Surface Chemistry of Gold Nanoparticles Produced by Laser Ablation in Aqueous Media
Bibliographic record
Abstract
The femtosecond laser ablation of a gold target in aqueous solutions has been used to produce colloidal Au nanoparticles with controlled surface chemistry. A detailed chemical analysis showed that the nanoparticles formed were partially oxidized by the oxygen present in solution. The hydroxylation of these Au−O compounds, followed by a proton loss to give surface Au−O -, resulted in the negative charging of the nanoparticles. The partial oxidation of the gold nanoparticle surface enhances its chemical reactivity and consequently has a strong impact on its growth. In particular, the oxidized surface reacted efficiently with Cl - and OH - to augment its net surface charge. This limited the coalescence of the particles, due to electrostatic repulsion, and led to a significant reduction of their size. Taking advantage of the repulsion effect, efficient size control was achieved using different salts (7 ± 5 nm for 10 mM KCl, 5.5 ± 4 nm for 10 mM NaCl, 8 ± 5 nm for NaOH, pH 9.4), a considerable improvement comparatively to particles prepared in deionized water, using identical ablation conditions, where particles of 1−250 nm were produced. The partially oxidized gold surface was also suitable for surface modification through both covalent and electrostatic interactions during particle formation. Using solutions of N -propylamine, we showed an efficient control of nanoparticle size (5−8 ± 4−7 nm) by the involvement of these interactions. The results obtained help to develop methodologies for the control of laser-ablation-based nanoparticle growth and the functionalization of nanoparticle surfaces by specific interactions.
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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".