) media constitute a rich and diverse set of functional materials, with applications ranging from catalysis to sensor fabrication. Owing to the low to negligible volatility of some ILs, it is possible to use novel synthesis methods such as laser-mediated synthesis, radiolysis, and plasma reductions in these solvents, making nanofabrication an exquisitely precise process, and enabling scientists to produce NSs with unprecedented control over shape, size, and uniformity. Furthermore, the possibility of variation of IL cations and anions, which leads to large changes in the three-dimensional structure of the IL itself, can assist in the formation of highly anisotropic metal NSs within the IL matrix. This makes ILs attractive solvents for the synthesis of morphologically diverse metal NSs, which are expected to be highly active for catalysis. The large electrochemical windows associated with some ILs also enables the application of metal NS/IL composites in electrochemical applications, with or without other supporting materials. Finally, the interaction of metal NS surfaces with electromagnetic radiation and the influence of chemical entities in the proximity of the metal surface on this interaction give us an opportunity to use these composite materials in chemical and biological sensing, often with detection limits far better than those offered by alternative protocols. Ag NPs, in particular, are also well-known for their biocidal effects, which may be enhanced or diminished in the presence of ILs. This chapter gives a broad overview of Au, Ag, and Cu NSs in IL media – including recent developments in associated synthesis protocols – and discusses selected examples of their applications in catalysis, electrochemistry, plasmonics, sensors, and therapeutics.
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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 | 0.001 |
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".