Nanoparticle Doping in Nematic Liquid Crystals: Distinction between Surface and Bulk Effects by Numerical Simulations
Bibliographic record
Abstract
Doping nematic liquid crystals with small amounts of nanoparticles can significantly alter the electro-optic response of the nematic host. Some of these effects result from nanoparticles influencing the liquid crystal/substrate interface, while other effects are caused by nanoparticles in the bulk. So far, little attention has been paid to the influence of surface interactions on the determination of bulk properties. In the present study, these effects are investigated experimentally and confirmed by numerical simulations. The splay-type Fréedericksz-transition of the nematic liquid crystal 5CB doped with CdSe quantum dots is investigated, as these dispersions are known from earlier studies to affect the initial alignment layers. In comparison, dispersions of chemically and thermally stable silanized gold nanoparticles in the apolar nematic host FELIX-2900-03 are analyzed, which are expected to be bulk-active only. A data fitting routine is presented which allows a distinction between bulk and surface effects of nanoparticle doping. For the quantum dots, an increase of pretilt angle proportional to the doping concentration is found, as well as a slight decrease of the anchoring energy of molecules at the confining substrates. The silanized gold particles show no influence on the boundary conditions up to doping concentrations of 2.5 % (w). For higher concentrations an increase of pretilt angle is reported.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".