Langmuir Films of <i>n</i>-Alkanethiol-Capped Gold Nanoparticles and <i>n</i>-Alkanes: Interfacial Mixing Scenarios Assessed by X-ray Reflectivity and Grazing Incidence Diffraction
Bibliographic record
Abstract
A series of n -alkanes cospread with alkanethiol-stabilized gold nanoparticles (AuNP) were studied as Langmuir monolayers by synchrotron X-ray reflectivity and diffraction. Tetradecanethiol capped gold nanoparticles with core diameters close to 2 nm were used to make films at 20 °C, below the ligand order–disorder temperature. A variety of n -alkane chain lengths (C n = C n H 2 n +2, where n = 12, 15 and 16) were tested to assess the interfacial assembly of nanoparticle films as a result of different mixing scenarios indicated in their surface pressure versus area isotherms. Synchrotron grazing incidence X-ray diffraction (GIXD) and reflectivity (XR) confirm that mixtures of n -alkane and AuNP exhibiting improved fluidity in their compression isotherm are indeed incorporating n -alkane into the AuNP ligand shell and stabilizing it at the air–water interface. The resulting films show a doubling of their correlation lengths and thus a significant improvement on their ordering, as well as increased lattice spacing that is dependent upon the n -alkane chain length. Mixtures that do not exhibit changes in their surface pressure vs area isotherm similarly show little change in the interfacial assembly of the nanoparticle films except to promote monolayer collapse and multilayer formation. Improvements to the film order are assigned to the initial formation of larger nanoparticle domains. The nature of the chain length dependence and persistence of the n -alkane through compression suggest a favorable interaction with the nanoparticle ligand shell that results in the extension of methylene units of the longer alkanes beyond the thiol layer, which has implications for influencing nanoparticle 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) | 0.001 | 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".