Dynamics of ethyl cellulose nanoparticle self-assembly at the interface of a nematic liquid crystal droplet
Bibliographic record
Abstract
Design and fabrication of many next-generation liquid crystal (LC)-based devices rely on nematic LC domains in the form of drops or emulsions. In addition to surfactants, solid nanoparticles may be used to stabilize LC-in-water Pickering emulsions, possibly adding new dimensions to device functionality. In this work we quantitatively study the adsorption of ethyl cellulose (EC) nanoparticles, as a colloid model system, on the 4-cyano-4'-pentylbiphenyl (5CB)-water interface via a series of dynamic interfacial tension measurements. It is found that the planar alignment of 5CB molecules at the interface with water is unaffected by particle adsorption, but a significant reduction of the interfacial tension over time occurs. It is also found that adsorption of EC nanoparticles to the LC-water interface is irreversible and results in close hexagonal packing. This study demonstrates a systematic approach to quantitatively investigate the effect of nanoparticles on the stabilization of LC emulsions.
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 | 0.000 |
| 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) | 0.002 | 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".