Effect of Surface Oxidation on the Interaction of 1-Methylaminopyrene with Gold Nanoparticles
Bibliographic record
Abstract
The effect of the surface chemistry of gold nanoparticles (GNPs) on the GNP-amine (-NH(2)) interaction was investigated via conjugating an amine probe--1-methylaminopyrene (MAP) chromophore--with three Au colloidal samples of the same particle size yet different surface chemistry. The surface of laser-irradiated and ligand-exchanged-irradiated GNPs is covered with acetonedicarboxylic ligands (due to laser-introduced citrate oxidization) and citrate ligands, respectively, and both surfaces contain oxidized Au species which are essentially lacking for the citrate-capped GNPs prepared by the pure chemical approach. Both laser-irradiated samples show inferior adsorption capacity of MAP as compared with the purely chemically prepared GNPs. Detailed investigations indicate that MAP molecules mainly complex directly with Au atoms via forming Au-NH(2)R bonds, and the oxidization of the GNP surface strongly influences the ratio of this direct bonding to the indirect bonding originating from the electrostatic interaction between protonated amine (-NH(3)(+)) and negatively charged surface ligands. The impact of the oxidized GNP surface associated with the laser treatment is further confirmed by aging experiment on GNP-MAP conjugation systems, which straightforwardly verifies that the surface oxidation leads to the decrease in the MAP adsorption on GNPs.
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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 | 0.001 |
| 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".