Ligand Shell Composition-Dependent Effects on the Apparent Hydrophobicity and Film Behavior of Gold Nanoparticles at the Air–Water Interface
Bibliographic record
Abstract
Nanoparticles with well-defined interfacial energy and wetting properties are needed for a broad range of applications involving nanoparticle self-assembly including the formation of superlattices, stability of Pickering emulsions, and for the control of nanoparticle interactions with biological membranes. Theoretical, simulated, and recent experimental studies have found nanometer-scale chemical heterogeneity to have important effects on hydrophobic interactions. Here we report the study of 4 nm gold nanoparticles with compositionally well-defined mixed ligand shells of hydroxyl-(OH) and methyl-(CH3) terminated alkylthiols as Langmuir films. Compositions ranging from 0-25% hydroxyl were examined and reveal nonmonotonic changes in particle hydrophobicity at the air-water interface. Unlike nanoparticles capped exclusively with a methyl-terminated alkylthiol, extensive particle aggregation is found for ligand shells containing <2% hydroxyl-terminated chains. This aggregation was lessened upon increasing the quantity of OH-terminated chains. Nanoparticles capped with 25% OH yield films of well-separated nanoparticles exhibiting a fluid-phase regime in the surface pressure vs area isotherm. Compression-expansion hysteresis, monolayer collapse, and mean nanoparticle area measurements support the TEM-observed changes in film morphology. Such clear changes in the hydrophobicity of nanoparticles based on very small changes in the ligand shell composition are shown to impact the process of interfacial nanoparticle self-assembly and are an important demonstration of nanoscale wetting with consequences in both materials and biological applications of nanoparticles that require tunable hydrophobicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".