Role of Collective Interactions in Self‐Assembly of Charged Particles at Liquid Interfaces
Bibliographic record
Abstract
Abstract Charged nano‐colloidal particles self‐assemble and display ordered arrays or other structures at liquid interfaces. We used Monte Carlo (MC) simulations to examine the effect of long‐range repulsive collective inter‐particle interactions on structural—transitions from liquid‐like to crystal‐like. We used the asymptotic pair interaction potential proposed by Hurd (J. Phys. A. Math Gen 18, L1055 (1985)), which includes both the screened Coulombic contribution and the dipole‐dipole interaction. The effects of the collective inter‐particle interactions on the interfacial 2‐D colloid structure formation were quantified by the radial distribution function and the potential of the mean force. The MC simulations agreed with the experimentally observed particle structural transitions at both the air‐water and oil‐water interfaces. The effects of the particle charge and interfacial coverage on the 2‐D structure formation were analyzed. The significance of the results lies in their potential applications in inducing 2‐D structural transitions in interfacial colloids to form ordered structures; this controls the emulsion and foam stability, and aids in the fabrication of patterned materials with desirable properties.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".