Effects of functionalized metal and semiconductor nanoparticles in nematic liquid crystal phases
Bibliographic record
Abstract
To capitalize on the unique size and shape-dependent optical and electronic properties of nanoscale particles for liquid crystal (LC) applications, detailed structure and size-property relationship studies are critical. To enhance our understanding of the thermal, optical and electro-optic effects of nanoparticles in nematic LCs we produced numerous different nematic LC mixtures containing small quantities of dispersed metal nanoparticles (i.e. gold and silver nanoclusters) or semiconductor quantum dots (i.e. CdTe nanocrystals) and studied their optical (texture, alignment, defect formation, luminescence) and electro-optic properties. Depending on several experimental parameters such as nanoparticle functionalization and concentration, as well as thermal history in combination with an applied electric field, these nanoparticle/LC mixtures with the nanoparticles differing in surface functionality, size, and core material gave rise to unique alignment effects and electro-optic responses in the two investigated nematic LC (N-LC) hosts.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".