Insight into Chloride Induced Aggregation of DMAP-Monolayer Protected Gold Nanoparticles Using the Thermodynamics of Ideally Polarized Electrodes
Bibliographic record
Abstract
The influence of halide ions on the stability of gold nanoparticles nominally protected by a monolayer of dimethylaminopyridine (DMAP) or its conjugate acid (DMAPH + ) is reported. The addition of NaF or NaCl to aqueous suspensions of DMAP(H + )-Au nanoparticles does not lead to flocculation or aggregation at high pH. In more acidic solutions, the presence of chloride ions causes the particles to aggregate whereas NaF has no effect. We have shown that this aggregation is not due to simple electrostatic screening but rather from the competitive adsorption between chloride and DMAP(H + ) on the surface of the gold nanoparticles. Chronocoulometry on polycrystalline gold electrodes has been used to quantitatively describe the pH dependent competitive adsorption. At high pH, chloride ions are unable to displace DMAP from the gold surface, while at low pH the gold surface is covered by very high chloride coverages, and nearly no DMAP (or its conjugate acid DMAPH + ) is found at the Gibbs interface. Only at intermediate pH (ca. 7.5), do the inorganic and organic species coexist on the gold surface. At pH 7.5, and for an equimolar mixture of chloride ions and DMAPH +, we have shown that each chloride ion displaces roughly one molecule and that this replacement is increasingly favorable as the surface charge of the metal becomes increasingly positive. We have also used zeta potential measurements to determine the electrical state of DMAP(H + )-Au nanoparticles. Our results indicate that stable DMAP(H + )-Au nanoparticles are characterized by a positive surface charge on the metal core even though the adsorbed monolayer is uncharged.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".