Effects of size, capping agent, and concentration of CdSe and CdTequantum dots doped into a nematic liquid crystal on the optical and electro-optic properties of the final colloidal liquid crystal mixture
Bibliographic record
Abstract
To investigate size and capping agent-dependent alignment and electro-optic effects of quantum dots (QDs) in nematic LCs, mixtures of a nematic phenylpyrimidine LC (LC1) doped with hexadecylamine-capped CdSe and thioglycolic acid-capped CdTe QDs were prepared and investigated by polarized optical microscopy between plain, untreated glass slides and in ITO-coated glass cells with rubbed polyimide alignment layers as well as by electro-optic analysis. The five investigated CdSe QDs, varying in size from 2.5 to 5.2 nm, show fluorescence emission maxima ranging from 480 to 610 nm, and the four CdTe QDs, varying in size from 3.2 to 4.0 nm, emit light from 530 to 610 nm depending on their size. The CdSe QDs favoring homeotropic anchoring of the nematic LC induce vertical alignment at 1 and 2 wt% in LC1 between plain glass slides and only at 1 wt% in the planar cells, except for CdSe QD emitting at 590 nm (CdSe590), which induces vertical alignment independent of the substrate only at 1 wt%. Concentrations above 2 wt% of the CdSe QDs in LC1 show planar alignment mainly due to aggregation of the QDs in LC1 at these higher concentrations. On the contrary, the CdTe QDs favoring planar anchoring of the nematic LC induce homeotropic alignment of LC1 at concentrations above 2 wt% between plain glass slides. All QDs are effective in reducing the threshold voltage (Vth) and the splay elastic constant (K11). The most drastic reduction over the entire temperature range, however, was observed for the smallest CdSe480 QDs, which showed the least tendency of all investigated QDs to aggregate in LC1.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".