Surface Structure of Organosulfur Stabilized Silver Nanoparticles Studied with X-ray Absorption Spectroscopy
Bibliographic record
Abstract
With the recent determination of the unexpected surface structure for thiolate-protected gold nanoparticles (−SR–Au–SR– staple-like motif for Au 102 ), it is of great interest to determine whether or not similar systems such as silver exhibit this special surface structure. A detailed study of the structure and composition of a series of organosulfur-stabilized silver nanoparticles (AgNPs) was carried out using X-ray absorption near-edge (XANES) and extended X-ray absorption fine structure (EXAFS) from a multielement (Ag, S) and multicore (Ag K- and L-edge) perspective. It was determined that AgNPs of varied sizes prepared with dodecanethiol did not exhibit either a staple-like surface structure or the traditional metal–thiolate structure (e.g., thiolate on 3-fold hollow site of metal surface), and instead adopted a layer of silver sulfide on the surface of metallic silver cores. The amount of the sulfide formed was found to be dependent on the AgNP size. Moreover, a comparison of the surface structure of thiolate-AgNPs with those coated with didodecyl sulfide indicated that the formation of a sulfide layer was inhibited when didodecyl sulfide was used achieving a surface structure more akin to the traditional thiolate bonding. These results show that AgNPs can be tailored to have different surface structure and bonding depending on the silver/sulfur molar ratio of the starting materials and type of organosulfur ligand used and, importantly, that the resulting bonding between silver and sulfur is very different from that of gold and sulfur.
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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".