Tunable Approach to Induce the Formation of Flexible Nanofilms from Small (3 nm) Gold Nanoparticles at Oil–Water Interfaces
Bibliographic record
Abstract
The self-assembly of gold nanoparticles (AuNPs) into thin films at the liquid–liquid interface has promising applications in industries such as catalysis, optics, and sensors. However, precise control over their formation is complex, influenced by several factors which scale differently with core size. Due to their small free energy of adsorption, there are few examples of AuNPs with core diameters <10 nm. The present research evaluates the adsorption of ∼3 nm AuNPs from either side of the oil–aqueous interface with variations in ligand shell composition, the oil phase composition, and the structure of alcohol additives to best drive thin-film formation. Film formation and quality are evaluated, and a recent thermodynamic model is used to gain insight into the primary forces promoting this adsorption. Results demonstrate that longer-chain alcohol additives (namely, n -butanol and n -hexanol) induced adsorption more efficiently than shorter-chain alcohols (ethanol). The volume of alcohol additive needed to induce adsorption was dependent upon the ligand composition, suggesting that the mechanism for induced interfacial adsorption is via interaction with the AuNP ligand shell. Comparison with the thermodynamic model indicates that the driving force for this induced adsorption is the alteration of the three-phase contact angle. Additionally, the use of various oils demonstrates that as oil–water interfacial tension increases, more AuNPs adsorb to the interface. This relationship is also supported by the model. Insight gained for favorable conditions of adsorption for AuNPs < 10 nm as well as the underlying thermodynamic mechanism is important in working toward the ability to fine-tune such films for industrial applications.
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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 | 0.001 |
| 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".