Irreversible Adsorption-Driven Assembly of Nanoparticles at Fluid Interfaces Revealed by a Dynamic Surface Tension Probe
Bibliographic record
Abstract
Adsorption-driven self-assembly of nanoparticles at fluid interfaces is a promising bottom-up approach for the preparation of advanced functional materials and devices. Full realization of its potential requires quantitative understanding of the parameters controlling the self-assembly, the structure of nanoparticles at the interface, the barrier properties of the assembly, and the rate of particle attachment. We argue that models of dynamic surface or interfacial tension (DST) appropriate for molecular species break down when the adsorption energy greatly exceeds the mean energy of thermal fluctuations and validate alternative models extending the application of generalized random sequential adsorption theory to nanoparticle adsorption at fluid interfaces. Using a model colloidal system of hydrophobic, charge-stabilized ethyl cellulose nanoparticles at neutral pH, we demonstrate the potential of DST measurements to reveal information on the energy of adsorption, the adsorption rate constant, and the energy of particle-interface interaction at different degrees of nanoparticle coverage of the interface. These findings have significant implications for the quantitative description of nanoparticle adsorption at fluid interfaces.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".