Formation of periodic stripe patterns in nematic liquid crystals doped with functionalized gold nanoparticles
Bibliographic record
Abstract
Mixtures of nematic liquid crystals (LCs) were produced by doping small quantities of gold nanoparticles coated with non-chiral hexane- (Au1), dodecane- (Au2) or chiral Naproxen-functionalized dodecane thiolates (Au3, Au4). Circular dichroism (CD) spectroscopy confirmed the optical activity for both Naproxen-functionalized gold nanoclusters. The small CD measured for Au1 and Au2 as well as the weak CD above 400 nm measured for Au3 and Au4 is attributed to scattering artifacts of dense particles aggregating in solution. For all mixtures, characterization of the nanoparticle doped nematic phase by polarized optical microscopy revealed the formation of uniform stripe textures or patterns separated by areas of homeotropic alignment due to a spatial separation of particle-rich and particle-poor domains. Similar characteristic textures were also observed for mixtures of the chiral nematic phase produced by doping either only the Naproxen-functionalized thiol 3b or Naproxen and additionally dodecane thiolate-protected gold nanoparticles Au2. On the basis of these findings, observed for the first time for alkane thiolate-capped gold nanoclusters doped into nematic LCs, two different scenarios are suggested. In the first scenario, the optically active gold nanoparticles Au3 and Au4 transfer chirality to the non-chiral nematic LC host. In the second scenario, all functionalized gold nanoclusters Au1–Au4 form topological defects resulting in chain-like particle aggregates, separated by areas of homeotropic alignment due to particles residing at the LC–glass interface.
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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 | 0.000 |
| 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 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".