Dispersion Stability, Ligand Structure and Conformation, and SERS Activities of 1-Alkanethiol Functionalized Gold and Silver Nanoparticles
Bibliographic record
Abstract
Dispersion stability, ligand structure and conformation, and SERS activities of 1-alkanethiol (C n H 2 n +1 SH, n = 2–14) functionalized gold and silver nanoparticles (AuNPs and AgNPs) were studied as a function of alkanethiol carbon chain length and nanoparticle (NP) type and size. The dispersion stability of alkanethiol functionalized NPs in water increases with increasing alkanethiol chain length and NP size, and the stabilities of the alkanethiol-containing AuNPs are higher than their AgNP counterparts. C 3 H 7 SH and longer alkanethiols are highly ordered on AgNPs but disordered on AuNPs. The SERS intensity of the C–S stretch band for the model alkanethiols on AgNPs and AuNPs decays exponentially ( I = I 0 exp(− N c / N 0 )) with increasing number of carbon atoms ( N c ). The empirical decay length N 0, in terms of the number of the carbon atoms, is 1.29, 0.53, and 0.10 for AgNPs with diameters of 50, 30, and 10 nm, respectively. This decay length is less than 1 for AuNPs of difference sizes. These results show that changing the NP gap size by a distance equivalent to a single chemical bond can have a significant impact on the NP integrated SERS activities.
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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".