Evaluation of Interfacial Structure of Self-Assembled Nanoparticle Layers: Use of Standard Deviation between Calculated and Experimental Drop Profiles as a Novel Method
Bibliographic record
Abstract
The self-assembly of nanoparticles (NPs) at interfaces is currently a topic of increasing interest due to numerous applications in food technology, pharmaceuticals, cosmetology, and oil recovery. It is possible to create tunable interfacial structures with desired characteristics using tailored nanoparticles that can be precisely controlled with respect to shape, size, and surface chemistry. To address these functionalities, it is essential to develop techniques to study the properties of the underlying structure. In this work, we propose an experimental approach utilizing the standard deviation of drop profiles calculated by the Laplace equation from experimental drop profiles (STD), as an alternative to the Langmuir trough or precise microscopic methods, to detect the initiation of closely packed conditions and the collapse of the adsorbed layers of CTAB-nanosilica complexes. The experiments consist of dynamic surface/interfacial tension measurements using drop profile analysis tensiometry (PAT) and large-amplitude drop surface area compression/expansion cycles. The results demonstrate significant changes in STD values at the onset of the closely packed state of nanoparticle-surfactant complexes and the monolayer collapse. The STD trend was explained in detail and shown to be a powerful tool for analyzing the adsorption and interfacial structuring of nanoparticles. Different collapse mechanisms were reported for NP monolayers at the liquid/liquid and air/liquid interfaces. We show that the interfacial tension (IFT) is solely dependent on the extent of interfacial coverage by nanoparticles, while the surfactants regulate only the hydrophobicity of the self-assembled complexes. Also, the irreversible adsorption of nanoparticles and the increasing number of adsorbed complexes after the collapse were observed by performing consecutive drop surface compression/expansion cycles. In addition to a qualitative characterization of adsorption layers, the potential of a quantitative calculation of the parameter STD such as the number of adsorbed nanoparticles at the interface and the distance between them at different states of the interfacial layer was discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".