Structural control of Au and Au–Pd nanoparticles by selecting capping ligands with varied electronic and steric effects
Bibliographic record
Abstract
Weakly interacting ligands including three Gemini surfactants, didodecyldimethylammonium bromide (DDAB), and amines (RNH 2 , R 2 NH, and R 3 N) were used to prepare Au nanoparticles (NPs). Aqueous Au NPs capped with DDAB and Gemini surfactants showed similar sizes (3–4 nm), whereas toluene-based NPs stabilized with DDAB, amines, and their mixtures range from 2.5 to 9.3 nm. Ligand effect on Au–Pd NP structure was also studied with EXAFS. These findings were consistently accounted for by considering the ligand’s electronic/steric effects and mixed ligands coadsorption, and suggest useful ways to control NP structure by manipulating the two effects and using mixed capping ligands.
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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) | 0.001 | 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".