Irreversible Adsorption-Driven Assembly of Nanoparticles at Fluid Interfaces Revealed by a Dynamic Surface Tension Probe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it