Use of Thiols as Protecting Ligands in Reflective Surface Films of Silver Nanoparticles
Bibliographic record
Abstract
We report the preparation of metal liquid-like films (MELLFs) of silver nanoparticles stabilized by thiolate surface ligands. These surface films, composed of particles with diameters of about 100 nm, are highly reflective and can be employed in the fabrication of liquid mirrors. A number of different thiols are considered as stabilizing ligands, including alkanethiols, aromatic thiols and dithiols. Under identical preparation conditions, some lead to the spontaneous formation of reflective surface films, whereas others do not. Shorter chain alkanethiols (C2 to C8), thiophene and thiophenol are found to be effective whereas longer chain alkanethiols (C10 and C12) and short dithiols (C2 and C3) do not produce reflective films. Ethanethiol and propanethiol protected particles form surface films with reflectivities in the near-IR that surpass those of a previous generation of MELLFs prepared with 1,10-dimethylphenanthroline as the ligand. This enhanced reflectivity is attributed to a more closely packed nanoparticle film with a higher metal volume fraction. The closer proximity of the particles, however, leads to enhanced coupling of their surface plasmon resonance and increased absorption in the visible region of the spectrum. Short chain dithiols do not produce MELLFs but rather provoke particle aggregation. In the case of 1,2-ethanedithiol, the particles are found to precipitate in a continuous organic matrix, presumably caused by oxidative polymerization of the dithiol to a polydisulfide. Finally, preliminary investigations indicate that a large variety of organic solvants can be employed in the preparation of thiol protected MELLFs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".